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TRANSCRIPT
Remaining Natural Vegetation in the Global Biodiversity Hotspots
Supplementary Online Information
Sean Sloana,1
Clinton Jenkins2
Lucas N. Joppa3
David L.A. Gaveau4
William F. Laurance1
1Centre for Tropical Environmental and Sustainability Science and School of Marine and
Tropical Biology, James Cook University, Cairns, Queensland 4870, Australia;
2Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695,
USA;
3Computational Science Laboratory, Microsoft Research, Cambridge CB1 2FB, UK;
4Centre for International Forestry Research, Bogor 16000, Indonesia
aCorresponding Author: [email protected]; tel: +61 7 4042 1835; fax: +61 7 4042 1319
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Contents:
Text A1 – Methodology and NIV Examinations
Text A2 – Sensitivity Analysis
Text A3 – NIV, Biomes, and Historical Agricultural Affinities
Figure A1 – Variation Amongst Prior Estimates of Natural Areas in the Hotspots
Figure A2 – Ecoregions of the Mesoamerican Hotspot
Figure A3 – Burned Areas of 1995-2012 Extending from Cultivated and Grazed Lands into Fringe ‘Forest’, MesoAmerican Hotspot
Figure A4 – Global View of Percent Natural Intact Area in the Hotspots, by Ecoregion
Figure A5 – Deviations from Current NIV Estimate Due to Variations in Parameter Values for Night Lights and Minimum Fragment Area Disturbance Filters
Table A1 – Area (km2) of Remaining Natural Vegetation, by Hotspot and Study. Figures in Brackets Express Areas as Percentages of Originally-Vegetated Area
Table A2 – The Coefficient of Concentration Describing the Degree to which NIV is Unevenly Distributed Amongst Biomes per Hotspot
Table A3 – The Global 200 Ecoregions of Olson and Dinerstein (1998, 2002) and Corresponding Ecoregions of Olson et al. (2001) Selected for Analysis in Table 3, by Biogeographical Realm and Biome
Table A4 – Examination of Natural Intact Vegetation for the Atlantic Forest Hotspot
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Text A1 – Methodology and NIV Examinations
MethodsWe used a recent global satellite-derived land-cover classification in conjunction with very
high-resolution imagery and ecoregion-specific analyses to estimates NIV area for each
ecoregion of each hotspot. Our methods are summarised as follows. First, the land-cover
classes of the moderate-resolution GlobCover 2009 land-cover classification (Bontemps et
al., 2011) were combined with the ecoregions of Olson et al. (2001) to yield 6863 unique
combinations of land -cover class by ecoregion in the hotspots. For each class-by-ecoregion
‘combination’, a determination of the likelihood that its class was naturally occurring was
assessed via consideration of its local ecological appropriateness, spatial pattern, , and
vegetative condition using moderate- and high-resolution imagery, and combinations were
classified as ‘naturally occurring’ or ‘other’ accordingly. Second, this initial classification
was refined by applying a series of human-disturbance maps to remove locally-disturbed
areas, yielding the final NIV delineation. This methodology was designed to integrate
precision with transparency, simplicity, and the possibility of comparable future updates by
non-specialists in the wider conservation community. The methodology is elaborated below,
as are examinations of the final NIV delineation for two hotspots.
Initial Classification of Naturally-Occurring Areas
The GlobCover 2009 classification maps 22 globally-consistent land-cover classes (Table 2)
at a 300-m spatial resolution on the basis of their spectral characteristics and seasonal
phenologies observed separately over one year and 21 biogeographic regions (Bontemps et
al., 2011). We combined these 22 classes with the ecoregion dataset of Olson (2001) to thus
produce 6863 unique class-by-ecoregion combinations within the 35 hotspots. Ecoregions are
“[biogeographic regions] containing distinct assemblages of communities and species, with
boundaries that approximate the original extent of natural communities prior to major land-
use change” (Olson et al., 2001: 933; Fig. A2). We hereafter analysed each class-by-
ecoregion combination individually in order to account for the fact that a given land-cover
class may be naturally occurring in one biogeographical context (ecoregion) but not in the
next. In this way we account for the biogeographic heterogeneity within the hotspots to some
degree.
