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Biogeographic Patterns of Lemur Species Richness and Occurrence in a Fragmented Landscape
by
Travis Scott Steffens
A thesis submitted in conformity with the requirements for the degree of PhD
Department of Anthropology University of Toronto
© Copyright by Travis Scott Steffens 2017
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Biogeographic Patterns of Lemur Species Richness and
Occurrence in a Fragmented Landscape
Travis Scott Steffens
Doctor of Philosophy
Department of Anthropology University of Toronto
2017
Abstract
Determining the factors that affect species richness and occurrence is vital to the study of
primate biogeography. In this study, I investigate the biogeographic patterns of a lemur
community within a fragmented landscape in Ankarafantsika National Park, Madagascar. The
landscape consists of 42 deciduous dry forest fragments ranging in size from 0.23 to 117.7 ha. I
conducted a total of 1218 surveys for lemurs between June and November 2011 in the 42
fragments. I recorded a total of 1023 individual or group sightings of six species. I conducted
vegetation sampling in 38 of 42 forest fragments. I measured human disturbance within each
fragment and determined fragment isolation and proximity to human settlements. I explored
biogeographic patterns of lemur species richness and occurrence using the species-area
relationship, metapopulation dynamics, and landscape ecology. I found that lemurs in a
fragmented landscape show a species-area relationship in the form of a convex power model. I
did not find a sigmoidal pattern for the species-area relationship and I found no evidence of a
“small island effect.” Human disturbance and tree height also influence species richness, but it is
unclear how. Lemur species form different metapopulations within the same landscape.
Metapopulation dynamics suggest that area was a stronger factor determining individual lemur
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species occurrence than fragment isolation. However, for Microcebus species, area seems to
have less influence than for other species (Cheirogaleus medius and Eulemur fulvus). I
investigated the landscape ecology of lemur species occurrence and found species-specific scale
responses to habitat amount. Area predicted species occurrence for C. medius and M. murinus,
but not for M. ravelobensis. M. ravelobensis occurrence may be mediated by factors other than
area, such as dispersal ability and edge tolerance. My study shows the importance of a multi-
scale approach to lemur biogeography and how it is critical for understanding how lemur species
respond to high amounts of forest loss and fragmentation.
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Acknowledgments This dissertation would not have been possible without the incredible support of many people,
institutions, and funding agencies. I would like to thank my advisor Shawn Lehman. His
feedback and support helped turn a silly idea into a dissertation. Shawn has given me every
opportunity to seek my own path. He provided me with the necessary encouragement and
advice, allowing me to complete an expensive and logistically challenging project. Shawn has
continuously supported my academic career and helped me develop as a researcher.
I would like to thank the Department of Palaeontology at the University of Antananarivo. I am
very grateful for the support and assistance from the staff of the Madagascar Institut pour la
Conservation des Ecosystèmes Tropicaux (MICET/ICTE), including the director Benjamin
Andriamihaja, Benji Randrianambina, Jean Rakotoarison, Haza Rasoanaivo, and Tina.
I would like to thank Mamy Razafitsalama and Rindra Rakotoarvony. Both their efforts were
indispensable for this research project. Mamy especially provided unwavering support for my
project. He became a leader and took responsibility of many elements of my research. He was
not only a great field assistant but also an amazing project manager and friend. Of all the people
I have met in Madagascar, Mamy is the single most intelligent, personable, and hard working. I
look forward to a long and illustrious collaboration with Mamy on current and future research
and conservation efforts.
I would like to thank the tireless efforts of the residents of Andranohobaka and Maevatanimbary
whose help was essential to the completion of this project. The community members provided
our food, water, and personnel, without which this project could not have survived past the first
week. Specifically, I would like to thank Jean Paul, Velontsara, Nada, Rollin, and also Alpha
(who is from Ambodimanga village). I would like to thank Madagascar National Park and their
staff who helped me to conduct my research in a remote portion of Ankarafantsika National
Park. Specifically, I would like to thank the head of research, Jacqueline Razaiarimanana, the
head of conservation and research, Justin Rakotoarimanana, as well as the Park Director at the
time, Rene Razafindrajery.
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I send my warmest thanks to Fanja and her family at La Maison du Pyla, whose comforting
home and amazing food was a welcome respite to the challenges faced in the field.
I would like to thank my friends and colleagues who helped support me and provide invaluable
assistance for this dissertation. Specifically, I am grateful for the help and support of Jarred
Heinrich, who basically taught me how to use R and was a sounding board for any and all ideas.
I thank Yuri Fraser for his immense efforts in Madagascar. I would also like to thank Vincent
Dorie, Kim Valenta, Abigail Ross, Ryan Burke, Steve Miller, Jamie Sharpe, and Greg Bridgett
for the varied and important contributions to this dissertation. I would like to thank my
committee members for their support and feedback for my dissertation.
I would like to thank Ken and Sheena McGoogan for their immeasurable support during my
dissertation.
I would like to thank my mother, Cecile Patterson. She gave me life, she provided me with
support through the most trying times, she gave me every opportunity to succeed, and she
encouraged me to live my dream.
Finally, I would like to thank my partner Keriann McGoogan who not only helped me collect
data, edit manuscripts, negotiate contracts, navigate through the wilderness, and help answer all
my questions, but also was there for me in a way no other person could be. Her care and support
helped me go from idea to dissertation and she is the biggest reason for any success I receive.
Financial support for this work was provided by the following intuitions and organizations:
Sigma Xi Grants in Aid of Research, American Society of Primatology (Conservation Small
Grant), Calgary and Edmonton Valley Zoos, Primate Conservation, Inc., The Explorers Club
(Exploration Fund), University of Toronto School of Graduate Studies Travel Grant, the Ontario
Government (Ontario Graduate Scholarship), and the Natural Sciences and Engineering
Research Council of Canada (Discovery Grant).
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Table of Contents
Acknowledgments .......................................................................................................................... iv
Table of Contents ........................................................................................................................... vi
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
List of Appendices ....................................................................................................................... xiii
Chapter 1: Area, Patches, Landscapes and the Determinants of Lemur Species Occurrence .......................................................................................................................................1
1.1 Introduction ..........................................................................................................................1
1.2 Background on Madagascar and Lemurs .............................................................................1
1.3 Habitat Loss and Fragmentation ..........................................................................................3
1.4 Species Richness and Occurrence ........................................................................................3
1.5 Patch and Landscape Effects and Their Relation to Habitat Loss and Fragmentation ........4
1.6 Patch-Level Effects on Primate Communities: The Species-Area Relationship .................5
1.7 Patch-Level Effects on Primate Occurrence: Metapopulation Dynamics ...........................6
1.8 Landscape-Level Effects on Primate Occurrence ................................................................6
1.9 A Note on the Format of the Thesis .....................................................................................7
1.10Dissertation Goals ................................................................................................................7
Chapter 2: Species-Area Relationships of Lemurs in a Fragmented Landscape in Madagascar ......................................................................................................................................9
2.1 Introduction ..........................................................................................................................9
2.2 Choosing a Species-area Model ...........................................................................................9
2.3 Types of Species-Area Curves ...........................................................................................13
2.3.1 Convex Models without an Asymptote ..................................................................13
2.3.2 Convex Models with an Asymptote .......................................................................14
2.3.3 Sigmoidal Models without an Asymptote ..............................................................14
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2.3.4 Sigmoidal Models with an Asymptote ...................................................................15
2.4 Processes Determining the Species-Area Pattern ..............................................................15
2.5 Other Non-Area Factors Affecting Primate Species Richness ..........................................16
2.6 Species-Area Relationships in Primates ............................................................................18
2.7 Testing for a Species-Area Relationship ............................................................................19
2.8 Justification ........................................................................................................................19
2.9 Goals ..................................................................................................................................20
2.10Methods..............................................................................................................................21
2.10.1 Study Site and Study Species .................................................................................21
2.10.2 Question 1: What is the SAR Pattern? ...................................................................24
2.10.3 Question 2: What Other Non-Area Factors Affect Lemur Species Richness? ......28
2.10.4 Statistical Analysis .................................................................................................29
2.11Results ................................................................................................................................32
2.11.1 Fragment Area and Survey Results ........................................................................32
2.11.2 Question 1: What is the SAR Pattern? ...................................................................34
2.11.3 Question 2: What Other Non-Area Factors Affect Lemur Species Richness? ......36
2.12Discussion ..........................................................................................................................37
2.12.1 Question 1: What is the SAR Pattern? ...................................................................37
2.12.2 Question 2: What Other Non-Area Factors Affect Lemur Species Richness? ......39
2.12.3 Suggestions for Conservation ................................................................................41
2.13Conclusion .........................................................................................................................42
Chapter 3: Population Dynamics of Lemurs in a Fragmented Landscape in Madagascar .......43
3.1 Introduction ........................................................................................................................43
3.2 Types of Metapopulations ..................................................................................................44
3.3 Metapopulation Models .....................................................................................................46
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3.4 Dispersal ............................................................................................................................48
3.5 Effect of Additional Variables within Incidence Function Models ...................................49
3.6 Simulating Metapopulation Dynamics Over Time ............................................................50
3.7 Metapopulation Dynamics: Forest Loss and Fragmentation Effects on Primate Occurrence .........................................................................................................................50
3.8 Justification ........................................................................................................................52
3.9 Goal ....................................................................................................................................53
3.10Methods..............................................................................................................................55
3.10.1 Study Site and Study Species .................................................................................55
3.10.2 Question 1: Do Lemur Species Form Metapopulations? .......................................57
3.10.3 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) within a Lemur Metapopulation? ....................................................................................................62
3.10.4 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence? What are the Conservation Implications? ..........................................................................................................63
3.11Results ................................................................................................................................64
3.11.1 Question 1: Do Lemur Species Form Metapopulations? .......................................64
3.11.2 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) on a Lemur Metapopulation? .....67
3.11.3 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence? .....................................................68
3.12Discussion ..........................................................................................................................71
3.12.1 Question 1: Do Lemur Species Form Metapopulations? .......................................71
3.12.2 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) on a Lemur Metapopulation? .....76
3.12.3 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence? What are the Conservation Implications? ..........................................................................................................78
3.13Suggestions for Conservation ............................................................................................79
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3.14Conclusion .........................................................................................................................79
Chapter 4: Lemur Species-Specific Scale Responses to Habitat Loss in Fragmented Landscapes in NW Madagascar .....................................................................................................81
4.1 Introduction ........................................................................................................................81
4.2 Landscape-Level Effect of Habitat Amount on Species Occurrence. ...............................82
4.3 Species-Specific Scale Responses to Habitat Amount. .....................................................84
4.3.1 Scale and Landscapes. ...........................................................................................84
4.3.2 “Scale of Effect.” ...................................................................................................84
4.3.3 Species-Specific “Scale of Effect” .........................................................................85
4.4 Justification ........................................................................................................................86
4.5 Goal ....................................................................................................................................86
4.6 Methods..............................................................................................................................87
4.6.1 Study Site and Study Species .................................................................................87
4.6.2 Question 1: What is the Scale of Species Response to Habitat Amount? .............87
4.6.3 Question 2: What is the Landscape-Level Effect of Habitat Amount on Species Occurrence? ..............................................................................................92
4.7 Results ................................................................................................................................93
4.7.1 Description of Spatial Autocorrelation Among Landscapes. ................................93
4.7.2 Question 1: What is the Scale of Species Response to Habitat Amount? .............94
4.7.3 Question 2: What is the Landscape-Level Effect of Habitat Amount on Species Occurrence? ..............................................................................................96
4.8 Discussion ..........................................................................................................................96
4.8.1 Question 1: What is the Scale of Species Response to Habitat Amount? .............97
4.8.2 Question 2: What is the Effect of Habitat Amount on Species-Specific Occurrence of Lemurs at the Landscape-Level? .................................................101
4.9 Suggestions for Conservation ..........................................................................................102
4.10Conclusion .......................................................................................................................102
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Chapter 5: Patch and Landscape Determinants of Lemur Species Richness and Occurrence ...................................................................................................................................104
5.1 Conclusion .......................................................................................................................104
5.2 Summary of Results .........................................................................................................104
5.2.1 Species-Area Relationships in a Lemur Community ...........................................104
5.2.2 Metapopulation Dynamics of Lemur Species ......................................................106
5.2.3 Landscape Effects on Lemur Species ..................................................................107
5.3 Implications ......................................................................................................................108
5.3.1 Species-Area Relationship ...................................................................................108
5.3.2 Metapopulation Dynamics ...................................................................................108
5.3.3 Landscape Effects ................................................................................................109
5.4 Directions for Future Research ........................................................................................110
5.4.1 Species-Area Relationships .................................................................................110
5.4.2 Metapopulation Dynamics ...................................................................................111
5.4.3 Landscape Ecology ..............................................................................................112
5.5 Implications for Lemur Conservation ..............................................................................113
5.6 Significance ......................................................................................................................114
References ....................................................................................................................................116
Appendices ...................................................................................................................................131
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List of Tables Table 2.1: Primate Species in Ankarafantsika National Park. ..................................................... 23
Table 2.2: Survey Data of 42 Fragments in a 3,000 ha Fragmented Landscape. ........................ 26
Table 2.3: 10 Candidate Species-Area Models. ........................................................................... 28
Table 2.4: Potential Influence of 10 Predictor Variables on Species Richness. .......................... 31
Table 2.5: Fragment Characteristics and Species Richness. ........................................................ 33
Table 2.6: Fitted Parameters of 10 Candidate Species-Area Models Using Non-linear Least
Squares Regression for Primate Species in the 42 Fragments. .................................................... 34
Table 2.7: Non-linear Least Squares Regression Model Selection of Species-Area Models. ..... 35
Table 2.8: Comparison of Seven Generalized Additive Models Using Poisson Link Function
Predicting Lemur Species Richness. ............................................................................................ 37
Table 3.1: Primate Species in Ankarafantsika National Park Found within Study Site. ............. 55
Table 3.2: Lemur Patch Occupancy in a Fragmented Landscape. ............................................... 64
Table 3.3: Metapopulation Models of Six Lemur Species in 42 Fragments in a Fragmented
Landscape. ................................................................................................................................... 66
Table 3.4: T-test Results for the Mean Probability of Occurrence (Ji) for Four Lemur Species in
the Largest Versus Smallest Fragments. ...................................................................................... 67
Table 3.5: T-test Results for the Mean Probability of Occurrence (Ji) for Four Lemur Species in
the Most Versus Least Connected Fragments. ............................................................................. 68
Table 4.1: Study Species Characteristics and Landscape Scale Sizes ......................................... 88
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List of Figures Figure 2.1: Sigmoidal Species-Area Model. ................................................................................ 10
Figure 2.2: Convex Species-Area Models. .................................................................................. 12
Figure 2.3: Study Site and Distribution of Forest within Madagascar. ....................................... 22
Figure 2.4: Study Site. ................................................................................................................. 24
Figure 2.5: Five Competing Species-Area Models. ..................................................................... 35
Figure 2.6: Hierarchical Partitioning Model. ............................................................................... 36
Figure 3.1: Five Types of Metapopulations. ................................................................................ 45
Figure 3.2: Probability of Occurrence Among Patches for Four Lemur Species in a Fragmented
Landscape. ................................................................................................................................... 65
Figure 3.3: Simulations of Metapopulation Dynamics for Four Lemur Species Over 200 Time
Steps in a Fragmented Landscape When the Five Largest and Five Smallest Fragments Are
Removed. ..................................................................................................................................... 69
Figure 3.4: Simulations of Metapopulation Dynamics for Four Lemur Species Over 200 Time
Steps in a Fragmented Landscape When the Five Most Connected and Five Least Connected
Fragments Are Removed. ............................................................................................................ 70
Figure 4.1: DigitalGlobe Satellite Image of Field Site. ............................................................... 90
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List of Appendices Appendix A: Cheirogaleus medius Occurrence Responses to Amount of Habitat within 10
Landscape Scales. ...................................................................................................................... 131
Appendix B: Microcebus murinus Occurrence Responses to Amount of Habitat within 10
Landscape Scales. ...................................................................................................................... 131
Appendix C: Microcebus ravelobensis Occurrence Responses to Amount of Habitat within 10
Landscape Scales. ...................................................................................................................... 136
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Chapter 1: Area, Patches, Landscapes and the Determinants of Lemur Species Occurrence
1.1 Introduction We are living through the sixth great extinction event in the earth’s history (Ceballos et al.
2015). There have been 903 extinctions reported in modern history, including two primate
species (Palaeopropithecus ingens and Xenothrix mcgregori) (IUCN, 2016). While
comparatively few primates have become extinct, 263 of 423 extant primate species are
currently threatened with extinction (IUCN, 2016) and one group—lemurs—is the most
threatened group of large animal taxa in the world (Schwitzer et al. 2014). As with most other
species (Brooks et al. 2002; Hanski, 2005; Groom et al. 2006), the main threat to primates,
including lemurs, is habitat loss and fragmentation (Schwitzer et al. 2014; IUCN, 2016).
However, scientists do not understand how primate species respond to these phenomena
(Chapman et al. 2003; Marsh, 2003; Arroyo-Rodriguez & Dias, 2010). More research is needed
on a wide range of primate taxa to identify the factors that influence primate responses to habitat
loss and fragmentation, and why so many primates are currently threatened with extinction. To
complicate matters, some species appear to fare well under certain habitat loss and
fragmentation scenarios while others do not (Boyle et al. 2013). Even within some species,
habitat loss and fragmentation responses vary (Arroyo-Rodriguez & Dias, 2010). Therefore, it is
crucial to determine why some primate species are impacted more than others. Understanding
primate extinction and responses to habitat loss and fragmentation will help us understand
primate evolution, ecology, and behavior, and will ultimately help us to conserve primate
species.
1.2 Background on Madagascar and Lemurs Madagascar is an island off the south east coast of mainland Africa. The fourth largest island in
the world, at approximately 587,000 km2 in size, it is home to approximately 23.6 million
people (World Bank, 2014). The island was attached to the mainland of Africa approximately
155 million years ago, and to the Indian subcontinent approximately 87 million years ago
(Wells, 2003). Therefore, Madagascar features a mix of African and Asian flora and fauna, plus
a large complement of unique species found only on the island (Goodman & Beanstead, 2005).
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Madagascar’s biodiversity is one of the most diverse and endemic of any country (Goodman &
Beanstead, 2005) and Madagascar is the only country where lemurs are naturally found.
Lemurs are a group of Strepsirrhine primates endemic to Madagascar, and are themselves one of
the most diverse groups of primates. There are 99 recognized lemur species and 103 taxa
(species and subspecies; Schwitzer et al. 2013). Lemurs vary dramatically: in size from the
diminutive mouse lemur (40 g) to the Indri (9.5 kg); in diet, including fauni-frugivores,
frugivores, and folivores; and in, activity pattern, such as diurnal, nocturnal and cathemeral
species (Mittermeier et al. 2010). Lemurs occupy a wide variety of habitat types including, but
not limited to, cloud forest, montane forest, moist lowland tropical forest, tropical deciduous dry
forest, and spiny forest (Mittermeier et al. 2010).
Madagascar is an ideal country in which to study the impact of habitat loss and fragmentation on
primate species. Habitats exploited by lemurs in Madagascar are fragmented and disappearing
rapidly. Since the 1950s, Madagascar has lost approximately 40% of its total forest (Harper et
al. 2007), which has been converted to agricultural fields or deforested grasslands. Therefore,
fragments of varying size surround much of the remaining continuous forest in Madagascar
(Harper et al. 2007). Forest loss and fragmentation in Madagascar are largely a result of small-
scale forest removal for rice production, and slash-and-burn agriculture to create pasture for
grazing cattle (Gade, 1996; Bloesch, 1999; Harper et al. 2007). Malagasy tropical dry forest in
particular is highly fragmented due to increased small-scale deforestation from fire along forest
edges in the 1990s (Harper et al. 2007). This pattern of deforestation in NW Madagascar has left
multiple fragments of varying sizes throughout the landscape and along the perimeter of
continuous forest tracts, such as Ankarafantsika National Park. Additionally, lemur species in
dry forest are more prone to variables that reduce their numbers than species in other forest
types (Ganzhorn, 1997). Deforestation through habitat loss and fragmentation is the leading
threat to lemurs in Madagascar (Schwitzer et al. 2014). Indeed, lemurs are the most endangered
group of animals in the world. Of the 103 taxa currently recognized, 94% are threatened with
extinction (Schwitzer et al. 2014). It is imperative that we determine how lemurs respond to
habitat loss and fragmentation in order to prevent this unique group of primates from going
extinct.
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1.3 Habitat Loss and Fragmentation Habitat loss is simply the removal of habitat from a landscape, while habitat fragmentation
involves splitting habitat into smaller, isolated fragments (Fahrig, 2003). Habitat loss and
fragmentation effects are inextricably linked because habitat fragmentation cannot occur without
some habitat loss (McGarigal & Cushman, 2002). Habitat loss and fragmentation are landscape-
level phenomena (McGarigal & Cushman, 2002; Fahrig, 2003). Yet most studies on habitat loss
and fragmentation are conducted at the patch-level (McGarigal & Cushman, 2002; Fahrig,
2003). A landscape is an area that is heterogeneous with respect to a character of interest
(Turner et al. 2001), while a patch is a discrete measurable portion of habitat. You can observe
habitat loss at either a landscape- or patch-level. However, you can only appropriately observe
habitat fragmentation at a landscape-level (Fahrig, 2003).
Based on a great deal of research, we know that primate species richness and occurrence is
impacted by habitat loss and fragmentation (Gray et al. 2010; Lawes et al. 2000; Chapman et al.
2003; Rodriguez-Toledo et al. 2003; Anzures-Dadda & Manson, 2007; Arroyo-Rodríguez et al.
2008; Arroyo-Rodriguez & Dias, 2010; Boyle & Smith, 2010; Marshall et al. 2010; Thorton et
al. 2011; Arroyo-Rodríguez et al. 2013a). For example, Rodriguez-Toledo et al. (2003) found
that habitat loss had a strong impact on Alouatta palliata in a highly fragmented landscape in
Mexico because A. palliata occurred in only 25% of remaining habitat fragments and were only
found in all of the largest (>10 ha) fragments. In the chapters to come, I will use three different
methods—species-area relationship, metapopulation dynamics, and landscape ecology—to
identify variables impacting both lemur community and species responses to habitat loss and
fragmentation in northwestern Madagascar.
1.4 Species Richness and Occurrence Understanding the factors that determine species richness (the number of species) and
occurrence (presence/absence of a species) is critical to understanding species ecology. A
species’ ability to occur in an area will relate to many aspects of their evolution, life history,
ecology, and behavior. Knowledge of species richness and occurrence can ultimately be used to
plan and evaluate conservation measures and understand biogeography. Researchers found that
area plays a major role in determining species richness and occurrence in numerous taxa,
including primates (Lomolino, 2000; Harcourt & Doherty, 2005; Whittaker & Triantis, 2012).
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With habitat loss and fragmentation increasing worldwide (Butchart et al. 2010), assessing the
role of area as a determinant of species richness and occurrence is crucial. It is also important to
determine how area impacts species richness and occurrence at both patch- and landscape-
levels.
1.5 Patch and Landscape Effects and Their Relation to Habitat Loss and Fragmentation
I define a patch as a discrete, measurable portion of habitat that is used by a species for
acquiring resources necessary for life. I define a fragment as a discrete portion of habitat that
has been separated from a larger whole. Patches can be considered fragments if they were
separated from a larger whole, and a fragment may be made up of more than one patch.
However, often the two terms are used synonymously in ecological literature. Research
investigating patch-level effects considers a patch or habitat fragment as the unit of analysis.
Comparing patches with different attributes can help us determine how those attributes affect
communities and species. Using a patch-level analysis is convenient because patches are easily
defined, measured, surveyed, and a useful unit for conservation management.
There is considerable debate in the ecological literature surrounding whether to use a patch- or
landscape-level study to determine the impact of habitat loss and fragmentation on species
occurrence and richness. The process of habitat loss, and habitat fragmentation are now
understood to be landscape-level phenomena (McGarigal & Cushman, 2002; Fahrig, 2003).
Therefore, many researchers have noticed the limitations of using only patch attributes to
evaluate what determines species richness and occurrence (McGarigal & Cushman, 2002;
Fahrig, 2003). More commonly, researchers are employing a landscape-level approach instead
of a patch-level approach to investigate how attributes of heterogeneous landscapes affect
species richness and occurrence. Although a landscape-level approach aligns with the scale of
the process of habitat loss and fragmentation, it introduces logistical and scale issues that must
be considered for effective study design. Logistical issues are a major factor inhibiting research
using a landscape-level approach because it is often necessary to use larger areas for analysis
using a landscape- compared to a patch-level study. To compare landscape attributes in a
landscape-level study researchers need to measure and survey multiple landscapes and
determine the appropriate measurement scale for a landscape (Turner et al. 2001; McGarigal &
Cushman, 2002; Turner, 2005). Some researchers have suggested that responses to habitat loss
5
and fragmentation at a landscape-level are species-specific (Jackson & Fahrig, 2012). More
specifically the scale by which species’ respond or the “scale of effect” is species-specific
(Jackson & Fahrig, 2012). Therefore, it can be difficult to determine the size of a landscape a
priori. Both patch- or landscape-level approaches can yield important information and the
choice of what level to use is determined by many factors including the research questions,
logistical issues, and management implications. Thus, it is appropriate to use a multi-scale
approach when logistic issues and lack of information are present.
1.6 Patch-Level Effects on Primate Communities: The Species-Area Relationship
One of the oldest and most common approaches to assess what impacts species richness is to use
the species-area relationship (Lomolino, 2000; Whittaker & Triantis, 2012). The species-area
relationship describes a pattern of increasing species richness with increasing habitat area
(Lomolino, 2000). Species-area relationships are so common that the relationship is considered
axiomatic in biogeography (Whittaker & Triantis, 2012). There are two main types of species-
area relationships. The first type includes the study of the species-area relationship within
sample areas that make up a greater whole (Lomolino, 2000; Tjørve, 2003). In sample area
studies, researchers sample and survey portions of habitat within greater and greater areas to
assess how many species occur within each size of sampled area. Sample areas can be nested
because a species can occur within multiple samples due to species movement between areas.
The second type of species-area relationship is the species-area relationship in isolates, such as
islands or habitat fragments (Lomolino, 2000; Tjørve, 2003). In isolate studies, the area
surrounding an isolate is considered hostile and limits the ability of a species to move between
isolates (Lomolino, 2000; Ricketts, 2001; Tjørve, 2003). Studying the species-area relationship
in isolates has led to large body of theory, such as the island biogeography theory (MacArthur &
Wilson 1967), and has improved our understanding of how communities respond to habitat loss.
Researchers have found the species-area relationship in most taxa including primates (Reed &
Fleagle, 1995; Cowlishaw, 1999; Cowlishaw & Dunbar, 2000; Lehman, 2004; Harcourt &
Doherty, 2005; Marshall et al. 2010). For example, Reed and Fleagle (1995) found that the
number of primate species increased with increasing area of tropical rainforest at continental
scales. Similarly, I found during my pilot study for this dissertation that lemur species richness
was linearly related to the log-area of habitat fragments (Steffens & Lehman, 2013). Looking at
the pattern and process of the species-area relationship in lemurs, a highly endangered group of
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primates (Schwitzer et al. 2014), at a larger scale will give us a better understanding of the
community-level response to habitat loss.
1.7 Patch-Level Effects on Primate Occurrence: Metapopulation Dynamics
The species-area relationship operates at the community-level and is species irrelevant, meaning
it is concerned only with the number of species in an area, rather than species composition or
identity. But to understand how species occurrence is impacted by habitat loss and
fragmentation, we need to investigate how individual species are impacted by these factors. One
way to determine the impact is to use metapopulation dynamics (Hanski, 1994a; Hanski, 1999;
Hanski & Ovaskainen, 2003). A metapopulation is a collection of many local populations of a
species that are connected to one another through dispersal (Levins, 1969). Metapopulation
models rely on the existence of measurable, discrete habitat patches such as habitat fragments
(Hanski & Ovaskainen, 2003). Metapopulation dynamics is a theoretical framework that is used
to describe how species become spatially arranged within a landscape and is particularly useful
for understanding the impact of habitat fragmentation. Primates have been shown to form
metapopulations (Lawes et al. 2000). For example, Lawes et al. (2000) used metapopulation
dynamics to determine the minimum area requirements for Cercopithecus mitis labiatus, and to
see if other variables such as human disturbance and isolation impacted their occurrence. They
found that the minimum area required for C. m. labiatus occupancy was approximately 44 ha
and that fragment area was the greatest factor explaining C. m. labiatus occurrence.
Understanding metapopulation dynamics in primates within a community can help us determine
what drives species-area relationships and how individual species are impacted by patch area
and isolation in fragmented landscapes.
1.8 Landscape-Level Effects on Primate Occurrence While the species-area relationship and metapopulation dynamics investigate aspects of species
richness and occurrence respectively at a patch-level, habitat loss and fragmentation are
landscape-level phenomena (McGarigal & Cushman, 2002; Fahrig, 2003). Therefore, a
landscape-level approach will yield additional insights into how species respond to habitat loss
and fragmentation, and may help determine why area is an important driver of species richness
and occurrence. A landscape is an area that is heterogeneous with respect to a character of
interest (Turner et al. 2001). For example, a landscape may vary in the amount of forest within
7
its boundaries. Scale becomes increasingly important in landscape ecology studies because
species responses are typically scale dependent and are probably species-specific (Jackson &
Fahrig, 2012). Some studies on primates have found that landscape-level responses are species-
specific (Gray et al. 2010; Thorton et al. 2011; Ordóñez-Gómez, 2014). For example, Ordóñez-
Gómez (2014) found that Ateles geoffroyi showed a species-specific scale of effect to the
amount of forest cover at 126 ha for most response variables at a landscape-level. By looking at
how species respond to habitat amount in a landscape, we may be better able to understand
species responses to habitat loss and fragmentation, and to determine what factors drive species
responses to area.
1.9 A Note on the Format of the Thesis My dissertation is written as a collection of three independent manuscripts (Chapters 2–4) that
investigate how habitat loss and fragmentation affect lemur species richness and occurrence. To
reduce redundancy, in the methods section of each independent chapter, I refer to methods from
previous chapters when there is overlap. References are supplied at the end of the dissertation in
a single references section. My dissertation flows from community level relationships to species
level relationships at a patch-level and then lemur occurrence patterns at a landscape-level.
Chapter 2 investigates the role of the species-area relationship on a lemur community to
determine if area is the largest driver of species richness. Chapter 3 assesses how
metapopulation dynamics relate to lemur species occurrence and compares the impact of area
and isolation on individual species occurrence. Chapter 4 evaluates how habitat loss as
measured by the amount of habitat within species-specific landscape impacts lemur species
occurrence. In Chapter 5, I draw conclusions about how lemur species richness and occurrence
are influenced by habitat loss and fragmentation. I also explore the implications of these
findings, detail directions for future research, and provide a comprehensive review of possible
conservation actions related to the results of my research.
1.10 Dissertation Goals Habitat loss and fragmentation are proceeding rapidly in Madagascar. It is critical that we
understand what drives local lemur species extinction in landscapes that are being increasingly
fragmented. My goal is to investigate how area impacts lemur species richness and occurrence
at different scales, using a patch-level analysis within a single large landscape, and a landscape-
8
level analysis involving many small landscapes. Specifically, I will determine if lemurs show a
species-area relationship, if that relationship is sigmoidal, and if factors other than area
influence lemur species richness in a fragmented landscape. I also want to determine if
metapopulation dynamics explain lemur species occurrence and how area and isolation
independently affect lemur species occurrence. Finally, I want to evaluate if the role of area on
lemur species occurrence is the same from a landscape ecology perspective as from a patch-
level perspective. With this dissertation, I will contribute to resolving a recurring debate in
ecological theory over how species respond to habitat loss and fragmentation, demonstrating the
intertwined utility of the species-area relationship, metapopulation dynamics, and landscape
ecology.
9
Chapter 2: Species-Area Relationships of Lemurs in a Fragmented Landscape in Madagascar
2.1 Introduction The species-area relationship (SAR) occurs throughout nature (Whittaker & Triantis, 2012). The
relationship between species and area describes the pattern of increasing species richness (the
total number of species) with increasing habitat area (Lomolino, 2000) and has been
demonstrated in virtually all taxa studied, including primates (Lehman, 2004; Harcourt &
Doherty, 2005; Marshall, et al. 2010). Although well-known since the 19th century, the species-
area relationship lacked formal description as the species-area curve until the 20th century
(McGuinness, 1984; Scheiner, 2003). Species-area curves are graphical representations of
mathematical models describing the pattern of the species-area relationship.
In this chapter, I will introduce how to choose a species-area model based on various criteria,
and describe the different types of species-area curves, focusing on the nature of convex and
sigmoidal models. I will then discuss some competing hypotheses for what determines the
species-area relationship. I will assess how other variables, in addition to area, influence primate
species richness in a fragmented landscape. I will follow with a literature review of the species-
area relationship research on primates, and explore why Madagascar is an ideal locality to study
the pattern and process of the species-area relationship in primates. I will end the introduction
by presenting my objectives, hypotheses, and predictions for this study.
2.2 Choosing a Species-area Model When choosing a species-area model, it is important to consider how species-area relationship
patterns are influenced by isolate data versus sample area data, scale, and arithmetic versus
statistical space (Scheiner et al., 2000; Tjørve, 2003; Triantis et al., 2012). In isolates (such as
islands or habitat fragments surrounded by matrix), species are typically bound by the confines
of the island or habitat patch. In these situations, minimum area effects for a species will impact
whether or not certain species exist within isolates below a particular size. The “small island
effect” occurs when species richness is impacted by factors other than area in small islands, or
when the sizes of the islands are too small for the species to survive (Lomolino, 2000). There
should be a maximum number of species when there is limited movement between isolates and
10
there is no other source population (Lomolino, 2000). Therefore, in surveys of isolates, species-
area curves should be sigmoidal shaped with the lower j-portion of the curve representing the
“small island effect” and upper asymptote representing the maximum number of species (Fig.
2.1; Tjørve, 2003; Lomolino, 2000).
Figure 2.1: Sigmoidal Species-Area Model. In sigmoidal models, there is a “small island effect” in the lower j-portion of the curve where species richness is affected by factors other than area. Above the “small island effect” species number accumulate in a similar fashion to convex models. Sigmoidal models may or may not have an upper asymptote. Some sigmoidal models have flexible inflection points (the point on the curve where the slope changes from an increasing slope to a decreasing slope) while others do not. This figure is an example of a sigmoidal model with a flexible inflection point. If researchers do not consider scale when investigating the species-area relationship, then it is possible that a truncated sigmoidal model may appear to be convex (area between red arrows).
