evolution of shape morphologic variation of the genus undaria
TRANSCRIPT
University of IowaIowa Research Online
Theses and Dissertations
Spring 2010
Evolution Of shape morphologic variation of thegenus Undaria (Scleractinia, Agariciidae)Kristopher J S RhodesUniversity of Iowa
Copyright 2010 Kristopher J S Rhodes
This thesis is available at Iowa Research Online: https://ir.uiowa.edu/etd/586
Follow this and additional works at: https://ir.uiowa.edu/etd
Part of the Geology Commons
Recommended CitationRhodes, Kristopher J S. "Evolution Of shape morphologic variation of the genus Undaria (Scleractinia, Agariciidae)." MS (Master ofScience) thesis, University of Iowa, 2010.https://doi.org/10.17077/etd.iluilxla
EVOLUTION OF SHAPE
MORPHOLOGIC VARIATION OF THE GENUS UNDARIA
(SCLERACTINIA: AGARICIIDAE)
by
Kristopher J. S. Rhodes
A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Geosciences in the Graduate College of The University of Iowa
May 2010
Thesis Supervisor: Professor Ann F. Budd
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
____________________________
MASTER’S THESIS
_________________
This is to certify that the Master’s thesis of
Kristopher J S Rhodes
has been approved by the Examining Committee for the thesis requirement for the Master
of Science degree in Geoscience at the May 2010 graduation.
Thesis Committee: Ann Budd, Thesis Supervisor Hallie Sims Gene Hunt
ii
To AJ
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ACKNOWLEDGMENTS
Thanks to the University of Iowa Dept. of Geoscience for funding, as well as Nancy
Budd, Hallie Sims, Gene Hunt, Jim Klaus, Don McNeill, Tiffany Adrain and the SUI
Paleontology Repository, and Tom Stemann. Much gratitude is also due to Abby
Michaelson, and all of my fellow graduate students, for providing insight, encouragement
and all others kinds of necessary assistance.
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TABLE OF CONTENTS
LIST OF TABLES -------------------------------------------------------------------------- v
LIST OF FIGURES ------------------------------------------------------------------------- vi
CHAPTER I INTRODUCTION AND BACKGROUND ------------------------ 1
Coral morphology and plasticity --------------------------------------------------------------- 1
The family Agariciidae and the genus Undaria ----------------------------------------------- 3
Change through time: Stasis and gradualism ------------------------------------------------- 4
Previous Work ------------------------------------------------------------------------------------ 6
Location and Geologic Setting ----------------------------------------------------------------- 6
Geometric Morphometrics --------------------------------------------------------------------- 8
CHAPTER II MATERIALS AND METHODS ------------------------------------ 11
Specimens used in this study ----------------------------------------------------------------- 11
Geometric morphometric analysis ----------------------------------------------------------- 12
CHAPTER III RESULTS ----------------------------------------------------------------- 26
Canonical variates analysis -------------------------------------------------------------------- 26
Principal components analysis --------------------------------------------------------------- 26
Evolutionary model fits ----------------------------------------------------------------------- 27
Regression --------------------------------------------------------------------------------------- 28
Partial least squares----------------------------------------------------------------------------- 28
CHAPTER IV DISCUSSION ----------------------------------------------------------- 39
CHAPTER V CONCLUSIONS -------------------------------------------------------- 41
REFERENCES ------------------------------------------------------------------------ 43
v
LIST OF TABLES
Table 1 Specimens used 18 Table 2 Landmarks used for this project. 21 Table 3 CVA cross validation results. 29 Table 4 Aikiele information criteria for three models of evolution for shape and size
variables. 37
vi
LIST OF FIGURES
Figure 1 Geologic setting and locality data. 9 Figure 2 Interpreted local sea level and time for Cibao valley deposition. 10 Figure 3 Landmark diagram. 22 Figure 4 Growth vectors associated with width by species 23 Figure 5 Deformation along the first principal component. 30 Figure 6 Deformation along the second principal component. 31 Figure 7 Deformation along the third principal component. 32 Figure 8 Deformation along the fourth principal component. 33 Figure 9 Time series of shape and size variables by species. 34 Figure 10 Regression of Undaria crassa shape vs. environmental variables. 38
1
CHAPTER I INTRODUCTION AND BACKGROUND
Great advances have been made in understanding the patterns of morphologic
change observed in the fossil record in the past 40 years. Starting with Eldredge and Gould’s
(1972) punctuated equilibrium, a number of increasingly appropriate and informative models
have been used to explain patterns of stasis and change in the fossil record (Bookstein 1987,
Roopnarine, Byars, and Fitzgerald 1999, Hunt 2006, Estes and Arnold 2007). It has
increasingly been recognized that different patterns are active over geologic time including
random walks, directional change, and stasis. What must now be focused on is applying
these methods across disparate taxa, in the hopes of understanding when these disparate
modes of evolution are active and what controls them.
Scleractinian corals are an important group today, serving literally as the framework
for reefs worldwide. These reefs provide habitat for a complex ecosystem with great value of
mankind, serving as a source of food, protection, and recreation. However, these reefs are
under threat today (Hoegh-Guldberg et al. 2007, Carpentor et al. 2008). While it is clear that
environmental stresses are wreaking havoc on coral populations in the short term, it is less
clear how corals will respond to a changing environment over geologic time. The genus
Undaria (Scleractinia: Agariciidae) is a common fossil coral in the Neogene of the Caribbean.
This study will focus on three species of Undaria from the Yaque group of the Dominican
Republic, to understand how shape at the corallite level has changed through time.
Coral morphology and plasticity
Most scleractinian corals are colonial animals that, in adult form, produce skeletal
hard parts by depositing layers of aragonite, with growth of structure controlled by
deposition of organic phases (Stolarski 2003). Individual polyps inhabit cuplike depressions
2
called calices, on the growing end of the individual corallites. The size and shapes of
corallites differ among coral lineages, and very little is known about relative advantages of
functionality of the different shapes observed. Corals have been shown to show a great deal
of ecophenotypic plasticity. These differences range in scope from differences in growth
form, polyp and corallite morphology and robustness of corallum, and as responses to
factors including light, depth, water movement, contact with surface, intraspecific
interactions, genetic differences, gravity, and initial arrangement (Todd 2008).