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Each class-by-ecoregion combination was initially classified as ‘naturally occurring’
or ‘other’ via consideration of three criteria: the ecological appropriateness of the land-cover
class for the local ecoregion, its spatial pattern, and its vegetation condition. Consideration of
the ‘ecological appropriateness’ of a class in an ecoregion simply entails consideration of the
biophysical characteristics of the class relative to those of the ecoregion and, on this basis
determining, the likelihood that the class is naturally occurring in a mature state in the
ecoregion . For example, the class “closed to open (>15%) shrubland (broadleaved or
needleleaved, evergreen or deciduous, <5m)” was considered unlikely to be naturally
occurring within the ‘Araucaria Moist Forests’ ecoregion of the Atlantic Forest hotspot,
which is naturally dominated by tall, closed-canopy moist tropical forests exclusive of
shrublands. However, this class was considered likely to be naturally occurring within the
‘Atlantic Coast Restingas’ ecoregion of the same hotspot, which is characterised by mixed
shrubs, grasses and low tree cover on infertile costal soils. Classes describing ‘bare cover’,
‘sparse vegetation’, ‘snow and ice’ and ‘artificial surfaces’ were not considered, with the
exception of the ‘sparse vegetation’ class in arid hotspots or montane regions (Table A3).
The spatial patterns of each combination were then visually scrutinised for additional
insight regarding whether a given combination was naturally occurring or perturbed. Visual
pattern analysis was undertaken using principally the Idrsisi Selva GIS at the 300-m
resolution of the GlobCover dataset, although high-resolution imagery in Google Earth was
also consulted. The visual analysis considered the following aspects of pattern:
I. SPATIAL ASSOCIATION: Combinations having consistent and ostensible spatial
associations with perturbed land covers (e.g., cropland), or with the roads, settlements,
and fires disturbance datasets described below were unlikely to be considered
naturally occurring. Association typically entailed consistent adjacency between the
class in question and the fringes of known disturbed class(s), or coincidence with
disturbances such as roads or fires (Figure 2a-c). Associations frequently flagged
moderately-disturbed covers by the consistency with which they separated highly-
disturbed covers from more intact covers, as for example where a narrow band of
‘mosaic forest / grassland’ class was consistency concentrated adjacent on the fringes
of agricultural classes and separated these from expanses of closed intact forest
(Figure 2c). The ‘separating’ classes tended to be distributed in abrupt, elongated, or
mottled patterns reflecting their underlying anthropogenic origins, as described below;
II. SPATIAL CONTIGUITY, SHAPE, SIZE, AND TEXTURE: Naturally occurring, intact
vegetation formations will generally be characterised by contiguous expanses, smooth
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lines/edges, and gradual transitions befitting the local biogeographic context (see
below). Therefore, combinations fitting such a characterisation – for example,
unbroken expanses of shrubland in arid valleys or homogenous expanses of montane
scrubland gradually giving way to grasslands or bare lands according to elevation –
were taken as likely to be naturally occurring (Figure 2d-f). Contrarily, combinations
having abruptly distributed, fragmented, dispersed, jagged-edged, and/or ‘ecologically
inappropriate’ classes were more likely to be considered disturbed (Figure2e). Such
aspects of pattern often helped identify unmanaged but still disturbed remnant patches
in larger managed landscapes, e.g., forest/grassland mosaic patches in pastoral
landscapes within a larger humid tropical forest settings;
III. BIOGEOGRAPHIC CONTEXT: Non-forest or open-forest/mosaic classes were more
confidently considered to be naturally occurring with reference to biogeographical
gradients and patterns which naturally incorporated such classes, e.g., bio-elevational
gradients from closed forest to montane steppe in mountainous lands, precipitation
gradients from coastal forests to inland grasslands in arid lands, and similar soil-
moisture gradients immediately surrounding floodplains in arid regions (Figure 2d, f).
Finally, we undertook a visual inspection of the condition and status of combinations
using high-resolution imagery in Google Earth (Figure 3). Inspection was performed
individually for all indeterminate combinations as well as the vast majority of non-forest and
open-forest combinations and most closed-forest combinations as a matter of routine. This
inspection afforded a very detailed view of the condition and state of a combination, e.g.,
whether the ‘Closed to Open Herbaceous Vegetation’ class was largely fenced and grazed
pasture or rather open grasslands, or whether the ‘Closed to Open Shrubland’ class was
largely degraded forest and fallows or rather natural heath. The visual inspections also
afforded a means of identifying misclassified or otherwise incompletely classified land
covers in a given ecoregion and treating them appropriately when determining whether their
extent was ‘naturally occurring’, e.g., in certain Mediterranean environments, where the
‘Mosaic Grassland / Shrubland or Forest’ class was found to incorporate disperse low-
intensity agricultural plots mingled with extensive grazed lands and shrubs; or in certain
temperate high alpine environments, where steep slopes illuminated by the sun and originally
classified as Rainfed Agriculture were actually observed to host natural montane grasslands
misclassified due to illumination effects. Where misclassification or incomplete
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classification was detected, the nominal land-cover label was updated accordingly prior to its
consideration by the three criteria determining ‘naturally occurring’ vegetation. In this way,
the inspection of high-resolution imagery verified not only that a given land-cover class in a
given locale was natural and intact, but also that its actual land-cover composition was as
described by the GlobCover global land-cover classification and treated accordingly if
otherwise. Thus this third step in determining ‘naturally occurring’ vegetation proved most
useful of the three.