Species-area relationships measured within portions of large continuous habitat are known as
sample area relationships. In sample areas, there is no “small island effect” because species are
Area
Spe
cies
Ric
hnes
s
0 20 40 60 80 100 120 140 160 180
010
2030
4050
Island EffectSmall
Upper Asymptote
Inflection Point
11
not restricted within continuous habitat as is the case for species in isolates (Lomolino, 2000;
Tjørve, 2003). Smaller sample areas are arbitrarily chosen and do not reflect potential biological
barriers, whereas in isolates, a small island is potentially surrounded by hostile matrix (Ricketts,
2001). Species accumulation during surveys of plots of increasing size will at first increase
dramatically with the most common species being discovered rather easily (Fig. 2.2). However,
the number of new species discovered typically decreases as sample area increases (Preston,
1962). If there is a finite species pool then the most appropriate model will be a convex upward
model with an upper asymptote (Tjørve, 2003).
Scale can impact the choice and nature of the species-area curve (Scheiner et al., 2000). For
example, in isolates a sigmoidal curve is theoretically correct because of the “small island
effect” and likely upper asymptote. If a study does not include a large range of isolate sizes, then
the predicted sigmoidal nature of the relationship may be obscured (Lomolino, 2000; Tjørve,
2003; Triantis et al. 2012). A truncated sigmoidal curve may appear to be convex if the scale of
observation does not include areas that would fall within the “small island effect” (Fig. 2.1).
Alternatively, if the areas sampled are too small, then the upper asymptote may be lost. Scale
can also influence the shape of a model. When using a power model, the slope of the species-
area curve (z-values) can change depending on the scale of the study. For example, larger
islands will have lower z-values than smaller islands (Martin, 1981).
12
Figure 2.2: Convex Species-Area Models. Neither model has a “small island effect,” but instead species richness increases rapidly at first but slows down as habitat area increases indefinitely (no upper asymptote) or until a maximum of species is found (upper asymptote).
Tjørve (2003) suggests that investigating the pattern of species-area curves is most appropriately
conducted in arithmetic space. The differential use of arithmetic space versus statistical space in
species-area studies can lead to confusion and misunderstandings of different models. Most
measurements of species-area relationships occur in arithmetic space and are later transformed
into log-log or log-linear statistical space for analysis. However, the shape of a species-area
curve is not consistent across different statistical transformations (Tjørve, 2003; Triantis et al.
2012). A problem occurs when transformations are applied to species richness data in attempts
to normalize or linearize the relationships without considering how such transformations may
alter the biological interpretation of subsequent models. Transforming a species-area
Area
Spe
cies
Ric
hnes
s
0 20 40 60 80 100 120
010
2030
4050
Upper Asymptote
No Upper Asymptote
13
relationship into a linearized relationship can result in a statistically valid model, but that model
will have little biological validity (Tjørve, 2003).
Researchers should select species-area models based on existing knowledge about the system
being studied (isolates versus continuous habitat), and consider the impact of scale on species-
area models. If the study focuses on true islands that may have a “small island effect,” then a
sigmoidal model with an upper asymptote is the most appropriate. If the study focuses on
sample areas within continuous habitat, then a convex model with or without an asymptote is the
most appropriate. If there is ambiguity in the isolated nature of the study area (for example, in
habitat fragments that are not as isolated as oceanic islands), then researchers should consider
both sigmoidal and convex models with and without asymptotes. Also regardless of the system
being studied researchers should consider the effect of scale. For example, if the scale is too
small in isolate species-area relationships, a convex model may appear to be more appropriate
than a sigmoidal model (Fig. 2.1). Finally, because linearizing species-area relationships can
result in inconsistencies in how researchers interpret different models, it is more appropriate to
use arithmetic space in data analysis.
2.3 Types of Species-Area Curves Species-area curves can be categorized as those that have asymptotes versus those that do not,
and those that are of convex shape versus those that are sigmoidal. An asymptote occurs when
the distance between a line and a curve approaches zero (Fig. 2.2). In models without an
asymptote the distance between a line and a curve continually increases or decreases. Sigmoidal
models can be further divided into those that are flexible about where their inflection point
occurs (the point on the curve where the slope changes from an increasing slope to a decreasing
slope) and upper asymptote versus those that are symmetrical because their inflection point is
fixed relative to the upper asymptote (Fig. 2.1).
2.3.1 Convex Models without an Asymptote
Species-area curves are typically fitted using two classic models: the power model and the
exponential model (Connor & McCoy, 1979; Coleman, 1981; Tjørve, 2003). The power model
was introduced by Arrhenius (1921) and is sometimes called the Arrhenius model. The
exponential model was introduced as a competing model by Gleason (1922) and is sometimes
referred to as the Gleason model. Both the power and exponential models do not have an upper
14
asymptote, meaning that the number of species will increase infinitely with area. The power and
exponential models are both simplistic, relatively monotonic views of the species-area
relationship, and do not fully capture the pattern as well as other models that provide upper
asymptotes and account for possible “small island effects” (Lomolino, 2000; Tjørve, 2003).
However, when applied to sample areas, both the power and exponential models have
consistently performed well (Tjørve, 2003). The difference between the two models is that in the
exponential model species number increases rapidly at first but the rate of increase declines
sooner than in a power model. The power model is used regularly in many studies on
mammalian species richness (Lomolino, 1982; Frey et al. 2007; Triantis et al. 2012). Botanists
most frequently used the exponential model to explain species-area relationships among plant
species until the power model became the model of choice (Connor & McCoy, 1979).
2.3.2 Convex Models with an Asymptote
When considering convex models, most research has focused on the power and exponential
models (Connor & McCoy, 1979; Lomolino, 2000; Tjørve, 2003). However, there are other
convex models that may be applied to species-area relationships that have upper asymptotes,
including the Monod, negative exponential, asymptotic regression, and rational function models
(Tjørve, 2003). Of these convex models with an upper asymptote, only the negative exponential
model (Rakatowsky, 1990) always passes through the origin (Tjørve, 2003). These are potential
candidate models for species-area relationships because of their ability to reach an upper
asymptote, where researchers can infer a total species number.
2.3.3 Sigmoidal Models without an Asymptote
Although many different sigmoidal models potentially fit species-area relationships, few models
have actually been applied to real-world data (Tjørve, 2003; Lomolino, 2000). The benefit of a
sigmoidal model is most apparent in its ability to explain biological relationships between
species and area in isolates. In isolates there is an expectation that some species will exhibit a
“small island effect” (Lomolino, 2000; Tjørve, 2003). A sigmoidal model without an upper
asymptote is more appropriate in isolates where species can enter the system and no maximum
number of species is expected. The result is an s-shaped curve with no upper asymptote. The
persistence function (referred to as the persistence P2 by Tjørve, 2009) presented by Ulrich and
Buszko (2003, 2004) represents such a sigmoidal model without an upper asymptote. The
15
persistence P2 function is a parameterization of the power model to allow for either a convex or
sigmoidal shape. The persistence P2 function is very flexible because it does not have an upper
asymptote and may be useful for species-area relationships where the “small island effect”
occurs at very small scales (Tjørve, 2009).
2.3.4 Sigmoidal Models with an Asymptote
The closed nature of isolates means that an upper limit of species number should result in an
upper asymptote for a species-area curve. If species have minimum area requirements, then the
“small island effect” should occur. The result is an s-shaped curve with an upper asymptote.
Differences in models may impact their application to species-area relationships. Sigmoidal
models with an upper asymptote that are proposed to work with species-area relationships
include the logistic function, Lomolino function, the cumulative Weibull distribution, and the
extreme value function. The ability to be flexible about the inflection point is important for
species-area relationships because the “small island effect” typically only occurs in the lower
end of area (Tjørve, 2009; Tjørve & Turner, 2009). The logistic function and extreme value
function have inflection points that are fixed at 50% and 63.2% of the upper asymptote,
respectively, while the cumulative Weibull distribution and Lomolino models are flexible about
the inflection point. Therefore, if there is any asymmetry in the shape of the species-area
relationship, then the cumulative Weibull or Lomolino models may be more appropriate than the
logistic or extreme value functions.
2.4 Processes Determining the Species-Area Pattern The pattern of species-area relationships can be influenced by the type of system under study
(isolate versus sample data), scale, and by analyzing arithmetic versus statistical relationships
(Scheiner et al. 2000; Tjørve, 2003; Triantis et al. 2012). However, what are the biological
mechanisms that determine the pattern of species-area relationships? There are three main
hypotheses explaining the process behind the pattern of the species-area relationship. The first,
the habitat heterogeneity hypothesis, was developed by Williams (1964) and is invoked in
numerous studies (Mac Arthur &Wilson, 1967; Fox & Fox, 2000; Scheiner, 2003). The habitat
heterogeneity hypothesis states that an increase in area results in more habitat types (i.e. large
areas are more heterogeneous relative to smaller areas), which results in increased niche space
and type that can be filled by more species (Williams, 1964; MacArthur & Wilson, 1967;
16
Connor & McCoy, 1979; Rosenzweig, 1995; Scheiner, 2003). Given variation amongst taxa in
their niche requirements, the degree of habitat heterogeneity depends on variables that are taxa-
and scale-specific. Many studies have found a connection between species richness and habitat
heterogeneity (e.g., Ganzhorn et al. 1997; Fox & Fox, 2000; Williams et al. 2002). For example,
Fox and Fox (2000) found that habitat diversity better predicted small mammal species richness
than area from eleven sites in Myall Lakes National Park, Australia.
The second hypothesis to explain the cause of the species-area relationship is called the area per
se hypothesis. The area per se hypothesis states that species richness is the product of extinction
probabilities. Species in larger areas will have larger populations and will therefore have lower
extinction probabilities and are less prone to stochastic disturbance events than species in
smaller areas (MacArthur & Wilson, 1967). Therefore, larger areas should have more species
than smaller areas.
Finally, the third hypothesis to explain the cause of the species-area relationship is the passive
sampling or random placement hypothesis (Connor & McCoy, 1979). The passive sampling
hypothesis assumes that all individuals and species within a community are distributed
randomly. Therefore, the probability of finding a randomly distributed species within a given
sample is determined by a ratio of the size of the sample to the entire area. Thus, a species is less
likely to be detected in a smaller sample area than in a larger sample area. In this sense, the
passive sampling hypothesis is a product of sampling phenomena and not biological processes
as we see in the habitat heterogeneity or area per se hypotheses, and so the passive sampling
hypothesis may be considered a null model (Connor & McCoy, 1979). However, these three
hypotheses may not be mutually exclusive and each may be combined to explain observed
patterns of species-area relationships (Connor & McCoy, 1979; Ricklefs & Lovette, 1999).
2.5 Other Non-Area Factors Affecting Primate Species Richness Though powerful, species-area models are overly simplistic in determining what specifically
drives species richness in fragmented landscapes. Factors such as habitat structure,
anthropogenic disturbance, and isolation metrics can influence species-area relationships within
fragmented landscapes. Anthropogenic disturbances such as hunting, logging, capture for the pet
trade, fire, climate change, and disease have been shown to impact primate species (Chapman &
Peres, 2001; Estrada et al. 2006; Chapman et al. 2007; Mittermeier et al. 2010). Although there
17
is a great deal of research that has investigated the impact of human disturbance on primates,
most fragmentation research has neglected to directly measure the potential conflation of
multiple sources of anthropogenic disturbance on primate species abundance and occurrence
(Chapman et al. 2003; Arroyo-Rodriguez & Dias, 2010). Lawes et al. (2000) found no
measurable impact of anthropogenic disturbance (aside from habitat loss and fragmentation) on
primate occurrence, and found that fragment area was the strongest predictor of primate
occurrence. Similarly, using multivariate analysis, Marshall et al. (2010) found that human
disturbance variables did not add explanatory power to species-area relationship models when
applied to primates. However, Arroyo-Rodriguez et al. (2008) found that fragments further from
villages had a higher occupancy of Alouatta palliata than fragments closer to villages,
suggesting an indirect impact of human disturbance. Gillespie and Chapman (2006) noted that
increased human disturbance within fragments increased parasite risk for primates. Thus,
researchers should consider how human disturbance might impact species responses to habitat
loss and fragmentation when assessing the impacts of these processes and patterns on primates.
Like habitat heterogeneity, the structure and composition of habitat may predict species richness
in primates. For example, tree species richness and habitat structure variables (including tree
size) are correlated with lemur species richness in a positive but non-linear fashion (Ganzhorn et
al. 1997). Pyritz et al. (2010) found that understory density is negatively correlated with primate
species richness. Hanya and Aiba (2010) found that variation in fruit fall might explain some of
the variation in frugivore richness in primates. Thus, it is important to consider the potential
effects of habitat characteristics on species richness.
Isolation of fragments within a landscape can impact species richness. Island biogeography
theory states that islands that are smaller and more isolated will have lower species richness than
islands that are larger and less isolated (MacArthur & Wilson, 1967). Species richness decreases
when islands are more isolated because isolation reduces immigration rates. Inversely, species
richness increases when islands are less isolated due to increased immigration rates. Few studies
have investigated the impact of habitat isolation on primate species richness (Harcourt &
Doherty, 2005). Harcourt and Doherty found no relationship between primate species richness
and isolation. However, some researchers have investigated the impact of habitat isolation on
primate species occurrence (Lawes et al. 2000; Boyle & Smith, 2010). In a metapopulation
study, Lawes et al. (2000) found that isolation did not explain variation in primate species
18
occurrence, but did explain variation in occurrence for non-primate species. Boyle et al. (2010)
found a negative relationship between primate species occurrence and isolation, but isolation
was correlated with area. Because of the large amount of theory outside of primatology
suggesting isolation should impact species richness, it is important for studies to incorporate
isolation metrics in their analysis.
2.6 Species-Area Relationships in Primates Many researchers have demonstrated species-area relationships in primates (Reed & Fleagle,
1995; Cowlishaw, 1999; Cowlishaw & Dunbar, 2000; Lehman, 2004; Harcourt & Doherty,
2005; Marshall et al. 2010). Reed and Fleagle (1995) found that the number of primate species
is positively correlated with area of tropical rainforest at continental scales, and with rainfall at
local scales. Using data from several published studies, Cowlishaw and Dunbar (2000) found
that the species-area relationship explained 51% of the variation in primate species richness.
Cowlishaw (1999) used the species-area relationship to determine whether there was an
extinction debt for African primates. Using a power model he found that log-area significantly
explained 34% of variation in log-species richness (R2=0.34; p=0.002). Lehman (2004)
surveyed the number of primate species within different habitat types in Guyana and found that
habitat area explained 63% of the variation in primate species richness. In a global analysis of
primate species-area relationships, Harcourt and Doherty (2005) found that primate species
number was significantly greater in larger than in smaller fragments, with a few exceptions in
Africa. Marshall et al. (2010) investigated the species-area relationship for diurnal primate
species among habitat fragments in Tanzania and found that of the 17 variables measured, area
was the strongest predictor of primate species richness (R2=0.846; p<0.01). These researchers
also noted a “small island effect,” where primate species richness did not relate to area in
fragments smaller than 12–40 km2. In 2010 I conducted a pilot study for this project,
investigating the possible species-area relationship of lemur species in eight fragments of dry
deciduous forest. I found that there was a linear relationship between log-area of a fragment that
significantly explained 71.3% of the variation in log-species richness (R2=0.713; p<0.01;
Steffens & Lehman, 2013). However, none of these studies considered different candidate
models to explain species-area relationships in primates, and only Marshall et al. (2010) and
Lehman (2004) considered the impact of variables other than area that may influence species-
area relationships.
19
2.7 Testing for a Species-Area Relationship To determine the pattern of the species-area relationship it is important to avoid simple curve
fitting, and to extend studies to determine biological mechanisms to predict species-area
relationships a priori (Tjørve, 2003). For example, a species-area relationship should take the
form of a sigmoidal model in isolates, especially if there is a suspected “small island effect”
(Lomolino, 2000; Tjørve, 2003; Tjørve, 2009). Conversely, in sample areas a convex model is
more appropriate (Tjørve, 2003). Researchers can assume an asymptote in sample areas if there
are reasons to suspect a maximum number of species (Lomolino, 2000). Primates, like all
species, require a minimum area of habitat for survival (Tjørve & Turner, 2009). However, for
most species we do not know what that minimum area is (Gurd et al. 2001). The minimum area
requirement for a sexually breeding species should fall somewhere near the minimum area that
could support a breeding pair (Lambeck, 1996). The minimum area for an individual should be
positively related to its body size, with smaller individuals requiring smaller ranges (Milton &
May, 1976; Lehman et al. 2005). Additionally, researchers can expect an upper limit to species
diversity in studies that limit their investigation of species-area requirements to a particular
taxon, such as primates, because at any particular moment there is a finite maximum number of
primate species (Tjørve & Turner, 2009).
2.8 Justification Madagascar is home to approximately 99 species and 103 taxa of lemurs, many of which have
overlapping geographic ranges (Schwitzer et al. 2013). Habitats exploited by lemurs in
Madagascar are fragmented and disappearing rapidly—since the 1950s 40% of forest cover in
Madagascar has been converted to non-forest habitats (Harper et al. 2007). Forest loss and
fragmentation in Madagascar are largely a result of small-scale forest removal for rice
production, slash-and-burn agriculture, and to create pasture for grazing cattle (Gade, 1996;
Bloesch, 1999; Harper et al. 2007). Malagasy dry forest in particular is highly fragmented due to
increased incidence of small-scale deforestation from fire along forest edges in the 1990s
(Harper et al. 2007). Deforestation in northwestern Madagascar has left multiple fragments of
varying sizes throughout the landscape and along the perimeter of continuous forest tracts, such
as in Ankarafantsika National Park. Madagascar is an ideal country in which to study the impact
of habitat loss and fragmentation on primate species-area relationships because of the large
number of primate species and the highly fragmented nature of the primate habitat.
20
Although researchers conduct SAR studies on numerous taxa and in numerous situations, we are
still unclear on which SAR pattern best describes SARs in primates in fragmented landscapes.
Primates are ideal taxa in which to investigate the pattern and processes creating SARs because
many species are arboreal and require forest for travel. Therefore, discrete patches such as
habitat fragments may represent truly insular islands. If fragments occur below a particular size,
then populations of primate species may not be viable due to minimum area requirements.
Studying arboreal primates provides an opportunity to determine if a priori assumptions, such as
a sigmoidal nature of SARs in a fragmented landscape, are valid. In this study, I seek to increase
our understanding of the SAR pattern and process in primates by investigating species richness
and habitat characteristics in a highly fragmented landscape in Madagascar.
2.9 Goals The goals of my study are to determine the pattern of lemur species richness and to evaluate
how variables other than area affect species richness by investigating the following questions:
1) Which species-area model best describes the pattern of lemur species richness in a
fragmented landscape?
Hypotheses:
H0 Lemur species richness will vary randomly with respect to area.
H1 Lemur species richness will be positively related to area.
Predictions: The primate species in this study are mainly arboreal and therefore the
matrix between fragments should represent a barrier to their movement. Minimum area
requirements should limit lemur species to the largest fragments. Either the level of
isolation between fragments, the presence of minimum area requirements for lemur
species, or both should dictate whether or not I observe a small island effect and
subsequently a sigmoidal SAR.
2) Is area the main factor affecting lemur species richness, or do other variables such as
habitat characteristics also impact lemur species richness?
Hypotheses:
21
H0 Lemur species richness will vary randomly with respect to area and habitat
characteristics.
H1 Lemur species richness will be positively related to area, habitat characteristics,
decreased isolation, and decreased human disturbance.
Predictions: Lemur species richness in a fragmented landscape, in addition to area, will
be positively influenced by habitat characteristics, and will be positively associated with
decreased isolation and decreased human disturbance.
2.10 Methods
2.10.1 Study Site and Study Species
I conducted this study between June and November of 2011 in Ankarafantsika National Park,
Madagascar (Fig. 2.3). Ankarafantsika National Park is approximately 135,800 ha, and consists
of a mosaic of approximately 72,670 ha of dry deciduous forest (Alonso et al. 2002; Razafy
Fara, 2003) and grassland savannah (García & Goodman, 2003). The climate is mostly dry with
mean yearly rainfall of 1,000–1,500 mm occurring mostly in the rainy season between
November and April (Alonso et al. 2002). There are eight species of lemurs in the park (Table
2.1) including: the western wooly lemur (Avahi occidentalis), Coquerel’s sifaka (Propithecus
coquereli), the grey mouse lemur (Microcebus murinus), the golden brown mouse lemur
(Microcebus ravelobensis), Milne Edwards sportive lemur (Lepilemur edwardsi), the fat-tailed
dwarf lemur (Cheirogaleus medius), the mongoose lemur (Eulemur mongoz), and the common
brown lemur (Eulemur fulvus; Mittermeier et al. 2010). I conducted this study in the dry season
and early part of the wet season (June–November) to facilitate access, and because there is
increased visibility due to reduced foliage. All species were active during the entire study except
C. medius, which is in torpor between April and October (Dausmann et al. 2005).
22
Figure 2.3: Study Site and Distribution of Forest within Madagascar. a) Location of study site within Madagascar. b) Location of the study site within Ankarafantsika National park. c) Close up of study site showing the fragmented landscape, consisting of 42 fragments of dry deciduous forest separated by a mainly homogeneous matrix of grassland. Survey fragments are represented in dark grey and continuous forest in light grey and savannah in white.
23
Table 2.1: Primate Species in Ankarafantsika National Park.
Species Body Mass (g) Activity Pattern Diet Mean Home range
(ha) Cheirogaleus medius 120–270 Nocturnal Frugivore 1.55 ±0.42(1) Microcebus murinus 58–67 Nocturnal Fauni-frugivore 2.83 ±1.44(2)
Microcebus ravelobensis 56–87 Nocturnal Fauni-frugivore 0.59 ±0.11 (3) Propithecus coquereli 3700–4300 Diurnal Folivore 19.36(4)
Eulemur fulvus 1700–2100 Cathemeral Frugivore 13.5(5) Lepilemur edwardsi 1100 Nocturnal Folivore 1.096)
Data from: 1 Müller (1998); 2 Radespiel (2000); 3 Weidt et al. (2004); 4 McGoogan (2011); 5 Mittermeier et al. (2010); 6 Warren & Compton (1997).
I collected species richness data in 42 forest fragments and habitat characteristics data in 38 of
the 42 forest fragments in a fragmented landscape (Fig. 2.4). The forest fragments ranged in size
from 0.23 ha to 117.7 ha, and were surrounded by a relatively homogeneous matrix of grassland
savannah, mostly consisting of the species Aristida barbicollis (Bloesch, 1999). I defined habitat
fragments as patches of forest with a connected canopy, including primary and secondary
vegetation that were separated from other fragments by a clear gap in the canopy. I defined
continuous forest as forest that was not fragmented. There is no recorded history of
fragmentation within the landscape, but I investigated topographic maps from the 1950s and
found a similar spatial arrangement of fragments. Current land-use management in the
landscape allows for some degree of use and some degree of seasonal burning and subsequent
grazing by cattle of the savannah is tolerated. There are no permanent residents within the
landscape. However, during the wet season local people create temporary settlements to graze
their cattle. Although hunting is not permitted, I found evidence of traps set for lemurs and there
is evidence in other areas of the park that lemurs are hunted (García & Goodman, 2003).
24
Figure 2.4: Study Site. Close up of fragmented landscape showing fragments (dark grey) labeled in red, surrounded by savannah (white), and in proximity to continuous forest (light grey).
2.10.2 Question 1: What is the SAR Pattern?
To assess the pattern of species-area relationships, I measured the area of each fragment by
recording a track along its perimeter using a handheld global positioning device (Garmin GPS
map 60csx) and inputting the results into QGIS (2012; n=38). If obstructions prevented a
complete perimeter walk (n=4; Fragments 37, 39, 40, 42), I traced the perimeter on a high
resolution DigitalGlobe™ satellite image taken during the study (10/8/2011) from Google
Earth™ and input the polygon in QGIS to determine its area. I placed survey transects along the
25
longest axis of the fragment while going through the center of the fragment except in Fragment
12A where I placed the transect along the longest axis of the largest portion of the fragment.
To determine species richness, my team of ten and I conducted both early and late diurnal and
nocturnal surveys in each fragment. Early diurnal surveys occurred between 06:19 and 09:07
hours and late diurnal surveys occurred between 14:39 and 17:18 hours. Early nocturnal surveys
took place between 18:00 and 21:27 hours and late nocturnal surveys took place between 02:17
and 5:55 hours. To ensure temporal independence for each survey, we only conducted one of
each survey type (diurnal and nocturnal) per 24-hour period in each transect. We surveyed all
fragments at least twice during early June and between October and November to ensure an
accurate assessment of the occurrence of C. medius, who can be in torpor between April and
October (Fietz & Ganzhorn, 1999; Dausmann et al. 2005). In total, we conducted between 11
and 18 diurnal, and 11 and 21 nocturnal surveys in each fragment (Table 2.2). During survey
walks each researcher walked slowly (approximately one km/hour), scanning and listening for
all lemur species. During diurnal surveys one or two researchers scanned both sides of the
transect simultaneously. Two researchers walked together during all nocturnal surveys and each
researcher focused on one side of the transect for the entire duration of the survey. Each team
member used high-powered flashlights and headlamps during nocturnal surveys to observe eye
shine. One member of each pair of diurnal researchers was experienced in identifying each of
the eight different species within the park. When team members observed a group or individual
lemur, the team spent up to 15 minutes determining species identity. Each of the species is
easily identified by size, with the exception of the two Microcebus species. We used a suite of
characteristics to determine the species identity of the two Microcebus species. We determined a
positive identification of M. murinus when the team observed the following characteristics:
grey/brown fur, small body size, and a short tail that is thick at the base. We determined a
positive identification of M. ravelobensis when the team observed the following characteristics:
rufus fur, larger body size, and a long tail that is thin at the base. We found it difficult to identify
41% of Microcebus species sightings to the species level during surveys. For those instances, I
only assigned identification to the genus level. We easily identified all other species during
surveys. For each survey, we recorded the start and end time, the type of survey (diurnal or
nocturnal), the number of researchers conducting the survey, which side each researcher was
surveying for nocturnal surveys, direction of survey and general weather.
26
Table 2.2: Survey Data of 42 Fragments in a 3,000 ha Fragmented Landscape.
Fragment ID Transect Length (m)
Diurnal Surveys
Nocturnal Surveys Total Surveys Diurnal
Sightings Nocturnal Sightings
1 823.1 15 15 30 1 47 2 1629.9 15 15 30 3 133 3 1804.2 18 21 39 18 150 4 565.8 14 18 32 6 41 5 627.3 15 15 30 2 60 6 649.1 15 17 32 0 50 7 278 14 17 31 0 11 8 621.3 14 17 31 1 30 9 625.4 15 17 32 0 50
10 294.6 14 14 28 0 30 11 227.3 14 13 27 0 8 12a 430.7 14 13 27 0 25 12b 577 13 13 26 0 15 13 314 14 13 27 0 19 14 291.3 14 16 30 0 14 15 148.6 16 16 32 0 24 16 105 14 16 30 0 2 17 66.3 14 16 30 0 0 18 289.5 13 16 29 0 4 19 210 13 16 29 0 7 20 161.8 14 16 30 0 20 21 67.7 14 16 30 0 0 22 43.8 14 16 30 0 4 23 325.2 15 15 30 0 17 24 61.1 14 14 28 0 6 25 86.7 16 15 31 0 7 27 188.3 14 14 28 0 4 28 76.8 15 13 28 0 6 29 138.3 14 15 29 0 2 30 119.1 15 14 29 0 1 31 535.6 14 14 28 0 20 32 239.8 14 14 28 0 17 33 324.7 14 14 28 0 9 34 278.2 13 14 27 0 34 35 377.1 15 14 29 0 11 36 314.2 14 14 28 0 24 37 127.5 14 14 28 0 0 38 178.3 14 14 28 0 2 39 498.2 14 13 27 0 41 40 391 14 13 27 0 22 41 223 11 11 22 0 12 42 141.9 12 11 23 0 13
Total 15476.7 596 622 1218 31 992 The number of surveys conducted during the day (diurnal), night (nocturnal), and total. And the number of associated sightings of all lemur species.
I measured survey effort as the total area of a fragment divided by the total area surveyed. The
area surveyed was calculated using the following formula:
27
Survey Area = W(lxw)
Where l is the transect length, w is two times the mean perpendicular distance of all sightings
along a transect, and W is the number of transect walks or surveys. For the four fragments where
I did not observe any individuals, I used the mean perpendicular distance for all sightings for all
transects (M=5.07 m SD=4.15 m).
To determine the pattern of the species-area relationship in my study, I selected 10 different
potential species-area functions from the literature (Table 2.3). The 10 functions represent a
wide range of possible species-area relationship models. Five functions are convex, including
three with asymptotes (negative exponential, Monod, rational function) and two without
asymptotes (power and exponential), while five functions are sigmoidal including one without
an asymptote (persistence P2) and four with an asymptote (logistic, Lomolino, Weibull, extreme
value function). Among the sigmoidal models, some models have inflection points that are
flexible with respect to the asymptote (e.g., Weibull and Lomolino models) while others are
symmetrical with respect to the asymptote (e.g., Logistic and extreme value function).
28
Table 2.3: 10 Candidate Species-Area Models.
Name Formula1 Para-meters Shape
First parameter
(c)
Second parameter
(z)
Third parameter
(b)
Curve origin
through axis
Asymp-totic2
power S = cAZ 2 Convex Curve shape Shape* Yes No
exponential S = c + zlog(A) 2 Convex Curve
shape Shape* No No
negative exponential
S = c(1 - exp(-zA)) 2 Convex Upper
asymptote Shape Yes Yes (c)
Monod S = (cA) / (z + A) 2 Convex Upper
asymptote Shape* Depends
on parameters
Yes (c)
rational function
S = (c + zA) / (1 + bA) 3 Convex
Shape + y-axis
intersection
Curve shape + upper
asymptote
Shape + upper
asymptote
Depends on
parameters
Yes (z/b)
logistic S = c / (1 + exp(-zA+b) 3 Sigmoid
Upper asymptote + y-axis
intersection
Shape + y-axis
intersection
Shape + y-axis
intersection
Depends on
parameters
Yes (c/b)
Lomolino S = c / 1 + (zlog(b/A)) 3 Sigmoid Upper
asymptote Shape Shape Yes Yes
cumulative Weibull
distribution
S = c(1 - exp(-zAb)) 3 Sigmoid Upper
asymptote Shape Shape Yes Yes
persistence (P2) model
S = cAZ
exp(-b/A) 3 Sigmoid Upper asymptote Shape Shape Yes No
extreme value
function
S = c(1 - exp(-
exp(zA+b))) 3 Sigmoid
Upper asymptote + y-axis
intersection
Shape + y-axis
intersection
Shape + y-axis
intersection No Yes (c)
1 A represents area c, z and b are fitted constants affecting the shape of the curve. 2 Letters within parentheses under the asymptotic column represent which constants determine the asymptote of the curve. Parameters with an asterisk alter curve shape on both sides of a rotation point. In some cases, more than one constant determines the asymptote (Adapted from Tjørve, 2003; 2009).
2.10.3 Question 2: What Other Non-Area Factors Affect Lemur Species Richness?
Habitat Characteristics
We determined the habitat structure for 38 of the 42 fragments along the same transects as the
primate surveys. We measured all living stems of trees over five cm diameter at breast height
(DBH) within one meter of each side of the 38 survey transects. For each tree, we measured its
DBH (cm) using DBH tape (Forestry Suppliers, Inc., Jackson, USA), visually estimated its
height (m), and recorded its location using a handheld GPS.
29
Human disturbance and Isolation Metrics
In addition to area and habitat characteristics, we measured the amount of human impact within
each fragment, fragment proximity to settlements, and fragment isolation. We conducted human
disturbance surveys along each primate survey transect to determine human impact. We looked
for evidence of all human activities within one meter of each side of the transect. We marked
evidence locations with a GPS and recorded the number of cut trees, the number of holes local
residents dug to access a tuber called maciba (Dioscorea maciba), zebu (cattle; Bos primigenius)
dung, hunting traps, and any other human disturbance variables observed (trails, artifacts). Cut
trees represent direct damage to forest, hunting traps represent a direct threat to lemur
populations, and maciba holes, zebu dung, and other disturbances (trails, artifacts) represent
evidence of forest use by local residents. For analytical purposes, I treated all observations of
human disturbance as equivalent. I also conducted a census of all temporary settlements within
the landscape and recorded their location using a GPS. To determine the proximity of temporary
settlements to each fragment, I entered the fragments and temporary settlements within ArcGIS
software (ESRI, Redlands, USA) and I calculated the distance in meters between the nearest
edge of each fragment and the nearest temporary settlement (DNS) using the ArcGIS Spatial
Join tool. Within ArcGIS, I determined isolation metrics by measuring the nearest distance from
the edge of each fragment to the nearest edge of continuous forest (DCF) and by measuring the
distance from the center of each fragment to the center of the nearest neighboring fragment
(DNN) using the ArcGIS Spatial Join tool.
2.10.4 Statistical Analysis
Question 1: What is the SAR Pattern?
I fit each of the 10 candidate species-area functions (Tjørve, 2009) to the data using non-linear
least squares regression (NLS). I assessed all species-area relationships in arithmetic space and I
did not apply transformations to species richness because: 1) a log transformation is not
appropriate because the species richness data contains zero values (log0=infinity; Cameron &
Trivedi, 2001; O'Hara & Kotze, 2010), 2) log transformation is ineffective for count data
(O'Hara & Kotze, 2010) and may reduce biological interpretation (Lomolino, 2000; Tjørve,
2003), and 3) many statistics such as least squares regression are robust to variation in normality
30
as long as there is homoscedasticity as demonstrated in the plot of residuals against predicted
values of the model (Kundu, 1993).
During the NLS regression, I set the start value for each fitted constant using the following
values (c=1, z=0.1, b=1), which resulted in fitted models for all functions except for the
cumulative Weibull function. For the cumulative Weibull function, I assigned the first parameter
(upper asymptote) to eight, which is the total number of lemur species found in the park. This
decision resulted in a fitted model.
To determine which model was most likely among the models assessed, I selected the model
with the highest Akaike’s Information Criterion weights (wi), and using AIC that was corrected
for small sample size (AICc; Burnham & Anderson, 2002). I considered models as potential
competing models if the difference in AICc (∆i) between a particular model and the best model
was between ∆i<4–7 (Burnham et al. 2011). To determine to what extent the best model fit the
data compared with competing models (i.e. models with ∆i<4–7), I calculated the evidence
ratio:
Evidence Ratio= wj / wi
The evidence ratio represents how much more likely the most likely model (wj) is than another
model (wi). I then compared the likelihood of all models together (n=10).
Question 2: What Other Non-Area Factors Affect Lemur Species Richness?