Several studies have looked at these responses within the Agariciidae; in three
studies, Pavona cactus was ―found to be phenotypically stable‖ (Willis 1985), ―show perfect
association between of growth form with genotype‖ (Willis and Ayre 1985), or only very
limited plasticity when grouped into corallum-level growth forms, with most genotypes only
being represented by a single growth form (Ayre and Willis 1988). Vaughan (1911) found
differences in growth form when Agaricia fragilis was transplanted to a tile, but the artificiality
of this condition limits applicability to this study. These studies suggest that agariciids are
more phenotypically stable then most scleractinians, and thus a study such as this one has
less chance of falling victim to plastic effects instead of actual evolutionary changes.
Helmuth and Sebens (1993) proposed that differences in corallite level shape in
Agaricia agaricites reflected adaption to maximize particle consumption in different flow
orientations. They noted two general growth forms; upright/bifacial and flat/unifacial.
Comparing the growth patterns of several hundred in situ colonies, they found that
upright/bifacial colonies were consistently oriented perpendicular to the dominant flow
regime. Fluid modeling and live experiments showed that this orientation enhanced particle
capture ability, while there was no effect for orientation related to flat/unifacial colonies.
Helmuth and Sebens (1993) also reported a difference in corallite-scale morphology
3
associated with these two colony types. On bifacial colonies, the ridges that separate serials
of corallites were oriented upwards, while unifacial colonies possessed ridges that were
normal relative to the colony surface. Wave tank experiments showed that these traits were
adaptive to optimize particle capture associated with the different flow regimes found in
nature.
The family Agariciidae and the genus Undaria
Members of the family Agariciidae (Anthozoa: Scleractinia) are common reef
building corals in both the Pacific and Caribbean, with twelve genera defined in the most
recent Treatise on Invertebrate Paleontology (Wells 1956), of which seven genera are extant. Of
these seven extant genera, three are found in the Caribbean, represented by nine species
(Cairns, Hoekesma and van der Land 1999). Modern agariciids are colonial, with foliaceous
and encrusting growth forms most common. Agariciids tend to grow by concentric accretion
of corallites on the outer margin of the colony, and by intracalicular budding of mature
corallites along serial rows. This growth pattern, along with lacking coenosteum that
separates corallite serials, results in an unusual set of symmetries for a coral; much of the
hexagonal symmetry associated with septal insertion is lost at the corallite level, and a
secondary rectangular shape defines the corallites. Some species of Pacific Leptoseris break
from this pattern, with very few corallites and most of their skeleton consisting of
coenosteum; species exhibiting this trait would not be measureable with the landmark
scheme used in this study. There is further asymmetry in most agariciids, as the colline is
regularly inclined towards the corallum edge, resulting in asymmetries between the arms of
the ridges. Thus, to quantify morphological variation, the entire corallite structure must be
4
analyzed. Studies on other Scleractinia have used landmark schemes consisting of two
primary septa and those minor septa between them (Nehm and Budd 2008).
Scleractinian systematic have undergone a revolution since the onset of molecular
phylogenetic methods (Fukami et al. 2004, Fukami et al. 2008). Molecular studies have
consistently shown that many morphologically defined genera and families are not
monophyletic and do not reflect evolutionary history. Molecular analysis of the family
Agariciidae, however, has vindicated traditional taxonomy at the family level, with most
agariciids clustering together (Fukami 2008). This suggests that traditional taxonomy has
been largely successful for understanding the relationships of the agariciids.
This study focuses on three species of Undaria: Undaria agaricites, Linnaeus (1758),
Undaria crassa, Verrill (1901), and Undaria sp. A, (Stemann, 1991). While their corallite shapes
are generally similar, they are differentiable based on simple criteria. U. agaricites and U. crassa
are distinguished by length of the corallite series and level of organization. U. agaricites is
characterized by long, neat series, generally over 2cm, while U. crassa typically has short
series, under 2cm, and poor organization with many corallites effectively not in a series. U.
sp. has long, well organized series, but contrasts from the other two species by having larger
corallites and much shorter ridges between series.
Change through time: Stasis and gradualism
Eldredge and Gould’s (1972) theory of punctuated equilibrium has been extensively
tested, and is generally supported with stasis appearing to be a common pattern over
macroevolutionary time (Gould 2002, Hunt 2007). Cases fitting the pattern of gradualism
have also been discovered, possibly more commonly associated with gradually changing
ecosystems or environments instead of large, sudden disturbances (Gould 2002: ch. 9,
5
Sheldon 1996). More complex models have become increasingly popular to explain observed
evolutionary patterns of stasis and change (Hunt 2006, Estes and Arnold 2007). Estes and
Arnold demonstrated that a model that incorporated selection towards a displaced optimum
was able to explain most of the observed data and offered it as a general model for stasis.
While it remains unclear what microevolutionary or ecological processes drive the pattern of
stasis, collecting more data in interesting systems will be necessary to understand that
pattern. This research focuses on a time period of environmental change in an area with well
studied tectonic and biologic histories. The Caribbean provides several gradually changing
environmental factors that could drive a directional evolution in morphology, and thus
serves as a model system to test what would drive stasis and other evolutionary patterns.
The increased focus on using explanatory models has helped explain observed
evolutionary patterns. For example, Bell, Travis and Blouw (2006) used existing statistical
techniques to test for directional evolution in a 21.5ka sequence of three-spine stickleback.