Information from our three criteria (ecological appropriateness, spatial pattern, and
vegetative condition) was synthesised as follows in order to classify combinations as
‘naturally occurring’ or ‘other’. Where assessments of all three criteria agreed, a
combination was simply classified accordingly. In instances where any of the three criteria
disagreed or an assessment was indeterminate, the assessments were revisited and revised, at
which point the revised consensus determination was adopted. Typically this latter
determination conformed to the assessment of the high-resolution imagery.
Removal of Key Human Disturbances
Taking the initial classification described above, a series of disturbance filters describing
local human disturbances were then applied to remove remaining areas of non-natural
vegetation. All analyses were realised at the 300-m pixel resolution of the GlobCover 2009
dataset. The filters concerned the following disturbances, in order of application. The output
of the final filter is the final NIV delineation.
BURNED AREAS: Following Potapov et al. (2008), recently burned areas were
removed from the initial natural-area classification (Fig. A3), except in the following arid
and/or fire-adapted hotspots for which fires are not necessarily disturbances: California
Floristic Province, Cape Floristic Province, Caucuses, Cerrado, Forests of East Australia,
Horn of Africa, Irano-Anatolian, Mediterranean, New Zealand, South West Australia, and
Succulent Karoo. Burned areas are defined as active fire hotspots detected daily or near daily
over 1995-2000 using ATSR-2 World Fire Atlas satellite data (Arino and Rosaz, 1999; ESA
DUE, 2012) and over 2000-2012 using MODIS FIRMS satellite data (Davies et al., 2009;
NASA and University of Maryland, 2012), both at ~0.9 km spatial resolution. Such data do
not discriminate between anthropogenic fires (e,g., for swidden agriculture and grazing) and
wild fires, and for the hotspots in question all fire events were treated as indicative of
disturbance. This treatment in keeping with (i) the predominately fire-sensitive tropical and
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sub-tropical climates of the hotspots in question wherein naturally-occurring fires are rare or
absent (Mutch, 1970); (ii) the direct spatial and temporal relationships between fires and
other disturbances (Eva and Lambin, 2000), such as road building, forest fragmentation, and
agro-pastoral activities , which act in concert to alter species composition and land cover
(Ellis, 2011; Nepstad et al., 2001); (iii) the exponential growth of anthropogenic fire events
and fire regimes alongside agricultural expansion in the tropics and sub-tropics during the
20th Century (Mouillot and Field, 2005; Pechony and Shindell, 2010); such that (iv)
anthropogenic fires events now comprise the vast majority of fire events (Crutzen and
Goldammer, 1993; Saarnak, 2001), and the vast majority of the tropics and sub-tropics now
experience fire regimes divergent from their natural states (Hoekstra et al., 2010; Shlisky et
al., 2007).
ROADS: Swaths 0.9 km wide along roadways were removed from the initial
classification. The 0.9 km swath width was in keeping with the spatial resolution of our land-
cover data (300 m), observations of the proximate ecological effects of roadways (Forman,
2000; Forman and Deblinger, 2000), and similar efforts to map natural vegetation (Potapov et
al., 2008; Sanderson et al., 2002) Road networks were mapped after the VMap0 (NIMA,
2000) and the gROADS v.1 (CIESIN, 2013) global GIS datasets. The former is an updated,
globally-consistent map of major roads as of 2000 or earlier (depending on country),
originally based on 1:1 million navigational charts (Nelson et al., 2006). The latter is a
compilation of best-available national maps of major roads as of approximately 2010 or
earlier, at scales of 1:50 thousand to 1:2million, depending on the country and data source
(CIESIN, 2013). While less consistent amongst countries, the gROADs dataset corrects for
regional gaps in the VMap0 dataset (Nelson et al., 2006).
SETTLEMENTS: Settlements and their immediate surrounds were removed from the
initial classification. Settlements are mapped as electric night-time lights observed by the
DMPS-OLS satellite in 2010 at 0.5-km pixel resolution, with brightness values reflecting the
average of a 5x5 pixel grid centered on a given pixel (NOAA, 2010). No buffer distance was
applied to the observed illuminations because light diffuses beyond apparent ‘built’
settlement boundaries – from 10+ km for very large bright city centres to ~1-2 km for small
towns to probably less for more disperse villages – and because settlement ‘boundaries’ were
considered gradational rather than absolute. Brightness values were originally recorded on a
64 point scale, and values of ≥5 were taken as defining the extent of settlements here. This
particular range of brightness values incorporates dimmer villages and smaller towns,
disperse roadside settlements, some illuminated roads, and the like, but it also limits extreme
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low-level light diffusion from large bright cities. Analyses using alternative brightness value
ranges show the NIV estimates to be generally insensitive and recommend the present
parameters as the most precise and appropriate (Text A2).