To determine how variables in addition to area influence species richness, I conducted a variable
selection procedure using hierarchical partitioning analysis. Hierarchical partitioning is a
hierarchical form of multiple regression analysis that looks at all possible regressions
contemporaneously to select predictor variables that have a high independent (of each other)
correlation with the dependent variable (Mac Nally, 1996; Mac Nally, 2000). It differs from
normal multiple regression in that the objective is not to find the best fit model but rather which
response variables contribute the most to the variation in the dependent variable (Mac Nally,
2000). Another advantage over normal multiple regression is that hierarchical partitioning
procedure works well with highly correlated variables (Mac Nally, 2000). In the hierarchical
partitioning procedure, I included: area, five habitat variables, two human disturbance variables,
31
two isolation metrics, and survey effort (Table 2.4). I then ran a generalized linear model (GLM)
with species richness as the response variable. I selected the predictor variables, which
independently contributed greater than 10% to the variation in species richness based on the
hierarchical partitioning procedure and input them in the GLM. The hierarchical partitioning
procedure uses least squares estimation for model fitting and determines the independent causal
factors between the highly correlated variables and the dependent variable (Chevan &
Sutherland, 1991; Olea, et al. 2010). I chose to use the R-squared measure of goodness of fit for
the hierarchical partitioning procedure. Table 2.4: Potential Influence of 10 Predictor Variables on Species Richness.
Variable Category Potential Influence on Species Richness
Area Area + Tree Basal Area Habitat Characteristic +
Tree Stem Density Habitat Characteristic + Mean Tree Height Habitat Characteristic + Mean Tree DBH Habitat Characteristic +
Distance to Nearest Neighboring Fragment Isolation Metric -
Distance to Continuous Forest Isolation Metric -
Total Human Disturbance Human Disturbance - Distance to Nearest Temporary
Settlement Human Disturbance -
Survey Effort Effort Neutral + Represents a predicted positive relationship with species richness, – represents a predicted negative relationship with species richness and DBH represents diameter at breast height.
I assessed the linearity for all predictor variables by visually inspecting histograms, density
plots, and box-plots of each variable. I assessed the shape of the data by calculating skewness,
kurtosis, variance, and standard deviation. I statistically assessed normality by applying a
Shapiro Wilk’s test to each variable (Shapiro & Wilk, 1965). If a variable was not normally
distributed, I attempted to transform the variable using a natural log (ln), square root or another
appropriate transformation and retested after transformation for normality.
For the GLM model, I used a Poisson (log) link function for the model because the dependent
variable, species richness, is count data. I compared a global model with all selected predictor
variables with models consisting of all combinations of variables using AICc.
32
I conducted all statistical analyses in R statistical software (2013). I used the “stats” package for
the descriptive statistics in R (Version 3.02). For the hierarchical partitioning procedure, I used
the “heir.part” package in R (Version 1.0-4). For the GLM I used the “stats” package in R
(Version 3.02). I considered all p-values significant if p≤0.05. I conducted all spatial analyses in
QGIS (2012) and ArcGIS (2012).
2.11 Results
2.11.1 Fragment Area and Survey Results
Within the survey landscape, we found fragments had a mean size of 8.82 ± 19.6 ha. During
1,218 total surveys in fragments, we observed 1,023 individuals or groups of lemurs. We found
a total of six lemur species, including four nocturnal species (C. medius, M. murinus, M.
ravelobensis and L. edwardsi), one diurnal species (P. coquereli), and one cathemeral species
(E. fulvus). We identified 29% (n=269) of 933 Microcebus species sightings as M. murinus,
30% (n=283) as M. ravelobensis, and 41% as (n=381) unidentified individuals. The number of
species within a fragment ranged from zero to six (Table 2.5). We visually observed all lemur
species recorded in each fragment during surveys, with the exception of C. medius in Fragment
8, which we heard during a survey, then tracked to visually confirm its presence in this
fragment. We found some nocturnal species during diurnal surveys and vice-versa for diurnal
and cathemeral species. Of the 42 fragments we surveyed, we found diurnal species in only
14.3% (n=6) of the fragments. Conversely, we found nocturnal species in 92.9% (n=39) of
surveyed fragments. We did not see or hear any individuals or groups of Eulemur mongoz or
Avahi occidentalis, although these species are present elsewhere in Ankarafantsika National
Park.
33
Table 2.5: Fragment Characteristics and Species Richness.
Fragment ID
Species Richness
Area (ha)
No. Trees Measured
µTree DBH (cm)
µTree Height
(m)
Stem Density (ind/ha)
Total Dist.
DNN (m)
DCF (m)
DNS (m)
1 4 34.51 296 10.35 6.52 1798.08 172 276.04 2198.56 707.75 2 5 45.34 430 17.89 6.80 1319.10 66 527.57 636.08 1191.77 3 6 117.70 583 11.86 6.24 1615.67 116 584.68 819.02 1272.94 4 4 19.46 197 N/A 7.37 1740.90 94 474.07 1319.98 1841.82 5 4 16.01 168 5.53 6.73 1339.07 132 584.68 1153.15 1048.08 6 4 11.58 271 7.44 7.81 2087.51 81 601.36 179.74 2361.76 7 3 4.16 99 6.73 6.12 1780.58 79 462.74 1385.74 705.33 8 4 13.55 184 8.97 6.74 1480.77 111 464.75 1154.63 1170.86 9 3 15.38 277 7.93 7.68 2214.58 56 416.35 110.77 2525.35
10 2 2.78 103 10.38 5.20 1748.13 33 839.64 105.92 2441.02 11 2 2.57 23 17.42 4.04 505.94 53 471.70 210.12 1632.44 12a 2 14.22 113 11.70 5.77 1311.82 49 588.49 656.29 311.29 12b 3 5.18 N/A 7.61 N/A N/A N/A 527.57 842.41 868.53 13 2 3.75 52 10.29 4.85 828.03 39 1089.28 277.72 785.14 14 2 4.08 102 12.79 5.87 1750.77 24 510.06 2008.92 872.92 15 2 1.18 50 N/A 6.02 1682.37 8 276.04 2481.39 938.80 16 1 0.76 22 9.33 4.64 1047.62 26 442.90 2044.61 1367.79 17 0 0.42 23 12.24 5.09 1734.54 17 706.74 1831.83 1844.52 18 1 1.14 N/A 12.22 N/A N/A N/A 395.00 1058.66 1,627.80 19 2 1.40 10 10.35 6.00 238.10 29 395.00 1088.53 2002.92 20 2 1.43 47 17.89 6.26 1452.41 33 601.36 180.49 1765.76 21 0 0.38 27 11.86 5.44 1994.09 8 462.74 1090.85 783.65 22 2 0.23 28 N/A 7.50 3196.35 28 416.35 103.72 2496.82 23 2 2.48 33 5.53 5.85 507.38 87 395.16 2051.63 299.25 24 2 0.28 21 7.44 5.95 1718.49 20 730.57 452.31 1855.57 25 2 0.57 15 6.73 7.21 807.38 16 571.27 992.58 116.84 27 1 1.97 N/A 8.97 N/A N/A N/A 571.27 747.52 458.01 28 2 0.64 12 7.93 4.5 781.25 1.00 151.54 1929.84 967.99 29 2 0.71 9 10.38 4.67 325.38 13.00 151.54 2074.78 850.34 30 1 0.31 22 17.42 4.68 923.59 21.00 395.16 2478.30 443.75 31 3 17.03 85 11.70 5.09 793.50 10.00 575.17 1186.92 291.50 32 2 0.98 43 7.61 5.02 896.58 6.00 336.34 803.73 373.33 33 2 2.16 78 10.29 5.28 1201.11 9.00 336.34 433.05 695.28 34 3 1.69 91 12.79 6.57 1635.51 44.00 580.35 268.28 1026.21 35 2 5.00 65 N/A 6.09 861.84 69.00 704.14 413.70 1259.84 36 2 6.97 74 9.33 5.35 1177.59 13.00 311.38 1336.03 1456.95 37 0 0.64 19 12.24 6.53 745.10 40.00 698.22 788.00 1581.47 38 1 0.52 40 12.22 6.28 1121.70 5.00 311.38 1182.11 1483.15 39 2 5.17 N/A 10.35 N/A N/A N/a 241.78 321.61 2,092.55 40 2 3.78 126 17.89 5.70 1611.25 12.00 231.41 95.09 2229.73 41 2 1.56 28 11.86 5.25 627.80 5.00 231.41 17.06 2016.44 42 2 0.79 46 N/A 6.89 1620.86 1.00 241.78 263.28 2215.00
Landscape fragment characteristics. I measured species richness as the number of species observed within a fragment. No. Trees Measured refers to the total number of trees measured in each fragment during habitat structure surveys along primate survey transects. µTree DBH is the mean diameter at breast height of all trees measured within each fragment. µTree Height is the mean tree height. Total Dist. is the number of all human disturbance observations recorded along each transect. DNN is the distance to nearest neighbor as measured from the center of each fragment. DCF is the distance from the nearest edge of the fragment to the nearest point edge of the continuous forest. DNS is the distance from the center of each fragment to the nearest temporary settlement. I
34
report N/A for four fragments that I could not measure habitat characteristics because there were either no living stems along the transect (Fragment 27) or the transect ran along a dry riverbed (Fragments 12b, 18, and 39).
2.11.2 Question 1: What is the SAR Pattern?
I found significant fitted parameter estimates for all models except the Lomolino and persistence
models (P2; Table 2.6). In the Lomolino model, the Z parameter was a significant contributor to
the shape of the model (Z=0.73, p<0.01), while the c and b parameters were not significant
contributors to the shape of the model (c=0.27, p=0.74; b=0.43, p=0.64). In the persistence (P2)
model, the c and Z parameters were significant contributors to the shape of the model (c=1.48,
p<0.01; Z=0.30, p<0.01) while the b parameter was not a significant contributor to the shape of
the model (b=-0.05, p=0.58). Table 2.6: Fitted Parameters of 10 Candidate Species-Area Models Using Non-linear Least Squares Regression for Primate Species in the 42 Fragments.
Name Formula Shape Asymp-totic2
Parameter Estimates and Associated p-values
power S = cAZ Convex No c=1.56; p<0.01
Z=0.28; p<0.01
exponential S = c + zlog(A) Convex No c=1.60; p<0.01
Z=0.68; p<0.01
negative exponential S = c(1 - exp(-zA)) Convex Yes (c) c=3.59;
p<0.01 Z=0.39; p<0.01
Monod S = (cA) / (z + A) Convex Yes (c) c=4.10; p<0.01
Z=2.11; p<0.01
rational function S = (c + zA) / (1 + bA) Convex Yes (z/b) c=1.33;
p<0.01 Z=0.291; p<0.01
b=0.04; p=0.03
logistic S = c / (1 + exp(-zA+b) Sigmoid Yes (c/b) c=5.29;
p<0.01 Z=0.10; p<0.01
b=0.89; p<0.01
Lomolino S = c / 1 + (zlog(b/A)) Sigmoid Yes c=0.27;
p=0.74 Z=0.73; p<0.01
b=0.43; p=0.64
cumulative Weibull distribution
S = c(1 - exp(-zAb)) Sigmoid Yes c=8.001 Z=0.21;
p<0.01 b=0.36; p<0.01
persistence (P2) model S = cAZ exp(-b/A) Sigmoid No c=1.48; p<0.01
Z=0.30; p<0.01
b=-0.05; p=0.58
extreme value function S = c(1 - exp(-exp(zA+b))) Sigmoid Yes (c) c=5.01;
p<0.01 Z=0.07; p<0.01
b=-0.99; p<0.01
S is species richness. A represents area. c, z and b are fitted constants affecting the shape of the curve. 1 For the cumulative Weibull distribution model, I manually fitted parameter c to equal eight to represent the hypothetical maximum species number. Bold Indicates p≤0.05. 2 Letters within parentheses under the asymptotic column represent which constants determine the asymptote of the curve. In some cases, more than one constant determines the asymptote.
When comparing all models together, I found that the power model had the best fit (wi=0.49)
followed by the persistence (P2: wi=0.18), Lomolino (wi=0.16), Weibull (wi=0.08), and rational
function models (wi=0.07). None of the remaining models had ∆i values within 4–7 of the
power model (Table 2.7). A scatterplot fitted with the five competing models demonstrates the
similarity between model fits (Fig. 2.5a).
35
Table 2.7: Non-linear Least Squares Regression Model Selection of Species-Area Models. Model Log Likelihood K AIC AICc Δi wi Power -41.20 2 86.41 86.72 0.00 0.49
persistence (P2) model -41.07 3 88.14 88.77 2.06 0.18 Lomolino -41.15 3 88.30 88.94 2.22 0.16
cumulative Weibull distribution -41.83 3 89.66 90.29 3.57 0.08 rational function -41.98 3 89.96 90.59 3.88 0.07
exponential -45.17 2 94.35 94.66 7.94 0.01 logistic -44.31 3 94.61 95.24 8.53 0.01
extreme value function -45.24 3 96.48 97.11 10.40 0.00 Monod -51.21 2 106.42 106.72 20.01 0.00
negative exponential -55.92 2 115.84 116.15 29.43 0.00 Non-linear least squares model selection using Akaike’s information criterion (AIC). I compared each candidate model against one another using corrected AIC values (AICc). K is the number of parameters in the model. I determined the difference between the lowest AICc values and the rest to determine the change in AICc (Δi) and the weight (wi) of that difference was calculated.
To see how much more likely the power model was over the other models, I investigated the
evidence ratios between the power model and the four competing models. I found that the power
model is 2.72, 3.06, 6.13 and 7.00 times more likely to fit the data than the persistence (P2),
Lomolino, Weibull, and rational function models, respectively. To confirm the underlying shape
of the data, I applied a lowess curve with a 50% smoothing parameter over a scatterplot of
species richness and area. The lowess curve follows a downward convex shape over the data
similar to the above competing models (Fig. 2.5b).
Figure 2.5: Five Competing Species-Area Models. a) Five competing SAR models for lemur species richness including the Persistence (P2) model, Weibull model, power model, Lomolino model and rational function model. b) 50% lowess curve fitted to species richness values.
0 20 40 60 80 100 120
01
23
45
6
(a)
Fragment Area (ha)
Lem
ur S
pece
s R
ichn
ess
Persistence (P2)WeibullPowerLomolinoRational
0 20 40 60 80 100 120
01
23
45
6
(b)
Fragment Area (ha)
Lem
ur S
pece
s R
ichn
ess
Lowess Line
36
2.11.3 Question 2: What Other Non-Area Factors Affect Lemur Species Richness?
I measured a total of 4,042 trees in fragments within the entire landscape. I measured as few as
nine trees in Fragment 29 (area=0.71 ha) and as many as 583 trees in Fragment 3 (area=117.70
ha). There is a statistically significant correlation between the number of trees measured and
fragment area (r=0.90; p<0.01). I measured 1626 incidences of human disturbance within the
landscape. Within fragments mean human disturbance was 42.79 (SD=41.02), ranging from one
to 172 incidences within fragments. There was a significant positive correlation between ln area
and ln total human disturbance (r=0.60, p<0.01). I found a mean of 477.51 m (SD=195.60m) for
distance to nearest neighbor and a mean of 994.86 m (SD=759.03 m) for distance to continuous
forest. Survey effort ranged between 0.19 and 4.95, with a mean effort of 1.18 m (SD=1.09 m)
across all fragments. There was a significant positive correlation between ln survey effort and ln
area (r=0.64, p<0.01).
Based on the hierarchical partitioning procedure, I found that area (47.84%) had the highest
independent contribution to lemur species richness followed by survey effort (27.53%), total
human disturbance (13.49%), and mean tree height (10.34%). None of the other variables
contributed greater than 10% of the variation in lemur species richness
(Fig. 2.6).
Figure 2.6: Hierarchical Partitioning Model. A hierarchical partitioning model, that shows the independent contributions of each predictor variable on species richness. ln represents natural log transformation and sqrt represents square root transformation. THD represents total human disturbance, DBH represents diameter at breast height, StDens represents stem density, THM represents mean tree height, BA represents basal area, DCF represents distance from fragment to continuous forest, DNN represents fragment nearest neighbor distance, and DCF represents distance to nearest settlement.
lnArea lnEffort lnTHD THM DNS StDens lnBA meanDBH DNN sqrtDCF
Predictor Variables
Inde
pend
ent E
ffect
s (%
)
010
2030
4050
60
37
I ran 15 GLM models including all possible combinations of the predictor variables: ln area, ln
effort, ln total human disturbance, and mean tree height. The model with the highest wi value
was the model with only ln area as a predictor (Table 2.8; wi=0.35). Ln area was the only
variable was significant in the top models (highest wi values). Ln effort, mean tree height and ln
total human disturbance variables were significant only when ln area was not included as a
variable in the model (Table 2.8). Table 2.8: Comparison of Seven Generalized Linear Models Using Poisson Link Function Predicting Lemur Species Richness.
Bold represents variables that are significant. SR represents species richness, THM represents mean tree height, THD represents total human disturbance. I transformed each predictor variable with a natural log transformation (ln) accept THM. I compared each candidate model against one another using corrected AIC values (AICc). I determined the difference between the lowest AICc values and the rest to determine the change in AICc (Δi) and calculated the weight of that difference (wi).
2.12 Discussion
2.12.1 Question 1: What is the SAR Pattern?
I predicted that the pattern of the species-area relationship among lemurs in a fragmented
landscape would be sigmoidal with a “small island effect” and an upper asymptote. I found
multiple competing models that describe the species-area relationship in this study. Contrary to
predictions, I found that the power model, and not one of the competing sigmoidal models, was
the best-fit model. Of the remaining four competing models, three were flexible sigmoidal
models (persistence (P2), Lomolino, and cumulative Weibull distribution) that were able to look
Model Parameters Δi wi Area THM THD Effort
β SE β SE β SE β SE SR=Area 0.00 0.35 0.27 0.07
SR=Area+THM 1.42 0.17 0.25 0.07 0.12 0.12 SR=Area+Effort 2.35 0.11 0.29 0.14 0.03 0.27 SR=Area+THD 2.35 0.11 0.27 0.09 0.01 0.12
SR=Area+THM+THD 3.88 0.05 0.26 0.09 0.13 0.13 -0.03 0.12 SR=Area+THM+Effort 3.91 0.05 0.26 0.14 0.12 0.13 0.04 0.27
SR=Effort 4.27 0.04 -0.46 0.13 SR=Area+THD+Effort 4.85 0.03 0.28 0.17 0.01 0.13 0.03 0.28
SR=THM+Effort 5.02 0.03 0.15 0.12 -0.42 0.14 SR=THD+Effort 5.20 0.03 0.12 0.11 -0.38 0.15
SR=Area+THM+THD+Effort 6.51 0.01 0.28 0.17 0.13 0.13 -0.03 0.13 0.06 0.28 SR=THM+THD+Effort 6.86 0.01 0.12 0.13 0.09 0.11 -0.36 0.15
SR=THD 9.57 0.00 0.26 0.10 SR=THM+THD 10.21 0.00 0.16 0.13 0.20 0.11
SR=THM 11.73 0.00 0.26 0.11
38
convex, while the remaining model was the rational function (convex model). The two best fit
models; power and persistence (P2) did not have asymptotes. The shape of the species-area
relationship in my study was clearly convex (Fig. 2.5a). I was not able to detect a sigmoidal
shape to the species-area relationship when visually inspecting the data using a lowess curve. I
predicted a sigmoidal model because of the perceived hostility of the matrix and the small size
of the smallest fragments. However, many other studies have also found the power model to fit
SARs in other species (Lomolino, 1982; Frey, 2007; Triantis et al. 2012). Two of the competing
sigmoidal models, Lomolino and persistence (P2), appeared to be convex because these models
are very flexible in their shape. Despite the Akaike's weights indicating that the sigmoidal
models are potential competing models, my results suggest that they are not appropriate models
to assess the pattern of lemur species richness within the fragmented landscape I studied.
Detecting a sigmoidal model is difficult without a large range of fragment sizes (Triantis et al.
2012). In my study, the fragment size range was too small to detect a sigmoidal model and the
larger fragments were too small to observe an asymptote. To detect a sigmoidal pattern, Triantis
et al. (2012) suggests that studies incorporate a size difference between the smallest and largest
isolates of at least three orders of magnitude. In my study, the largest fragment (117.7 ha) was
2.5 orders of magnitude greater than the smallest (0.23 ha). Regional species richness is eight
species, so for my models to reach an asymptote, I would have needed to observe all eight
species in the fragments. However, it is possible to reach an asymptote if I included the
remainder of the park as a data point. Although I included very small fragments (<1 ha) and the
matrix is assumed to be hostile to lemur species, I was not able to detect a small island effect.
The two most likely possibilities to explain why I did not observe a small island effect are: (1) in
my sample, there is no minimum area effect for the two widest ranging lemur species
(Microcebus species), or (2) the two widest ranging species (Microcebus species) are able to
move between fragments and may only be transitory within a fragment at any given time. Life
history characteristics of Microcebus provide evidence to support the first explanation.
Microcebus species differ from the other species that occur within this landscape in their
physical size, ranging behavior, and diet. Microcebus has the smallest body mass (Rasoloarison
et al. 2000), home ranges (Radespiel, 2000; Weidt et al. 2004), and is the most insectivorous of
all the lemur species within the landscape (Table 2.1). Having a low body size and small home
range requirements may make it possible for Microcebus to survive in the smallest (<1 ha)
39
fragments. In fragments of this size, species that rely more on fruit or leaves would have
difficulty finding food, while Microcebus can rely on insects that occur with greater absolute
availability than fruits or leaves in extremely small fragments (due to edge effects; Corbin &
Schmid, 1995).
The second explanation for my inability to detect a small island effect is likely related to the
lack of isolation between fragments within the study landscape (Fig. 2.4 and Table 2.5),
suggesting that some species are able to move through the matrix between fragments. Marshall
et al. (2010) suggested that there must be enough isolation between fragments so that species
cannot move between them. In my study, there were low levels of isolation between some
fragments as measured by nearest neighbor distance, allowing for possible movement between
fragments.
The only species that I observed in the matrix were Microcebus species (n=9), found in shrubs
within the matrix, suggesting that Microcebus use the matrix to move between fragments and
that the matrix may not be as hostile as I had assumed. Even though savannah matrix is a
significant barrier for M. ravelobensis (Radespiel et al. 2008), it appears that the degree of
isolation between fragments is low within my study landscape. Low fragment isolation reduces
the insular nature of the fragments and would thus explain the convex rather than sigmoidal
nature of the observed SAR. A lack of asymptote is likely due to the largest fragments being too
small to support continual populations of the two missing lemur species (A. occidentalis and E.
mongoz). Future research should use mark-recapture methods on Microcebus species to
determine their ability to move throughout the matrix and their ability to survive within the
smallest fragments.
2.12.2 Question 2: What Other Non-Area Factors Affect Lemur Species Richness?
The hierarchical partitioning procedure showed that ln area, ln survey effort, ln total human
disturbance, and mean tree height were possible causal factors influencing lemur species
richness. I expected ln area and mean tree height to both have a positive influence on species
richness, survey effort to have a neutral impact on species richness, and human disturbance to
have a negative influence on species richness. Post hoc analysis shows that species richness is
significantly positively correlated with ln area, mean tree height, and ln total disturbance but
40
significantly negatively correlated with ln survey effort. A positive relationship with ln area and
ln tree height falls in line with expectations of the species-area relationship and the possible
reliance of arboreal primates on tall trees, which may indicate increased food and shelter
availability, and may be necessary for locomotion for large vertical clingers and leapers such as
Propithecus coquereli. A positive relationship between species richness and ln human
disturbance indicates that the relationship between human disturbance and species richness is
more complicated. A negative relationship between survey effort and species richness suggests
that I over-surveyed smaller fragments that contained the fewest species. However, the GLM
analyses found that only the ln area only model was the most likely model. The GLM models
suggest that biogeographic factors such as area have a stronger positive influence than the
possible negative influence of human disturbance. However, during my study I did not find two
species of lemur (A. occidentalis and E. mongoz) that exist within the park in any of my survey
fragments. Although it is difficult to determine absence for any species, it is unlikely that I did
not observe individuals of E. mongoz or A. occidentalis due to methodological considerations
because both species are very conspicuous primates and have loud, distinct vocalizations.
During a shorter pilot study for this project between June and August 2010, we found one
individual A. occidentalis within the Fragment 3 (Figure 2.4) after only seven surveys, and
heard repeated vocalizations from the same location. However, during the 2011 seven-month
study, we conducted 21 nocturnal surveys in the same fragment and we failed to detect (observe
or hear) any A. occidentalis individuals, suggesting that they were recently extirpated in my
study site or are transient within the site. As a cautionary note, determining absence is extremely
difficult and other studies have found species within survey sites only after years of survey
effort. For example, Lehman (2014) unexpectedly observed a single Indri indri individual
walking to camp but not during 1,318 km of surveys in the wet forests of Vohibola III.
Minimum area requirements, limited numbers of large trees, and hunting pressure may also
relate to the absence of Avahi in the forest fragments. In eastern littoral forests, Norscia (2008)
only found Avahi meridionalis in forest fragments larger than 75 ha and no correlation between
fragment area and Avahi density (Norscia, 2008). Although A. occidentalis have small home
ranges and rely on low quality abundant leaves, suggesting that they would be able to inhabit
relatively small fragments, it is possible that the number and distribution of large trees impacts
Avahi occurrence (Norscia, 2008). Norscia (2008) found that the percentage of large trees above
41
3.2 cm DBH was significantly positively related to Avahi density. Illegal hunting does occur
within the park, and the although the most commonly hunted species are the larger-bodied and
more common P. coquereli and E. fulvus, García and Goodman (2003) did identify remains of
A. occidentalis from a hunt within the park.
In a rapid assessment, researchers only found E. mongoz in one of four survey sites within
Ankarafantsika National Park (Schmid & Rasoloarison, 2002). Shrum (2008) only found E.
mongoz in fragments larger than 250 ha. In my study, the largest fragment was only 117.7 ha.
Hunting may also contribute to my failure to detect E. mongoz. For example, local informants
indicated that E. mongoz is a preferential food source (Steffens, personal communication). Thus,
the absence of E. mongoz and A. occidentalis was likely due to hunting pressure, that some
aspect of the habitat structure being unsuitable, and/or that there were too few large fragments to
support populations of either species.
Considering lemurs are mainly arboreal, it is surprising that most of the habitat variables did not
appear to influence lemur species richness. Besides ln area and total human disturbance, the
hierarchical partitioning indicated that the only habitat variable that influenced lemur species
richness was mean tree height. Either fragment area is too great an influence on species richness
in this landscape that it overrides the possible influence of habitat characteristics, or my
measures were poor indicators of lemur habitat. Future research should look at other variables
such as food tree species diversity and presence of possible competitors and predators.
2.12.3 Suggestions for Conservation
Area is the main driver of lemur species richness at a patch-level. Preserving larger fragments
will help ensure more species of lemur are protected from extinction. Although I did not find
many habitat variables that appeared to influence lemurs at a community level, conservation
managers should continue to consider habitat quality and especially measures to maintain mean
tree height in a fragment. Two species are currently presumed locally extinct in fragments
within my study site. Measures that increase fragment size, protect existing fragment size, and
discourage anthropogenic use of large trees are needed to help ensure future local extinctions are
avoided.
42
2.13 Conclusion Lemur species do show a species-area relationship in a fragmented landscape in Ankarafantsika
National Park, Madagascar. Contrary to predictions, I found that the lemur SAR was convex
rather than sigmoidal and I did not detect a small island effect for primates in my study
landscape. The lack of a small island effect may be due to the movement of lemurs between
fragments and/or the small body size, small habitat requirements, and fauni-frugivorous diet for
the two most widely distributed species (Microcebus species) found within the landscape. Of the
15 GLM models I ran, the ln area only model was the most likely considering my data. I found
that mean tree height and ln human disturbance appeared to have little effect on lemur species
richness in this landscape. Therefore, the SAR found among lemurs within this landscape is
driven mainly by the biogeographic factor: area. However, the possible local extinction of A.
occidentalis and E. mongoz may be the result of both reduced fragment size and hunting
pressure. No measures of habitat characteristics or fragment isolation appear to influence the
SAR I observed among the lemur species in my study. Future research should investigate other
habitat measures, such as food species diversity, habitat structural heterogeneity, the ability of
lemurs to move throughout the matrix, the presence of competitors, and potential predators to
develop a more comprehensive understanding of how lemurs respond to habitat loss and
fragmentation.
43
Chapter 3: Population Dynamics of Lemurs in a Fragmented Landscape in Madagascar
3.1 Introduction In Chapter 2, I investigated the community level effects of habitat loss and fragmentation on
primate species richness. I found that even in the context of high human disturbance, lemur
species at the community level continue to follow a classic species-area relationship. But how
are individual lemur species affected by habitat loss and fragmentation?
There are eight species of lemurs within Ankarafantsika National Park and six occur within my
study site including: the fat-tailed dwarf lemur (Cheirogaleus medius), the grey mouse lemur
(Microcebus murinus), the golden brown mouse lemur (Microcebus ravelobensis), Coquerel’s
sifaka (Propithecus coquereli), the common brown lemur (Eulemur fulvus), and Milne Edwards
sportive lemur (Lepilemur edwardsi; see Chapter 2; Mittermeier et al. 2010). Of the six species
found within the study site, two are considered least concern (M. murinus and C. medius), one is
considered near threatened (E. fulvus) and three are considered in endanger of extinction (M.
ravelobensis, P. coquereli, and L. edwardsi; IUCN Red List, 2016).
The main reason lemur species are threatened with extinction is habitat loss and fragmentation
of the forests in Madagascar (Schwitzer et al. 2014). The high degree of habitat disturbance in
Madagascar even affects protected forests such as those within Ankarafantsika National Park.
The processes of habitat loss and fragmentation create landscapes with discrete patches of
habitat (Fahrig, 2003). Metapopulation dynamics is a useful approach to find out how individual
species respond to these processes (Hanski, 1994a; Hanski, 1999; Hanski & Ovaskainen, 2003).
A metapopulation is a population of populations (Levins 1969, 1970). Within a metapopulation
local populations of breeding individuals live within discrete definable patches of habitat. Each
local population within each patch has a probability of going extinct. The probability of
extinction is a function of the area of a patch, because smaller areas have fewer individuals and
are more prone to extinction, while larger areas support a greater number individuals and are
less prone to extinction (MacArthur & Wilson 1967). Local populations within a
metapopulation are connected via dispersal. Dispersal is the movement of an individual from
their natal home range to a breeding range (natal dispersal) or from their breeding home range to
a new breeding range (secondary or breeding dispersal; Matthysen, 2012). Patch colonization
44
potential for dispersers is a function of the connectivity or, inversely, isolation, between patches
and the species-specific ability to disperse among patches (Hanski, 1999).
A metapopulation system is dynamic (Hanski & Gilpin, 1991). Over time, some patches may
go extinct while others may not. The patches that continue to have populations provide migrants
to colonize extinct patches. Occasionally, the number of individuals within a small patch may
decline and near extinction. However, colonists from a larger patch may arrive to the smaller
patch and “rescue” it from extinction. This process is termed the rescue effect (Hanski 1999). If
there is no change in the area or isolation of the patches within a landscape, then a dynamic
equilibrium should emerge where there is a balance between extinction and colonization within
the metapopulation (Hanski 1994a).
3.2 Types of Metapopulations There are different types of single species metapopulations with variable characteristics,
including but not limited to: the Levins metapopulation (Levins 1969, 1970), mainland-island
metapopulation (Hanski, 1994abc), patchy population metapopulation (Harrison & Taylor,
1997), non-equilibrium metapopulation (Harrison & Taylor, 1997), and the intermediate
metapopulation (Harrison & Taylor, 1997). The Levins metapopulation (Levins 1969, 1979) is
often referred to as the classic metapopulation (Harrison, 1991; Hanski, 1997; Baguette, 2004)
and occurs where there are a large number of small patches with local populations that are
equally prone to extinction with low levels of migration between patches (Fig. 3.1a; Hanski,
1997). Hanski (1997) identifies four conditions that need to be met for a Levin’s metapopulation
model to exist: suitable habitat is in discrete patches, all populations large and small have a high
extinction risk, habitat patches are connected enough to allow for recolonization, and population
dynamics are not synchronous in local populations. This is a simple model and it applies poorly
in fragmented habitats because fragments are not all the same size and are not equally isolated.
As a result, local populations do not have similar levels of extinction risk. Therefore, a more
complicated metapopulation occurs when patches vary in size and distance.
45
Figure 3.1: Five Types of Metapopulations. Filled circles represent occupied patches, empty circles represent un-occupied patches, dotted lines represent local population boundaries, arrows represent dispersal distance and direction: a. Levins (classic); b. mainland-island; c. patchy population; d. non-equilibrium population; and e. intermediate metapopulation. (Adapted from Harrison, 1991).
A mainland-island metapopulation occurs where a patch or population within a fragmented
landscape is particularly large (mainland) and is surrounded by smaller patches. The large
mainland has a large population of individuals that is unlikely to become extinct (MacArthur &
Wilson 1967). However, patches vary in their extinction risk based on their size (Hanski 1994a),
with smaller patches at relatively higher extinction risk than larger patches. Because of its large
size the mainland produces an unlimited supply of migrants called propagule rain (Hanski,
1994ab). The mainland’s unlimited supply of migrants is independent on the number of patches
occupied within the system (Hanski, 1994ab). The colonization potential or isolation of island
patches is related to their distance from the mainland (Hanski, 1994ab). A mainland-island
metapopulation may help explain the source-sink dynamics observed in some metapopulations
(Harrison, 1991). Source-sink dynamics are situations where reproduction among individuals is
lower than mortality (sink) and a population is maintained through migration from a nearby
population that is growing (source; Pulliam 1998).
a. b. c.
d. e.
46
A patchy metapopulation occurs when the habitat patches are not separate enough to create
effective isolation between patches (Harrison, 1991; Fig. 3.1c). In patchy populations, local
populations are so close that it is unlikely that any will become extinct because they are
connected to one another by as much movement between patches as within patches (Harrison,
1991). This high degree of movement between patches violates the assumption of the classical
metapopulation of low levels of colonization between patches and is not generally considered a
true metapopulation (Hanski, 1997; Harrison & Taylor, 1997).
Non-equilibrium metapopulations occur when the assumption of classic metapopulation
dynamics - that extinction and colonization will balance over time - fails (Hanski, 1997). In non-
equilibrium metapopulations, local extinctions occur more than recolonization, resulting in a
declining metapopulation (Harrison, 1991). The classic metapopulation model assumes that
local extinctions within a patch create a patch that can be potentially recolonized. If
deterministic processes such as human disturbance reduce habitat quality or size below a species
threshold, then some patches may be unavailable for recolonization (Harrison, 1991; Fig. 3.1d).
Intermediate metapopulations combine elements of some or all of the above metapopulations
(Harrison, 1991; Fig. 3.1e). The timescale under consideration and the spatial arrangement of
patches make an intermediate metapopulation likely (Harrison, 1991). For example, a
metapopulation is intermediate if it has one large patch that produced an indefinite supply of
migrants (a mainland) but there is some level of migration between the remaining smaller
patches (Harrison, 1991). In such cases, rescue effects may result in patches with a higher
probabilities of being occupied because they are closer to other occupied patches (Harrison,
1991).