The authors took advantage of excellent temporal precision and large samples sizes due to
annual laminations in the lacustrine deposits their samples came from. Because a great deal is
known about the genetic basis of morphology in the stickleback, as well as the ecological
cause of the observed directional change, they were confident that directional selection did
occur within their sample. However, the statistical methods they used were unable to reject
the null hypothesis of a random walk. Reanalyzing this data using a model comparison
method showed that selection was a far better model then that of the random walk (Hunt,
Bell and Travis 2008). While the temporal and stratigraphic resolution available in the area of
the present study is not as good as that seen by Bell et al., it is clear that model fitting
techniques will provide a great advantage in understanding the evolutionary dynamics of
Undaria compared to the methods previously used in this group.
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Previous Work
Stemann (1991) did extensive work on the Agariciidae, revising the taxonomy using
various traditional characters, including series length, calices per series, valley width, calical
spacing, calical diameter, pit diameter, number of septa, number of major septa, number of
septocostae, length and width of largest septum, length and width of septa first/second to
left of largest, length and width of septum opposite the largest, columella length and width,
and size of largest septal bead on largest septum. These characters differentiated
morphotypes within previously designated species, prompting designation of several new
species. Additionally, he tested whether these characters showed any change through time
using a canonical variable analysis (CVA). Canonical variable (CV) 1 was generally defined by
size characters, with CVs two and three defined by combinations of the characters,
indicating differences in ratios and thus generally shape. He compared the means between
formations of individual’s CV scores using Student’s t-test. In the same three species of
Undaria studied here, he found a significant change between formations for mean value of
CV1 and 3, but these differences were small compared to standard deviations and were
based on small samples with significant outliers. For most CV’s across most of the taxa
studied, he was unable to reject the null hypothesis of no change through time.
Location and Geologic Setting This study focuses on fossil corals from a single region; the Yague Group,
Dominican Republic. The Yague Group provides an ideal system to address the objectives
of this study, consisting of approximately 3 million years (6.43-3.4 ma) of marine siltstones
and sandstones with abundant fossils. Since uplift 3mya, a series of modern subparallel rivers
have incised channels through the uplifted Neogene marine sediments, allowing dense
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sampling both laterally and temporally (figure 1). Fossil material is typically unaltered
aragonite with little weathering, and the relatively unconsolidated nature of the sediments
allows collection of even delicate material in near original condition (Lutz et al. 2008, Nehm
and Budd 2008).
During the Neogene, the Central American Seaway was closing, leading to profound
changes in local ocean conditions (Lutz et al. 2008), including major shifts in current regimes
and a gradual increase in salinity. Additionally, the island of Hispaniola was uplifted, resulting
in a regional reduction in ocean depth (figure 2). This geologic backdrop resulted in
changing environmental conditions similar to those that have been documented as evoking
specific morphological responses in modern corals.
An existing age-depth curve (McNeill et al. 2010, in press) uses biostratigraphy,
paleomagnetic data, and strontium-isotope ages to interpolate ages at each stratum within
the Yague group. Various efforts have been undertaken to record environmental variation
through this period. Jain and Collins (2004) estimated many characteristics of the paleo-
Caribbean sea using microfossils, including paleoproductivity, current velocity, oligotrophy,
dissolution, and ventilation over a time period from 8.3ma to 2.5 ma. Each environmental
condition was calculated from known proxies determined by ecological characteristics of
various foraminifera species, isotopes, or sedimentological characteristics. The result was a
temporal record of traits through the time period that this study focuses on, allowing for
correlation between these traits and corallite shapes in various species of the Agariciidae.
McNeill et al.(2008, 2010, in press) have established a local sea level model for the Cibao
basin, based on sedimentary structures and foraminifera data. This curve is the best data
available for local conditions through the section under study. Lutz et al. 2008 examined
local conditions through the Yaque group using relative abundance data from various species
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of foraminifera. These provide local proxies of sea surface temperature, salinity, and
paleoproductivity, and thus will be used as environmental proxies for this study.
Geometric Morphometrics
Three dimensional geometric morphometrics (GM), which were used in this study,
present several advantages to the traditional linear distance techniques using by Stemann
(1991). They allow for more information to be collected from the same specimens, as each
landmark is comprised of three data, the {X,Y,Z} coordinates, while a distance must be
computed from two sets of {X,Y,Z} coordinates, yielding only a single datum. They also
allow for analysis of shape. In addition, specimens too small or fragmentary to be included in
the traditional data set can be analyzed with GM as long as a single corallite is whole. GM
methods also allow more detailed analysis of shape, which we compare to the results of
Stemann (1991).
9
Figure 1- Geologic setting and locality data. Image from McNeill et al., 2008. The specimens
from this study are from the Rio Cana and Rio Gurabo valleys (Table 1).
10
Figure 2- Interpreted local sea level and time for Cibao valley deposition. From McNeill et
al. 2008.
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CHAPTER II MATERIALS AND METHODS
Specimens used in this study
Neogene fossil specimens of Undaria from the Yaque group, Dominican Republic
housed at the University of Iowa Paleontology Repository (SUI) were examined for
suitability. I rejected specimens for use in this study that showed taphonomic alteration that
prevented accurate measurement of corallite shape. Shape was most commonly altered by
abrasion and rounding, usually of the ridge structures, while accurate measurement was often
obscured by encrusting organisms and diagenetic cementation of sediment within the calice.
Some specimens were prepared using a toothbrush, dental pick, and ultrasonic scrubber to
clear the calice of sediment, but these procedures were not effective in preparing all corallites
for measurement. Only mature corallites from the center of meandroid series were selected
for measurement. Additionally, corallites whose shapes were influenced by extraneous
factors such as inserted corallites or rows were not measured.
The method for selecting specific corallites was dependent on how many corallites
were of acceptable quality on each colony. When only a few corallites met the above criteria,
they were all measured. When more than five were adequate, at attempt was made to not
measure adjacent corallites. When many corallites were adequate, corallites along a transect
perpendicular to the ridges were inspected, with a maximum of only one corallite from each
serial included. This provides for greater independence, since the shapes of corallites within
a given serial tend to be similar.