PATCH SIZE AND EDGES: The edges of fragments as well as fragments <100 ha were
removed from the initial classification. ‘Naturally-occurring’ pixels immediately adjacent to
a non-natural pixel were re-classified as non-natural, both to recognize ecological ‘edge
effects’ in fragmented landscapes (Broadbent et al., 2008b; Cochrane and Laurance, 2002;
Laurance et al., 1997) and to diminish the extent of speckled ‘salt-and-pepper’ vegetation
cover, which is highly indicative of degraded and highly-fragmented yet still semi-vegetated
landscapes, e.g., semi-treed, tropical, non-mechanised agro-pastoral landscapes. Removal of
a 300-m edge is in keeping an authoritative review citing 273m as the mean distance to which
‘edge effects’ penetrate tropical forests patches (Broadbent et al., 2008a). Subsequently, all
remaining natural-vegetation patches of <100 ha were removed. The decision to remove only
fragments <100 ha reflects our caution not to discount the potential biological importance of
fragments (Turner and Corlett, 1996) while also recognizing that smaller fragments are
unlikely to retain their biodiversity for long (Gibson et al., 2013; Laurance et al., 2002).
Analyses using alternative minimum fragment size thresholds are discussed in Text A2.
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Examinations of NIV in the Sundaland and Atlantic Forest HotspotsThe final NIV delineation was examined the Atlantic Forest hotspot and the Sundaland
hotspot wherein fine-scale, visually-interpreted spatial data on vegetation cover and its
disturbances were available. The following describes these examinations.
Examination Parameters for the Sundaland Hotspot
In recent decades the Sundaland Hotspot has experienced extensive selective logging,
expansion of tree and tree-crop (oil-palm) plantations, and forest degradation (Bryan et al.,
2013; Curran et al., 2004; Gaveau et al., 2013; Gaveau et al., In Press; Miettinen et al., 2012;
Miettinen et al., 2011). In Borneo, for example, some 46% of all remaining forest cover has
been logged (Gaveau et al., In Press). The detection of such disturbances and attendant local
forest degradation is challenging for global natural-area estimations (Wright, 2010). Such
disturbances may occur far from readily observable human infrastructure or agricultural
activity, are occasionally situated in close proximity to contiguous forest, and/or are often
difficult to spectrally distinguish from naturally forest cover in satellite imagery (Hansen et
al., 2013; Miettinen et al., 2011). Global land-cover classifications such as GlobCover
(Bontemps et al., 2011), GLC2000 (ECJRC, 2003) and the MODIS Collection 5 Land Cover
Type product (Friedl et al., 2010) therefore necessarily incorporate such
anthropogenic/perturbed forest covers within their more general forest-cover classes (Table
2), significantly underestimating the apparent areas of these anthropogenic/perturbed forest
covers and thus the extent of disturbance (see Section 2 and Table 1 of main text).
The Sundaland examination entailed assessing the degree of overlap between the final
NIV delineation and all active and abandoned logging roads established between 1972-2010
in Borneo (Gaveau et al., In Press) and 1990-2000 in Sumatra (Gaveau et al., 2009), and all
oil-palm and tree plantations in Borneo as of 2010 (Gaveau et al., In Press). Borneo and
Sumatra are two of the four principal regions comprising the Sundaland hotspot – the others
being Peninsular Malaysia and East Java Island – and comprise 81% of its total area. The
logging-road maps and plantation maps were derived from exhaustive manual digitisation of
time-series false-colour composites of Landsat satellite imagery, in which all such features of
interest were clearly discernible (cf. de Wasseige and Defourny, 2004; Margono et al., 2012).
Oil-palm and tree plantations established as of 2010 effectively include all plantations
established since the advent of industrial plantation agriculture in Borneo in the early 1980s,
as mapping was performed for 1990, 1995, 2000 and 2005, in addition to 2010. The mapping
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of logging roads in Borneo was similarly performed retrospectively for these years as well as
and 1972, and is therefore similarly comprehensive of virtually all logging roads established
since the advent of industrial logging.
Examination Parameters for the Atlantic Forest Hotspot
The Atlantic Forest hotspot is highly disturbed and fragmented, even compared to other
hotspots (Table 1), and its representation within a global inventory based on moderate-
resolution satellite imagery and public global datasets is therefore particularly challenging.