3.3 Metapopulation Models The three types of metapopulation models used to describe metapopulations (Hanski, 1994c)
are: spatially implicit models, spatially explicit models, and spatially realistic models.
Researchers can chose which model to use depending on their question, the data available, and
the nature of the landscape configuration within a study.
Spatially implicit models are simple models with fewer variables that incorporate assumptions
about spatial structure in a landscape, such as all patches are the same size (Hanski, 1994c).
47
Spatially implicit models do not consider spatial elements such as patch size or isolation within
a landscape. The earliest type of metapopulation described, the classic Levin’s metapopulation,
is an example of a spatially implicit model. The assumptions of the classic Levin’s
metapopulation model include: extinction risk is equal between each patch because all patches
are the same size, (Hanski, 1994c), local populations are equally connected, population
dynamics within a patch are independent from those among patches, and spatial relationships
among patches are not considered. In the classic Levin’s metapopulation, the proportion of
patch occupancy at any one time is the variable of interest, not patch size or isolation (Hanski,
1997). Applying a classic metapopulation approach to real landscapes is problematic because
the actual configurations and sizes of patches often do not fall within the assumptions of a
classic metapopulation where patches are similar in size with similar levels of isolation.
Spatially explicit models are more complicated with more variables than spatially implicit
models and can include the spatial location and therefore isolation of patches relative to one
another, but they do not incorporate size or quality of patches into the model (Hanski, 1994c).
Spatially explicit models arrange multiple patches as cells in a lattice. Patches are either
occupied or not with no variation in patch size. The distance to adjacent patches is equivalent
but migration between occupied and unoccupied patches are distance dependent (Hanski,
1994c).
Spatially realistic models can incorporate more spatial information about a landscape including
the size, shape, and location of patches and the distances between them (Hanski, 1994c).
Spatially realistic models can even incorporate patch quality (Hanski, 2001). Therefore, they are
more representative than spatially implicit or explicit models. Because landscapes do not always
look like classic metapopulation assumptions (Fig. 3.1a), Hanski (1994a) developed a spatially
realistic stochastic-patch occupancy model, called the incidence function model (IFM), to test
species vulnerability to habitat fragmentation in a realistic context. In an IFM, incidence is a
measure of the probability of occurrence of a species within a patch and is a function of patch
extinction probability and colonization potential (Hanski, 1999). Measuring patch extinction
probability and colonization potential directly is difficult (Hanski, 1999). To determine the
extinction probability of a species within a patch or patch network, it is necessary to acquire
long-term data on mortality of individual primates, who are long lived, within patches of
varying sizes. To determine colonization potential of a patch or patch network, it is necessary to
48
know primate species dispersal abilities between patches over more than one year. For primates
this requires long-term data on species dispersal patterns, which is difficult to measure.
Researchers can determine incidence in a metapopulation model without data on species
extinction or colonization rates. Fortunately, using an IFM, we can infer the extinction
probability of a patch and its colonization potential using simple occurrence data
(presence/absence) gathered from a single-survey period among patches within a fragmented
landscape (Hanski, 1994ab). An IFM uses area as a proxy for extinction risk and isolation as a
proxy for colonization potential. Thus, it describes the probability that a species occurs
(incidence) within a patch as a function of both the area (extinction risk) and isolation
(colonization potential) of that patch (Hanski, 1994ab). The only additional data required are the
sizes and locations of each patch and knowledge of the median-dispersal range of a species
within the landscape. The benefit of an IFM is that it is more realistic because it incorporates
patch area and isolation directly measured from the landscape and it can be easily parameterized
based on occurrence data of species within a fragmented landscape at one particular point in
time.
You can use a mainland-island IFM if you apply three simplifying assumptions. First, in
mainland-island dynamics the mainland can never go extinct (Hanski, 1994b). Second, migrants
only come from the mainland; there is no movement between island patches (Hanski, 1994b).
Third, the colonization potential of a patch is a negative exponential function of the distance
from an island to the mainland (Hanski 1994b).
3.4 Dispersal Like patch extinction probability and colonization potential, gathering dispersal information on
primate species is quite difficult. Understanding dispersal requires long-term data on the
movements of multiple individuals within a population within the landscape of interest
(Sutherland et al. 2000; Bowman et al. 2002; Schliehe-Diecks et al. 2012). We know little about
dispersal for many of the lemur species that occur in Ankarafantsika National Park. However,
researchers have studied dispersal in Microcebus murinus (Radespiel et al. 2003; Schliehe-
Diecks et al. 2012) and M. ravelobensis (Radespiel et al. 2009). Researchers found greater
dispersal distances for M. murinus than M. ravelobensis. Using mark recapture methods on M.
murinus, Radespiel et al. (2003) found that the male median-dispersal range was 251 m and the
49
female median-dispersal range was 63 m. Looking at dispersal movements in M. murinus in
Kirindy Forest, Schliehe-Diecks et al. (2012) found that successful male dispersal events ranged
between 180 m to 960 m. Radespiel et al. (2009) found that M. ravelobensis male natal dispersal
ranged between 7 m to 193 m and female natal dispersal ranged from 11 m to 193 m in
Ankarafantsika National Park.
Although it is difficult to determine a value for primate species median-dispersal ability, it is an
important parameter (α) within an IFM. Fortunately, IFMs are not overly sensitive to variations
in the value of α (Hanski, 1994a). There are also solutions to determining 𝛼 when dispersal data
are lacking. One solution is to parameterize α using the occurrence data from a single-survey
period. This method involves inputting different values of α into the IFM and selecting the best-
fitting value (Hanski, 1999). The effect of using occurrence data to generate one of the
parameters used in the IFM also means that the standard errors of the model are biased
(Oksanen, 1994). Another solution is to use a proxy for dispersal. Mammal vagility affects both
home range size and dispersal distance, independent of body size (Bowman et al. 2002). In a
study on terrestrial and arboreal North American mammals, Bowman et al. (2002) demonstrate
that dispersal distance is related to a single constant value, the linear dimension of home range
size. Whitmee & Orme (2013) also found that home range was the most important predictor
variable related to median-dispersal distance in mammals. Although similar to Bowman et al.
(2003), Whitmee & Orme (2013) used mostly mammals from the Nearctic and Palearctic, they
also included five arboreal primate species. From a biogeographic perspective, Beaudrot and
Marshall (2011) demonstrate that dispersal limitation determines community structure in
primates including lemurs. Bannar-Martin (2014) also found spatial and dispersal limitations
determine lemur community assembly while other researchers found that lemurs were
ecologically limited (Kamilar, 2009). If lemurs are dispersal limited like other primates, then the
results from Bowman et al. (2002) may not hold for this group. However, the constant Bowman
et al. (2002) created allows for the use of home range data, which is readily available for many
primate species, to determine dispersal ability.
3.5 Effect of Additional Variables within Incidence Function Models
One potential benefit of IFMs is their ability to incorporate other variables that could impact the
extinction risk or colonization potential of a patch (Moilanen & Hanski, 1998). It is
50
straightforward to add an additional variable to the regression form of a metapopulation model
but it is very difficult to tease out how the additional variable affects either or both extinction
risk and colonization potential of a patch within the model (Oksanen, 2004). To do so requires
in-depth knowledge on how the additional variables impact extinction risk and colonization
potential a priori (Moilanen & Hanski, 1998).
The results of incorporating additional variables into an IFM are mixed (Moilanen & Hanski,
1998; Lawes et al. 2000; Jaquiéry et al. 2008). Incorporating additional variables that affect
species occurrence, such as habitat quality and landscape structure, did not improve the fit over
metapopulation models that only incorporated area and distance metrics (Moilanen & Hanski,
1998). Jaquiéry et al. (2008) found that population size of greater white-toothed tree shrews was
better explained by using habitat quality (factoring in human disturbance) over patch size.
Lawes et al. (2000) did not find any measures of human disturbance that improved the fit of
metapopulation models on Cercopithecus mitis labiatus (samango monkey). However, they did
find that the addition of human disturbance variables improved the fit of metapopulation models
for Philantomba monticola (blue duikers) and Dendrohyrax arboreus (tree hyraxes). I found
that area was the main factor influencing lemur species at the community level and
incorporating additional variables into metapopulations met with mixed results (Chapter 2).
Therefore, I will not add additional variables to metapopulation models in this chapter.
3.6 Simulating Metapopulation Dynamics Over Time Metapopulation dynamic models produce two results: species-specific extinction and
colonization potentials for animals in each patch within a set of patches. Once the extinction and
colonization probabilities of a patch are known, simulations can be run to model species
occurrence over time (Hanski, 1998). Simulating metapopulation dynamics is very useful to
determine whether a metapopulation is in decline. These simulations are also useful for
artificially altering the system through the addition or removal of patches to determine how
changes in landscape composition and structure affect species occupancy over time.
3.7 Metapopulation Dynamics: Forest Loss and Fragmentation Effects on Primate Occurrence
There is no research on metapopulation dynamics in lemurs and few studies on primates (Lawes
et al. 2000; Chapman et al. 2003; Mandujano & Escobedo-Morales, 2008). Some researchers
51
have used metapopulation theory as a conservation tool to: predict species persistence in a
fragmented landscape under varying conservation strategies (Swart & Lawes, 1996; Chapman et
al. 2003), assess extinction risk (Zeigler et al. 2013), determine factors impacting species
occurrence (Lawes et al. 2000), determine species minimal critical patch size (Lawes et al.
2000), and for population viability analyses (Mandujano & Escobedo-Morales, 2008). Other
studies have investigated the impact of patch size and isolation within suspected
metapopulations but they did not use metapopulation dynamics per se (Rodriguez-Toledo et al.
2003). In forest fragments along the periphery of Kibale National Park, Uganda, Chapman et al.
(2003) fitted a mainland-island incidence function model to occurrence data on four primate
species. The metapopulation models accounted for a substantial amount of variation in each
species occurrence. However, they found low confidence in the estimated coefficients for the
models. For both Procolobus badius (red colobus) and Colobus guereza (black and white
colobus), Chapman et al. (2003) found a strong area effect on occurrence but little influence of
connectivity on each species occurrence. For Cercopithecus ascanius (redtail monkey) fragment
size or distance did not affect occurrence while Chapman et al. (2003) found Pan troglodytes
(chimpanzees) were an unsuitable species for the application of metapopulation dynamics
because of their highly mobile nature. In a fragmented portion of Podocarpus forest in
KwaZulu-Natal Province, South Africa, Lawes et al. (2000) applied a mainland-island incidence
function model to C. m. labiatus occurrence and additional land use and environmental factors.
Lawes et al. (2000) found that the best-fit model incorporated only area as a factor determining
C. m. labiatus occurrence. Model fit was not improved by the inclusion of isolation, land use, or
other environmental factors.
The effects of forest loss and fragmentation on primate species occurrence are well studied
(Chapman et al. 2003; Rodriguez-Toledo et al. 2003; Arroyo-Rodriguez & Dias, 2010; Raboy et
al. 2010; Arroyo-Rodríguez et al. 2013). Typically, primate species occurrence decreases with
increased forest loss (Anzures-Dadda & Manson, 2007; Arroyo-Rodríguez et al. 2008; Boyle &
Smith, 2010; Raboy et al. 2010). For example, Anzures-Dadda & Manson (2007) show that for
Alouatta palliata, the probability of occurrence in a fragment was positively related to area.
Similarly, Boyle et al. (2010) found that for five of six primate species, occurrence was also
positively related to fragment size in the Brazilian Amazon.
52
Forest separated into smaller and more isolated patches reduces the amount of habitat and can
impact fragment connectivity. Several studies have focused on how habitat connectivity (or
isolation) between fragments affects primates (Lawes et al. 2000; Cristobal-Azkarate et al. 2005;
Marshall et al. 2010; Raboy et al. 2010). Although community structure may be determined by
geographic distance across the island of Madagascar (Beaudrot & Marshall, 2011), isolation has
so far been observed to have little to no effect on individual primate occurrence when compared
to fragment area (Lawes et al. 2000; Cristobal-Azkarate et al. 2005; Raboy et al. 2010). Thus,
scale dependence in biogeographic research represents an important consideration when seeking
to apply models from patches through landscapes to regions. For example, Cristobal-Azkarate et
al. (2005) found no relationship between isolation, as measured by the shortest distance to
nearest fragment and shortest distance to nearest fragment with monkeys, and monkey
occurrence. Marshall et al. (2010) did find that isolation of forests by farmland had a significant
effect on species richness. However, they suggest that the type of matrix between fragments was
more important than isolation distance. Raboy et al. (2010) found that patch isolation did not
predict golden-headed lion tamarin (Leontopithecus chrysomelas) occurrence. The effect of
habitat fragmentation separate from habitat loss on primate occurrence is not well understood
(Chapman et al. 2003; Marsh, 2003; Cardillo et al. 2008; Arroyo-Rodríguez et al. 2013b). In
order to better understand how fragmentation impacts primate species, researchers need to
further assess how connectivity, independent of habitat loss, affects primate occurrence (Hanski
& Ovaskainen, 2003; Arroyo-Rodríguez et al. 2013b).
3.8 Justification Although lemurs represent the largest biomass and biodiversity of mammals in Madagascar
(Garbutt, 2007), the forested habitats preferred by lemurs have decreased and continue to
decrease dramatically (Schwitzer et al. 2014). Forty percent of forest cover was converted to
non-forested habitat in Madagascar between the 1950s and 2000s (Harper et al. 2007; Schwitzer
et al. 2014), mostly due to human-induced disturbance (Schwitzer et al. 2014). As a result of
this, lemurs are currently considered the most endangered mammal group in the world, with
94% of the species threatened with extinction (Schwitzer, 2013).
In western Madagascar, the forest is mostly tropical dry deciduous forest, which is extremely
sensitive to fire (Bloesch, 1999). Fire has resulted in a high degree of forest loss and increased
53
habitat fragmentation (DeFries et al. 2005; Harper et al. 2007). There are a few bastions of intact
dry forest in the west and Ankarafantsika National Park contains the second largest portion of
continuous dry forest. However, habitat loss and fragmentation continue along the periphery of
these continuous forest patches (DeFries et al. 2005). Therefore, lemurs along the forest edge in
protected areas such as Ankarafantsika could be subject to increased habitat fragmentation.
Most researchers conduct their primate research near the center of Ankarafantsika at Ampijoroa
(Warren & Compton 1997; Radespiel 2000; Radespiel et al. 2001; Radespiel et al. 2003a;
Radespiel et al. 2003b; Rendigs et al. 2003; Weidt et al. 2004; Braune et al. 2005; Radespiel et
al. 2006; Olivieri et al. 2008; Radespiel et al. 2009; Thorén et al. 2010; Thorén et al. 2011;
Crowley et al. 2011,2012; Eichmueller et al. 2013; Kun-Rodrigues et al. 2014;) with some
exceptions (García & Goodman, 2003; Radespiel et al. 2008; Craule et al. 2009;
Rakotondravony & Radespiel 2009). Preliminary research indicates considerable differences in
lemur biogeography between continuous and fragmented forests (Craule et al. 2009; Crowley et
al. 2013; Steffens & Lehman, 2016). For example, Steffens and Lehman (2016) found that
contrary to a previous study of M. murinus and M. ravelobensis in nearby continuous forest
(Rakotondravony & Radespiel 2009), there were significant, positive correlations between
density and abundance for both species in fragments. Knowing that there are differences in
biogeographic patterns in some species in continuous versus fragmented habitat the question
arises as to how other species will respond to increased habitat fragmentation? Thus,
understanding spatial variations in lemur responses to forest fragmentation is critical to a more
informed understanding of lemur conservation biogeography.
My study is important because it is the first of its kind to use metapopulation dynamics to
determine how six lemur species (including three endangered species) respond to reduction in
habitat and changes in habitat connectivity. Moreover, it is imperative that we determine how
lemurs are affected by habitat loss and fragmentation because they face high extinction
probabilities, are the most endangered animal group in the world, and their habitat is
increasingly fragmented and disturbed.
3.9 Goal The goal of this study is to apply a spatially realistic metapopulation approach to investigate the
vulnerability to habitat loss and fragmentation of six lemur species living in a fragmented
54
landscape in Ankarafantsika National Park. Using metapopulation dynamics, I will answer the
following questions:
1) Do lemur species form metapopulations?
Hypotheses:
H0 Individual lemur species occurrence will vary randomly with respect to patch area
and isolation.
H1 A spatially realistic metapopulation model (incorporating area and connectivity) will
predict lemur species occurrence.
2) What are the separate effects of area (extinction risk) and connectivity/isolation
(colonization potential) within a lemur metapopulation?
Hypotheses:
H0 Incidence of occurrence and extinction probability will not differ between large and
small patches.
H1 Incidence of occurrence and extinction probability will be higher and lower
respectively in large versus small patches.
H0 Incidence of occurrence and colonization probability will not differ between more
and less connected patches.
H1 Incidence of occurrence and colonization probability will be higher in more
connected versus less connected patches.
3) Within simulated metapopulations over time, how do area and connectivity/isolation
affect occurrence? What are the conservation implications?
55
3.10 Methods
3.10.1 Study Site and Study Species
I conducted this study in Ankarafantsika National Park, Madagascar. I describe the study site in
detail in Chapter 2. The study landscape is entirely within the park boundaries. There are eight
species of lemurs within four families in the park (Table 3.1): two Indriidae: the western wooly
lemur (A. occidentalis) and Coquerel’s sifaka (P. coquereli); three Cheirgaleidae: the grey
mouse lemur (M. murinus), the golden brown mouse lemur (M. ravelobensis) and the fat-tailed
dwarf lemur (C. medius); one Lepilemuridae: Milne Edwards sportive lemur (L. edwardsi); and
two Lemuridae: the mongoose lemur (E. mongoz), and the common brown lemur (E. fulvus)
(Mittermeier et al. 2010). Table 3.1: Primate Species in Ankarafantsika National Park Found within Study Site.
Species Body mass (g)
Activity pattern Diet Mean home
range (ha)
Median-dispersal
distance (m) based on
home range*
Median/ Mean- dispersal
distance reported in
literature (m) Cheirogaleus
medius 120-270 Nocturnal Frugivore 1.55 ±0.42(1) 873 N/A
Microcebus murinus 58-67 Nocturnal Fauni-
frugivore 2.83 ±1.44(2) 1177 Median=251(7)
Microcebus ravelobensis 56-87 Nocturnal Fauni-
frugivore 0.59 ±0.11 (3) 530 Mean=54(8)
Propithecus coquereli
3700-4300 Diurnal Folivore 19.36(4) 3080 N/A
Eulemur fulvus 1700-2100 Cathemeral Frugivore 13.5(5) 2572 N/A
Lepilemur edwardsi 1100 Nocturnal Folivore 1.09(6) 731 N/A
*Median-dispersal distance was calculated as seven times the square root of the mean reported home range from each study. Data from: 1 Müller 1998; 2 Radespiel (2000); 3 Weidt et al. (2004); 4 McGoogan (2011); 5 Mittermeier et al.2010; 6 Warren & Compton (1997); 7 Radespiel et al. (2003); 8 Radespiel et al. (2009).
Cheirogaleidae
The Cheirogaleidae are characterized by their nocturnal behavior and small body size. Most
species are omnivorous but many, such as Microcebus species, rely mainly on fruit and insects
(Lahann, 2007). In the study landscape, two species of the same genus potentially occur: M.
murinus and M. ravelobensis. Morphological differences between the two Microcebus species
include color of pelage in that M. murinus has greyish brown pelage, while M. ravelobensis has
golden brown pelage (Zimmermann et al. 1998). Differences also include the size and thickness
of tail with M. murinus having a shorter, thicker tail than M. ravelobensis (Zimmermann et al.
56
1998). There are also interspecific differences in diet and behavior between the two Microcebus
species. Although the there are similarities in both Microcebus species’ diet, M. ravelobensis
appears more generalized than M. murinus (Radespiel et al. 2006). M. murinus exhibits
behavioral torpor during the dry season while M. ravelobensis is more active (Thorén et al.
2011). M. murinus prefers higher elevation drier forest and M. ravelobensis prefers lower
elevation forest closer to water (Rakotondravony & Radespiel, 2009).
The single Cheirogaleus species C. medius is larger than the two Microcebus species, ranging in
mass from 120 g to 270 g (Mittermeier et al. 2010). This species consumes greater amounts of
fruit than either Microcebus species, and hibernates during much of the dry season from April to
October (Fietz & Ganzhorn, 1999; Dausmann et al. 2005). To survive these long bouts of
hibernation, C. medius stores fat in its tail, thus increasing its body mass (Fietz & Ganzhorn,
1999). Prior to hibernation, C. medius reduces its activity while increasing high sugar fruit
consumption (Fietz & Ganzhorn, 1999). However, following hibernation C. medius increases its
activity level (Fietz & Ganzhorn, 1999).
Indriidae
There are two species from the Indriidae family that occur within the study landscape: P.
coquereli and A. occidentalis. P. coquereli is the largest species found in Ankarafantsika
(between 3700 g and 4300 g). It is diurnal and social, living in groups of between 2 and 10
individuals (McGoogan, 2011). This species is mainly folivorous, but also consumes fruit
(McGoogan, 2011). A. occidentalis is nocturnal, highly folivorous, and lives in adult pairs with
offspring (Mittermeier et al. 2010). This species is smaller in size than P. coquereli but similar
in size to Lepilemur edwardsi. A. occidentalis are distinguished from L. edwardsi by the white
patches on the back of their legs (Mittermeier et al. 2010).
Lemuridae
Two Lemuridae species are known to occur within the park: E. mongoz and E. fulvus. Both
Eulemur species are highly frugivorous, but they supplement their diet with leaves and flowers
during periods of fruit scarcity (Mittermeier et al. 2010). Both species are cathemeral in that
they change their activity pattern seasonally (Curtis et al. 1999; Rasmussen, 1999). E. fulvus
forms groups of 3 to 12 individuals while E. mongoz groups are smaller, usually consisting of
57
one breeding pair and their offspring. E. mongoz males are easily distinguished from E. fulvus
by their orange-colored chins. Female E. mongoz are greyer than E. fulvus females.
Lepilemuridae
There is only one member of the Lepilemuridae family found in the park: L. edwardsi. This
species is mainly folivorous, nocturnal, solitary, and is similar in size to A. occidentalis, but
darker in color with a more pointed face (Mittermeier et al. 2010).
3.10.2 Question 1: Do Lemur Species Form Metapopulations?
Although there are some differences in meaning between the terms patch and fragment they are
often used interchangeably. I define patches as discrete and measurable portions of habitat that
differ from neighboring portions, while I define fragments as discrete and measurable portions
of habitat that have been separated from a larger whole. A fragment may contain many patches
and a patch may not necessarily be a fragment. In this chapter, I will use the term patches when
referring to the metapopulation literature and fragments when a referring to my study site where
each fragment is considered a single habitat patch.
The data needed for a metapopulation model include at least a single survey of patch occupancy
within a network of patches, the x and y coordinates of each patch to determine the distance
between each patch, patch area, and the species-specific dispersal ability within the landscape
(Hanski, 1999). It is relatively easy to gather occurrence data for primates compared to
determining their population density. It is also easy to measure patch area and distances using
current GPS and GIS technology. However, it is very difficult to know the dispersal ability of
primates within a landscape.
To test the hypothesis that metapopulation dynamics explain occupancy patterns in lemur
species, I compared five or six potential metapopulation models including a null model for each
of the six species found in the study site. The metapopulation models include four incidence-
function models with rescue effect (IFM) and one mainland-island incidence function model
without rescue effect (MI-IFM). I applied the MI-IFM and null models to all six lemur species
occurrence data. The remaining four IFMs are similar but differ in the way the dispersal
parameter (α) was defined. In the first IFM, α is parameterized based on occupancy data from
one survey period (IFM). I applied the IFM model to all six lemur species occurrence data. The
58
next two IFMs, use a proxy for the dispersal parameter (α) based on the home range reported for
each species in the literature (IFMproxy and IFMprox2). I calculated the first proxy for median-
dispersal ability as the square root of the mean home range multiplied by seven (Equation (17);
IFMproxy) and I calculated the second as the square root of the mean home range (IFMproxy2).
I applied the IFMproxy and IFMproxy2 models to all six species occurrence data. For the final
IFM model, the dispersal parameter (α) is based on actual dispersal data from the literature
(IFMlit). I applied the IFMlit model only to those species where there was data on dispersal
ability (M. murinus and M. ravelobensis).
For each model, I input data on patch occupancy for each species, a measure of species-specific
dispersal distance (α), patch area, and isolation. I collected data on patch occupancy, patch area,
and isolation using the methods outlined in Chapter 2. I then took a linearized version of the
IFM models (see equation (9) below) and the MI-IFM model (see equation (15) below) and ran
binomial generalized linear models (GLM) with a logit link function using the glm{stats}
function in R (version 3.02), for each species. For each GLM I ran I input species occurrence as
the response variable and species-specific dispersal distance (α), patch area, and isolation as the
predictor variables.
Model Comparison
To determine the relative strength of the six fitted models (IFM, IFMproxy, IFMproxy2, IFMlit,
MI-IFM, and Null), I calculated corrected Akaike's information criterion (AICc) and then
performed a model averaging procedure to produce AIC weights (wi; Burnham, Anderson, &
Huyvaert, 2011). I considered the model with the highest wi as the model with the highest
likelihood of being selected among the six models (Burnham et al. 2011). I considered models
with AICc values within two of the model with the lowest AICc as potential candidate models. I
did not consider any models with AICc values greater than two of the lowest AICc as candidate
models.
Incidence Function Model with Rescue Effect (IFM; IFMproxy; IFMproxy2; IFMlit):
The IFM with rescue effect takes the following form (Hanski, 1999):
𝑱𝒊 =𝑪𝒊
𝑪𝒊&𝑬𝒊(𝑪𝒊𝑬𝒊(1)
59
𝐸+ =𝑒𝐴+. , 𝑓𝑜𝑟𝐴 ≥ 𝑒
4.(2)
𝑀+ = 𝛽𝑆 = 𝛽(3)
𝐶+ =𝑀+=
𝑀+= + 𝑦=
=𝑆+=
𝑆+= + 𝑦,𝑤ℎ𝑒𝑟𝑒𝑦𝑎𝑏𝑠𝑜𝑟𝑏𝑠𝛽(4)
𝐽+ =𝑆+=𝐴+.
𝑆+=𝐴+. + 𝑒𝑦=
1
1 + 𝑒𝑦𝑆+=𝐴+.
= 1 +𝑒𝑦𝑆+=𝐴+.
(4
(5)
where Ji is patch incidence in patch i, Ei is the extinction probability, Ci is the colonization
probability, Si is a measure of connectivity for patch i, and Ai is the area of patch i. It is difficult
to estimate extinction and colonization directly (Hanski, 1999), however, it is possible to
calculate e and y with data on patch occupancy 𝒑𝒋, patch size A, and connectivity S collected
during a single time period survey. Connectivity is estimated using the following:
𝑆+ = exp −𝛼𝑑+P 𝑝PR
PS+𝐴P(6)
where 𝜶 is inverse of the median species-specific dispersal distance and 𝒅𝒊𝒋 is a distance matrix
among patches. It is possible to change equation (5) by applying a linear model for the log-odds
of incidence:
𝐽+ 1 +𝑒𝑦𝑆+=𝐴+.
(4
=1
1 + 𝑒𝑥𝑝 𝑙𝑜𝑔 𝑒𝑦 − 2 𝑙𝑜𝑔 𝑆+ − 𝑥𝑙𝑜𝑔 𝐴+(7)
𝑙𝑜𝑔𝐽+
1 − 𝐽+= − 𝑙𝑜𝑔 𝑒𝑦 + 2 𝑙𝑜𝑔 𝑆+ + 𝑥𝑙𝑜𝑔 𝐴+ (8)
A logit transformation results in:
𝑙𝑜𝑔𝑖𝑡 𝐽+ = 𝛽^ + 2𝑙𝑜𝑔𝑆 + 𝛽4×𝑙𝑜𝑔𝐴(9)
The mainland-island incidence function model without rescue effect takes the following form
(MI-IFM; Hanski, 1994b):
60
𝐽+ =𝐶+
𝐶+ + 𝐸+(10)
𝐶+ = 𝑞𝑒𝑥𝑝 −𝛽𝐷+ 11
where q and 𝛽 are two parameters. Assuming that all the species are common on the mainland,
where 𝐶+ approaches one when 𝐷+ approaches zero than q=1 and equation (11) can be simplified
further (Hanski, 1994b):
𝐸+ =𝑒𝐴+. , 𝑓𝑜𝑟𝐴 ≥ 𝑒
4. 12
𝐽+ = 1 +𝜇efg𝐴+.
(4
13
It is possible to linearize equation (13) by applying linear model for the log-odds of incidence:
𝑙𝑜𝑔(𝐽+) = −log 𝜇 − 𝛽𝐷+ + 𝛽= log 𝐴k (14)
A logit transformation results in:
𝑙𝑜𝑔𝑖𝑡(𝐽+) = 𝛽^ − 𝛽4𝐷 + 𝛽= log(𝐴) (15)
Patch Occupancy
I determined patch occupancy of each fragment using standard line-transect methods (see
Chapter 2 for a full description). If we did not observe a species during any survey, I considered
it absent within a fragment. I conducted between 11 and 18 diurnal and 11 and 21 nocturnal
surveys of each fragment.
Dispersal distance
To determine connectivity within each IFM, I needed to determine the dispersal parameter (α).
However, there is limited data on dispersal ability in most primates, especially lemurs. For the
species in this study, previous researchers reliably measured dispersal distance only in M.
murinus (Radespiel et al. 2003; Schliehe-Diecks et al. 2012) and to a lesser degree M.
ravelobensis (Radespiel et al. 2009). Therefore, I ran multiple IFM models incorporating
61
different dispersal parameters. For each species, I ran three IFMs using different measures for
dispersal (α), except for M. murinus and M. ravelobensis for which I ran four IFMs with
different measures for dispersal (α). For the first IFM, I determined which α fit the survey data
by running a function in R that ran all possible values for α and selected the one that provided
the lowest deviance (IFM; Oksanen, 2004). For the second IFM, I used a proxy for median-
dispersal distance based on a function of the home range size of each species (IFMproxy) from
Bowman et al. (2002). Bowman et al. (2002) found that median-dispersal distance could be
calculated as the linear dimension (square root) of the mean home range multiplied by a factor
of 7. Therefore, I took the mean reported home range for each species and determined its
dispersal ability with the following formula:
ℎ𝑜𝑚𝑒𝑟𝑎𝑛𝑔𝑒(𝑘𝑚=)×7 = 𝑚𝑒𝑑𝑖𝑎𝑛𝑑𝑖𝑠𝑝𝑒𝑟𝑠𝑎𝑙𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑘𝑚 (16)
Alpha (𝛼) is calculated as:
4pqr+str+uvqwusxr+uystzq
(17)
The proxy for dispersal using the formula from Bowman et al. (2002) overestimated the median-
dispersal of the two known species (M. murinus and M. ravelobensis; Table 3.1). For the third
IFM (IFMproxy2), I created a second proxy where I took the linear dimension of the mean
reported home range (i.e. ℎ𝑜𝑚𝑒𝑟𝑎𝑛𝑔𝑒(𝑘𝑚=)), because lemurs like many arboreal primates
may be dispersal limited (Bannar-Martin, 2014) and the value derived from formula (16) may
overestimate median-dispersal distance. The values derived using the linear dimension of the
home range better fit the known home range and dispersal distances for M. murinus and M.
ravelobensis (Table 3.1). For the final IFM (IFMlit: M. murinus and M. ravelobensis only), I
used the largest median-dispersal distance reported for each species regardless of sex (M.
murinus = 251 m (Radespiel et al. 2003); M. ravelobensis = 54 m (Radespiel et al. 2009).
Patch Area and Isolation
I measured the area and isolation of each fragment using the methods detailed in Chapter 2.
62
3.10.3 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) within a Lemur Metapopulation?
To test the hypothesis that incidence probability was higher in larger fragments than smaller
fragments, I determined the incidence probability for each patch (Ji) based on equation (1;
among patch models) or (10; mainland-island model) depending on which candidate models I
selected in section 2.2. However, in order to calculate incidence probability (Ji), I needed to
determine the extinction probability (Ei) and colonization probability (Ci) for each patch. For
the among patch models, with known patch sizes, patch occupancies, and connectivity, I used a
generalized linear binomial model with logit link function (equation (9)) to determine the
coefficients 𝛽{ and 𝛽4 = 𝑥 and to separate e from y to calculate the extinction and colonization
probabilities for each patch using the following steps:
𝑒𝑦 = exp −𝛽{ (18)
𝑒 = minvS{
𝐴. (19)
𝑦 = 𝑒𝑦/𝑒 (20)
Once I separated e and y, I calculated the extinction probability (𝐸+) and colonization probability
(𝐶+) and subsequently incidence probability (Ji) of each patch using equations (2) and (4)
respectively.
For the mainland-island model, I also used a generalized linear binomial model from a single
survey of patch occupancy on equation (15). Using this equation, I was able to determine the
coefficients 𝛽{ = 𝜇, 𝛽4 = 𝛽 and 𝛽= = 𝑥 to calculate the colonization and extinction
probabilities using equations (11) and (12) respectively. I then input the values from the
colonization and extinction probabilities into equation (10) to determine the incidence
probability for each patch.
For both the among patch and the mainland-island model, I sorted fragments by descending size
and divided them in half: the top half represented the largest fragments and the bottom half
represented the smallest fragments. To test the hypothesis that incidence probability was higher
in larger fragments than smaller fragments, I compared the mean incidence probability for the
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largest fragments with the mean incidence probability for the smallest fragments using a one-
tailed t-test for each species in R.
Using the incidence probability calculated above, I then determined whether incidence
probability was higher in more connected/closer fragments than less connected/further
fragments. In the among patch models, fragment connectivity (Si; equation (6)) incorporates a
species-specific dispersal parameter. Therefore, patch connectivity was species-specific. For
each species, I sorted connectivity (Si) in descending order and divided Si in half —the top half
representing the most connected fragments, and the bottom half representing the least connected
fragments. In the mainland-island model, I sorted connectivity (the distance from the mainland)
and divided this distance in half - the top half representing the closest fragments (most
connected), and the bottom half representing the furthest fragments (least connected). To test the
hypothesis that incidence probability was higher in more connected than in less connected
fragments, I compared the mean incidence probability for the most connected/closer fragments
with the mean incidence probability of the least connected fragments using a one-tailed t-test for
each species in R.
3.10.4 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence? What are the Conservation Implications?
To see if there was a difference in how area affected occurrence compared to connectivity, I
simulated metapopulation dynamics for each species over time based on the extinction and
colonization probabilities derived from the IFM in R. I simulated the metapopulation dynamics
in the following manner: If a patch is empty (pi = 0) at time (t) then the probability that it will
become occupied (pi=1) is a function of the colonization probability (𝐶+(𝑡) =�g�(y)
�g�(y)&�
) for that
patch. If the patch is occupied (pi=1) at time (t) the probability that it will become extinct (pi =
0) is a function of the extinction probability for that patch (Ei(t)=𝐸+(𝑡) = 𝑒𝐴+(.).