A total of 281 corallites on 74 individuals were measured (Table 1). I measured five
individuals of U. crassa and 19 individuals of U. agaricites from the Mao formation, ranging
from approximately 6.7 mya to 6.2 mya. I measured 11 individuals of U. crassa, 17 individuals
of U. agaricites, and 17 individuals of U. sp. from the Gurabo formation, spanning from 5.8
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mya to 4.2 mya. I used one colony of U. crassa and four U. sp from the Mao formation,
representing approximately 3.9 mya to 3.4 mya. A. For each specimen, an approximate age
date was calculated using the age depth curve of McNeill et al (2010, in press). This was also
used to calculate a rate of sedimentation through time for each basin for later analysis against
shape variables.
Geometric morphometric analysis
In order to analyze corallite shape, 3D geometric morphometrics techniques were
used. Previous studies using geometric morphometrics techniques in corals have utilized a
group of landmarks incorporating a 1/6th slice wedge of the corallite, encompassing adjacent
primary septa and associated minor septa. In the agariciids, corallites have a secondary
bilateral symmetry associated with their meandroid rows and ridges between them.
Accordingly, a new landmark scheme had to be developed. Various methods were explored.
These methods focused on integrating landmarks along primary septa as well as points
defined by the intersections of septa and ridges. Additionally, sliding semi-landmarks were
examined as a potential method to define the variation along septal ridges. The use of sliding
semi-landmarks seems ideal for measuring this feature, but visual inspection of test data with
and without the semi-landmarks showed that they added little power to analysis while adding
both time to measurement and a decrease in the number of specimens that could be utilized.
Additionally, it was decided to limit measurements to ½ of each corallite in order to increase
the number of measureable specimens. The final landmark scheme consists of 13 landmarks
(table 2, figure 3).
Landmarks were measured using a 3d reflex microscope. Data analysis was
undertaken using Microsoft Excel, SAS, R, and the Integrated Morphometrics Package
(IMP) (Zelditch et al. 2004). Analysis of geometric morphometric data was undertaken using
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the standard methods of Zelditch et al. (2004). In order to compare shapes between different
specimens, the data must be standardized so that location, scale and rotational effects are
removed. This procedure is known as Procrustes superimposition. The first step in doing
this is to calculate the centroid of each individual, which is the mean location of its landmark
coordinates. All individuals are then moved so that their centroids are at the same location.
Scaling is implemented by dividing each measurement be centroid size, which is the square
root of the summed squared distances of each landmark from the centroid. All individuals
are then rotated to minimize the summed squared distance between them. Finally, in order
to reduce the number of variables so that the correct degrees of freedom are present, the
coordinates are decomposed into partial warp scores. Partial warp scores contain the same
information as the superimposed coordinates, and can be used directly in statistical analysis.
Shape change associated with ontogenetic growth was removed from each species.
While it is common to use centroid size as the metric for growth, this is inappropriate for
agariciid corals. Growth is tightly constrained perpendicular to the axis of the ridges, while
growth associated with division of a corallite largely occurs in the direction parallel to the
ridges. As such, the width of each corallite was calculated by projecting the average location
of the landmarks on the left margin of the corallite and the columella onto a surface
perpendicular to the corallite surface, then measuring the horizontal distance between those
two points. For each species, the IMP program ThreeDStand6 was used to normalize each
corallite’s shape to that of the median width for that species. The vector of change
associated with growth for the different species was similar, but not identical (figure 4).
These data were then analyzed using canonical variates analysis (CVA), principal
components analysis (PCA), fitting of time-sequence evolutionary models, and partial least
squares (PLS).
14
CVA was used in order to test the morphologic difference between species using
SAS. Analyses were run on all specimens, and for each formation. In cases where a large
proportion of corallites in a specimen were misidentified, the specimen was reexamined
using the criteria of Stemann (1991), and were reassigned if appropriate. The analysis was
then rerun to test the results of that reassignment on other specimen assignment, and the
procedure repeated if necessary. This procedure was also done by formation to see if there
were significant shifts through time in the patterns of differentiation.
Principal components analysis was undertaken with all available specimens using the
IMP program ThreeDPCA6. Data from each of the significant components was examined
to understand the trends through time in various species. Each of the principal components
for each species was then analyzed using the fit3models function of the paleoTS package for
R, which uses Akaike information criterion (AIC) to determine which of three evolutionary
models – stasis, directional evolution (DE) or unbiased random walk (URW) best fits the
time series. The stasis model has two parameters, the phenotype optimum and a variance
term. The trait mean is allowed to vary, but no net change is allowed to accumulate over
time. The DE model also consists of two parameters, the μstep and the σ2step, which
respectively are the mean change in the trait and the variance of that change. This models a
directional trend over time. URW is a special case of the DE model, where the μstep is 0; this
means that the trait mean is allowed to change but has no directional trend through time
(Hunt 2006).
For each model, the AIC is calculated as the log likelihood of the model fit penalized
by the number of parameters (for discussion in this context, see Hunt 2006). Additionally,
there is a correction used for samples with a small size, the AIC corrected (AICc), which is
15
used in this study. For each data set, the model with the lowest AICc is accepted as best
fitting the data.
Differences between species means through time were examined using the student’s
t-test (Sokal and Rohlf 1994). For each species, individuals from a formation were grouped
together and the first four principal components were compared against individuals from the
other formations.
Four environmental proxies were calculated for each specimen using the data of
McNeill et al. (2010, in press) and Lutz et al. (2008). These proxies were deposition rate,
water depth, sea surface temperature/salinity, and upwelling. For each segment of the
time/depth plot in the McNeill et al. paper, the rate of deposition was calculated by dividing
the section’s thickness by the time represented by the sequence. Rates of deposition
estimated this way ranged from 0.13 mm/year to 0.64mm/year. Local depth was inferred
from the age/depth plot in figure 15, and coded into four categories of relative depth, with 1
representing shallow and 4 deep.