In the Atlantic Forest Hotspot, the NIV delineation was compared to a 2004/2005 map of
total forest cover produced by SOS Mata Atlântica and the Instituto Nacional de Pesquisas
Espaciais of Brazil (SOSMA/INPE, 2008), as described in Ribeiro et al. (2009). The
SOSMA map exhaustively delimits forest cover for the Atlantic Forest hotspot via visual
interpretation of 1:50,000-scale false-colour composites of Landsat and CCD/CBERS-2
satellite imagery, having 30-m and 20-m spatial resolution, respectively. The minimum
mappable area was ~3 ha, or ~33 Landsat pixels. The SOSMA map encompass total forest
cover generally, including increasingly ascendant successional forests (Aide et al., 2013;
Baptista and Rudel, 2006; Redo et al., 2012), and delimitations include many ‘residual’ forest
patches in addition to more integral constellations of fragments. Indeed, forest fragments
<100 ha comprise 29.7% of the total forest area in the SOSMA map, fragments <50 ha
comprise 20% of the total forest area, and nearly half of the total forest area is <100m from
the forest edge (Ribeiro et al., 2009). For these reasons, as well as slight methodological
differences, the 2005 SOSMA map presents a total forest area 40-50% greater than that of the
previous 2000 SOSMA map similarly based on visual interpretation of Landsat imagery
(Galindo-Leal and Câmara, 2003; Ribeiro et al., 2009; SOSMA, 2012; SOSMA/INPE, 2000).
Like forest-cover maps generally, the 2005 SOSMA map therefore depicts total remaining
forest cover that, while finely delineated and exclusive of plantations and very young fallows,
is for many landscapes not necessarily ‘natural’ nor ‘intact’ as understood here.
The Atlantic Forest examination entailed assessing the frequency with which 5778
points randomly distributed across the 2005 SOSMA forest fragments coincided with NIV,
and then accounting for any lack of coincidence. A lack of coincidence between the SOSMA
and NIV maps for a given point was explained by one of four pre-determined possibilities,
detailed below.
i. One of the spatial disturbance filters (see above) was applied coincidently to the point
in question when mapping the NIV area. Such filters removed areas initially
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classified as ‘naturally occurring’, on the basis that these were disturbed. No such
considerations of the proximity of forest cover to disturbances featured in confection
of the SOSMA map;
ii. The geometry of the medium-resolution (300m) initial classification was sufficiently
coarse relative to the SOSMA map so as to just exclude a sample point by a negligible
distance, where the larger forest patch actually hosting the point was otherwise
captured by the initial classification and later removed from the NIV delineation by
the disturbance filters. We account for this possibility by counting non-coincident
points <100 m from the initial classification subject to disturbance filters.
iii. The non-coincident point was excluded by >100 m and the SOSMA forest patch
hosting the point was small (≤100 ha) relative to the resolution of the moderate-
resolution imagery (300-m) from which the NIV delineation was derived. The 100-ha
threshold area was selected as the area beneath which the moderate-resolution
imagery of the GlobCover classification was relatively unlikely to reliably detect a
given forest fragment, particularly in light of the irregular shapes of many forest
fragments. We account for this possibility simply be counting non-coincident points
excluded by >100 m and within fragments ≤100 ha.
iv. The non-coincident point was excluded by >100 m and the SOSMA forest patch
hosting the point was not small (>100 ha) relative to the resolution of the moderate-
resolution imagery (300-m) from which the NIV delineation was derived. The point
was therefore relatively detectable by the GlobCover classification and our NIV
delineation, notwithstanding portions of fragments difficult to resolve due to irregular
shapes, e.g., narrow spindles of a larger fragment. We account forthis possibility
simply by counting the number of points excluded by >100 m and within fragments
>100 ha.
Possibilities i through iv represent a gradient from exclusion to omission of SOSMA
total forest cover. We emphasise that these possibilities should be taken as illustrative of the
scope and nature of the regional NIV delineation relative to fine-scale visual delineations of
total forest cover, and not as indicative of ‘error’ per se, given differences between these
concepts and input data. A determination of ‘error’ would entail that the SOSMA forest
fragments are universally natural and intact vegetation as understood here, which is not the
case. Further, fragmented heterogeneous landscapes are resolved differently by moderate-
and high-resolution satellite imagery, both in terms of the geometry of features as well as the
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relative areas of land covers. This is particularly the case here, given that the GlobCover map
observes some 22 wide ranging land-cover classes (Table 2) whereas the SOSMA map
observes only two (forest/non-forest). Excepting the smallest fragments that a moderate-
resolution image may fail to resolve, the agreement between general landscape patterns
observed by fine-scale and moderate-scale imagery may be greater than per-point
comparisons would indicate (Benson and MacKenzie, 1995; Turner et al., 1989).