One can run simulations as a single-time step or multiple-time steps. However, in multiple time
steps patch occupancy is based on the previous step only. I ran two sets of simulations. The first
set represents a worst case scenario where I ran a simulation separating out the five largest
fragments and a second simulation where I separated out the five most connected fragments.
The second set represents the opposite scenario where fragments that are smaller and least
64
connected were removed from the simulation. For each species, I then ran the two sets of two
simulations for 200 time steps (equivalent of 200 years). In the first half of the simulation, for
the first 100 steps all 42 fragments contribute to the metapopulation. At time step 101, I
separately continue simulations on the five removed fragments (i.e. five largest, five smallest,
five most connected, five least connected) from the remaining 37 fragments for 99 more time
steps. This method allows the ability to look at what happens to lemur species occupancy in the
remaining 37 fragments when the five largest/smallest and five most/least connected fragments
are removed. In addition, this method gives the ability to look at what happens to lemur species
occupancy in the five largest/smallest and five most/least-connected fragments independent of
the remaining 37.
3.11 Results
3.11.1 Question 1: Do Lemur Species Form Metapopulations?
There were 42 fragments within the study landscape with a mean size of 0.09 ± 0.20 km2. The
mean distance between centroids of each fragment was 2.82 ± 1.36 km with a range of 0.15 km
to 6.48 km. The proxy for median-dispersal distance ranged from 700 m (M. ravelobensis and L.
edwardsi) to 3357 m (P. coquereli; Table 3.1). Patch occupancy differed among species.
Smaller-bodied Cheirogaleids occupied the largest number of fragments while the remaining
three larger species occupied the fewest (Table 3.2). However, a linear regression of occupancy
versus body size yielded no relationships. I did not observe any A. occidentalis or E. mongoz
individuals during this study. Therefore, I did not include these species in the analysis. Table 3.2: Lemur Patch Occupancy in a Fragmented Landscape.
Species Number of occupied patches Number of unoccupied patches
Cheirogaleus medius 12 30 Microcebus murinus 35 7
Microcebus ravelobensis 34 8 Propithecus coquereli 3 39
Eulemur fulvus 7 35 Lepilemur edwardsi 2 40
The probability of occurrence differed among species (Fig. 3.2). Both Microcebus species had
the highest probability of occurrence in the landscape followed by C. medius. E. fulvus had the
lowest probability of occurrence within the landscape.
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Figure 3.2: Probability of Occurrence Among Patches for Four Lemur Species in a Fragmented Landscape. Colors represent the probability of occurrence: red reflects the highest probability of occurrence for a species within a fragment and white the lowest. The probability of occurrence is based on the fitted incidence function model with α parameterized from the data (IFM) for C. medius, M. murinus, and E. fulvus and the IFM with α determined from the literature (IFMlit) for M. ravelobensis. The size of each circle represents the size of each fragment relative to one another. The position of fragments is based on Universal Transverse Mercator (UTM) coordinate system. Northing is equivalent to latitude and easting is equivalent to longitude.
I found differences in model selection results between species (Table 3.3). For C. medius, two
models had nearly identical low AICc values. The first was the IFM (model where I determined
dispersal (α) based on the survey data) and the second was the IFMproxy2 (model where I
determined dispersal (α) based on the linear dimension of the mean reported home range). For
M. murinus, the model with the lowest AICc value was the MI-IFM and the only other model
within two AICc values was the IFM. I found a different model for M. ravelobensis to have the
lowest AICc value, IFMlit (where I determined dispersal (α) from the literature), followed by
three other models within two AICc values, MI-IFM, IFMproxy2, and IFM. In fact, all four
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models were roughly equivalent. For both P. coquereli and L. edwardsi, there were no models
with values lower than or within two of the null model. For E. fulvus, the model with the lowest
AICc value was IFM with IFMproxy (where I determined dispersal (α) a proxy based on home
range) as the only model within two AICc values. Therefore, I was able to reject the null
hypothesis that occurrence varied randomly with respect to area and connectivity for all species
except for P. coquereli and L. edwardsi. Table 3.3: Metapopulation Models of Six Lemur Species in 42 Fragments in a Fragmented Landscape.
Species Modela Null Deviance
Residual Deviance DoF P-value AICc ΔAICc wi
Cheirogaleus medius
IFM 52.78 26.69 41 <0.01 31.00 0.00 0.37 IFMproxy2 52.98 26.74 41 <0.01 31.05 0.05 0.36 IFMproxy 57.72 29.28 41 <0.01 33.00 2.00 0.14 MI-IFM 50.25 26.49 41 <0.01 33.12 2.12 0.13
Null 8.57 8.57 41 N/A 56.75 25.75 0.00
Microcebus murinus
MI-IFM 37.85 25.25 41 <0.01 31.88 0.00 0.53 IFM 39.86 29.35 41 <0.01 33.66 1.78 0.22
IFMproxy2 40.76 30.07 41 <0.01 34.38 2.50 0.15 IFMlit 41.94 31.00 41 <0.01 35.30 3.43 0.10 Null 5.83 5.83 41 N/A 40.59 8.71 0.01
IFMproxy 56.62 41.77 41 <0.01 46.07 14.20 0.00
Microcebus ravelobensis
IFMlit 42.29 28.94 41 <0.01 33.25 0.00 0.25 MI-IFM 40.9 26.65 41 <0.01 33.28 0.03 0.25
IFMproxy2 42.48 20.07 41 <0.01 33.38 0.13 0.24 IFM 42.71 29.22 41 <0.01 33.53 0.28 0.22
IFMproxy 48.04 32.59 41 <0.01 36.90 3.65 0.04 Null 6.48 6.48 41 N/A 44.98 11.73 0.00
Propithecus coquereli
Null 2.77 2.77 41 N/A 9.55 0.00 0.56 IFM 27.0 7.54 41 <0.01 11.84 2.30 0.18
IFMproxy2 27.65 7.76 41 <01 12.06 2.52 0.16 MI-IFM 21.61 6.32 41 <0.01 12.95 3.41 0.10
IFMproxy 47.72 13.32 41 <0.01 17.63 8.08 0.01
Eulemur fulvus
IFM 40.18 8.4 41 <0.01 12.71 0.00 0.48 IFMproxy 41.65 8.44 41 <0.01 12.74 0.04 0.47
IFMproxy2 37.36 14.22 41 <0.01 18.53 5.82 0.03 MI-IFM 37.85 12.9 41 <0.01 19.53 6.82 0.02
Null 5.83 5.83 41 N/A 40.59 27.88 0.00
Lepilemur edwardsi
Null 1.91 1.91 41 N/A -6.42 0.00 1.00 MI-IFM 16.08 6.17 41 <0.01 12.80 19.22 0.00
IFM 25.96 14.87 41 <0.01 19.17 25.59 0.00 IFMproxy2 25.97 14.87 41 <0.01 19.18 25.60 0.00 IFMproxy 29.03 16.41 41 <0.01 20.72 27.14 0.00
a IFM= incidence function model where α was parameterized based on occupancy data from one survey period; MI-IFM= mainland-island incidence function model; IFMproxy= IFM where α was calculated as a proxy for dispersal ability based on the square root of the mean home range multiplied by seven reported for each species in the literature; IFMproxy2= IFM where α was calculated as a proxy for dispersal ability based on the square root of the mean home range reported for each species in the literature IFMlit= IFM where α was based on the literature for species where data has been reported on dispersal ability; DoF= Degrees of freedom.
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3.11.2 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) on a Lemur Metapopulation?
I found that the mean probability of occurrence for all species was significantly greater in large
fragments compared to smaller fragments (Table 3.4). However, there were species- and model-
specific differences among the study species in the probability of occurrence between the least
connected/further and more connected/closer fragments (Table 3.5). For C. medius, there was no
significant difference in the probability of occurrence in more or less connected fragments. For
M. murinus, there was a significant negative difference in the probability of occurrence in
further rather than closer fragments within the MI-IFM. However, I found no significant
difference in the IFM for M. murinus. For M. ravelobensis, for both the IFMlit and MI-IFM the
probability of occurrence was significantly lower in more connected/closer fragments than less
connected/further fragments. However, I found no significant difference in probability of
occurrence in the IFM for M. ravelobensis. For E. fulvus, I found no significant difference in the
probability of occurrence in the IFM but did find a significantly higher probability of occurrence
in more connected rather than less connected fragments. Therefore, area contributes more to
lemur species occurrence than connectivity and connectivity for some species (M. murinus and
M. ravelobensis) has a negative effect on species occurrence. Table 3.4: T-test Results for the Mean Probability of Occurrence (Ji) for Four Lemur Species in the Largest Versus Smallest Fragments.
Species Modela Ji Mean Largest N Ji Mean Smallest N T-statistic p-value
Cheirogaleus medius IFM 0.54 21 0.09 21 6.52 <0.01
IFMproxy2 0.54 21 0.09 21 6.70 <0.01 IFMproxy 0.53 21 0.11 21 5.30 <0.01
Microcebus murinus MI-IFM 0.94 21 0.55 21 6.52 <0.01 IFM 0.97 21 0.70 21 7.38 <0.01
Microcebus ravelobensis
IFMlit 0.97 21 0.64 21 7.46 <0.01 MI-IFM 0.97 21 0.65 21 6.47 <0.01
IFMproxy2 0.97 21 0.65 21 7.4 <0.01 IFM 0.97 21 0.87 21 14.59 <0.01
Eulemur fulvus IFM 0.33 21 0.004 21 3.91 <0.01 IFMproxy 0.33 21 0.06 21 3.00 <0.01
a See Table 2.3 for definitions.
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Table 3.5: T-test Results for the Mean Probability of Occurrence (Ji) for Four Lemur Species in the Most Versus Least Connected Fragments.
Species Model Ji Mean most connected N Ji Mean least
connected N T-statistic
p-value
Cheirogaleus medius IFM 0.29 21 0.33 21 -0.44 0.66
IFMproxy2 0.24 21 0.38 21 -1.50 0.10 IFMproxy 0.37 21 0.27 21 -1.00 0.30
Microcebus murinus MI-IFM 0.63 21 0.86 21 -2.98 <0.01
IFM 0.80 21 0.87 21 -1.29 0.21
Microcebus ravelobensis
IFMlit 0.69 21 0.93 21 -4.22 <0.01 MI-IFM 0.72 21 0.89 21 -2.47 0.02
IFMproxy2 0.76 21 0.86 21 -1.50 0.20 IFM 0.91 21 0.93 21 -0.91 0.37
Eulemur fulvus IFM 0.28 21 0.06 21 2.44 0.02
IFMproxy 0.35 21 0.05 21 3.43 <0.01 a See Table 2.3 for definitions.
3.11.3 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence?
Fragment area has a greater influence on overall species occurrence than fragment isolation
does. Removal of the five largest fragments via simulation caused all species to decline in
occurrence (Fig. 3.3A). The most extreme example was C. medius, which became extinct in the
remaining fragments. None of the species showed any appreciable change in occurrence among
the five largest fragments when they were separated from the remaining 37, although occurrence
of C. medius did vary between three and five in the five largest fragments. Removing the five
smallest fragments (Fig. 3.3B) caused no noticeable decline in species occurrence in the 37
remaining fragments. Separation of the five smallest fragments from the remaining 37 caused a
decline in occurrence of all species in the five smallest fragments. Removal of the five most
connected fragments via simulation resulted in no obvious changes in occurrence for any
species (Fig. 3.4A). However, occurrence for all four species declined within the five most
connected fragments following separation from the remaining 37. Removal of the five least
connected fragments caused no change in occurrence for any of the species except C. medius,
which saw a declining trend in occurrence following removal of the five least connected
fragments. There was a decline to zero occurrence for all species, accept M. murinus, in the five
least connected fragments when they were separated from the remaining 37.
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Figure 3.3: Simulations of Metapopulation Dynamics for Four Lemur Species Over 200 Time Steps in a Fragmented Landscape When the Five Largest and Five Smallest Fragments Are Removed. Simulated species occurrence over time using a Markov chain process. The five largest (A) and five smallest (B) fragments (black lines), respectively are removed from the rest of the fragments (n=37; red lines) at time period 101(vertical line). After this point I ran simulations, to time period 200, separately to demonstrate the impact of either removing the largest (A) or smallest (B) fragments (black). I ran simulations using the IFM with α parameterized from the data (IFM) for C. medius, M. murinus, and E. fulvus and the IFM with α determined from the literature (IFMlit) for M. ravelobensis.
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Figure 3.4: Simulations of Metapopulation Dynamics for Four Lemur Species Over 200 Time Steps in a Fragmented Landscape When the Five Most Connected and Five Least Connected Fragments Are Removed. Simulated species occurrence over time using a Markov chain process. I removed the five most connected (A) and five least connected (B) fragments, respectively (black lines) from the rest of the fragments (n=37; red lines) at time period 101(vertical line). After this point I ran simulations, to time period 200, separately to demonstrate the impact of either removing the most (A) or least (B) connected fragments (black). I ran simulations using the IFM with α parameterized from the data (IFM) for C. medius, M. murinus, and E. fulvus and the IFM with α determined from the literature (IFMlit) for M. ravelobensis.
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3.12 Discussion In the first study of its kind on lemur biogeography, I applied a metapopulation approach to
determine how lemur species occurrence was affected by habitat loss and fragmentation. Using
a single-season survey of patch occupancy, I found that lemur species respond differently to
area and connectivity/isolation in a fragmented landscape. Simulations of metapopulation
dynamics provide support that lemur species occurrence was more affected by area than
isolation. Based on the simulation results, I suggest that the most connected fragments are not as
important to the maintenance of the metapopulation as has been previously predicted. For some
species separating the most connected fragments from the remaining fragments actually resulted
in declines in occurrence within the most connected fragments.
3.12.1 Question 1: Do Lemur Species Form Metapopulations?
A metapopulation approach is appropriate when habitat is in discrete patches, when ecological
processes occur at the local and metapopulation scale, when habitat within the discrete spatial
unit is large enough for local breeding populations, and when the patches are relatively
permanent (Hanski, 1999). The level of habitat loss and fragmentation in Madagascar provides
an ideal situation in which to apply a metapopulation approach to studying with lemur
populations: there is only a fraction of habitat remaining, habitat patches (fragments) are
discrete units separated by non-habitat, many patches are large enough to maintain local
breeding populations, and local populations are connected to one another through dispersal, thus
creating metapopulations.
Two of the species that I studied, P. coquereli and L. edwardsi, did not form a metapopulation
within my study site. Both of these species only occurred within a small number of fragments
within the landscape (three for P. coquereli and two for L. edwardsi). There are three scenarios
that could explain why neither P. coquereli nor L. edwardsi formed metapopulations: area
effects, dispersal ecology and anthropogenics. First, the existing populations of the two species
within the fragments declined and became locally extinct over time because fragment size was
too small to support populations. McGoogan (2011) found that P. coquereli home ranges can be
as large as 23.34 ha (using 95% kernel density methods), which is much larger than the majority
of fragments occurring within my study landscape. Warren and Crompton (1997) found that for
L. edwardsi, yearly home range size is 0.81-1.70 ha, which is much smaller than many of the
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fragments where L. edwardsi was found absent in my study. This home range size suggests that
L. edwardsi should be able to tolerate smaller fragments, however their mean horizontal
distance travelled per day was quite large. Warren and Crompton (1997) found that L. edwardsi
could travel as much as 463 m (horizontal travel distance) per day. Few fragments in my study
site had linear dimensions greater than 463 m. L. edwardsi occurrence appears to differ in
continuous versus fragmented habitats. For example, Craul et al. (2009) found L. edwardsi to
occur in 13 of 17 continuous forest sites surveyed but only two of six habitat fragments, a
finding that supports the hypothesis that this species is not tolerant to habitat loss and
fragmentation. I found both P. coquereli and L. edwardsi to occur only in larger fragments (the
three largest for P. coquereli and the largest and fourth largest for L. edwardsi), and absent in all
fragments smaller than 11.58 ha. Therefore, the explanation of fragments being too small for
survival is plausible for both P. coquereli and L. edwardsi. The second scenario is that these
species have limited ability to disperse through the matrix and therefore cannot colonize
fragments. Both species have large day ranges between 260 m and 1909 m (McGoogan, 2011;
Warren & Crompton, 1997) and both are highly arboreal vertical clingers and leapers. It’s likely
that these species would avoid matrix habitat that has few or no trees as is in my study area. It is
also likely that the distances between fragments are too large for P. coquereli and L. edwardsi to
attempt crossing. I found P. coquereli in three adjacent and nearby fragments (Fragment 1, 3,
and 4; Figure 2.4). I found L. edwardsi in the largest fragment (Fragment 3) and another
fragment adjacent and close to the continuous forest (Fragment 6; Figure 2.4). The third
scenario is anthropogenics. Both P. coquereli and L. edwardsi are preferred species to hunt in
Ankarafantsika National Park (García & Goodman, 2003). It is possible that these species may
be hunted out of smaller fragments or they avoid moving between fragments due to hunting
pressure. I suggest that these species exist within a declining non-equilibrium metapopulation.
Without intervention it is likely that these populations will become locally extinct within the
area similar to E. mongoz and A. occidentalis.
For the four lemur species that I found that had formed a metapopulation, the type of
metapopulations differed between species. Although the landscape formed a classic mainland-
island metapopulation, I only was able to select this model as a potential candidate model in M.
murinus and M. ravelobensis. However, there were differences in model selection between even
these two similar species. For M. murinus, the mainland-island model was the most likely model
73
followed by the IFM where I estimated the dispersal parameter (α) using the survey data (IFM).
For M. ravelobensis, four models were equivalently likely, including the IFM where I
determined the dispersal parameter (α) based on the literature (IFMlit), the mainland-island
model (MI-IFM), the IFM where I determined dispersal as the linear dimension of the reported
home range (IFMproxy2), and the IFM where I estimated the dispersal parameter (α) using the
survey (IFM). Differences in the habitat preferences and dispersal abilities of Microcebus
species may explain why these similarly related species appear to form different
metapopulations. M. murinus prefers higher elevation and dryer forests than M. ravelobensis
does (Rakotondravony & Radespiel, 2009). There was very little difference in elevation between
fragments within this study and I selected the landscape due to its relatively homogeneous forest
separated by relatively homogeneous grassland savannah. There were some differences in
habitat between fragments. For example, some fragments had dry riverbeds, which may indicate
a difference in habitat type. Although there were dry riverbeds within some of the fragments,
both species were present in all fragments that had a dry riverbed. In fact, both species had
similar occupancy rates within the landscape with M. ravelobensis occurring in one fewer
fragment than M. murinus. Steffens and Lehman (2016) found that abundance in both M.
murinus and M. ravelobensis were related to similar factors including: dendrometrics, fragment
area, and isolation within the same fragmented landscape. In continuous forest, Burke and
Lehman (2015) found differences between M. murinus and M. ravelobensis in their capture rates
of each species and the body mass of female M. ravelobensis between the edge and interior
habitat. They captured more M. ravelobensis and fewer M. murinus along the edge than in the
interior habitat. They found female M. ravelobensis along the edge had greater body mass than
those in the interior habitat. Therefore, preference for different microhabitats may not explain
why models differed between these species but edge effects might.
Dispersal ability differs between M. murinus and M. ravelobensis in continuous forest
(Radespiel et al. 2003; Radespiel et al. 2009; Schliehe-Diecks et al. 2012) and may explain their
different metapopulation dynamics in a fragmented landscape. Although both species appear to
be dispersal limited, in continuous forest M. ravelobensis may be more dispersal limited than M.
murinus with 0.05 km (Radespiel et al. 2009) and 0.25 km (Radespiel et al. 2003) median-
dispersal distances respectively. I was able to determine median-dispersal ability for both
species using metapopulation dynamics in a fragmented landscape. Based on metapopulation
74
dynamics, I found that both Microcebus species had the same median-dispersal ability of 0.10
km. A median-dispersal distance of 0.10 km is less than half the distance reported from the
literature for M. murinus (0.25 km; Radespiel et al. 2003) and double the distance reported for
M. ravelobensis (0.05 km; Radespiel et al. 2009). The most likely model for M. ravelobensis
contained the smaller median-dispersal distance reported in the literature (0.05 km; Radespiel et
al. 2012) and the most likely model for M. murinus was the mainland-island model. In a
mainland model, immigrants continually spread from the continuous forest to the fragments
(MacArthur & Wilson, 1967). M. murinus is less dispersal limited and edge intolerant than M.
ravelobensis in continuous forest. Yet neither species showed major differences in occurrence in
a fragmented landscape. A mainland-island metapopulation model may be more suitable for M.
murinus because of their greater ability to disperse from continuous forest while M. ravelobensis
may not be as dispersal limited as thought and is more edge tolerant and therefore capable of
surviving in fragments once established.
For both C. medius and E. fulvus, the most likely model was the among patch incidence function
model where I estimated the dispersal parameter (α) using the survey data (IFM). Based on this
model, I suggest that their ability to move among patches exceeded their ability to move from
the continuous forest to occupied fragments. For both species, I selected additional candidate
models: the IFM where I estimated the dispersal parameter (α) using home range as a proxy (E.
fulvus and C. medius; IFMproxy), and IFM where I estimated (α) using the linear dimension of
home range (C. medius, IFMproxy2). The dispersal parameter suggests E. fulvus has the highest
dispersal ability of all the species in the study. In fact, both species have large median-dispersal
abilities, although I found E. fulvus to have a roughly 10 times greater median-dispersal distance
(2.35 km) than C. medius (0.21 km), and E. fulvus has roughly 10 times the body mass of C.
medius (Table 3.1). Moreover, C. medius and E. fulvus were the only two frugivores in the study
site. It is predicted that larger species have greater dispersal ability than smaller species
(Sutherland et al. 2000) and frugivorous primates have larger home ranges than folivorous
primates (Clutton-Brock & Harvey 1977; Richard 1985). Thus, home range size may predict
dispersal ability (Bowman et al.2002) and frugivores have greater dispersal ability than
folivores. However, E. fulvus occupied fewer fragments than C. medius. Within this landscape
there were likely few fragments large enough for E. fulvus to live in, which required E. fulvus to
move between fragments. E. fulvus may be transient within patches that are smaller than they
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would normally need to be able to survive. For example, I found E. fulvus in one fragment that
was smaller (4.16 ha) than their reported home range yet absent in four fragments that were
within their reported home range. Although E. fulvus is smaller than P. coquereli they both have
similar home range sizes (Table 1; McGoogan, 2011; Mittermeier et al. 2010). However, E.
fulvus occupied seven fragments and P. coquereli only two fragments. P. coquereli may be
limited by edge effects that reduce habitat suitability (McGoogan, 2011; Kun-Rodriguez et al.
2014) where E. fulvus may be more edge tolerant. Lehman et al. (2006a; 2006b) found that
contrary to predictions Eulemur rubriventer was edge tolerant. Lehman (2006b) suggests that E.
rubriventer behaved more like a folivore/frugivore than a strict folivore. It is possible that E.
fulvus behaved the same way. However, we need further study to determine if E. fulvus is more
folivorous or frugivorous within the fragments and to determine what is the availability of
fruiting trees within the fragments.
E. fulvus is an important seed disperser and so its ability to disperse has secondary benefits to
habitat maintenance (Sato, 2012; Ganzhorn et al. 1999). It is important to understand this
species’ response to habitat fragmentation as losing an important seed disperser may have
cascading negative effects throughout the landscape (Valenta et al. 2014; Sato, 2012; Ganzhorn
et al. 1999). C. medius is also an important seed disperser but with its small body mass and
hibernation patterns, may be better suited to survive in more fragments than E. fulvus. I found C.
medius in one fragment that was 1.69 ha but the remaining fragments where it occurred were
larger than 11.58 ha. Unlike any of the other species observed within the fragments, C. medius is
capable of extended hibernation (Dausmann et al. 2005). Like other Cheirogaleus species, C.
medius consumes large amounts of high-sugar fruits prior to hibernation in order to build up fat
reserves (Fietz & Ganzhorn, 1999). Therefore, C. medius may be able to survive only in
fragments that have a high availability of fruit during this crucial period. Tree holes used by C.
medius must be carefully selected in order to allow sufficient maintenance of body temperature
during the months that they hibernate (Dausmann et al. 2005). Like fruit availability, tree holes
may be a limiting resource for C. medius in the fragments.
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3.12.2 Question 2: What are the Separate Effects of Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) on a Lemur Metapopulation?
Area
I found that larger fragments had a higher probability of occurrence for all four species that I
conducted the analysis on, regardless of the selected candidate model. In Chapter 2, I found that
area was the strongest predictor of species richness within the same landscape. Area not only
predicts species richness but individual species occurrence whereas connectivity seems to have
little effect on lemur species occurrence; with some exceptions (see below). The pattern of area
predicting lemur occurrence at the species level may be driving the same pattern at the
community level. Hanski (2010) suggests that species-area relationships can be derived from
single species metapopulation models by summing the predicted incidences for each species. In
metapopulation dynamics as local populations grow and reach their carrying capacity
individuals are forced to leave the patch to find a new suitable habitat patch (Hanski 1991). An
empty patch is considered colonized when an immigrant arrives and subsequently survives in
that patch. The quality and size of the habitat determines survival of an individual in a
previously unoccupied patch (Hanski, 2010). However, assessing habitat quality is more
difficult than measuring area of a patch. Hanski (2010) argues that how much habitat quality
versus area contributes to species occurrence is dependent on specific circumstances. Many
studies on primates found that area was the largest predictor of primate species occurrence
(Lawes et al. 2000; Chapman et al. 2003; Rodriguez-Toledo et al. 2003; Arroyo-Rodriguez &
Dias, 2010; Marshall et al. 2010). For example, Lawes et al. (2000) found that area was the only
factor that impacted occurrence in Cercopithecus mitis in a fragmented landscape even when
considering other factors such as isolation and habitat disturbance. We need future research to
evaluate the relative contribution of habitat quality versus area to lemur species occurrence.
Connectivity/Isolation
I hypothesized that connectivity would have a significant positive effect on lemur species
occurrence. The only species to show a positive relationship in occurrence probability and
connectivity was E. fulvus using the IFMproxy model but not with the IFM model. Contrary to
predictions, occurrence probability was the same between more and less connected fragments
for one species (C. medius using all candidate models) and negative between more and less
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connected fragments in M. murinus (MI-IFM model) and M. ravelobensis (IFMlit model). For
E. fulvus there was a significant positive difference in occurrence between more and less
connected fragments. This result means that although E. fulvus had the greatest dispersal ability
among the six species in this study, it is more likely to occur in more versus less connected
fragments. Migration between fragments is risky for arboreal lemurs. For example, species
travelling through the matrix have increased predation risk (Irwin, 2009) and there is the
possibility of arriving at an unsuitable fragment requiring further migration. Therefore, E.
fulvus, although capable of migrating to any fragment, appears to stay within the largest and
most connected fragments. Although occurring in multiple fragments, C. medius tended to occur
near the largest fragment (Fragment 3; Figure 2.4 and Table 2.5) or the continuous forest.
Therefore, they are either not limited by dispersal or they form an intermediate metapopulation.
In an intermediate metapopulation, they would be able to move between fragments but one or
more of the larger fragments would act as a mainland source of more colonists. If Fragment 3
acted as a second mainland source, this pattern would explain a lack of difference in occurrence
probability between more and less connected fragments (Harrison, 1991). For both Microcebus
species, the differences in occurrence probability were negative, meaning that the probability of
occurrence was lower in more connected than in less connected fragments. One explanation for
this negative probability is that the two species of Microcebus have higher occurrence
probability in more isolated fragments because they are not area limited and are able to survive,
possibly in the long-term, within the smallest fragments regardless of isolation. Other studies
have recorded Microcebus species in all but the smallest (<1 ha) fragments (Ganzhorn, 2003;
Schad et al. 2004; Olivieri et al. 2008) However, these studies did not include as many small (<1
ha) fragments as my study. I found Microcebus to occur in fragments as small as 0.23 ha. It is
possible that there are source-sink dynamics occurring within the study landscape, where large
patches and possibly the nearby continuous forest provide a constant source of potential
immigrants for smaller patches (Harrison 1991). For example, I only observed one Microcebus
individual in the smallest fragment, suggesting that occupancy in this patch is ephemeral and
thus maintained through colonization. Ganzhorn and Schmid (1998) also found a potential
source-sink relationship occurring for M. murinus in secondary forests within a fragmented
landscape. They observed poorer conditions (smaller, fewer trees and warmer temperatures)
within the secondary versus primary forest and within the secondary forest they never re-
captured any individuals but were able to recapture seven within the continuous forest.
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Habitat loss and fragmentation
We know little about the independent effects of habitat loss and fragmentation on primates
(Arroyo-Rodríguez et al. 2013b). Most studies research habitat loss and fragmentation at the
patch-level (Arroyo-Rodríguez et al. 2013b). However, habitat fragmentation is a landscape-
level process, and the combined effects of habitat loss and fragmentation are hard to separate at
a patch-level (Fahrig, 2003). In metapopulation dynamics, area affects a species extinction
probability and connectivity/isolation impacts the colonization potential of a patch (Hanski,
1999). Although a metapopulation approach uses data from a patch-level, it is in essence a
pseudo-landscape approach because it incorporates landscape-level connectivity measures. It
also allows for the analysis of how area versus connectivity/isolation impact species occurrence.
My study shows that area strongly affects species occurrence and that isolation has species-
specific neutral, negative and positive impacts on lemur species occurrence.
3.12.3 Question 3: Within Simulated Metapopulations Over Time, How Do Area and Connectivity/Isolation Affect Occurrence? What are the Conservation Implications?
One of the advantages of a metapopulation approach is that it is possible to model extinction
(area) and colonization (connectivity/isolation) probability, which allows for the simulation of
their effects on occurrence over time. When I removed the five largest fragments from the
metapopulation, all four species in the remaining fragments collapsed. Conversely, when I
removed the five smallest fragments no species populations in the remaining fragments
collapsed. When I removed the five most connected fragments, the remaining metapopulation
was marginally or not at all affected. When I removed the five least connected fragments, C.
medius showed a marked decline in occurrence. However, none of the other species showed any
appreciable decline. E. fulvus was the only species to not show extinction in the five most
connected fragments separated from the remaining 37 fragments. Lemur occurrence collapsed
for all but one species (M. murinus) when I separated the five least connected fragments from
the rest. It is possible that large yet more isolated fragments help maintain metapopulation
dynamics, in essence creating an intermediate metapopulation with multiple mainland sources
for colonists.
The conservation implications of altering the amount of habitat in this landscape are clear. Large
fragments are crucial to maintaining lemur species metapopulations. What is less clear is the
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role of connectivity within the landscape. Simulation models suggest that removing the most
connected fragments will not affect the remaining metapopulation. However, lemurs occupying
the most connected fragments are unlikely to survive when separated from the remaining
metapopulation. Therefore, connectivity has a reduced overall effect on the metapopulation
dynamics of the entire system but matters when particular fragments are removed. These
patterns are important to consider if decisions are being made on the fate of particular fragments
within the system. Loss of fragments due to un-regulated anthropogenic disturbance may
remove fragments that are the most valuable to maintain each lemur species’ metapopulation
dynamics. For example, my simulations suggest that maintaining the size and number of large
fragments within the population is most important and care must be taken when considering
fragment connectivity. The loss of fragments with high connectivity values will have little
impact on the lemur occurrence over the entire system but contrary to previous thinking
removing less connected fragments may have dramatic negative impacts on lemur species
occurrence.
3.13 Suggestions for Conservation Ankarafantsika National Park contains different zones of protection including multiuse zones,
regeneration zones and fully protected zones. My study area was within one of the multiuse
zones where burning of grass for grazing cattle is tolerated. This activity has resulted in habitat
loss and fragmentation, and has created a fragmented landscape near a large portion of
continuous forest. My simulation models suggest that increased attention should be paid to
maintaining fragment size. I would recommend measures that reduce the impact of fire on
fragment size. Currently, fires are lit within the zone and forest is incidentally burned (personal
observation). I suggest the creation of a fire management plan following Bloesch (1999). If
source-sink dynamics are occurring with the landscape for species like M. murinus and M.
ravelobensis, then I also recommend considering methods to improve connectivity between
fragments, although connectivity does not appear to be influencing occurrence within the greater
metapopulation.
3.14 Conclusion A metapopulation approach is useful for determining the effect of area and
connectivity/isolation on lemur species occurrence in a fragmented landscape. This study shows
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that lemurs form metapopulations in fragmented landscapes. Within their metapopulation,
lemurs are impacted by both habitat area and isolation. However, fragment area has a larger
impact on lemur occurrence than isolation. I identified dispersal ability as potentially explaining
differences in model selection between lemur species. P. coquereli and L. edwardsi do not form
traditional metapopulations and likely form a non-equilibrium metapopulation that is declining
toward local extinction. It is possible that source-sink dynamics are impacting populations of M.
murinus and M. ravelobensis within the landscape. The two most frugivorous lemurs, E. fulvus
and C. medius, are able to maintain stable metapopulations likely through dispersal and their
ability to survive within the largest fragments. To perpetually maintain metapopulations for each
species, I recommend a fire management strategy that reduces further habitat loss and isolation
among fragments in the landscape. We should pay special attention to connecting the largest
fragments to reduce the likely local extinction of P. coquereli and L. edwardsi, as well as
allowing seed dispersers of large fruits such as E. fulvus and C. medius to increase the area of
potential seed deposition.
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Chapter 4: Lemur Species-Specific Scale Responses to Habitat Loss in Fragmented Landscapes in NW Madagascar
4.1 Introduction In Chapters 2 and 3, I investigated the patch-level effects of habitat loss and fragmentation on
lemur species richness and individual lemur species occurrence, respectively. In these two
chapters, I found that area was a major determinant of both species richness and individual
species occurrence. However, I did not determine the response of lemur species to habitat loss
and fragmentation at a landscape-level. In this chapter, I investigate the impact of habitat loss
and fragmentation on lemur species using a landscape-level approach derived from landscape
ecology.
Landscape ecology is concerned with the study of the relationship between spatial pattern and
ecological processes at various scales (Turner et al. 2001). A landscape is defined as “an area
that is spatially heterogeneous in at least one factor of interest” (Turner et al. 2001:7), for
example the amount of forest within an area. Landscape ecology focuses on larger spatial scales
than typical ecological research. However, the choice of the scale for a study is both dependent
on the question asked and the species of interest (Wiens, 1989; Turner et al. 2001). Researchers
mainly conduct research on the impact of habitat loss and fragmentation at a patch- versus a
landscape-level, despite habitat loss and fragmentation being landscape-level processes
(McGarigal & Cushman, 2002; Fahrig, 2003; Arroyo-Rodriguez et al. 2013b; Arroyo-Rodriguez
& Fahrig, 2014).