From Lutz et al. (2008), relative abundance data for the planktonic foraminifera
Globigerinoides sacculifer and G. ruber were used as proxies for sea surface temperature (SST)
and salinity (SSS). Percent abundance curves of both species were very similar, and the
regression of percent abundance vs. stratigraphic position from their figure 5 was used for
this study. The abundances increase through the section, with the slope increasing between
the 400 and 500 meter mark in the section, indicating increasing SST and SSS. Percent
abundance of another foram, Dentoglobigerina altispira, provides a proxy for just SST which
roughly agrees with the record from the other species except for an excursion at around 500
meters, a section from which no specimens were included in this study. The relationship
between foram abundance and SST is complicated and recent attempts to understand these
16
systems have relied on artificial neural networks calibrated to large localized data sets
(Kucera 2007). However, a first order approximation at a world wide scale of G. ruber
abundance vs. SST does show a linear relationship for the abundances shown here (Kucera
2007, figure 5).
Abundance of G. bulloides (Lutz et al. 2008, figure 6) is positively associated with
primary productivity, and the record shows two basic levels of abundance; a mean of
approximately 3% for the lower part of the section from 150m to 450m, coded as ―1‖ in this
study, and a mean abundance of 1% for the section from 450m to 900m, coded as ―0‖.
The environmental proxies were regressed against the shape variables for each
corallite, by species, to understand the association between shape and environment, using
the IMP program ThreeDRegress6.
Additionally, the proxies were scaled to a maximum of 1 and PLS analysis was
undertaken using the IMP program PLS3D. PLS is similar to regression in that is allows the
investigation of the relationship between two sets of data, but differs in several important
ways. PLS does not assume that one set of variables causes the other; it instead assumes that
some underlying latent attribute causes both sets of data to covary (Zelditch et al. 2004).
In order to minimize the possibility for type 1 error for the regression and PLS tests,
a more conservative sample set was used. Following Procrustes superimposition, the average
shape of a colony’s measured corallites was calculated by averaging the X, Y, and Z
coordinates for each landmark. This step significantly reduces power, but more accurately
represents a sample of population means since we know that all corallites in a colony are
genetically identical. Additionally, in order to test for significance, a permutation test was
used (Zelditch et al. 2004). Permutation tests repeatedly draw a sample, without replacement,
from the data set and then compare the results of this against the test sample. After doing
17
this a number of times, the number of times where the variance explained by the
permutations exceeds the sample is divided by the number of permutations, resulting in a p-
value for exceedance by chance. Permutation tests seem best applicable for this data set due
to the relatively low sample sizes. In this study, 1000 permutations were ample for all tests.
18
Table 1- Specimens used. See methods for explanation.
SUI # Locality Corallites
measured Formation
Estimated
age (mya)
Dep. rate
mm/y
SST SSS
Depth Upwelling
Undaria crassa
63738 NMB15830 2 Mao 3.54 0.64 0.75 1 0
63807 NMB16817 4 Gurabo
5.24 0.21 0.15 3 1
63714 NMB15837 4 Gurabo 5.07 0.13 0.16 2 1
63654 NMB15848 2 Gurabo 5.48 0.30 0.12 4 1
63658 NMB15848 5 Gurabo 5.48 0.30 0.12 4 1
63655 NMB15848 5 Gurabo 5.48 0.30 0.12 4 1
63656 NMB15848 4 Gurabo 5.48 0.30 0.12 4 1
63591 NMB15850 3 Gurabo 5.49 0.30 0.12 4 1
63590 NMB15850 5 Gurabo 5.49 0.30 0.12 4 1
63589 NMB15850 5 Gurabo 5.49 0.30 0.12 4 1
63695 NMB15862 5 Gurabo 5.58 0.42 0.11 4 1
63694 NMB15862 3 Gurabo 5.58 0.42 0.11 4 1
107158 KB05-05 3 Cercado
6.27 0.47 0.04 4 1
105136 Evans-S4 5 Cercado
6.27 0.47 0.04 4 1
105099 Evans-S8/9 2 Cercado
6.27 0.47 0.04 4 1
107159 Evans-S11 5 Cercado
6.28 0.47 0.04 4 1
107160 Evans-S11 1 Cercado
6.28 0.47 0.04 4 1
Undaria agaricites
105183 NMB16859 2 Gurabo 5.18 0.21 0.15 2 1
63809 NMB16817 3 Gurabo 5.24 0.21 0.15 3 1
63651 NMB15848 5 Gurabo 5.48 0.30 0.12 4 1
63653 NMB15848 3 Gurabo 5.48 0.30 0.12 4 1
63652 NMB15848 5 Gurabo 5.48 0.30 0.12 4 1
63689 NMB15848 5 Gurabo 5.48 0.30 0.12 4 1
63617 NMB15862 5 Gurabo 5.58 0.42 0.11 4 1
63788 NMB15893 3 Gurabo 5.80 0.42 0.09 4 1
63789 NMB15893 5 Gurabo 5.80 0.42 0.09 4 1
63787 NMB15893 4 Gurabo 5.80 0.42 0.09 4 1
63783(1) NMB15893 5 Gurabo 5.80 0.42 0.09 4 1
63783(2) NMB15893 4 Gurabo 5.80 0.42 0.09 4 1
63784 NMB15893 5 Gurabo 5.80 0.42 0.09 4 1
63785 NMB15893 5 Gurabo 5.80 0.42 0.09 4 1
63786 NMB15893 4 Gurabo 5.80 0.42 0.09 4 1
63781 NMB15893 4 Gurabo 5.80 0.42 0.09 4 1
63791 NMB15893 4 Gurabo 5.80 0.42 0.09 4 1
105095 JK06-18 2 Cercado
6.27 0.47 0.04 4 1
63836 Evans-S4 8 Cercado
6.27 0.47 0.04 4 1
continued on next page
19
Table 1 continued.