Examination Results
The examination confirms that the NIV delineation is largely confined to natural, intact,
unperturbed vegetation. In Borneo, only 5% of the area of all tree plantations and oil-palm
plantations and fell within the NIV area. In Sumatra, only 17% of the length of all logging
roads of 1990-2000 fell within NIV. The proportion of logging-road length within NIV was
greater in Borneo, at 36%, although so too was the period of observation (1972-2010) and the
pervasiveness of logging on that island, which was very considerable (Bryan et al., 2013;
Curran et al., 2004; Gaveau et al., 2013; Gaveau et al., In Press). Some 46% of overlapping
Bornean logging-road lengths were established before 1990, and 81% before 2000,
suggesting that much overlap is attributable to post-logging forest regrowth that is not
detectible using optical satellite imagery. All overlaps between NIV and plantations or
logging roads constituted a negligible fraction of total NIV area, and all were largely
confined to the edges of typically smaller NIV patches.
In the Atlantic Forest hotspot, the NIV delineation is conservative relative to the
SOSMA map of total forest cover. Just over 14% of the random points situated within
SOSMA forest fragments were deemed NIV, and a further 41% were excluded from the NIV
delineation by the disturbance filters, e.g., roads, settlements, fires, forest edges (Table A4).
Discrepant geometries / resolution of the SOSMA and NIV maps as well as the small size
(≤100 ha) of many SOSMA fragments account for a further 24% of the points. The omission
of points within larger fragments in the SOSMA map accounts for 21% of points. Visual
examination of these later points highlights three explanatory factors. The first is the highly
irregular, often ‘skinny’ or ‘spindly’ shapes of many fragments in the SOSMA map, e.g.,
those following a sharp ridge line, which correspondingly evade complete resolution by
moderate-resolution imagery despite their area. The second factor is the not-insignificant
extent of successional forest encompassed by the SOSMA map, which was not a target of the
NIV classification. Successional forest is a highly variable land cover class (Sloan, 2012)
and was therefore represented by a variety of GlobCover classes. Given different treatments
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across the ecoregions, these classes sometimes ultimately delineated only part of a larger
successional fragment. The third factor is the ‘spatial ‘lumpiness’ of the NIV delineation.
For example, a SOSMA forest fragment spanning two adjacent ecoregions might be captured
only partially if the various GlobCover forest classes (Table 2) composing the fragment were
classified differently between the two ecoregions, as is possible where a given cover is
generally perturbed in one ecoregion but not in the other. In such a scenario, the NIV
classification of the SOSMA fragment would be erroneously absent from one of the two
ecoregions. We emphasise however that, of these 21% of points, some 56% would have been
subject to exclusion due to their immediate proximity to settlements, roads, or fires alone.
Taking this into account, we estimate an omission rate of total potential natural vegetation
cover at less than 12-21% for the Atlantic Forest hotspot.
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Text A2 – Sensitivity Analysis
We explored the sensitivity of our NIV estimates to variations of the parameters of certain
disturbance filters, and find the current estimates to be robust. Specifically, we varied the
parameters of the night-lights disturbance filter and the minimum-fragment area disturbance
filter in order to observe the magnitude of deviations from the current NIV estimates. These
filters were selected for adjustment because they have a range of more and less conservative
potential parameter values and no clear empirical precedent for selecting a particular value.
Variations in their values entail trade-offs between comprehensiveness of spatial coverage
and accuracy and meaningfulness of NIV delineations across different contexts.
MethodsFor the night-lights disturbance filter and the minimum fragment-area disturbance filter, a
total of six variations of their parameters were developed to produce six alternative NIV
estimates. Three variations were more conservative than the present estimates, and three
were less conservative than the present estimates. The variations of parameters and their
implications are described below overleaf, from the least conservative to the most
conservative. The six resultant alternative NIV estimates were compared to the current NIV
estimates, and the magnitude of their deviations from the current estimate measured per
hotspot as a percent of total hotspot area.
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Least Conservative
VARIATION 1 : The night lights disturbance filter was relaxed by raising the minimum brightness value above which an area is excluded, from the current brightness value of 5 to a value of 10 on the 64-point brightness scale. In practice, this meant that the corresponding alternative NIV estimate overlooked dimmer villages, illuminated roadways/infrastructure, peri-urban fringes and the lightly settled areas spanning such features to a much greater degree than the current estimate, but also that it more precisely circumscribed and retained natural vegetation immediately adjacent to larger, brighter settled areas such as cities and coastlines. Also, the minimum fragment size disturbance filter was also omitted altogether, rather than being set at the current value of 100 ha. The meant that the alternative NIV estimate incorporated all fragments surviving other filters, including the relaxed night lights filter. The minimum possible fragment size is the area of one pixel, being 9 ha.
Less Conservative
VARIATION 2 : The night lights disturbance filter was relaxed as described for Variation 1. The minimum fragment size parameter remained unchanged.