Although it is recognized that habitat loss and fragmentation effects are landscape-level
phenomena (McGarigal & Cushman, 2002; Fahrig, 2003), including within the field of
primatology (Arroyo-Rodriguez et al. 2013b; Arroyo-Rodriguez & Fahrig, 2014), few studies
have actually measured habitat loss and fragmentation at a landscape-level. Researchers
continue to use a patch-level analysis because of the ease of data collection and its historical use
in ecological theory, such as the island biogeography theory (MacArthur & Wilson, 1967) and
metapopulation dynamics (Hanski, 1999). One of the major issues with applying a landscape-
level approach is scale, which involves choosing what size of landscapes to study. The choice of
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scale depends on the research question, the species studied, and the process of interest (Turner et
al. 2001; McGarigal & Cushman, 2002; Turner, 2005). Although fragmentation research should
be conducted at relevant species-specific scales, such as the area of a home range (McGarigal &
Cushman, 2002), few studies have taken steps to determine the species-specific responses to
habitat loss and fragmentation.
4.2 Landscape-Level Effect of Habitat Amount on Species Occurrence.
Landscape ecology is a sub-discipline of ecology, studying how landscape structure impacts
species abundance and distribution (Fahrig, 2005). One can divide landscape structure into two
elements: landscape composition and configuration (Fahrig, 2005; McGarigal & McComb,
1995). Landscape composition refers to the types and amount of habitat within a landscape
(Fahrig, 2005), while landscape configuration refers to the arrangement of habitat within a
landscape (McGarigal & McComb, 1995). Habitat loss and fragmentation are two separate yet
related landscape-level phenomena (Fahrig, 2003). Habitat loss is simply the removal of habitat
from a landscape (McGarigal & Cushman, 2002; Fahrig, 2003). Habitat fragmentation is the
separation of habitat into smaller less connected portions (McGarigal & Cushman, 2002; Fahrig,
2003). The impact of habitat loss on species richness and occurrence is well studied (Fahrig,
2003). However, fewer studies have focused on the impact of habitat fragmentation independent
of habitat loss (Fahrig, 2003).
We now understand that habitat loss and fragmentation are landscape-level processes where the
size and shape of patches within a landscape are altered to change the composition and
configuration of habitat (McGarigal & Cushman, 2002; Fahrig, 2005; Arroyo-Rodriguez &
Fahrig, 2014). Although habitat loss and fragmentation are landscape-level phenomena,
researchers still study these processes using fragments as the unit of analysis in patch-level
studies. Patch-level studies can provide important insight into the mechanisms that result in
landscape patterns, such as patch occupancy (Arroyo-Rodriguez & Fahrig, 2014). In Chapter 3,
I introduced many examples of patch-level research on the impact of habitat loss and
fragmentation on primate species occurrence. The research clearly showed that species
occurrence is strongly related to patch/fragment area (Anzures-Dadda & Manson, 2007; Arroyo-
Rodríguez et al. 2008; Boyle & Smith, 2010). Fragment isolation appears to have a lesser effect
on primate species occurrence (Lawes et al. 2000; Cristobal-Azkarate et al. 2005). In Chapter 2,
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I investigated the species-area relationship on a lemur community. Regardless of the other
variables in the analysis, I found that area was the main driver of species richness. In Chapter 3,
I investigated how habitat loss and fragmentation affect individual lemur species occurrence at a
patch-level. I found that fragment area was the primary driver of lemur species occurrence and
that habitat fragmentation (isolation) per se did not affect lemur species as much as habitat loss
(fragment area). A major issue with using just a patch-level approach is that patch-level studies
typically ignore the context within which the patch exists. For example, these studies may
ignore the matrix (Ricketts, 2001) or the amount of continuous forest near a fragment (Arroyo-
Rodriguez & Fahrig, 2014). Therefore, patch-level studies provide a very localized view of the
impact of habitat loss on primate species occurrence.
A landscape-level approach provides a broader view of the impact of habitat loss on primate
species occurrence (Arroyo-Rodriguez & Fahrig, 2014). Landscape-level studies investigating
the impact of primate species richness, occurrence, and abundance are increasing in number. A
landscape-level approach is appropriate when one is interested in determining the effect of
landscape composition and configuration on a response variable (Fahrig, 2005). Arroyo-
Rodriguez & Fahrig (2014) suggest two approaches for landscape studies in primatology. The
first is a patch-landscape scale study where habitat variables and species responses are measured
in focal patches and their surrounding landscape (Arroyo-Rodriguez & Fahrig, 2014). The
second is a pure landscape-scale study where habitat variables and species responses are
measured within entire landscapes (Arroyo-Rodriguez & Fahrig, 2014).
A few primate researchers have used a landscape-level approach to study the impact of habitat
loss and fragmentation on primate occurrence (Anzures-Dadda & Manson, 2007; Thornton et al.
2011; Arroyo-Rodriguez et al. 2013b; Benchimol et al. 2014; Sales et al. 2015). However, there
were few consistent results among these studies even within similar species. Anzures-Dadda and
Manson (2007) found that patch occupancy in mantled howler monkeys (Alouatta palliata) was
positively related to both forest cover and fragmentation, whereas Thornton et al. (2011) found
that patch occupancy in Central American black howlers (A. pigra) was not related to forest
cover or fragmentation. Sales et al. (2015) found that models containing local predictors, such as
canopy height and canopy openness, better explained primate occupancy than models containing
landscape metrics, such as habitat amount and fragmentation.
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The apparent differences in primate responses to landscape-level variables between studies may
be the result of actual differences among species or differences in study design. Although scale
effects are apparent and well documented in the ecological literature (Wiens, 1989; Holland et
al. 2004; Bradter et al. 2013) the scale of effect for primate species is poorly studied (Arroyo-
Rodriguez & Fahrig, 2014).
4.3 Species-Specific Scale Responses to Habitat Amount.
4.3.1 Scale and Landscapes.
All ecological patterns and processes occur in various temporal and spatial scales (Hutchinson,
1965). However, spatial scale is frequently ignored in primatology (Arroyo-Rodriguez & Fahrig,
2014). Recently, researchers have applied landscape ecological methods and theory to studies of
primate behavior and ecology, and biogeography (Gray et al. 2010; Thorton et al. 2011;
Ordóñez-Gómez et al. 2014). These studies have begun to recognize the need to look at
ecological patterns and processes at the appropriate scale.
4.3.2 “Scale of Effect.”
Researchers undertaking landscape-level studies are interested in finding how patterns and
processes affect species responses (richness, occurrence, abundance; Turner et al. 2001).
Although a landscape-level approach is suitable to look at questions concerning how landscape-
level patterns and processes, such as habitat loss and fragmentation, affect species responses,
what is less clear is at what scale or size of landscape do species respond to habitat loss and
fragmentation (Jackson & Fahrig, 2012). This “scale of effect” occurs when the landscape
structure is identified and measured at scales relevant to the species. Studies on numerous taxa
demonstrate species-specific scale responses to habitat loss and fragmentation including
amphibians (Eigenbrod et al. 2011), birds (Bradter et al. 2013; Gilroy et al. 2015; Thorton &
Fletcher, 2014), insects (Holland et al. 2004; Bellier et al. 2007), arboreal mammals (Patterson
& Malcolm, 2010), terrestrial mammals (de Knegt et al. 2011), and plants (Borcard et al. 2004).
A few studies have assessed the “scale of effect” in primate species (Gray et al. 2010; Thorton et
al. 2011; Ordóñez-Gómez et al. 2014). Gray et al. (2010) investigated different scale responses
of red-cheeked gibbon (Nomascus gabriellae) habitat preferences in a fragmented landscape.
They found that gibbon occurrence responded to different predictor variables at different scales.
For example, semi-evergreen forest at a small scale (1500 m radius), dipterocarp forest at an
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intermediate scale (3000 m radius), and evergreen forest at a large scale (5000 m radius) best-
explained variance in gibbon occurrence. In a landscape-scale study, Thorton et al. (2011) found
that spider monkeys (Ateles geoffroyi) responded negatively and strongly to habitat
fragmentation, particularly at a 500 m scale, and negatively to habitat loss, while the Central
American black howler monkeys (Alouatta pigra) did not respond to habitat fragmentation or
habitat loss at any spatial scale. Ordóñez-Gómez et al. (2014) investigated the “scale of effect”
in how different landscape attributes affected spider monkey diet and behavior. These
researchers found that most response variables were related to landscape variables at a 126 ha
landscape-level. However, they found that the “scale of effect” differed between response
variables and landscape characteristics, suggesting a multi-scale approach may be the most
appropriate.
Most researchers typically determine the “scale of effect” post hoc by assessing species
responses at multiple scales and selecting the scale with the strongest effect (Holland et al. 2004;
Jackson & Fahrig, 2012; Bradter et al. 2013). The problem with this approach is that it results in
a less efficient study design (Jackson & Fahrig, 2012) and may not actually detect the actual
“scale of effect” due poor scale selection (Jackson & Fahrig, 2014). However, until we
understand why certain scales produce a “scale of effect” for a species we are limited to
determining the “scale of effect” post hoc. Jackson and Fahrig (2014) suggested that one way of
dealing with this issue is by selecting a large ranges of scales ranging from the size of a single
territory/home range to greater than the average dispersal distance.
4.3.3 Species-Specific “Scale of Effect”
We do not fully understand what determines species-specific “scale of effect” (Jackson &
Fahrig, 2012). In the first study of its kind, Jackson & Fahrig (2012) determined that a species
dispersal distance was strongly positively related to “scale of effect,” and in a meta-analysis of
avian body size, Thorton & Fletcher (2015) found that body size mediated the “scale of effect.”
For primates, what determines species-specific “scale of effect” is still unknown. However, as
indicated by the above research, dispersal ability and body size, which are correlated with
mobility (Bowman et al. 2002; Whitmee & Orme, 2013), likely mediate the “scale of effect.”
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4.4 Justification I described in Chapter 3 that lemur species are at high risk of extinction mainly due to habitat
loss and fragmentation (Schwitzer et al. 2014). All researchers have applied a patch-level
approach to determine how lemurs respond to habitat loss and fragmentation, even though
habitat loss and fragmentation are landscape-level phenomena. To date, no research on lemurs
has explicitly addressed how lemurs respond to habitat loss and fragmentation using a
landscape-level approach.
Preliminary studies on primates suggest that primate responses to area are species-specific and
that there is no typical primate response to the amount of habitat in a landscape (Gray et al.
2010; Thorton et al. 2011; Ordóñez-Gómez et al. 2014). My study is important because it is the
first of its kind to determine the species-specific scale responses of lemurs to habitat loss and
fragmentation using a landscape-level approach. My study has the added benefit of determining
whether two sympatric species from the same genus (Microcebus) respond to habitat loss and
fragmentation in the same way. By using a landscape-level approach, I will be able to not only
determine how habitat loss and fragmentation impacts lemur species occurrence but, more
importantly, at what scale, all while accounting for spatial autocorrelation in the data.
4.5 Goal The goal of this study is to assess how lemur species occurrence is related to habitat amount at a
landscape-level. To solve how lemur species occurrence is related to habitat amount I must first
determine at what scale each species responds to habitat amount. Therefore, I will determine
how lemur species occurrence is related to habitat amount within species-relevant scales while
testing the following questions and hypotheses:
1) At what scale do species respond to habitat amount?
Hypotheses:
H0 Lemur species occurrence with respect to habitat amount will vary randomly with
respect to scale.
H1 Scales relating to home range or dispersal ability will predict lemur occurrence with
respect to habitat amount.
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2) What is the effect of habitat amount on species-specific occurrence of lemurs at the
landscape-level?
Hypotheses:
H0 The probability of occurrence for each lemur species will vary randomly with respect
to habitat amount at a species-relevant scale.
H1 The probability of occurrence for each lemur species will increase with increasing
habitat amount at a species-relevant scale.
4.6 Methods
4.6.1 Study Site and Study Species
My study site and study species are described in more detail in Chapters 2 and 3. In this chapter,
I analyzed landscape-level data on three of the six species found in the study site (Cheirogaleus
medius, Microcebus murinus, and Microcebus ravelobensis). I excluded three of the species
because I had either observed too few occurrences to make a meaningful statistical assessment
(Propithecus coquereli and Lepilemur edwardsi), or because the nature of the spatial
autocorrelation among landscape sizes was too complex to make a meaningful determination as
to what scale a species responded to habitat amount (Eulemur fulvus).
4.6.2 Question 1: What is the Scale of Species Response to Habitat Amount?
Species Presence and Pseudo-Absence
For each species, I determined presence along line transects using the same methods that I
described in Chapter 3. I then plotted each observation location within ArcGIS. Using the
observer to animal sighting distance, I determined the effective detection width for each species
following Müller et al. (2000) and Steffens and Lehman (2016). I then created an effective
detection buffer by creating a buffer along both sides of each transect equivalent to the effective
detection width (m) for each species within ArcGIS. I removed any presence points located
outside of the transect detection buffers from the analysis. To create pseudo-absence points for
logistic regression analysis, I buffered each presence point with a circle representing one home
range size for each species; clipped the effective transect buffers by the home range buffers,
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leaving survey areas where no individuals were observed; and randomly placed approximately
the same number of points as the number of observed presence points within the clipped
effective detection buffers. I searched for duplicate points using the dup.coords function in R
and I manually deleted duplicate points and their associated variables for all analyses. I
employed this method to better reflect actual absence points rather than creating pseudo-absence
points using a simple random sample of the landscape.
Landscape-Level
To determine the scale at which each species responds to habitat loss I created landscapes of
varying sizes in ArcGIS for each species. For each species presence and pseudo-absence points,
I created 10 arbitrary buffers representing approximately ¼, ½, 1, 2, 4, 8, 16, 32, 64 and 128
times each species’ mean home range (Table 4.1; Landscape Scale Sizes). Jackson and Fahrig
(2014) recommended using a large range size of landscape scales, from the size of a single
home range/territory to greater than the average dispersal distance for a species. I went further
by including two scales smaller than a single home range for each species. To make the results
easier to compare between species, I rounded the home range for C. medius and M. ravelobensis
up from 1.55 to 2.00 ha and from 0.59 to 1.00 ha respectively and rounded the home range for
M. murinus down from 2.83 to 2.00 ha. I then measured the amount of forest within each
species occurrence buffer using the below methods. Table 4.1: Study Species Characteristics and Landscape Scale Sizes
Species Body Mass (g)
Activity Pattern Diet Mean Home
Range (ha) 10 Landscape Scale
Sizes (ha) Cheirogaleus
medius 120–270 Nocturnal Frugivore 1.55 ±0.42(1) 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256
Microcebus murinus 58–67 Nocturnal Fauni-frugivore 2.83 ±1.44 (2) 0.5, 1, 2, 4, 8, 16,
32, 64, 128, 256 Microcebus ravelobensis 56–87 Nocturnal Fauni-frugivore 0.59 ±0.11(3) 0.25, 0.5, 1, 2, 4, 8,
16, 32, 64, 128 Data from: 1 Müller (1998); 2 Radespiel (2000); 3 Weidt et al. (2004)
Amount of habitat
I determined the amount of forest from a DigitalGlobe four band, 2x2 m resolution satellite
image within an extent within 8199684.36 and 8191497.94 northing and 679451.29 and
685941.31 easting UTM coordinates. The size of the image is approximately 8 km north to
south and 6.2 km east to west (Fig. 4.1). I added the DigiGlobe image as a raster to ArcGIS and
georeferenced the image using the georeference tool and 16 reference points taken from the base
map within ArcGIS. I classified the raster image into forest and non-forest using an
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unsupervised classification and checked this classification against the original RGB raster for
accuracy. I removed all non-forest values from the classified raster to create a raster consisting
of only forest cells. I measured the amount of forest within all 10 species occurrence buffers
using zonal statistics tool in ArcGIS. Some of the larger buffers exceeded the extent of the raster
image. I retained sightings for species where all buffers had at least 80% of their area within the
extent of the raster. Both Microcebus species had 20 sightings each that had more than 20% of
their landscape buffers outside of the extent of the raster image. I removed these 40 sightings
from analyses.
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Figure 4.1: DigitalGlobe Satellite Image of Field Site.
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Analysis
I used logistic regression analysis and Akaike’s Information Criterion (AIC) to assess how
habitat amount affects species occurrence. Logistic regression is useful for analysis of
presence/absence data because it does not expect a linear relationship between the response and
independent variables like linear regression does (Quinn and Keough, 2002). Instead, the error
term can be modeled using a binomial logit link identity structure (Quinn & Keough, 2002).
Like most regression models, logistic regression assumes that the error terms are independent
(Quinn & Keough, 2002). Although each observation must be independent from one another,
ecological data are rife with spatial autocorrelation, resulting in a violation of the assumption
that the error terms are independent because of residual spatial autocorrelation (de Knegt et al.
2010; Zuckerberg et al. 2012). For example, sampling landscapes surrounding observation
points may result in large amounts of overlap between sample landscapes thereby increasing the
likelihood of residual spatial autocorrelation. To solve the problem of overlapping landscapes,
Holland et al. (2004) developed a method to determine the largest number of spatially
independent sites (non-overlapping) from occurrence data at various scales. However, non-
overlapping sites do not necessarily eliminate or even reduce residual spatial autocorrelation
(Zuckerberg et al. 2012). For example, Zuckerberg et al. (2012) found that overlapping and non-
overlapping sites show similar levels of spatial autocorrelation. These researchers suggest that
we should not be as concerned with finding spatially independent sites, but rather should look at
increasing independence in the errors in regression analysis (i.e. reducing residual spatial
autocorrelation). One technique to reduce residual spatial autocorrelation in GLMs is to use
Moran’s Eigenvector (ME) filtering, an extension of principal coordinate analysis of neighbor
matrices PCNM (Dray et al. 2006; Griffith & Pere-Neto, 2006). Bocard and Legendre (2002)
first developed PCNM to investigate the spatial structure of data. Later other researchers
developed a filtering technique to select a subset of eigenvectors for inclusion in linear models
and GLMs (Dray et al. 2006; Griffith & Pere-Neto, 2006).
I applied an ME filtering approach to reduce residual spatial autocorrelation in my data using
the ME function in the spdep 0.5-88 package in R (Bivand et al. 2013; Bivand & Piras, 2015).
ME filtering in the spdep package uses a brute force selection technique to determine which
eigenvectors reduce spatial autocorrelation within a GLM below a particular alpha value of
Moran’s I (Bivand et al. 2013; Bivand & Piras, 2015). Then you add the selected fitted values of
each vector as independent variables to the GLM. The resulting spatial GLM will have either no
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or reduced spatial autocorrelation among the error terms. I ran each ME filtering procedure
using 1000 simulations and a 0.20 Moran’s I alpha value for inclusion. In some cases, the
filtered eigenvectors created near perfect separation among the data and resulted in poorly
specified although spatially un-correlated models. For situations where the ME filtering
procedure produced poorly specified models, I re-ran the ME filtering step with 0.01 and 0.001
Moran’s I alpha values. I added the fitted values from the selected eigenvectors to create a
spatial GLM and selected among the different alpha values based on which resulting GLM had
the lowest AIC value. I tested for spatial autocorrelation in the residuals of the spatial models
using moran.test function in the spdep package in R (Bivand et al. 2013; Bivand & Piras, 2015)
to determine if they met the assumption of independence of errors. I considered data to be
independent if there was no significant spatial autocorrelation or if the spatial autocorrelation
was significant but low (i.e. P-value <0.05 and Moran’s I value <0.33). I ran all GLMs using the
glm function with a binomial “logit” link function in R (R Core Team, 2015).
To determine at which scale species respond to habitat amount, I ran the above ME filtering
procedure on species that showed residual spatial autocorrelation, and ran the resulting spatial
GLMs (which included the fitted Moran’s eigenvectors as variables in the model) on all 10
species occurrence buffer sizes, including a null model (with spatial filtering applied). The one
exception was for C. medius, which showed no residual spatial autocorrelation. For C. medius, I
ran non-spatial GLM’s without the added step of the ME filtering procedure. I selected the
models with the smallest AIC to determine at which scale each species responds to habitat
amount. If for any species analysis the null model had the lowest AIC value of all the models, I
determined that there was no scale of response to habitat amount for that species.
4.6.3 Question 2: What is the Landscape-Level Effect of Habitat Amount on Species Occurrence?
To assess if the amount of habitat determined species occurrence, I ran non-spatial (C. medius)
and spatial GLMs (Microcebus spp.; including the fitted Moran’s eigenvectors as variables in
the model) from the scale that was selected based on AIC values. I assessed the sign and
magnitude of the coefficients and tested the significance of the model using the ANOVA
function in R (R Core Team, 2015), thereby determining if the amount of habitat influenced
occurrence. I then calculated McFadden’s R2 as a measure of pseudo R2 for each GLM
regression model.
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4.7 Results
4.7.1 Description of Spatial Autocorrelation Among Landscapes.
I found significant positive spatial autocorrelation in all non-spatial models for all species
except C. medius. For C. medius there was no spatial autocorrelation among any of the non-
spatial models (Table 4.2), likely due to the fact that there were fewer and more spatially
dispersed (less overlap among landscapes) presence/pseudo-absence points for this species than
the other species. Therefore, for C. medius I was able to report on non-spatial GLMs without
using the ME filtering approach. I found higher (>0.67) significant positive spatial
autocorrelation among presence/pseudo-absence points in non-spatial models and lower (<0.33)
significant spatial autocorrelation among presence/pseudo-absence points in spatial models for
both M. murinus and M. ravelobensis (Table 4.2). For M. murinus, I had to use Moran’s I alpha
values of 0.01 and 0.001, instead of 0.2 for the ME spatial filtering procedure to produce
properly specified models for two scales: 2 ha and 4 ha respectively. The resulting spatial GLMs
incorporated fewer parameters (fewer fitted Moran’s eigenvectors) and had more spatial
autocorrelation than models based on 0.2 alpha but the spatial autocorrelation was still low
(<0.33). Table 4.2: Results of Residual Spatial Autocorrelation Tests.
Species Model Residuals (Landscape Scale in ha) Moran’s I Expected P-value
Cheirogaleus medius
P = A(0.5) -0.03 -0.02 0.51 P = A(1) -0.04 -0.02 0.53 P = A(2) -0.01 -0.02 0.48 P = A(4) 0.03 -0.02 0.39 P = A(8) 0.07 -0.02 0.32
P = A(16) 0.10 -0.02 0.27 P = A(32) 0.10 -0.02 0.25 P = A(64) 0.11 -0.02 0.23
P = A(128) 0.15 -0.02 0.18 P = A(256) 0.18 -0.02 0.14
Null 0.18 -0.02 0.15
Microcebus murinus
P = A(0.5) 0.90 -0.002 <0.01 P = A(1) 0.91 -0.002 <0.01 P = A(2) 0.91 -0.002 <0.01 P = A(4) 0.91 -0.002 <0.01 P = A(8) 0.91 -0.002 <0.01
P = A(16) 0.91 -0.002 <0.01 P = A(32) 0.92 -0.002 <0.01 P = A(64) 0.92 -0.002 <0.01
P = A(128) 0.91 -0.002 <0.01 P = A(256) 0.91 -0.002 <0.01
Null 0.91 -0.002 <0.01
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Microcebus murinus
P = A(0.5)+21 Fitted ME 0.10 -0.002 0.03 P = A(1) +21 Fitted ME 0.10 -0.002 0.03 P = A(2) +17 Fitted ME 0.20 -0.002 <0.01 P = A(4) +15 Fitted ME 0.24 -0.002 <0.01 P = A(8) +21 Fitted ME 0.09 -0.002 0.05
P = A(16) +21 Fitted ME 0.13 -0.002 0.01 P = A(32) +21 Fitted ME 0.10 -0.002 0.03 P = A(64) +21 Fitted ME 0.10 -0.002 0.03
P = A(128) +20 Fitted ME 0.11 -0.002 0.02 P = A(256) +20 Fitted ME 0.11 -0.002 0.02
Null+20 Fitted ME 0.11 -0.002 0.02
Microcebus ravelobensis
P = A(0.25) 0.81 -0.002 <0.01 P = A(0.5) 0.81 -0.002 <0.01 P = A(1) 0.81 -0.002 <0.01 P = A(2) 0.82 -0.002 <0.01 P = A(4) 0.81 -0.002 <0.01 P = A(8) 0.82 -0.002 <0.01
P = A(16) 0.82 -0.002 <0.01 P = A(32) 0.82 -0.002 <0.01 P = A(64) 0.83 -0.002 <0.01
P = A(128) 0.83 -0.002 <0.01 Null 0.83 -0.02 <0.01
Microcebus ravelobensis
P = A(0.25)+21 Fitted ME 0.13 -0.002 <0.01 P = A(0.5)+21 Fitted ME 0.12 -0.002 <0.01 P = A(1)+22 Fitted ME 0.12 -0.002 <0.01 P = A(2)+22 Fitted ME 0.09 -0.002 0.04 P = A(4)+22 Fitted ME 0.13 -0.002 <0.01 P = A(8)+21 Fitted ME 0.13 -0.002 <0.01
P = A(16)+11 Fitted ME 0.13 -0.002 <0.01 P = A(32)+22 Fitted ME 0.13 -0.002 <0.01 P = A(64)+22 Fitted ME 0.10 -0.002 0.03 P =A(128)+22 Fitted ME 0.13 -0.002 <0.01
Null +21 Fitted ME 0.10 -0.002 0.03 P= Occurrence, A= Amount of Forest, ME= Moran’s Eigenvectors
4.7.2 Question 1: What is the Scale of Species Response to Habitat Amount?
Species occurrence buffers ranged in size from 0.5 to 256 ha for C. medius and M. murinus, and
from 0.25 to 128 ha for M. ravelobensis (Table 4.3). Scale responses differed between species.
For C. medius, the scale of effect to habitat amount was between 1 ha (ΔAIC=0.35) and 4 ha
(ΔAIC=0.35), with 2 ha having the lowest ΔAIC (0.00) which is an area slightly larger than their
mean home range size. Therefore, for C. medius I was able to reject the null hypothesis that
lemur species occurrence with respect to habitat amount will vary randomly with respect to
scale. For M. murinus, the scale of effect to habitat amount was 8 ha (Table 4.3), which is
approximately three times the size of their reported mean home range size but within the
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median-dispersal distance (251 m) (Radespiel et al. 2003). Therefore, I was able to reject the
null hypothesis for this species as well. For M. ravelobensis, I was not able to reject the null
hypothesis because it appears there was no scale of effect to habitat amount for this species
because the null spatial model (accounting for spatial autocorrelation) had the lowest ΔAIC
value (Table 4.3). The scales with the next lowest ΔAIC values were at 64 ha (ΔAIC=2.14). Table 4.3: Lemur Species Scale Responses to Habitat Amount.
Species Model (Landscape Scale in ha) Null Deviance (DoF)
Residual Deviance
(DoF) ΔAIC McFadden’s
R2
Cheirogaleus medius
P = A(2) 61.00(43) 46.19(42) 0.00 0.24 P = A(4) 61.00(43) 46.54(42) 0.35 0.24 P = A(1) 61.00(43) 47.16(42) 0.97 0.23 P = A(8) 61.00(43) 48.68(42) 2.49 0.2
P = A(16) 61.00(43) 48.96(42) 2.77 0.2 P = A(0.5) 61.00(43) 49.29(42) 3.09 0.19 P = A(32) 61.00(43) 49.62(42) 3.43 0.19 P = A(64) 61.00(43) 50.90(42) 4.71 0.17
P = A(128) 61.00(43) 54.00(42) 7.81 0.11 Null 61.00(43) 61.00(43) 12.81 0
P = A(256) 61.00(43) 59.10(42) 12.91 0.03
Microcebus murinus
P = A(8)+21 Fitted ME 720.87(519) 99.92(497) 0.00 0.86 P = A(1)+21 Fitted ME 720.87(519) 113.26(497) 13.34 0.84
P = A(0.5)+21 Fitted ME 720.87(519) 123.52(497) 23.60 0.83 Null+20 Fitted ME 720.87(519) 139.94(499) 36.02 0.81
P = A(2)+17 Fitted ME 720.87(519) 144.11(501) 36.19 0.8 P = A(32)+21 Fitted ME 720.87(519) 136.72(497) 36.80 0.81
P = A(256)+20 Fitted ME 720.87(519) 139.11(498) 37.19 0.81 P = A(128)+20 Fitted ME 720.87(519) 139.84(498) 37.92 0.81 P = A(64)+21 Fitted ME 720.87(519) 138.32(497) 38.40 0.81 P = A(16)+21 Fitted ME 720.87(519) 144.25(497) 44.33 0.8 P = A(4)+15 Fitted ME 720.87(519) 161.71(503) 49.79 0.78
Microcebus ravelobensis
Null+21 Fitted ME 774.92(558) 228.67(536) 0.00 0.7 P = A(64)+22 Fitted ME 774.92(558) 228.61(535) 2.14 0.7
P = A(0.25)+21 Fitted ME 774.92(558) 240.11(536) 11.64 0.69 P = A(0.5)+21 Fitted ME 774.92(558) 242.01(536) 13.54 0.69 P = A(8)+21 Fitted ME 774.92(558) 242.63(536) 14.16 0.69
P = A(16)+11 Fitted ME 774.92(558) 242.83(546) 14.36 0.69 P = A(2)+22 Fitted ME 774.92(558) 242.07(535) 15.60 0.69 P = A(4)+22 Fitted ME 774.92(558) 242.18(535) 15.71 0.69
P = A(32)+22 Fitted ME 774.92(558) 243.08(535) 16.61 0.69 P =A(128)+22 Fitted ME 774.92(558) 243.08(535) 16.61 0.69 P = A(1)+22 Fitted ME 774.92(558) 247.55(535) 21.08 0.68
P= Occurrence, A= Amount of Forest, and ME= Moran’s Eigenvectors; DoF= Degrees of Freedom.
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4.7.3 Question 2: What is the Landscape-Level Effect of Habitat Amount on Species Occurrence?
The amount of forest in a landscape was a significant positive contributor to C. medius
occurrence in all of the models except the largest scale (256 ha) measured (Appendix A), where
amount of forest was positive but not a significant contributor to C. medius occurrence. C.
medius occurrence was most influenced by the amount of forest at landscape scales of 2 ha and
4 ha because the associated McFadden’s R2 values were the highest of all scales measured
(Table 4.3). The amount of forest in a landscape was a significant positive contributor to M.
murinus occurrence in the six smallest landscape scales. At the three larger scales (32, 64, and
128 ha) the amount of forest was a non-significant but positive contributor to M. murinus
occurrence (Appendix B). In the largest landscape scale (128 ha) the amount of forest was a
non-significant negative contributor to M. murinus occurrence (Appendix B). As with C. medius
the scale of effect selected based on ΔAIC for M. murinus (8 ha) had the highest McFadden’s R2
value (Table 4.3). The 8 ha landscape scale is where amount of forest had the greatest effect on
M. murinus occurrence. The amount of forest was a significant positive contributor to M.
ravelobensis occurrence at the three smallest scales (Appendix C). However, since the null
model had the lowest ΔAIC and highest McFadden’s R2 value, the GLM results for M.
ravelobensis suggest that they do not respond to habitat amount at any scale measured (Table
4.3).
4.8 Discussion In the first study of its kind on lemur biogeography, I modeled species-specific scale responses
to habitat amount using a landscape-centered approach accounting for the effect of spatial
autocorrelation from overlapping landscapes. Patterns and processes in ecological data are
related to spatial features of landscapes resulting in a high degree of spatial autocorrelation. Not
accounting for this spatial autocorrelation in presence/absence data may result in erroneous
results because of the assumption requiring independence of errors for logistic regression
(Quinn & Keough, 2002). For Microcebus spp., I found that spatial models (which incorporate
21 additional variables) had lower AIC values compared to univariate models that did not
account for spatial autocorrelation. I found that even related species responded differently to
habitat amount at a landscape-level. For two species (C. medius and M. murinus), the amount of
habitat predicted occurrence, but for M. ravelobensis, I found no effect of amount of habitat on
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species occurrence. Below, I will suggest that the species-specific scale of effect to habitat
amount was related to home range size (C. medius) or potential dispersal ability (M. murinus),
and was explained by population density (C. medius) and edge effects (M. murinus).
4.8.1 Question 1: What is the Scale of Species Response to Habitat Amount?
Scale responses to landscape characteristics are an important consideration in landscape ecology
(Hutchison, 1965; Turner et al. 2001). Species-specific scale responses to landscape
characteristics have been demonstrated in numerous taxa (Borcard et al. 2004; Holland et al.
2004; Bellier et al. 2007; Patterson & Malcolm, 2010; de Knegt et al. 2011; Eigenbrod et al.
2011; Bradter et al. 2013; Gilroy et al. 2015; Thorton & Fletcher, 2015), including primates
(Gray et al. 2010; Thorton et al. 2011; Ordóñez-Gómez, 2014). However, researchers have not
inspected species-specific scale responses in lemur biogeography.
C. medius and M. murinus showed species-specific scale responses to habitat amount and M.
ravelobensis did not. For C. medius, the scale of effect was similar to their reported mean home
range size. The greater the scale, the higher the ΔAIC value was for this species, suggesting that
habitat amount had less of an effect on C. medius occurrence at larger than smaller scales. For
M. murinus, the scale of effect was greater than their mean home range but within the reported
median-dispersal distance for this species (Radespiel et al. 2009). I found no scale responses for
M. ravelobensis to habitat amount. For this species, something other than area must be
contributing to their occurrence patterns in the landscape.
Thorton et al. (2011) found a scale of effect of 500 m (radius) for the negative response of
habitat fragmentation on Ateles geoffroyi occurrence in a fragmented landscape within Petén
region of Guatemala. A 500 m radius would be roughly equivalent to 78.54 ha and well within
reported home ranges sizes for this species (Chapman et al. 1995), a similar pattern of response
to C. medius. However, like my results for M. ravelobensis, Thorton et al. (2001) did not find a
species-specific scale response for habitat loss or fragmentation for A. pigra. Investigating the
scale of effect on A. geoffroyi diet and behavior, Ordóñez-Gómez et al. (2014) found that the
amount of forest cover influenced behaviors, such as resting and time spent feeding on leaves, at
a 126 ha landscape-level. Again, this landscape-level is well within the home range size reported
for this species. Conversely, Gray et al. (2010) found that gibbon (N. gabriellae) occurrence was
positively influenced by the amount of evergreen forest at large scales (3000 m), which is much
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larger than the home range in evergreen forest reported for this species (Traeholt et al. 2005).