SUI # Locality Corallites measured Formation
Estimated age
Dep. rate SST/SSS Depth Upwelling
Undaria agaricites continued
108304 KB05-01 5 Cercado 6.27 0.47 0.04 4 1
108112 JK06-18 1 Cercado 6.27 0.47 0.04 4 1
105120 Evans-S4 6 Cercado 6.27 0.47 0.04 4 1
105137 Evans-S4 3 Cercado 6.27 0.47 0.04 4 1
105134 Evans-S4 5 Cercado 6.27 0.47 0.04 4 1
105131 Evans-S4 1 Cercado 6.27 0.47 0.04 4 1
105130 Evans-S4 5 Cercado 6.27 0.47 0.04 4 1
105112 Evans-S4 5 Cercado 6.27 0.47 0.04 4 1
63835 Evans-S4 4 Cercado 6.27 0.47 0.04 4 1
105168 Evans-S4 3 Cercado 6.27 0.47 0.04 4 1
108416(1) Evans-S12 3 Cercado 6.28 0.47 0.04 4 1
108416(2) Evans-S12 4 Cercado 6.28 0.47 0.04 4 1
108416(3) Evans-S12 2 Cercado 6.28 0.47 0.04 4 1
108416(4) Evans-S12 2 Cercado 6.28 0.47 0.04 4 1
63820 Evans-S12 5 Cercado 6.28 0.47 0.04 4 1
63811 Evans-S12 2 Cercado 6.28 0.47 0.04 4 1
63811 Evans-S12 5 Cercado 6.28 0.47 0.04 4 1
105098 Evans-S12 1 Cercado 6.28 0.47 0.04 4 1
Undaria sp. A
105092 NMB16136 5 Mao 3.41 0.64 0.81 1 0
63761 NMB15828 5 Mao 3.69 0.64 0.69 2 0
63773 NMB15822 5 Mao 3.75 0.64 0.67 2 0
63774 NMB15822 2 Mao 3.75 0.64 0.67 2 0
105139 NMB16822 2 Gurabo 5.28 0.21 0.14 3 1
105164 NMB16822 3 Gurabo 5.28 0.21 0.14 3 1
105133 NMB16822 3 Gurabo 5.28 0.21 0.14 3 1
63660 NMB15848 4 Gurabo 5.48 0.30 0.12 4 1
63663 NMB15848 4 Gurabo 5.48 0.30 0.12 4 1
63662 NMB15848 4 Gurabo 5.48 0.30 0.12 4 1
63593 NMB15850 2 Gurabo 5.49 0.30 0.12 4 1
63594 NMB15850 4 Gurabo 5.49 0.30 0.12 4 1
63592 NMB15850 5 Gurabo 5.49 0.30 0.12 4 1
63700 NMB15862 2 Gurabo 5.58 0.42 0.11 4 1
63698 NMB15862 5 Gurabo 5.58 0.42 0.11 4 1
63697 NMB15862 3 Gurabo 5.58 0.42 0.11 4 1
continued on next page
20
Table 1 continued.
SUI # Locality Corallites
measured Formation
Estimated
age
Dep.
rate
SST
SSS Depth Upwelling
Undaria sp. A continued
63699 NMB15862 4 Gurabo 5.58 0.42 0.11 4 1
63622 NMB15862 5 Gurabo 5.58 0.42 0.11 4 1
63702 NMB15862 2 Gurabo 5.58 0.42 0.11 4 1
63620 NMB15862 5 Gurabo 5.58 0.42 0.11 4 1
63624 NMB15862 3 Gurabo 5.58 0.42 0.11 4 1
21
Table 2 – Landmarks used for this project. Inner/outer and left/right are relative to the
center of the colony. Top/bottom are relative to the colony surface (See figure 3).
________________________________________________________________________
# Landmark description
1 The point where the septum along the outer left corallite margin intercepts the outer
corallite ridge.
2 Following the ridge from the top left of the corallite, where the first major septum to
where it intersects with the corallite floor (the base).
3 Top of corallum.
4 As #2, mirrored to the inner side of the coralite.
5 The point where the septum along the inner left corallite margin intercepts the inner
corallite ridge.
6 The intersection of the septa along the outer left corallite margin and a septum that
reaches the corallite’s center.
7 As #6, mirrored to the outer edge of the corallite.
8 From point #6, move to the first major septa counterclockwise from it, at its base. If
the same septum creates the intersections responsible for both points 6 and 7, use
that septum.
9 The point where the septum from #8 meets the wall between corallites.
10 The point of greatest curvature along the septum indicated in #2. This approximates
the end of the line of organic deposition described in Stolarski (2003).
11 The intersection of the septum from #10 and the outer corallite ridge.
12 The point of greatest curvature along the septum indicated in #4. This point is the
mirror of #10 across the axis of the ridges.
13 As #11, except on the corresponding septum on the inner side of the corallite.
22
Figure 3 – Landmark diagram. Also, see table 2. Image based on Stemann (1991).
23
Figure 4- Growth vectors associated with width by species.
4.1- Undaria crassa. Model is regression of shape variables against width. Percent of variance
explained is 17.1%. Regression was significant by permutation test, p <0.001. Black outline is
the mean form, while blue lines show vectors of shape change associated with larger size.
24
Figure 4 continued.
4.2- Undaria agaricites. Model is regression of shape variables against width. Percent of
variance explained is 15.2%. Regression was significant by permutation test, p <0.001. Black
outline is the mean form, while blue lines show vectors of shape change associated with
larger size.
25
Figure 4 continued.
4.3-Undaria sp. A. Model is regression of shape variables against width. Percent of variance
explained is 18.8%. Regression was significant by permutation test, p <0.001. Black outline is
the mean form, while blue lines show vectors of shape change associated with larger size.
26
CHAPTER III RESULTS
Canonical variates analysis
For the combined Gurabo and Mao formations, 189 of 195 (96.9%) individuals were
correctly identified. Cross validation of this data set resulted in 184 of 195 (94.4%)
individuals correctly identified (Table 3.1). All of the observed misidentifications were
between U. agaricites and U. crassa.