Less
Conservative
VARIATION 3 : The minimum fragment size disturbance filter was omitted altogether as described in Variation 1. The night lights disturbance filter remained unchanged.
More Conservative
VARIATION 4 : The night lights disturbance filter was made more conservative by lowering the minimum brightness value above which an area is removed, from the current brightness value of 5 to a value of 1 on the 64-point brightness scale. In practice, this meant that the corresponding alternative NIV estimate now excluded the very dimmest of settlements and illuminated roads/infrastructure, or rather greater parts thereof, such as dispersed dwellings at the peripheries of small agricultural settlements. It also meant that disperse light pollution in the absence of local settlement was more problematic, particularly but not exclusively near larger settlements. Also, due to the ‘smoothing’ of brightness values using a 5x5 pixel grid during data processing, potential light dispersion up to one or two kilometres beyond actual settlements was now unaccounted for. The minimum fragment size disturbance filter remained unchanged.
More Conservative
VARIATION 5 : The minimum fragment size disturbance filter was made more conservative by increasing the minimum allowable fragment size from 100 ha to 1000 ha. The effect is that the resultant alternative NIV estimate excluded those fragments between 100 ha and 1000 ha surviving prior filters. These larger fragments are still typically in disturbed landscapes but may house appreciable biodiversity, roughly in proportion to the logarithm of their area, other factors being equal. The night lights disturbance filter remained unchanged.
Most Conservative
VARIATION 6 : Both the night lights disturbance filter and the minimum fragment size disturbance filter were simultaneously made more conservative, as described for Variation 4 and Variation 5.
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ResultsThe NIV estimates are generally robust to variations in the parameters. For Variation 1 and
Variation 6, being respectively the least conservative and most conservative variations, their
median (mean) deviations from the current estimates are modest, at +1.2% (+1.8%) and -
1.7% (-1.8) of hotspot area, respectively. The inter-quartile ranges of their deviations are also
modest, at 1.2% and 0.9% of hotspot area, respectively, indicating a limited range of
deviations amongst the hotspots.
Variation 1 and Variation 6 register a total of four outlying hotspots, taken here as
indicative of sensitivity to alternative parameter values. Outliers are defined as a deviation of
> 1.5 times the inter-quartile range from the 75th or 25th percentile value. Variation 1, again
being the least conservative, accounts for three of these outliers, namely Japan, the
Californian Floristic Province, and Western Ghats and Sri Lanka (Fig. A5). Their positive
deviations, ranging from +5.5 to +10.2% of hotspot area, are due almost entirely to the
relaxation of the night lights parameter (Variation 3), that is, to the disregard of dimmer,
smaller and/or more disperse settlement networks or parts thereof (Fig. A5). Such features
are very prominent in these hotspots, in keeping with their relatively high population
densities (Williams, 2011). The fourth and final outlier is the Cape Floristic Region on the
most conservative Variation 6, registering a negative deviation of -3.7% of hotspot area (Fig.
A5). This deviation owe to strong independent effects of the two conservative disturbance
filters, in contrast to other hotspots for which smaller negative deviations are due largely and
simply to the increase in the minimum fragment size (Variation 5) (Fig. A5).
These results recommend the present parameter values as the most appropriate. The
recognition of extremely extensive smaller, dimmer settlement networks by the current night
lights parameter value means that any correspondent under-estimation of NIV immediately
adjacent to larger settlements is certainly a necessary trade off that ensures greater overall
accuracy than the alternative. Further, applying a more conservative night lights parameter
(Variation 4) would not exclude appreciably more NIV than the current estimates on average
(Fig. A5) but would aggravate under-estimation near larger or more densely distributed
settlements and thus inflate measures in certain hotspots like Japan. While a more
conservative minimum fragment filter (Variation 5) would yield relatively larger negative
deviations (median [mean] -1.4% (-1.4%) by hotspot area), it would not significantly alter the
findings of the present analysis, and there is no basis for adopting it in lieu of the present
filter given the conservation value of intermediate-sized fragments (Turner and Corlett,
1996).
16
17
Text A3 – NIV, Biomes, and Historical Agricultural Affinities
Section 4.2 observed that certain biomes host critically low levels of remnant vegetation and
that these low levels probably reflect in large part a historical affinity between these biomes
and agricultural activity. Accounts of agricultural settlement in the MesoAmerican hotspot
elaborate this observation well. Jones (1989) for example recounts how colonial populations
historically concentrated in the Tropical Dry Broadleaf Forest biome of the Pacific lowlands
of the MesoAmerican isthmus, where even a short dry season permitted the use of fire to
clear and maintain permanent agricultural lands, necessary for permanent settlements,
amongst other attractions such as lesser rates of agricultural plagues and human illness.