These results differ from my models for C. medius and for those reported for A. geoffroyi
(Thorton et al. 2011; Ordóñez-Gómez et al. 2014). Thus, the scale of effect for C. medius was
similar to those reported for A. geoffroyi (Thorton et al. 2011; Ordóñez-Gómez et al. 2014), but
both are much smaller than what was reported for N. gabriellae (Gray et al. 2010). Home range
is and locomotor ability may explain why these three frugivores have such disparate scale
responses. Home range sizes are much larger in N. gabriellae (Kenyon, 2007) and A. geoffroyi
(Chapman et al. 1995) than in C. medius (Müller, 1998), although C. medius and A. geoffroyi
share a similar scale response (when accounting for home range size). Also in terms of mobility,
there is a gradient of ability and agility from the slow quadrupedal locomotion of C. medius
(Young et al. 2007) to the intermediate brachiation of A. geoffroyi (Cant, 1986) to the fast and
efficient richochetal brachiation found in N. gabriellae (Usherwood et al. 2003; Rawson &
Traeholt, 2011). Therefore ranging behaviour and locomotor ability are likely to explain why N.
gabriellae has a greater scale response than A. geoffroyi and C. medius.
Jackson and Fahrig (2012) suggested that species-specific responses to landscape characteristics
are related to some element of a species’ life history such as mobility, body size, and
reproductive rate. These researchers found that dispersal distance has a positive impact on scale
of effect and they suggested that landscape radius should be assessed at sizes four to nine times
greater than the median-dispersal distance for a species. While this landscape size may be
accurate for large-bodied, highly mobile species, my results and studies on other primate species
suggest that a smaller landscape size may be more appropriate (Gray et al. 2010; Thorton et al.
2011; Ordóñez-Gómez, 2014). Most primates are dispersal limited due to their highly arboreal
nature (Beaudrot & Marshall, 2011). Smaller landscapes would make sense for dispersal-limited
species because dispersal costs are greater for dispersal-limited species and would therefore
limit their ability to move long distances and may result in smaller home range sizes (Isbell &
van Vuren, 1996). Alternatively, researchers invoked body size to explain species differences in
scale of effect response to habitat loss and fragmentation, where some species showed a relation
to body size while others did not (see review in Jackson & Fahrig, 2012). However, body size
differences do not explain why N. gabriellae had a larger scale response than A. geoffroyi or C.
medius because both N. gabriellae and A. geoffroyi are similar in body size (Ford & Davis,1992;
Rawson & Traeholt, 2011) but both are larger than C. medius (Fietz, 1999).
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Habitat heterogeneity may impact the scale of effect for frugivorous primates (Ordóñez-Gómez
et al. 2014). For example, both A. geoffroyi and C. medius respond to habitat fragmentation at
scales well below their maximum home range size (Thorton et al. 2011; Ordóñez-Gómez et al.
2014) and these highly frugivorous species may respond at a smaller scale because they may be
sensitive to differences in habitat quality as a result of their diet, limiting its scale of effect. In
my study, highly frugivorous C. medius had a smaller scale response than the more diminutive
fauni-frugivore, M. murinus. However, frugivorous N. gabriellae had a much larger scale
response than would be predicted based on the research done on A. geoffroyi (Thorton et al.
2011; Ordóñez-Gómez et al. 2014) and my results on C. medius. Differences in diet among
frugivores may explain why N. gabriellae has a larger scale response than A. geoffroyi or C.
medius. Both A. geoffroyi and C. medius consume a greater proportion of fruit (A. geoffroyi fruit
is >70% of diet, Chapman et al. 1995; C. medius fruit is >60% of diet, Fietz & Ganzhorn, 1999)
than N. gabriellae (fruit is =30.31% of diet, Rawson & Traeholt, 2011). However, A. pigra, a
facultative folivore that eats as much fruit as is available but will switch to leaves if needed
(Pavelka & Knopff, 2004), showed no species-specific scale response (Thorton et al. 2011).
Therefore, it is unclear if diet is a major determining factor of species-specific scale of responses
to habitat amount.
Fahrig (2001) suggests reproductive rate as a potential mechanism to determine minimum
habitat requirements (Fahrig, 2001). In birds, species with higher reproductive rates required
less habitat (Vance et al. 2003). Although primates are known for their slow reproductive rates
(Jones, 2011), C. medius has a relatively high reproductive rate that can change with geography,
while other life history characteristics such as home range size remain static (Lahann &
Dausmann, 2011). However, C. medius, A. geoffroyi, and N. gabriellae all have similar birth
intervals of about 20–24 months (A. geoffroyi, Difore & Campbell, 2007; C. medius, Lahann &
Dausmann, 2011; N. gabriellae, Rawson & Traeholt, 2011). Therefore, reproductive rate does
not explain the large-scale response for N. gabriellae.
In terms of group composition and social behavior, both N. gabriellae and C. medius form
mostly monogamous pairs (Fietz, 1999), while A. geoffroyi form large fission/fusion groups
(Chapman et al. 1995). Although both N. gabriellae and C. medius form monogamous pairs, N.
gabriellae is highly territorial (Traeholt et al. 2006) while C. medius is not (Müller, 1998).
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Therefore, it is possible that territoriality will affect a species scale of effect to habitat loss and
fragmentation.
Another possible explanation for why A. geoffroyi and C. medius have smaller scale responses
than N. gabriellae may be related to their respective population densities. Both A. geoffroyi and
C. medius occur at much higher densities (Ganzhorn & Kappeler, 1996; Estrada et al. 2004) than
N. gabriellae (Rawson et al. 2009). For example, Estrada et al. (2004) found that A. geoffroyi
density ranged from 17.0 to 56.4 individuals/km2 and Schäffler and Kappeler (2015) found C.
medius density can occur at 180 individuals/km2 but, Rawson et al. (2009) found that N.
gabriellae occurs at a much lower density of 0.71 groups/km2. Because of low population
densities and their territorial nature, the smallest area where a breeding population of N.
gabriellae could occur would be much larger than it would be for A. geoffroyi and C. medius
irrespective of reproductive rate, diet, or group composition.
Even though M. murinus is much smaller than C. medius, it showed a larger scale response.
However, the closely related and similarly sized M. ravelobensis showed no scale of effect to
habitat loss and fragmentation. What differences between M. murinus and M. ravelobensis
would explain why one species shows a scale of effect while the other does not? M. murinus has
larger home ranges and greater dispersal ability in continuous forest (Radespiel, 2000; Radespiel
et al. 2003) than M. ravelobensis (Weidt et al. 2004; Radespiel et al. 2008). In Chapter 3, I
found evidence to suggest that M. ravelobensis may be more dispersal limited than M. murinus
in the same fragmented landscape. The two species are similar in size but M. murinus stores fat
in its tail seasonally (Zimmerman et al.1998). Although their diet is similar (Radespiel et al.
2006), one study showed that they have different microhabitat preferences in continuous forest
(Rakotondravony & Radespiel, 2009), with M. murinus preferring higher elevation drier forest
than M. ravelobensis. Reproductive rate between each species is similar in that they are both
seasonal breeders (Radespiel, 2000; Randrianambinina et al. 2003) and they are both solitary
foragers (Radespiel et al. 2003; Randrianambinina et al. 2003; Weidt et al. 2004). One
difference is that M. murinus typically sleeps in protected tree holes with only females sleeping
with conspecifics (Radespiel et al. 2003) while both sexes of M. ravelobensis may sleep with
conspecifics in less protected sites (Radespiel et al. 2003) and self-constructed leaf nests
(Thorén et al. 2011). Although I suggest that differences in scale responses between more
frugivorous species appear to be related to population density, this does not appear to be the
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case for M. murinus and M. ravelobensis. Steffens and Lehman (2016) found that there were no
significant differences in abundance or density between each Microcebus species within the
same study landscape. We found similar factors (dendrometrics and fragment size and distance
metrics) explained abundance for both species. However, we found that potential edge effects
explain density in M. ravelobensis. In continuous forest near my research landscape, Burke and
Lehman (2015) found that M. ravelobensis was more edge tolerant than M. murinus. A need for
more protected sleeping sites may preclude M. murinus from occupying edge habitats that are
characterized by having smaller trees (Murcia, 1995). Conversely, M. ravelobensis does not
require protected sleeping sites, even following birth (Thorén et al. 2011). Other primate species
that show no species-specific scale of effect to habitat loss and fragmentation such as A. pigra
(Thorton et al. 2011) are also thought to be edge tolerant (Arroyo-Rodríguez & Dias, 2010).
Thus, edge effects may play a crucial but largely unstudied role in determining a species scale of
effect in fragmented landscapes.
I have shown that for three nocturnal lemur species, the scale of effect for habitat loss and
fragmentation in a fragmented landscape is species-specific and may be moderated by things
other than typical life history characteristics (Jackson & Fahrig, 2012). Factors such as home
range size, locomotor ability, population density, lack of territoriality, and edge effects may
influence each species’ scale of effect. Future studies should incorporate additional variables to
determine at precisely what scale lemur species respond to habitat amount.
4.8.2 Question 2: What is the Effect of Habitat Amount on Species-Specific Occurrence of Lemurs at the Landscape-Level?
Many studies have looked at habitat amount (area) and its relation to species richness (e.g., the
species-area relationship; Chapter 2) and occurrence (e.g., metapopulation dynamics; Chapter
3). As I demonstrated in Chapter 2, area is the factor with the greatest influence on lemur
species richness in a fragmented landscape. In Chapter 3, I demonstrated that area had a greater
impact on individual lemur species occurrence than isolation/connectivity within a fragmented
landscape. What about when landscapes are used as the unit of analysis? Does the area pattern
continue to hold? For both C. medius and M. murinus, the amount of habitat (a measure of area)
within a landscape is a determinant of species occurrence at species relevant scales. However,
the amount of forest was not a determinant of species occurrence at larger scales. For M.
ravelobensis, the amount of habitat within a landscape had a positive effect at only smaller
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scales. However, I can accept the null hypothesis that area randomly affects M. ravelobensis
occurrence as the null model had the lowest ΔAIC values. This result is likely due to M.
ravelobensis’s tolerance for edge effects, and because, regardless of scale, occurrence in this
lemur species is not determined by how much habitat occurs within a landscape. Therefore, I
suggest that research is conducted investigating other landscape and biogeographic factors to
determine what impacts occurrence for this species.
4.9 Suggestions for Conservation Protecting sufficient area to maintain populations is important as I suggest in Chapter 3, based
on metapopulation dynamics. However, using a landscape-level approach, I found that
responses of three nocturnal lemur species occurred at small scales, suggesting that there is a
need to consider how small-scale disturbances may affect species occurrence. For example, tavy
(slash-and-burn agriculture) and selective logging may not negatively impact an area at scales
relevant to humans in the short term, but may have detrimental impacts for C. medius and M.
murinus at small spatial scales. For M. ravelobensis, whose occurrences was not impacted by
the amount of habitat within a landscape, we should assess other variables to determine what
drives occurrence in this species in fragmented landscapes. From a conservation perspective, it
appears that M. ravelobensis, the only species in this study considered endangered, is tolerant of
habitat loss and fragmentation. Although it is edge tolerant in continuous forests, this species
may be at greater risk of predation from both endemic and non-endemic predators due to its use
of unprotected sleeping sites and existence in very small fragments. I recommend a dual
approach that helps maintain or increase habitat amount at large and small scales to protect
lemur species with greater efficacy.
4.10 Conclusion Although both species-area relationships and metapopulation dynamics have uses in
understanding lemur biogeography, landscape ecology is very useful for determining how
individual species respond to habitat loss and fragmentation at species relevant scales. Area was
the main driver of species richness and occurrence in three nocturnal lemurs using patch-level
analyses, but I was able to demonstrate that this result is not always true when using a
landscape-level approach even among closely related species (e.g., two species of Microcebus).
Frugivorous species at higher densities, with more limited mobility, which are not territorial,
have small-scale responses to habitat amount. More versatile fauni-frugivores have
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comparatively larger scale responses (M. murinus) or no response at all (M. ravelobensis) to
habitat amount on species occurrence. My study demonstrates that the amount of habitat (a
measure of area) is not always a determinant of species occurrence despite its nearly axiomatic
use in the biogeographic literature (Whittaker & Triantis, 2012), suggesting that we still do not
fully understand what determines species occurrence. Therefore, further research that
incorporates variables in addition to the amount of habitat using a landscape-level approach
while considering scale and spatial autocorrelation are needed to determine exactly how primate
species, including the diurnal species sympatric with the three nocturnal species described in
this dissertation, respond to habitat loss and fragmentation.
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Chapter 5: Patch and Landscape Determinants of Lemur Species Richness and Occurrence
5.1 Conclusion My study aimed to investigate how area impacts lemur species richness and occurrence at
different scales, such as using a patch-level analysis within a single large landscape and a
landscape-level analysis using many small landscapes. I applied a patch-level approach to
investigate the nature of species-area relationships on primate communities (Chapter 2); I
applied metapopulation dynamics to look at how individual species occurrence is impacted by
patch area and isolation (Chapter 3); and I looked at landscape-level patterns of lemur species
occurrence with relation to habitat area in species-specific landscapes (Chapter 4). I found that
area was the main driver of species richness and occurrence at a patch-level, but that area did
not always determine species occurrence at a landscape-level.
5.2 Summary of Results
5.2.1 Species-Area Relationships in a Lemur Community
Species-area relationships in a fragmented landscape should follow a sigmoidal pattern
(Lomolino, 2000; Tjørve, 2003; Tjørve, 2009). However, I found that lemur species richness
patterns followed a convex pattern (power model) within a fragmented landscape. Sigmoidal
models may form s-shaped curves (Chapter 2, Fig. 2.1). In a sigmoidal species-area relationship
the lower, straight portion of the s-curve is hypothesized to be relatively flat due to the “small
island effect” (Lomolino, 2000). The “small island effect” occurs when something other than
area is predicting species richness or the area is too small to support a population for a particular
species (Lomolino, 2000). The curve steepens as area becomes the main predictor of species
richness, and eventually the curve plateaus as the total number of species is reached in a finite
species pool (Lomolino, 2000). On the other hand, power models form convex curves that are
relatively monotonic compared to sigmoidal models, and they do not have an upper asymptote
(Chapter 2, Fig. 2.2; Lomolino, 2000; Tjørve, 2003). With respect to the species-area
relationship forming a power model rather than a sigmoidal model in my study, either there is a
high degree of movement between fragments or some species are able to survive in even the
smallest habitat fragments. The results from Chapter 3, summarized below, demonstrate that
some species (C. medius and E. fulvus) are able to move between fragments while others (M.
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murinus and M. ravelobensis) may move but are more tolerant of edge effects (specifically M.
ravelobensis) and can survive in even the smallest fragments.
Determining which model best describes the species-area relationship in my study site is
impacted by certain limitations of the study landscape. The study landscape contains 42
fragments ranging from 0.23 to 117.7 ha and I only observed six of the eight potential lemur
species (I suggest two have gone locally extinct). Triantis et al. (2012) suggest a fragment range
size of three orders of magnitude (compared to the 2.5 in my study) to facilitate the detection of
a sigmoidal pattern in the species-area relationship. Therefore, there is some uncertainty if the
power model is more appropriate than a sigmoidal model, as I found in my study. However, my
study included the largest number of fragments investigating the species-area relationship on
lemurs in Madagascar and is one of the largest habitat fragmentation studies on primates in the
world. My study landscape contained too few large fragments to support all eight possible lemur
species. My study demonstrates that local extinction of certain larger-bodied, preferentially
hunted species (e.g., E. mongoz and A. occidentalis) occurred in fragments that continue to
support other large, preferentially hunted species (e.g., P. coquereli). As noted above, both
Microcebus species are able to survive in even the smallest fragments in my study site (e.g.,
0.23 ha), which would obscure the detection of a sigmoidal curve unless even smaller (< 0.23
ha) fragments occurred within my study landscape. Although other studies have found lemurs in
fragments greater than 1 ha (Ganzhorn, 2003; Schad et al. 2004; Olivieri et al. 2008), my study
is the first to find lemurs in fragments smaller than 1 ha.
I found that area was the strongest predictor of lemur species richness when comparing different
Generalized Additive Models. However, using a hierarchical partitioning procedure, both mean
tree height and the total amount of human disturbance contributed a combined 28.04% of the
variation in lemur species richness compared to 60.82% for ln area. It is unclear if mean tree
height or human disturbance had a positive or negative influence on lemur species richness but
human disturbance was positively related to fragment area. Additionally, the absence of E.
mongoz and A. occidentalis may be related to human disturbance factors. For example, I found
lemur traps located within fragments in the study site and local residents report that they
preferentially hunt E. mongoz. The results of this chapter demonstrate that at a patch-level, area
appears to be a driving factor determining lemur species richness, but future studies should
further investigate the potential role of human disturbance and mean tree height.
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5.2.2 Metapopulation Dynamics of Lemur Species
Not all lemur species formed stable metapopulations. Both P. coquereli and L. edwardsi
appeared to form non-equilibrium metapopulations, and these two species may be declining
towards extinction within my study site. The low occurrence of both P. coquereli and L.
edwardsi in the study site precluded my ability to analyze their metapopulation structure in more
detail. This raises the issue that the two most apparently vulnerable species in my study site
have populations below levels that could be analyzed to determine how they are impacted by
habitat loss and fragmentation. Two other species, E. mongoz and A. occidentalis, already
appear locally extinct in the landscape. Immediate conservation action is warranted and we need
further research in landscapes with intermediate stages of loss containing larger and more
connected fragments. This research and conservation action will help us better understand the
impact of habitat loss and fragmentation on P. coquereli and L. edwardsi occurrence to prevent
the local extinction of these species. The two frugivorous species (E. fulvus and C. medius)
formed stable metapopulations, but C. medius may be more sensitive to increased isolation than
E. fulvus. The two smaller-bodied, fauni-frugivores, M. murinus and M. ravelobensis are
virtually ubiquitous in fragments throughout the landscape inhabiting 35 and 34 of the 42
fragments, respectively. Although both species occupied even the smallest fragments, I found
small differences in model selection between each species. The most likely models for M.
murinus were the mainland-island model followed by the incidence function model (IFM),
where dispersal was estimated from the survey data. For M. ravelobensis, four of the five
models were roughly equivalent including the mainland-island model, IFMs where dispersal
was determined by the literature, occupancy data, and based on the square root of the mean
home range reported in the literature. Both species appear to form source-sink mainland-island
metapopulations within an intermediate metapopulation. For both species, I hypothesize that the
larger fragments and continuous forest likely act as sources and the smallest fragments act as
sinks. Moreover, some movement is likely occurring between all the fragments for M. murinus,
as evidence by their greater dispersal ability, while M. ravelobensis are more edge tolerant and
capable of surviving within small fragments. We should test these hypotheses using
metapopulation dynamics combined with mark recapture methods.
Area was the main driver of lemur species occurrence in my study site, while the relationship
between isolation/connectivity and lemur occurrence was less clear. E. fulvus was the only
species to show a positive relationship between connectivity and lemur occurrence. There was a
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neutral response to isolation/connectivity for C. medius. For both Microcebus species, the
probability of occurrence was actually lower in more connected than less connected fragments.
Differences in lemur species dispersal ability and edge tolerance may be driving differences in
metapopulation dynamics among lemur species. For both Microcebus species, and especially M.
murinus, source-sink dynamics may be occurring within my study site. The results of my study
on lemur species occurrence using metapopulation dynamics help explain why lemurs in this
fragmented landscape form non-sigmoidal species-area relationships. The results of this chapter
help inform the species-area relationship patterns observed in Chapter 2 and show that at a
patch-level lemur species occurrence in a fragmented landscape is mainly driven by area.
5.2.3 Landscape Effects on Lemur Species
I investigated the species-specific landscape-level effects of habitat loss on C. medius, M.
murinus, and M. ravelobensis within my study landscape. Although I found that area is a major
factor determining lemur species richness and occurrence at a patch-level, at a landscape-level
area was not as important for all lemur species. The results from Chapter 4 provide further
evidence to support my suggestion in Chapter 3 that the different metapopulation models
selected for each Microcebus species were the result of greater dispersal ability (M. murinus)
and edge tolerance (M. ravelobensis). M. murinus had a relatively large scale response to habitat
amount, which I suggest is due to its greater modeled and reported dispersal ability (Radespiel et
al. 2003; Schliehe-Diecks et al. 2012), while M. ravelobensis had no response to habitat amount
within a landscape, which I suggest is due to its reported greater edge tolerance (Burke and
Lehman 2015). C. medius had small scale responses to habitat amount within a landscape,
similar to reports for Ateles geoffroyi (Thorton et al. 2011), but contrary to results for Nomascus
gabriellae (Ordóñez-Gómez et al. 2014), all highly frugivorous species. I suggest that for
frugivorous species, population density, home range size, mobility, and territoriality may
influence species responses to habitat amount using a landscape-level of analysis. Using a
landscape approach, I was able to show that lemurs have species-specific responses to habitat
loss and fragmentation. Together with Chapter 3, the results of Chapter 4 help explain why a
sigmoidal SAR was not observed in Chapter 2. The results from Chapter 4 of my study are
important because they clearly show that area is not always the driving factor determining
species occurrence, and that other variables such as edge tolerance, dispersal ability, population
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density, and territoriality may play important roles in determining how species respond to
habitat loss and fragmentation.
5.3 Implications
5.3.1 Species-Area Relationship
Area has been the focus of biogeographic research since Watson (1859) published the first
species area curve. Since his work on the species-area relationship, countless other studies have
continued to find a re-occurring pattern of increasing species richness with increasing area
(Whittaker & Triantis, 2012). Whether areas are larger portions of continuous habitat or
fragments of habitat, the pattern of increasing species richness with increasing area has held.
Although the shape of the species-area relationship is still debated (Tjørve, 2003; Tjørve, 2009),
the species-area relationship itself is so strong that it is considered axiomatic in biogeography
(Whittaker & Triantis, 2012). However, what causes the species-area relationship is still poorly
understood. My work continues to demonstrate the ability of area to overwhelm other variables
that potentially impact species richness. I weigh in on the debate regarding the shape of species
area relationships. Although I predicted I would find a sigmoidal model to represent lemur
species-area relationships in a fragmented landscape with potentially hostile matrix, I found that
a model more suitable to continuous forest was the most likely. Other research on species-area
in primates also demonstrates that area is the main factor determining primate species richness
(Reed & Fleagle, 1995; Cowlishaw, 1999; Cowlishaw & Dunbar, 2000; Lehman, 2004;
Harcourt & Doherty, 2005; Marshall et al. 2010). However, only a few of these studies (Reed &
Fleagle, 1995; Lehman, 2004; Marshall et al. 2010) incorporated elements other than area.
Although my study confirms that area is an important contributor to primate species richness, it
does suggest that other variables may be involved, such as the amount of human disturbance and
mean tree height.
5.3.2 Metapopulation Dynamics
Following work on community level patterns of species distribution, researchers from
population biology such as Levins (1969, 1970) and later Hanski (1994abc) sought to explain
how individual species distribute themselves within patchy environments. Their work led to the
creation of metapopulation dynamics. Incorporating both area and isolation, metapopulation
models are useful to determine how individual species are impacted by habitat loss and
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fragmentation (Hanski 1994b). Metapopulation dynamics have been applied to primate species
but never to lemur species. As the first researcher to apply metapopulation dynamics to lemur
species within a fragmented landscape, I was able to determine how lemur species are affected
by habitat loss and fragmentation from a patch perspective. I show that area is a greater
determinant of primate occurrence than isolation. Other primate studies have found similar
results showing a limited isolation impact on primate occurrence (Lawes et al. 2000; Chapman
et al. 2003; Cristobal-Azkarate et al. 2005; Raboy et al. 2010).
An interesting use of metapopulation dynamics is in determining dispersal patterns of a species
within fragmented landscapes. I found that my metapopulation models generated 101 m
dispersal parameter (α) estimates for both Microcebus species that underestimate reported
dispersal distances for M. murinus (251 m; Radespiel et al. 2003) and overestimate reported
dispersal distances for M. ravelobensis (54 m; Radespiel et al. 2009). I generated my dispersal
estimates within a fragmented landscape while the reported dispersal distances were from a
continuous forest landscape. This suggests that dispersal patterns in continuous forest may differ
from dispersal patterns in fragmented forest landscapes. My use of the dispersal index, square
root of each species home range, provided a closer approximation to reported dispersal distances
(M. murinus = 168.2 m; M. ravelobensis = 76 m). We need further research to determine if
dispersal ability in primates is different in continuous forest versus fragmented forest
landscapes.
5.3.3 Landscape Effects
Both the species-area relationship and metapopulation dynamics look at community and species
level patterns, respectively, from a patch perspective. Meanwhile, since the early 2000s
researchers from landscape ecology, such as McGarigal and Cushman (2002) and Fahrig (2003),
have argued that viewing species richness and occurrence patterns from a patch perspective can
mask underlying trends that may explain the cause of the patterns themselves. Specifically,
work by Fahrig (2003) demonstrates how using landscapes as units of analysis as opposed to
patches yield important insights into how species richness responds to habitat loss and
fragmentation, as the latter process is a landscape-level phenomenon. A shift from a patch
perspective to a landscape perspective introduces some new problems, such as determining what
landscape size is appropriate and relevant to the species of interest (Jackson & Fahrig, 2012;
Arroyo-Rodriguez & Fahrig, 2014). In the first study of its kind on lemurs, I investigated
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species-specific landscape scales assessing lemur species occurrence in a fragmented landscape.
My study affirms the need to use a landscape-level approach when assessing the impact of
habitat loss and fragmentation on species occurrence. Using a landscape approach, I found that
for C. medius and M. murinus area was still the main variable impacting occurrence, but for M.
ravelobensis something other than area explains occurrence. As with similar studies on primates
(Gray et al. 2010; Thorton et al. 2011; Ordóñez-Gómez et al. 2014) and other arboreal mammals
(Patterson & Malcolm, 2010), I found differing scale responses among the species of my study.
By looking at species occurrence at a landscape-level, I demonstrated that area is not always the
main determinant of species occurrence, even in closely related species. Thus, other factors must
be influencing the ability of the lemur species I studied to occur in a fragmented landscape that
are partially masked at a patch-level, such as dispersal ability, edge tolerance, population
density, and territoriality in primates. My research also demonstrates the importance of looking
at species-specific landscape scale responses to habitat loss and fragmentation. Without
determining relevant scale responses, researchers may miss important information into how
species respond to habitat loss and fragmentation. My dissertation fits within the greater
discipline of biogeography by both reaffirming the role of area in determining primate richness
and occurrence at a patch-level, while also highlighting the importance of landscape-level
analysis to assessing species occurrence in a fragmented landscape.
5.4 Directions for Future Research
5.4.1 Species-Area Relationships
Future research on species-area relationships on lemurs should incorporate more variables and
should focus strongly on anthropogenic factors that could influence lemur species richness. A
study is needed with a greater range of both larger and smaller fragments than those used in my
current study in order to determine if a sigmoidal relationship is possible for lemur species.
Comparing species-area relationships among fragments of habitat to those within continuous
forest in Madagascar may help resolve some of the questions as to what species-area patterns
lemurs are expected to form under different conditions. Although a patch-level analysis, species-
area relationships are still a useful tool for conservation managers with limited time and budget
to determine the potential impacts of habitat loss and fragmentation on species richness.
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5.4.2 Metapopulation Dynamics
Other metapopulation dynamics research on primates has focused on determining: species
persistence in fragmented landscapes (Chapman et al. 2003; Swart & Lawes, 1996), occurrence
(Lawes et al. 2000), extinction risk (Zeigler et al. 2013), minimum critical patch size (Lawes et
al. 2000), and population viability (Mandujano & Escobedo-Morales, 2008). The results of my
study will provide a starting point for other researchers to use metapopulation dynamics on other
primate species, in other regions, and for other purposes. For example, the results of my study
contribute to the single large reserve or several small reserves (SLOSS) debate (Diamond,
1975). I found the same species richness in the largest habitat fragment as within a combination
of other fragments, but not including the largest. This fact suggests that either single large or
several small would be a suitable management choice for this landscape. However, when you
look at the simulated metapopulation dynamics over time, if the largest fragments are removed
then species occurrence collapses for all four species. Meanwhile species occurrence among the
five remaining fragments remained relatively constant. The collapse in the remaining fragments
and subsequent stability in the five largest provides evidence that protecting a single (or a few)
large fragments may protect more species than several small fragments. By using similar
metapopulation dynamics and simulation techniques as I have in this study, other researchers
will be able to expand on the SLOSS debate and other topics in primate population ecology and
conservation biogeography.
An obvious direction for future metapopulation research would be to look at the nature of
source-sink relationships in lemurs and other primates found in fragmented forests near
continuous forest. My study suggests that source-sink dynamics may be occurring for
Microcebus species. Other research on lemurs has shown that some lemurs may be attracted to
edge habitats (Ganzhorn, 1995; Lehman et al. 2006a; 2006b). It is unclear if species can persist
in small habitat fragments that may appear attractive in the short-term due to certain benefits of
edge effects (e.g., increased leaf and fruit productivity), but that may be detrimental in the long
term due to negative effects of small fragment size, such as increased predation pressure or
reduced mating opportunity. We need to understand how source-sink dynamics are operating on
primates and what impact they have on primate persistence in fragmented landscapes.
Knowledge of dispersal is limited for many primate species, especially in fragmented
landscapes. Future research should investigate dispersal patterns in primates within fragmented
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landscapes. Metapopulation dynamics can be used to generate fitted dispersal estimates.
However, dispersal can also be measured directly by incorporating mark recapture techniques
(Hanks 1994a), something that is possible for small-bodied primates such as Microcebus species
(Radespiel et al. 2003). Determining dispersal ability in primates under varying conditions is a
critical factor in assessing how species respond to habitat loss and fragmentation.
5.4.3 Landscape Ecology
There has been a recent acknowledgment of the role of landscape ecology in the field of
primatology. Influenced by Farhrig (2003) primate researchers have expressed the need for
more research using a landscape perspective and considering species-specific landscape
responses (Arroyo-Rodriguez & Fahrig, 2014). Those studies that have incorporated species-
specific landscape responses in primates have found that there are indeed differences (Gray et
al. 2010; Thorton et al. 2011; Ordóñez-Gómez et al. 2014). Building on the work of these early
pioneers in primate landscape ecology, I also found that lemur responses are species-specific at
the landscape-level. I suggest that future research continue to investigate how landscape-level
effects of habitat loss and fragmentation at species relevant scales impact primate species
richness and occurrence. Research on other mammals suggests that scales as large as four to
nine times the median-dispersal distance are appropriate (Jackson and Fahrig, 2012). However,
my research suggests that since many primates are dispersal limited, smaller scales should be
considered. My results for Microcebus species suggest that responses to habitat loss and
fragmentation reflect differences in dispersal ability and edge tolerance. Future studies should
measure dispersal ability in these species within fragmented habitats. For example, looking at
mark/recapture techniques using both metapopulation dynamics and landscape-level studies will
help determine how much dispersal ability influences these species occurrence. The influence of
edge effects on species occurrence is another avenue for future research. Future research should
look at how edge responses impact occurrence of species within habitat fragments and to
compare those results in edge habitats of nearby continuous forest. It is important to determine if
the results of my study are dependent on the nature of the fragmentation and loss within my
study landscape or if my results represent general patterns in lemur responses to habitat loss and
fragmentation. By comparing my study site to another with a different landscape configuration
but a similar lemur community will help us to better understand how the pattern of habitat loss
and fragmentation influence lemur species richness and occurrence.
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5.5 Implications for Lemur Conservation Lemurs are the most endangered mammal group in the world (Schwitzer et al. 2014). Habitat
loss and fragmentation are the greatest threats, followed by illegal hunting (Schwitzer et al.
2014). With a wealth of biodiversity, Madagascar is one of the worlds 36 Biodiversity Hotspots
(CEPF, 2016) and lemurs are endemic to the island nation (Schwitzer et al. 2013). Madagascar
is one of the poorest countries in the world with over 92% of its residents living on less than two
U.S dollars a day (World Bank, 2013). Although wealthy in natural resources, such as
gemstones and other minerals, forestry products, and oil and gas, Madagascar is under-
developed and is one of the most corrupt countries in the world. Since the 1950s an estimated
40% of the forest has been converted to non-forest habitats (Harper et al. 2007) while poverty
has increased. My research can provide a useful conservation framework to inform lemur
conservation actions and plan future directions for conserving lemurs.
The largest threat to lemurs in Ankarafantsika National Park is from habitat loss and
fragmentation from fire (Bloesch, 1999). Other threats include degradation of habitat by
resource extraction (Steffens, personal observation), hunting pressure from humans (García &
Goodman, 2003), and predation, especially from introduced carnivores (Steffens, personal
observation). Many of the threats are likely exacerbated by habitat loss and fragmentation.
The conservation implications of my study are complex. On the one hand, I found the two
smaller bodied Microcebus species to be quite tolerant of even extreme fragmentation and forest
loss. Albeit likely for different reasons, these two species are capable of surviving in small
fragments. One area of concern for these two species is that they may be forming a source-sink
metapopulation where migrants from the continuous forest or largest fragments move into the
smallest fragments into untenable populations (for example single individuals). For M. murinus,
the situation is not as problematic because they have a large distribution across western
Madagascar (Mittermeier et al. 2010) and are listed as least concern by the IUCN (2016).
However, M. ravelobensis has a very limited distribution, is only protected in Ankarafantsika,
and is considered endangered due to recent habitat loss and a suspected population crash (IUCN,
2016). Although I suggest M. ravelobensis is edge tolerant, there are many factors that are
compounded by habitat loss and fragmentation that may impact their population in a fragmented
landscape, such as increased predation pressure from introduced predators (Farris et al. 2014).
Therefore, for Microcebus species in fragmented landscapes, I conservatively recommend
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protecting large tracts of forest, and further research into the possibility of source-sink dynamics
and potential increase in predation risk related to edge effects within fragments.
For C. medius the situation is of moderate conservation concern. C. medius is classified as least
concern and is widely distributed, and occurs in many protected areas (IUCN, 2016). However,
the results of my study suggest that within fragmented landscapes C. medius needs medium to
large fragments that have low isolation. Therefore, I recommend protecting larger fragments and
improving connectivity among fragments.
For the large bodied lemurs in my study, I found two species (E. mongoz and A. occidentalis)
were likely extirpated from my study site, two species (P. coquereli and L. edwardsi) had too
few occurrence points to conduct meaningful statistical assessment of their distribution, and one
species (E. fulvus) formed a stable metapopulation but was less sensitive to habitat isolation. All
the larger bodied species except E. fulvus are considered endangered by the IUCN (2016). The
conservation implications for E. fulvus are similar for C. medius. From a species richness
perspective my work on species-area relationships demonstrates the immediate need to protect
as many large tracts of habitat as possible. However, this may not be enough to stem the local
extinction of L. edwardsi, and especially P. coquereli; the latter occurring only in the largest
fragments and not even within fragments nearest to the continuous forest. For these species and
the two (E. mongoz and A. occidentalis) extirpated from my site, I recommend four important
conservation measures: First, protection from fire, which is the greatest threat to habitat in my
study site; Second, increased education regarding hunting, as all of these species are
preferentially hunted (García & Goodman, 2003); Third, re-establishment of corridors and
increasing the size of habitat by planting; Finally, considering possible reintroduction of these
species when large tracts of forest have been re-grown, protected, and connected to other forest
through corridors. However, further study is needed to determine the efficacy of such measures.