For the individuals from the Cercado formation, 83 of 86 (96.5%) were correctly
identified. Cross validation of this data set resulted in 64 of 86 (74.4%) correctly identified
(Table 3.2). All individual corallites that were misassigned by this CVA had other corallites
from the same colony correctly assigned. All specimens with incorrect assignments from this
set were reexamined according to the criteria of Stemann (1991). While corallite morphology
between these two species are quite similar, especially from the Cercado formation,
traditional criteria such as corallite size and row length allow a consistent differentiation of
species, and no individuals were reassigned. Once again, all of the observed
misidentifications were between U. agaricites and U. crassa.
Principal components analysis
The first four principal components were statistically significant. The first principal
component explained 28.74% of the variance. Higher values of the value of the first
principal component are primarily associated with lower ridges and shallower calice (figure
5). U. crassa tended to have lower average values on PC1, and U. sp. A tended towards higher
values. The first principal component seems to correspond to the differing growth forms of
Helmuth and Sebens (1993), with higher scores on this axis relating to the upright/bifacial
growth form.
27
The second principal component explained 16.95% of the variance. Higher values on
this principal component are related to a shift in the central pit towards the colony center
relative to the ridges, as well a change in the geometry of landmarks 6 and 7 along the
corallite edge (figure 6).
The third principal component explains 11.47% of the variance. Higher scores on
this component are associated with higher walls separating the corallites in a single meander
(figure 7).Undaria sp. A tended had a lower mean value on this trait then the other two
species, which tracked each other through time. The fourth principal component explains
6.96% of the observed variance, and primarily describes a change in the distance of
landmarks 6 and 7 from the corallite center, with landmark 6 moving away from and 7
moving towards the center with increased values of this principal component (figure 8).
In U. crassa, the student’s t test revealed a significant change between the Gurabo and
Cercado formations for PC1 and PC3 (p = 0.044 and p < .0001, respectively). For U.
agaricites, there was a significant difference in mean of PC1 between the Gurabo and Cercado
formations (p = .007). For U. sp. A, there was a significant shift in PC1 between the Mao
and Gurabo formation (p = .013).
Evolutionary model fits
AICc values by model and species for each significant principal component and
centroid size are listed in table 4. Across the first four principal components, the stasis
model was preferred 8 times while the unbiased random walk (URW) model was favored 4
times. In those cases where the URW model was preferred, the AICc values were always
within two units of the stasis model. For log of centroid size, the preferred model for U.
crassa is stasis, while the preferred models for U. agaricites and U. sp. A is URW.
28
Regression
For U. crassa, the single specimen from the Cercado formation was excluded from
regression and PLS analysis. Deposition rate and SST/SSE were statistically significant
predictors of shape (figure 10), p = 0.010 and 0.044 respectively, while local depth and
upwelling were not significant. Regression against deposition rate resulted in a correlation of
.43, R^2 = 18.4%, while SST/SSE explained 14.3%. The change in shape associated with
these is shown in figure 10. For U. agaricites and U. sp. A, none of the environmental
variables were significant predictors of shape.
Partial least squares
All partial least squares results were not statistically significant (p > 0.05).
29
Table 3- CVA cross validation results. Each row shows how many individuals assigned a
priori to the various species are classified to each species by the CVA procedure. Priors are
the proportion of individuals assigned to each group a priori.
3.1- CVA cross validation results for all specimens from Gurabo and Mao formations. ______________________________________________________________
From sp. U. sp.A U. agaricites U. crassa Total U.sp.A 77 0 0 77 % 100.00 0.00 0.00 U. agaricites 0 67 4 71 % 0.00 94.37 5.63 U. crassa 0 7 40 47 % 0.00 14.89 85.11 Total 77 74 44 195 % 39.49 37.95 22.56 Priors 0.39487 0.3641 0.24103
3.2- CVA cross-validation for all specimens from Cercado formation. __________________________________________ From sp. U. agaricites U. crassa Total U. agaricites 58 12 70 % 82.86 17.14 100.00 U. crassa 10 6 16 % 62.50 37.50 100.00 Total 68 18 86 % 79.07 20.93 100.00 Priors 0.81395 0.18605
30
Figure 5- Deformation along the first principal component. PC1 explained 28.7% of the
variance. Black outline is the average shape, while red outline shows the shapes associated
with a PC score 0.2 higher than mean.
31
Figure 6- Deformation along the second principal component. PC2 explained 16.9% of the
variance. Black outline is the average shape, while red outline shows the shapes associated
with a PC score 0.1 higher than mean.
32
Figure 7- Deformation along the third principal component. PC3 explained 11.5% of the
variance. Black outline is the average shape, while red outline shows the shapes associated
with a PC score 0.1 higher than mean.
33
Figure 8- Deformation along the fourth principal component. PC4 explained 6.9% of the
variance. Black outline is the average shape, while red outline shows the shapes associated
with a PC score 0.1 higher than mean.
34
Figure 9-Time series of shape and size variables by species.
9.1- First principal component through time. Red: Undaria sp. A, Blue: Undaria agaricities,
Black: Undaria crassa. Bars show one standard error, with variance pooled.
9.2- Second principal component through time. Red: Undaria sp. A, Blue: Undaria agaricities,
Black: Undaria crassa. Bars show one standard error, with variance pooled.
35
Figure 9 continued.
9.3- Third principal component through time. Red: Undaria sp. A, Blue: Undaria agaricities,
Black: Undaria crassa. Bars show one standard error, with variance pooled.
9.4- Fourth principal component through time.Red: Undaria sp. A, Blue: Undaria agaricities,
Black: Undaria crassa. Bars show one standard error, with variance pooled.
36
Figure 9 continued.
9.5- Centroid size through time. Red Undaria sp. A, Blue: Undaria agaricities, Black: Undaria
crassa. Bars show one standard error, with variance pooled.
37
Table 4-Akaike information criteria for three models of evolution for shape and size variables. For explanation of models, see methods.
PC1 PC2 PC3 PC4 ln(Centroid size)
DE URW Stasis DE URW Stasis DE URW Stasis DE URW Stasis DE URW Stasis
U. crassa 1.6 -2.6 -5.5 -10.9 -13.8 -20.5 -13.0 -17.1 -16.5 -6.9 -11.1 -21.4 -5.3 -8.7 -12.2
preferred * * * * *
U. agaricites -10.6 -14.6 -13.5 -12.5 -16.0 -20.2 -15.6 -20.4 -20.3 -18.4 -20.6 -23.5 -9.8 -14.8 -12.1
preferred * * * * *
U. sp. A -5.9 -10.8 -9.2 -12.1 -16.1 -17.8 -13.7 -18.5 -25.9 -21.0 -25.8 -26.6 3.7 -1.3 -0.1
preferred * * * * *
38
Figure 10- Regression of Undaria crassa shape vs. environmental variables.
10.1- Shape change associated with deposition rate. Percent variance explained = 18.4%. P =
0.010 by permutation test. Red outline reflects a deposition rate 0.3mm/year higher then
black reference form.
10.2- Shape change associated with change in sea surface temperature/salinity proxy. Percent
variance explained = 14.1%. P = 0.044 by permutation test. Red outline reflects an increase
of Globigerinoides sacculifer abundance of 50%.
39
CHAPTER IV DISCUSSION
This study shows that stasis is the dominant evolutionary mode for the genus
Undaria through the Neogene of the Cibao Basin, Dominican Republic. The results broadly
agree with Hunt (2007a). He found that for 251 time series of organismal traits, 37% of the
examined size-related traits were best fit by the stasis model, and 60% of shape traits were
best fit by the stasis model. In this study, 1 of 3 (33%) size traits were best fit by the stasis
model, while 8 of 12 (67%) shape traits were best fit by the stasis model. Stasis may be more
pervasive in Undaria than these results suggest; even when preferred by the data, support for
the unbiased random walk model was usually only slightly higher than that for the stasis
model, while the stasis model was often strongly preferred over the others. This could easily
be the result of small sample sizes at some stratigraphic levels and a relatively small number
of stratigraphic levels included.
These results strengthen the conclusions of Stemann (1991), that stasis is the general
mode of evolution for Undaria through this section. In this study, positive evidence for stasis
was discovered, as opposed to finding a lack of change. Additionally, some evidence for
change was found that should be reconciled with the broader pattern of stasis.
The evidence for change through time found here does not mean that the pattern of
stasis is rejected. Stasis models allow for some change through time, and some small changes
have always been within its purview (Eldredge and Gould 1972, Gould 2002). The
microevolutionary and ecologic causes of the observed pattern of stasis are not well
understood (Eldredge et al. 2005). The pattern of stasis has been explained as both a result
of stabilizing selection (Estes and Arnold 2007, Gould 2002) and of ecological interactions
coupled with microevolutionary processes (Lieberman and Dudgeon 1996, Hansen and
Houle 2004). This study does not provide clear support for either of these.
40
Attempts to find potential causal factors of the observed changes were not
particularly successful. While regression showed a correlation between the shape of U. crassa
and two environmental variables, both of these variables were correlated with each other as
well as with time. If similar responses were seen across all the species, it would be possible to
suggest an adaptive response. In this case it seems premature to suggest this.
Additionally, patterns observed in the CVA analysis by formation may be
informative to understanding these changes. Cross validation of corallites from the Cercado
formation (6.5 – 6.0 mya) resulted in 74.4% being correctly identified, while the same
analysis from individuals from the Gurabo formation (5.8 – 4.0 mya) resulted in 94.4% of
corallites correctly identified. This results primarily from a shift in morphospace occupied by
U. crassa, from a location proximal to U. agaricites to a location farther away, most notably on
PC1 and PC3. One explanation for this shift would be a speciation event followed by
divergence into available morphospace. The oldest reported occurrences of these taxa both
occur in the Cercado formation (Budd et al. 2001). This kind of event – a gradual
morphologic divergence between closely related species – has been reported from the Cibao
group in mollusks (Nehm and Geary 1994), but is generally rarely observed (Gould 2002).
This may be an example of an event rarely seen in the fossil record: a nascent divergence that
occurs over a geologically significant time. Further study both on these taxa, and others from
this group, seems warranted.
41
CHAPTER V CONCLUSIONS
In this study, the corallite shapes of three species of the scleractinian genus
Undariafrom the Yague group, Dominican Republic, were examined through a period of
time stretching from 6.4 mya to 3.4 mya, a total of 3.0 ma. Corallite shape was measured
using 3 dimensional landmarks and manipulated using the well established procedures of
geometric morphometrics. Differences in shape and size through time were examined using
a variety of tools, including canonical variates analysis, principal components analysis, least
squares regression, partial least squares regression, and a variety of evolutionary model fits.
Evolutionary model fits were used to test three models against the shape and size variables:
directional evolution, which models a directional change through time; unbiased random
walk, which models random change through time; and stasis, which models stability through
time. In summary:
1. Stasis seems to be the most common pattern through the section, with a
proportion of support for stasis (9 of 15, 60%) and unbiased random walk (6 of
16, 40%) models similar to that observed in other studies for both shape and size
variables. None of the observed time series was best explained by the directional
evolution model. This strengthens the evidence for stasis in Undaria through this
section, as described by Stemann (1991).
2. While two of the examined environmental factors seemed to be related to change
through time in U. crassa, namely deposition rate and sea surface
temperature/salinity, they were correlated to both each other and time. As such, a
single underlying factor – also correlated to time – could explain the observed
pattern. The evidence that these environmental factors were the causal agent of
shape change is weak.
42
3. The first occurrences of U. crassa and U. agaricitesboth occur at the top of this
section. The distance in morphospace between these two species increases
through time, as represented by the results of CVA and PCA. One plausible
explanation for this would be a speciation event followed by divergence into
available morphospace.
43
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