5.6 Significance Understanding how primates and specifically lemurs respond to habitat loss and fragmentation
is of critical importance for primate conservation biogeography. Madagascar is facing one of the
greatest threats to biodiversity due to habitat loss, and lemurs are on the brink of losing
important remaining habitat. The goal of my study was to determine how lemur species, at the
community level and individually, respond to habitat loss and fragmentation at both the patch-
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and landscape-level in a fragmented landscape. My study continues to show the overwhelming
influence of area on primate species richness and occurrence at a patch-level but not necessarily
at a landscape-level. Therefore, it is important to consider primate biogeography at both a patch-
and landscape-level when developing a conservation action plan. A patch-level is important
because patches or habitat fragments are common units of management for conservation. Land
managers need to know what size of patches/fragments to protect in order to retain the
maximum number of species, or to protect particular species of interest. However, a landscape
approach, in addition to a patch-level approach, will yield important suggestions on how to
protect primate species. What we learn to protect lemur species can be applied to other primates
and arboreal mammals in other parts of the world where habitat loss and fragmentation are
rampant.
116
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Appendices Appendix A: Cheirogaleus medius Occurrence Responses to Amount of Habitat within 10 Landscape Scales.
Model Scale (ha) Variable Coefficient Value
Standard Error Z-Value P-value
0.5 Intercept -4.47 1.81 -2.47 0.01 Amount of Forest 1.06e-03 4.01e-04 2.65 <0.01
1 Intercept -4.26 1.53 -2.78 <0.01 Amount of Forest 5.41e-04 1.81e-04 3.00 <0.01
2 Intercept -3.52 1.18 -2.99 <0.01 Amount of Forest 2.45e-04 7.53e-05 3.25 <0.01
4 Intercept -2.76 0.92 -3.02 <0.01 Amount of Forest 1.11e-04 3.36e-05 3.30 <0.01
8 Intercept -2.09 0.75 -2.79 <0.01 Amount of Forest 4.95e-05 1.60e-05 3.10 <0.01
16 Intercept -1.88 0.70 -2.70 <0.01 Amount of Forest 2.70e-05 9.00e-06 3.01 <0.01
32 Intercept -1.68 0.66 -2.55 0.01 Amount of Forest 1.54e-05 5.49e-06 2.81 <0.01
64 Intercept -1.64 0.69 -2.39 0.02 Amount of Forest 9.44e-06 3.67e-06 2.58 0.01
128 Intercept -1.44 0.67 -2.14 0.03 Amount of Forest 4.58e-06 1.94e-06 2.36 0.02
256 Intercept -0.81 9.96e-07 -1.21 0.22 Amount of Forest 1.34e-06 1.34e-06 1.34 0.18
Null Intercept 6.70e-17 0.30 0.00 1.00
ME=Moran’s Eigenvectors
Appendix B: Microcebus murinus Occurrence Responses to Amount of Habitat within 10 Landscape Scales.
Model Scale (ha) Variable Coefficient Value
Standard Error Z-Value P-value
0.5
Intercept -2.88 1.11 -2.60 <0.01 Amount of Forest 7.21E-04 2.18E-04 3.32 <0.01
Fitted ME 139 -129.30 8.80 -1.47 0.14 Fitted ME 23 1418.00 1155.00 1.23 0.22 Fitted ME 73 38.76 9.071 4.27 <0.01 Fitted ME 14 -29.20 11.24 -2.60 <0.01 Fitted ME 33 45.13 18.68 2.42 <0.01 Fitted ME 47 -78.61 23.76 -3.31 <0.01 Fitted ME 10 28.65 10.32 2.78 <0.01 Fitted ME 30 -244.30 212.60 -1.15 0.25 Fitted ME 141 -71.84 67.480 -1.07 0.28 Fitted ME 46 -17.92 7.70 -2.33 <0.01
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Fitted ME 51 35.37 9.54 3.71 <0.01 Fitted ME 78 78.31 19.93 3.93 <0.01 Fitted ME 98 31.13 7.34 4.24 <0.01 Fitted ME 3 1606.00 1146.00 1.40 <0.01
Fitted ME 437 38.38 14.12 2.72 <0.01 Fitted ME 17 12.94 18.63 6.94 0.49 Fitted ME 436 26.06 9.13 2.85 <0.01 Fitted ME 435 -31.57 12.20 -2.59 <0.01 Fitted ME 26 -4622.00 3718.00 -1.24 0.21 Fitted ME 1 -69.48 44.93 -1.55 0.12 Fitted ME 18 359.50 252.40 1.42 0.15
1
Intercept -2.86 0.81 -3.55 <0.01 Amount of Forest 5.52E-4 1.13E-4 4.90 <0.01
Fitted ME 139 -120.70 80.74 -1.50 0.13 Fitted ME 23 117.40 79.43 1.48 0.14 Fitted ME 73 37.91 7.79 4.86 <0.01 Fitted ME 47 -25.61 19.28 -1.33 <0.01 Fitted ME 33 65.41 21.25 3.08 <0.01 Fitted ME 14 -44.48 14.04 -3.17 <0.01 Fitted ME 10 47.91 17.17 2.79 <0.01 Fitted ME 51 35.21 9.72 3.62 <0.01 Fitted ME 78 78.38 18.99 4.13 <0.01 Fitted ME 98 31.15 6.64 4.69 <0.01 Fitted ME 46 -99.58 35.73 -2.79 <0.01 Fitted ME 141 -74.85 73.68 -1.02 0.31 Fitted ME 28 -44.33 17.01 -2.61 <0.01 Fitted ME 437 40.43 13.22 3.06 <0.01 Fitted ME 26 -257.70 215.70 -1.20 0.23 Fitted ME 436 27.39 8.62 3.18 <0.01 Fitted ME 435 -31.86 10.87 -2.93 <0.01 Fitted ME 74 21.27 6.59 3.23 <0.01 Fitted ME 42 -58.30 22.51 -2.59 <0.01 Fitted ME 336 20.21 6.96 2.90 <0.01 Fitted ME 2 -10.16 9.83 -1.034 0.30
2
Intercept -1.71 0.55 -3.10 <0.01 Amount of Forest 1.97E-04 4.12E-05 4.78 <0.01
Fitted ME 139 -106.40 58.59 -1.82 0.07 Fitted ME 23 133.90 74.68 1.79 0.07 Fitted ME 73 38.38 8.42 4.56 <0.01 Fitted ME 33 55.94 18.42 3.04 <0.01 Fitted ME 47 -89.91 23.18 -3.88 <0.01 Fitted ME 14 -28.59 9.05 -3.16 <0.01 Fitted ME 10 28.53 10.26 2.78 0.01 Fitted ME 78 65.73 12.61 5.21 <0.01 Fitted ME 51 33.39 9.00 3.71 <0.01 Fitted ME 98 27.57 5.52 4.99 <0.01 Fitted ME 46 -16.98 7.23 -2.35 0.02
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Fitted ME 141 -62.55 50.33 -1.24 0.21 Fitted ME 28 -48.94 13.60 -3.60 <0.01 Fitted ME 437 31.25 12.04 2.60 0.01 Fitted ME 26 -294.90 203.90 -1.45 0.15 Fitted ME 436 21.97 7.51 2.93 <0.01 Fitted ME 435 -27.30 10.26 -2.66 0.01
4
Intercept -1.28 0.44 -2.89 <0.01 Amount of Forest 8.03E-05 1.75E-05 4.59 <0.01
Fitted ME 139 -102.10 54.49 -1.87 0.06 Fitted ME 23 127.70 78.28 1.63 0.10 Fitted ME 33 47.70 16.70 2.86 <0.01 Fitted ME 47 -80.53 21.91 -3.68 <0.01 Fitted ME 73 28.68 5.68 5.05 <0.01 Fitted ME 78 53.12 9.81 5.42 <0.01 Fitted ME 51 25.41 6.63 3.83 <0.01 Fitted ME 98 21.84 4.67 4.68 <0.01 Fitted ME 14 -25.98 8.44 -3.08 <0.01 Fitted ME 10 28.50 10.05 2.84 <0.01 Fitted ME 46 -15.55 6.72 -2.31 0.02 Fitted ME 141 -60.69 45.66 -1.33 0.18 Fitted ME 28 -39.26 11.43 -3.43 <0.01 Fitted ME 437 24.58 5.98 4.11 <0.01 Fitted ME 26 -286.30 215.40 -1.33 0.18
8
Intercept -1.373 0.59 -2.328 0.02 Amount of Forest 8.78e-05 1.82E-05 4.824 <0.01
Fitted ME 139 -110.36 54.61 -2.021 0.04 Fitted ME 23 161.11 67.13 2.400 0.02 Fitted ME 33 96.04 28.02 3.427 <0.01 Fitted ME 47 -25.09 18.22 -1.377 <0.01 Fitted ME 73 69.84 13.49 5.175 <0.01 Fitted ME 78 140.61 28.35 4.960 <0.01 Fitted ME 51 66.76 20.52 3.253 <0.01 Fitted ME 98 61.19 14.82 4.130 <0.01 Fitted ME 14 -37.76 11.46 -3.294 <0.01 Fitted ME 10 32.30 11.50 2.809 <0.01 Fitted ME 141 -56.65 45.47 -1.246 0.21 Fitted ME 46 -150.25 43.89 -3.424 <0.01 Fitted ME 28 -82.66e 22.12 -3.737 <0.01 Fitted ME 437 35.96 13.85 2.598 <0.01 Fitted ME 26 -330.08 174.70 -1.889 <0.01 Fitted ME 436 43.00 9.81 4.384 <0.01 Fitted ME 435 -61.59 14.790 -4.166 <0.01 Fitted ME 217 -14.77 6.18 -2.391 0.02 Fitted ME 440 91.19 19.37 4.707 <0.01 Fitted ME 42 -92.63 28.18 -3.287 <0.01 Fitted ME 32 -36.31 21.42 -1.695 0.09
16 Intercept -1.16 0.82 -1.41 0.16
134
Amount of Forest 2.20E-05 5.64E-06 3.89 <0.01
Fitted ME 139 -1711.00 121900.0
0 -0.01 0.99 Fitted ME 23 20.79 6.43 3.23 <0.01 Fitted ME 33 52.56 16.24 3.24 <0.01 Fitted ME 73 40.79 8.32 4.90 <0.01 Fitted ME 78 72.70 15.69 4.63 <0.01 Fitted ME 47 -95.15 24.82 -3.83 <0.01 Fitted ME 51 33.67 7.90 4.26 <0.01 Fitted ME 98 29.43 6.02 4.89 <0.01 Fitted ME 18 34.51 17.03 2.03 0.04 Fitted ME 14 -24.81 8.36 -2.97 <0.01 Fitted ME 10 29.99 10.23 2.93 <0.01 Fitted ME 46 -1034.00 77100.00 -0.01 0.99 Fitted ME 28 -15.06 8.17 -1.84 0.07 Fitted ME 437 -50.99 22.08 -2.31 0.02 Fitted ME 436 36.96 12.80 2.89 <0.01 Fitted ME 435 24.12 8.00 3.02 <0.01 Fitted ME 217 -29.40 10.64 -2.76 0.01 Fitted ME 3 -4.78 5.38 -0.89 0.37 Fitted ME 1 262.70 193.30 1.36 0.17
Fitted ME 150 -24.22 16.88 -1.44 0.15
32
Intercept 0.18 0.77 0.24 0.81 Amount of Forest 6.15E-06 3.50E-06 1.76 0.08
Fitted ME 139 -121.60 73.48 -1.65 0.10 Fitted ME 23 238.50 263.50 0.91 0.37 Fitted ME 78 77.27 15.68 4.93 <0.01 Fitted ME 73 36.86 7.38 4.99 <0.01 Fitted ME 33 52.24 16.19 3.23 <0.01 Fitted ME 47 -106.80 26.73 -4.00 <0.01 Fitted ME 51 35.09 7.65 4.59 <0.01 Fitted ME 98 30.05 5.85 5.14 <0.01 Fitted ME 18 81.64 36.82 2.22 0.03 Fitted ME 14 -25.71 10.09 -2.55 0.01 Fitted ME 10 32.67 10.71 3.05 <0.01 Fitted ME 46 -61.57 41.46 -1.49 0.14 Fitted ME 46 -19.01 8.92 -2.13 0.03 Fitted ME 3 326.30 248.50 1.31 0.19 Fitted ME 17 16.17 21.15 0.76 0.44 Fitted ME 437 39.37 12.42 3.17 <0.01 Fitted ME 217 -6.29 5.03 -1.25 0.21 Fitted ME 436 23.94 7.48 3.20 <0.01 Fitted ME 435 -27.62 9.64 -2.87 <0.01 Fitted ME 1 -22.98 15.90 -1.45 0.15 Fitted ME 26 -772.10 772.20 -1.00 0.32
64 Intercept 0.32 0.77 0.42 0.68 Amount of Forest 2.66E-06 2.10E-06 1.27 0.21
135
Fitted ME 139 -127.60 79.33 -1.61 0.11 Fitted ME 23 229.20 262.70 0.87 0.38 Fitted ME 78 77.60 15.57 4.98 <0.01 Fitted ME 73 35.15 7.01 5.02 <0.01 Fitted ME 47 -109.20 26.90 -4.06 <0.01 Fitted ME 51 34.74 7.61 4.56 <0.01 Fitted ME 33 50.80 15.83 3.21 <0.01 Fitted ME 18 29.91 5.76 5.19 <0.01 Fitted ME 10 83.43 36.72 2.27 0.02 Fitted ME 14 33.71 10.79 3.12 <0.01 Fitted ME 141 -25.94 10.05 -2.58 0.01 Fitted ME 46 -64.06 43.79 -1.46 0.14 Fitted ME 3 -19.89 9.14 -2.18 0.03 Fitted ME 17 336.10 247.80 1.36 0.18 Fitted ME 437 17.69 21.40 0.83 0.41 Fitted ME 217 39.55 12.19 3.25 <0.01 Fitted ME 436 -5.94 4.99 -1.19 0.23 Fitted ME 435 23.48 7.28 3.22 <0.01 Fitted ME 1 -26.08 9.18 -2.84 <0.01 Fitted ME 26 -23.15 15.69 -1.48 0.14
128
Intercept 0.33 0.71 0.47 0.64 Amount of Forest 4.00E-07 1.28E-06 0.31 0.76
Fitted ME 139 -128.10 78.06 -1.64 0.10 Fitted ME 23 121.40 138.60 0.88 0.38 Fitted ME 78 72.07 13.81 5.22 <0.01 Fitted ME 73 32.77 6.48 5.06 <0.01 Fitted ME 47 -98.75 24.61 -4.01 <0.01 Fitted ME 51 33.90 7.66 4.43 <0.01 Fitted ME 98 28.12 5.25 5.36 <0.01 Fitted ME 33 46.58 15.23 3.06 <0.01 Fitted ME 14 -58.58 20.67 -2.83 <0.01 Fitted ME 10 48.87 15.60 3.13 <0.01 Fitted ME 141 -67.49 44.74 -1.51 0.13 Fitted ME 46 -21.38 9.88 -2.16 0.03 Fitted ME 3 633.40 316.80 2.00 0.05 Fitted ME 17 510.30 180.30 2.83 <0.01 Fitted ME 437 36.70 11.09 3.31 <0.01 Fitted ME 26 -388.60 398.20 -0.98 0.33 Fitted ME 436 -31.73 16.48 -1.93 0.05 Fitted ME 435 21.12 6.59 3.20 <0.01 Fitted ME 6 -23.05 8.25 -2.80 0.01
256
Intercept 0.94 0.79 1.19 0.23 Amount of Forest -7.37E-07 8.24E-07 -0.90 0.37
Fitted ME 139 -130.50 79.97 -1.63 0.10 Fitted ME 23 167.10 238.90 0.70 0.48 Fitted ME 78 73.41 13.93 5.27 <0.01 Fitted ME 73 33.36 6.55 5.09 <0.01
136
Fitted ME 47 -102.90 25.80 -3.99 <0.01 Fitted ME 51 36.16 8.07 4.48 <0.01 Fitted ME 10 52.36 16.06 3.26 <0.01 Fitted ME 14 -57.64 20.10 -2.87 <0.01 Fitted ME 98 28.37 5.33 5.32 <0.01 Fitted ME 33 49.59 15.81 3.14 <0.01 Fitted ME 3 649.30 327.00 1.99 0.05 Fitted ME 17 -69.65 45.16 -1.54 0.12 Fitted ME 437 -24.45 10.93 -2.24 0.03 Fitted ME 1 524.30 185.50 2.83 <0.01 Fitted ME 26 37.00 11.20 3.30 <0.01 Fitted ME 436 -33.55 17.31 -1.94 0.05 Fitted ME 435 -520.20 683.70 -0.76 0.45 Fitted ME 6 21.17 6.65 3.18 <0.01
NULL
Intercept 0.46 0.58 0.80 0.42 Fitted ME 139 -129.66 79.59 -1.63 0.10 Fitted ME 23 125.61 154.95 0.81 0.42 Fitted ME 73 32.75 6.45 5.08 <0.01 Fitted ME 78 72.30 13.76 5.26 <0.01 Fitted ME 47 -99.48 24.70 -4.03 <0.01 Fitted ME 51 34.32 7.59 4.52 <0.01 Fitted ME 10 49.71 15.39 3.23 <0.01 Fitted ME 14 -58.85 20.55 -2.86 <0.01 Fitted ME 33 47.19 15.19 3.11 <0.01 Fitted ME 98 28.13 5.25 5.36 <0.01 Fitted ME 3 637.02 317.37 2.01 0.04 Fitted ME 46 -22.24 9.84 -2.26 0.02 Fitted ME 141 -68.34 45.02 -1.52 0.13 Fitted ME 1 513.66 180.11 2.85 <0.01 Fitted ME 17 36.78 11.05 3.33 <0.01 Fitted ME 436 -32.01 16.53 -1.94 0.05 Fitted ME 435 -401.26 444.52 -0.90 0.37 Fitted ME 6 21.09 6.57 3.21 <0.01
ME=Moran’s Eigenvectors
Appendix C: Microcebus ravelobensis Occurrence Responses to Amount of Habitat within 10 Landscape Scales.
Model Scale (ha) Variable Coefficient
Value Standard
Error Z-Value P-value
0.25
Intercept -1.69 0.57 -2.98 <0.01 Amount of Forest 7.23E-04 2.67E-04 2.71 0.01
Fitted ME 24 41.11 8.00 5.14 <0.01 Fitted ME 49 32.79 7.73 4.24 <0.01 Fitted ME 85 36.27 11.39 3.19 <0.01 Fitted ME 127 -56.99 15.35 -3.71 <0.01 Fitted ME 80 -27.05 6.61 -4.09 <0.01 Fitted ME 15 51.43 11.63 4.42 <0.01
137
Fitted ME 104 31.85 16.13 1.98 0.05 Fitted ME 81 28.69 8.62 3.33 <0.01 Fitted ME 105 15.31 6.94 2.21 0.03 Fitted ME 130 -56.46 15.53 -3.64 <0.01 Fitted ME 133 29.47 7.61 3.87 <0.01 Fitted ME 2 -335.50 16690.00 -0.02 0.98 Fitted ME 78 -24.05 7.06 -3.41 <0.01 Fitted ME 140 19.94 6.81 2.93 <0.01 Fitted ME 136 -14.37 8.96 -1.60 0.11 Fitted ME 138 14.88 4.12 3.61 <0.01 Fitted ME 120 18.01 14.26 1.26 0.21 Fitted ME 40 -12.99 5.67 -2.29 0.02 Fitted ME 116 19.51 10.61 1.84 0.07 Fitted ME 92 -31.42 11.39 -2.76 0.01 Fitted ME 17 5.02 3.55 1.41 0.16
0.5
Intercept -1.37 0.51 -2.70 0.01 Amount of Forest 2.97E-04 1.25E-04 2.37 0.02
Fitted ME 24 41.18 7.97 5.17 <0.01 Fitted ME 49 32.44 7.70 4.22 <0.01 Fitted ME 80 -27.28 6.64 -4.11 <0.01 Fitted ME 85 36.62 11.29 3.24 <0.01 Fitted ME 127 -56.86 15.28 -3.72 <0.01 Fitted ME 15 51.09 11.62 4.40 <0.01 Fitted ME 104 32.32 16.31 1.98 0.05 Fitted ME 105 15.35 6.95 2.21 0.03 Fitted ME 81 28.39 8.47 3.35 <0.01 Fitted ME 130 -56.78 15.44 -3.68 <0.01 Fitted ME 133 29.58 7.62 3.88 <0.01 Fitted ME 2 -333.10 16700.00 -0.02 0.98 Fitted ME 78 -24.29 7.03 -3.46 <0.01 Fitted ME 140 19.99 6.71 2.98 <0.01 Fitted ME 136 -14.19 8.86 -1.60 0.11 Fitted ME 138 15.08 4.12 3.66 <0.01 Fitted ME 120 18.21 14.44 1.26 0.21 Fitted ME 40 -13.00 5.65 -2.30 0.02 Fitted ME 116 19.04 10.22 1.86 0.06 Fitted ME 92 -31.78 11.22 -2.83 <0.01 Fitted ME 17 4.78 3.46 1.38 0.17
1
Intercept -1.34 0.45 -2.98 <0.01 Amount of Forest 1.29E-04 5.91E-05 2.18 0.03
Fitted ME 24 41.52 7.95 5.22 <0.01 Fitted ME 49 26.50 6.97 3.80 <0.01 Fitted ME 80 -21.17 4.49 -4.71 <0.01 Fitted ME 85 27.44 8.49 3.23 <0.01 Fitted ME 127 -66.78 17.02 -3.92 <0.01 Fitted ME 15 55.47 13.91 3.99 <0.01 Fitted ME 104 10.65 3.61 2.95 <0.01
138
Fitted ME 130 -66.40 20.69 -3.21 <0.01 Fitted ME 133 37.55 8.95 4.19 <0.01 Fitted ME 81 22.45 6.78 3.31 <0.01 Fitted ME 2 -333.80 16520.00 -0.02 0.98
Fitted ME 116 11.14 5.38 2.07 0.04 Fitted ME 78 -18.92 4.91 -3.85 <0.01 Fitted ME 140 18.84 5.24 3.60 <0.01 Fitted ME 134 -23.29 10.99 -2.12 0.03 Fitted ME 138 14.64 4.15 3.53 <0.01 Fitted ME 40 -15.24 7.93 -1.92 0.05 Fitted ME 129 -23.03 11.11 -2.07 0.04 Fitted ME 41 35.09 26.64 1.32 0.19 Fitted ME 203 9.74 6.44 1.51 0.13 Fitted ME 17 4.68 3.39 1.38 0.17 Fitted ME 75 -10.15 3.89 -2.61 0.01
2
Intercept -0.82 0.40 -2.07 0.04 Amount of Forest 3.25E-05 2.72E-05 1.20 0.23
Fitted ME 24 39.71 7.61 5.22 0.00 Fitted ME 49 30.07 7.32 4.11 <0.01 Fitted ME 80 -21.66 4.53 -4.78 <0.01 Fitted ME 85 28.99 8.57 3.38 <0.01 Fitted ME 15 52.13 12.31 4.24 <0.01 Fitted ME 127 -113.80 42.97 -2.65 0.01 Fitted ME 104 45.96 23.73 1.94 0.05 Fitted ME 130 -50.65 15.42 -3.28 <0.01 Fitted ME 133 36.16 10.07 3.59 <0.01 Fitted ME 81 23.32 6.68 3.49 <0.01 Fitted ME 2 -330.00 16440.00 -0.02 0.98
Fitted ME 116 27.52 12.28 2.24 0.03 Fitted ME 78 -19.42 4.93 -3.94 <0.01 Fitted ME 140 30.39 9.08 3.35 <0.01 Fitted ME 134 -13.28 8.92 -1.49 0.14 Fitted ME 138 25.71 7.65 3.36 <0.01 Fitted ME 40 -11.80 5.40 -2.19 0.03 Fitted ME 129 -35.12 15.37 -2.29 0.02 Fitted ME 75 -10.09 3.74 -2.70 0.01 Fitted ME 105 20.10 11.19 1.80 0.07 Fitted ME 203 9.70 6.50 1.49 0.14 Fitted ME 148 19.40 7.03 2.76 0.01
4
Intercept -0.76 0.36 -2.10 0.04 Amount of Forest 1.52E-05 1.32E-05 1.15 0.25
Fitted ME 24 39.91 7.62 5.24 <0.01 Fitted ME 49 30.00 7.32 4.10 <0.01 Fitted ME 80 -21.69 4.55 -4.77 <0.01 Fitted ME 85 29.08 8.54 3.41 <0.01 Fitted ME 15 52.18 12.32 4.24 <0.01 Fitted ME 127 -113.20 42.73 -2.65 0.01
139
Fitted ME 104 46.31 23.68 1.96 0.05 Fitted ME 130 -50.51 15.45 -3.27 <0.01 Fitted ME 133 36.12 10.05 3.60 <0.01 Fitted ME 81 23.29 6.66 3.50 <0.01 Fitted ME 2 -330.20 16430.00 -0.02 0.98
Fitted ME 116 27.41 12.25 2.24 0.03 Fitted ME 78 -19.38 4.93 -3.93 <0.01 Fitted ME 140 30.28 9.06 3.34 <0.01 Fitted ME 134 -13.16 8.92 -1.48 0.14 Fitted ME 138 25.66 7.64 3.36 <0.01 Fitted ME 40 -11.67 5.40 -2.16 0.03 Fitted ME 129 -34.86 15.34 -2.27 0.02 Fitted ME 75 -10.15 3.73 -2.72 0.01 Fitted ME 105 20.22 11.14 1.82 0.07 Fitted ME 203 9.72 6.54 1.49 0.14 Fitted ME 148 19.37 7.01 2.77 0.01
8
Intercept -0.74 0.33 -2.23 0.03 Amount of Forest 8.94E-06 6.82E-06 1.31 0.19
Fitted ME 24 40.10 7.65 5.24 <0.01 Fitted ME 49 30.32 7.37 4.12 <0.01 Fitted ME 80 -21.71 4.56 -4.77 <0.01 Fitted ME 85 29.38 8.53 3.44 <0.01 Fitted ME 127 -58.47 15.50 -3.77 <0.01 Fitted ME 15 51.83 12.22 4.24 <0.01 Fitted ME 104 33.93 17.84 1.90 0.06 Fitted ME 105 15.92 7.44 2.14 0.03 Fitted ME 130 -58.14 15.69 -3.71 <0.01 Fitted ME 133 30.72 7.93 3.87 <0.01 Fitted ME 81 23.25 6.63 3.51 <0.01 Fitted ME 2 -331.40 16530.00 -0.02 0.98 Fitted ME 78 -19.32 4.92 -3.93 <0.01 Fitted ME 140 20.47 6.56 3.12 <0.01 Fitted ME 136 -14.44 9.09 -1.59 0.11 Fitted ME 138 15.43 4.24 3.64 <0.01 Fitted ME 120 17.28 15.36 1.13 0.26 Fitted ME 40 -11.63 5.40 -2.15 0.03 Fitted ME 116 18.87 10.45 1.81 0.07 Fitted ME 75 -10.22 3.73 -2.74 0.01 Fitted ME 203 9.71 6.51 1.49 0.14
16
Intercept -0.70 0.32 -2.20 0.03 Amount of Forest 4.66E-06 3.78E-06 1.23 0.22
Fitted ME 24 40.15 7.66 5.24 <0.01 Fitted ME 49 30.41 7.37 4.12 <0.01 Fitted ME 80 -21.68 4.58 -4.73 <0.01 Fitted ME 127 -58.66 15.47 -3.79 <0.01 Fitted ME 85 29.47 8.46 3.48 <0.01 Fitted ME 15 52.10 12.27 4.25 <0.01
140
Fitted ME 104 34.08 17.73 1.92 0.05 Fitted ME 105 16.03 7.43 2.16 0.03 Fitted ME 2 -331.60 16530.00 -0.02 0.98
Fitted ME 130 -58.41 15.69 -3.72 <0.01 Fitted ME 133 30.79 7.93 3.88 <0.01 Fitted ME 81 23.25 6.57 3.54 <0.01 Fitted ME 78 -19.35 4.93 -3.92 <0.01 Fitted ME 140 20.52 6.54 3.14 <0.01 Fitted ME 136 -14.47 9.10 -1.59 0.11 Fitted ME 138 15.33 4.24 3.62 <0.01 Fitted ME 120 16.89 15.04 1.12 0.26 Fitted ME 40 -11.63 5.41 -2.15 0.03 Fitted ME 116 18.63 10.36 1.80 0.07 Fitted ME 75 -10.22 3.72 -2.75 0.01 Fitted ME 203 9.70 6.52 1.49 0.14
32
Intercept -0.57 0.31 -1.83 0.07 Amount of Forest 1.47E-06 2.24E-06 0.66 0.51
Fitted ME 24 40.21 7.63 5.27 <0.01 Fitted ME 49 30.11 7.32 4.11 <0.01 Fitted ME 85 29.47 8.41 3.51 <0.01 Fitted ME 127 -113.30 42.11 -2.69 0.01 Fitted ME 80 -21.77 4.61 -4.72 <0.01 Fitted ME 15 53.15 12.48 4.26 <0.01 Fitted ME 130 -50.82 15.65 -3.25 <0.01 Fitted ME 133 36.23 10.08 3.60 <0.01 Fitted ME 81 23.55 6.55 3.60 <0.01 Fitted ME 2 -330.40 16420.00 -0.02 0.98
Fitted ME 140 30.51 9.06 3.37 <0.01 Fitted ME 134 -12.88 8.63 -1.49 0.14 Fitted ME 78 -19.64 4.99 -3.94 <0.01 Fitted ME 116 27.39 12.09 2.27 0.02 Fitted ME 104 46.45 23.32 1.99 0.05 Fitted ME 138 25.55 7.59 3.37 <0.01 Fitted ME 129 -11.80 5.44 -2.17 0.03 Fitted ME 75 -34.96 15.26 -2.29 0.02 Fitted ME 105 -10.17 3.71 -2.74 0.01 Fitted ME 203 20.44 10.96 1.87 0.06 Fitted ME 148 9.70 6.58 1.48 0.14
64
Intercept -0.35 0.33 -1.09 0.28 Amount of Forest 3.60E-07 1.42E-06 0.25 0.80
Fitted ME 24 40.10 7.60 5.27 <0.01 Fitted ME 49 30.68 7.43 4.13 <0.01 Fitted ME 127 -3662.00 218800.00 -0.02 0.99 Fitted ME 85 30.63 8.64 3.54 <0.01 Fitted ME 130 -1656.00 106800.00 -0.02 0.99 Fitted ME 133 1242.00 72310.00 0.02 0.99 Fitted ME 80 -22.24 4.65 -4.78 <0.01
141
Fitted ME 15 50.02 11.32 4.42 <0.01 Fitted ME 81 24.29 6.63 3.66 <0.01 Fitted ME 2 -436.40 199500.00 0.00 1.00
Fitted ME 140 923.40 55900.00 0.02 0.99 Fitted ME 134 36.19 27100.00 0.00 1.00 Fitted ME 78 -20.11 5.01 -4.01 <0.01 Fitted ME 116 576.70 55830.00 0.01 0.99 Fitted ME 104 338.80 22620.00 0.02 0.99 Fitted ME 138 652.00 40000.00 0.02 0.99 Fitted ME 40 -11.73 5.31 -2.21 0.03 Fitted ME 129 -1258.00 78120.00 -0.02 0.99 Fitted ME 75 -10.21 3.68 -2.77 0.01 Fitted ME 148 161.00 47980.00 0.00 1.00 Fitted ME 252 641.80 38080.00 0.02 0.99
128
Intercept -0.60 0.35 -1.72 0.09 Amount of Forest 5.91E-07 9.03E-07 0.66 0.51
Fitted ME 24 40.59 7.65 5.31 <0.01 Fitted ME 49 30.08 7.27 4.14 <0.01 Fitted ME 127 -113.80 41.93 -2.71 0.01 Fitted ME 80 -21.77 4.60 -4.74 <0.01 Fitted ME 130 -50.99 15.61 -3.27 <0.01 Fitted ME 133 36.40 10.09 3.61 <0.01 Fitted ME 85 29.66 8.42 3.52 <0.01 Fitted ME 15 53.44 12.52 4.27 <0.01 Fitted ME 81 23.58 6.51 3.62 <0.01 Fitted ME 2 -330.10 16300.00 -0.02 0.98
Fitted ME 140 30.71 9.07 3.39 <0.01 Fitted ME 134 -12.87 8.50 -1.51 0.13 Fitted ME 78 -19.85 4.94 -4.02 0.00 Fitted ME 116 27.18 12.09 2.25 0.02 Fitted ME 104 46.39 23.29 1.99 0.05 Fitted ME 138 25.63 7.58 3.38 0.00 Fitted ME 40 -11.83 5.46 -2.17 0.03 Fitted ME 129 -35.31 15.22 -2.32 0.02 Fitted ME 75 -10.25 3.72 -2.75 0.01 Fitted ME 105 20.51 10.94 1.88 0.06 Fitted ME 203 9.84 6.73 1.46 0.14 Fitted ME 148 19.41 6.93 2.80 0.01
NULL
Intercept -0.29 0.21 -1.40 0.16 Fitted ME 24 40.10 7.59 5.28 <0.01 Fitted ME 80 -22.34 4.64 -4.81 <0.01 Fitted ME 127 -3665.00 218600.00 -0.02 0.99 Fitted ME 85 30.67 8.67 3.54 <0.01 Fitted ME 15 50.18 11.32 4.43 <0.01 Fitted ME 49 30.62 7.44 4.12 <0.01 Fitted ME 130 -1658.00 106800.00 -0.02 0.99 Fitted ME 133 1243.00 72290.00 0.02 0.99
142
Fitted ME 81 24.49 6.61 3.70 <0.01 Fitted ME 2 -435.30 198800.00 -2.00E-03 1.00
Fitted ME 140 924.20 55840.00 0.02 0.99 Fitted ME 134 36.64 26980.00 1.00E-03 1.00 Fitted ME 78 -20.26 5.01 -4.05 <0.01 Fitted ME 116 577.30 56080.00 0.01 0.99 Fitted ME 104 339.30 22940.00 0.02 0.99 Fitted ME 138 652.50 39920.00 0.02 0.99 Fitted ME 40 -11.83 5.30 -2.23 0.03 Fitted ME 129 -1258.00 78060.00 -0.02 0.99 Fitted ME 75 -10.18 3.68 -2.77 0.01 Fitted ME 105 161.40 48150.00 3.00E-03 1.00 Fitted ME 148 642.30 38030.00 0.02 0.99 Fitted ME 152 -832.10 48110.00 -0.02 0.99
ME=Moran’s Eigenvector