Correspondingly, satellite-based observations now confirm that the great majority of the
natural vegetation of this biome has been converted across the isthmus, and that most of what
remains is highly fragmented and subject to direct human disturbance (Miles et al., 2006).
Thus, by the 1980s, when many Central American governments undertook agricultural
resettlement programs to alleviate agricultural pressures in the their long-settled, seasonally-
dry Pacific regions, Jones (1988: 241) observed that:
“Modern [agricultural] colonists are now forced to occupy humid and very humid
tropical forest areas, since these are the only remaining forest lands [unoccupied
by agriculturalists and others]. These areas had been avoided in the past due to
problems inherent in their use. Heavy rains make overland transport difficult, in
addition to causing agricultural problems such as fungal disease, water logging,
and soil erosion.”
Similar scenarios played out elsewhere in the world for the same reasons. In eastern South
America (south of the Atlantic Forest hotspot), for example, significant agricultural
conversion was underway in the drier savanna and grassland biomes of The Pampas and
Chaco regions suited to extensive grazing and cropping a hundred years earlier and thus more
extensively than in the more northerly humid forest biome (Eva et al., 2002; Ramankutty and
Foley, 1999).
18
Figure A1 – Variation Amongst Prior Estimates of Natural Areas in the Hotspots (%
Hotspot Area).
Note: Hotspot identifier numbers on the x-axis as well as graphed data are as per Table 1. Warm
coloured + symbols pertain to Independent Estimates observing vegetation cover directly. Cool
coloured × symbols pertain to Independent Estimates focusing on the absence of human disturbance.
The purple circles pertain to the latest Expert Estimates having data for most hotspots. Note how the
current estimates (black triangles) generally track the purple dots and separate the + symbols above
from the × symbols below.
19
Figure A2 – Ecoregions of the Mesoamerican Hotspot.
Note: The north-western portion of the Mesoamerican hotspot is omitted from the main map in order
to enhance the visibility of the ecoregions therein.
20
Figure A3 – Burned Areas of 2000-2012 Extending from Cultivated and Grazed Lands into Fringe ‘Forest’, MesoAmerican Hotspot.
Notes: The proximity of fires (orange dots in B) to agriculture and forest/grassland mosaics describes their perturbation as extending into the forest, often
from small clearings.
21
Figure A4 – Global View of Percent Natural Intact Area in the Hotspots, by Ecoregion.
Note: Ecoregion boundaries not apparent where adjacent ecoregions have the same colour.
22
Figure A5 – Deviations from Current NIV Estimate Due to Variations in Parameter
Values for Night Lights and Minimum Fragment Area Disturbance Filters.
Note: Deviations from current estimates are in terms of percent of hotspot area.
23
Table A1 – Area (km2) of Remaining Natural Vegetation, by Hotspot and Study. Figures in Brackets Express Areas as Percentages of
Originally-Vegetated Area.
EXPERT ESTIMATES INDEPENDENT ESTIMATES NIV
Hotspot Total Area
(km2)
Myers
(1988)
Myers
(1990)
Mittermeier et
al.
(1999)/Myers
et al. (2000)
Mittermeier
et al. (2004)
McCloskey
& Spalding
(1989)
Hannah
et al.
(1995)k
Byrant et
al. (1997)
Sanderson
et al.
(2002)
Hoekstra et
al. (2005)
Schmitt
et al.
(2009)
Potapov
et al.
(2008)
Present
Study
Prim
ary
Fore
st
Prim
ary
Fore
st
Prim
ary
Vege
tatio
n
Prim
ary
Hab
itat
Wild
erne
ss
Und
istu
rbed
Area
s
Fron
tier
Fore
sts
Wild
Are
as
Und
istu
rbed
Hab
itat
Rela
tivel
y
Nat
ural
Fore
st C
over
Inta
ct F
ores
t
Land
scap
es
Nat
ural
Inta
ct
Vege
tatio
n
1. Atlantic Forest
of Brazil
1,236,664
20,000
(2)♦
--- 91,930(7.5)♦
99,944(8)
7,779 (0.63)
245,520
(20)♦
19,672(1.6)
5,192 (0.42)
489,848 (39.6)
246,000
(19.9)
2,729(0.2)j
42,674 (3.5)
2. California
Floristic
Province
294,463 --- 246,000
(76)
80,000(24.7)e
73,451(25)
12,912 (4.4)
61,560(19)
0 57,068(19.4)
237,206 (80.5)
155,000
(52.6)
8,940(3)j
102,572 (34.8)
3. Cape Floristic
Region
78,731 --- 89,000
(66)♦
18,000(24.3)
15,711(20)
0 13,495(17.1
)♦♦
0 18,495(23.5)
67,252(85.4)
15,000(19.1)
0j 25,901 (32.9)
4. Caribbean
Islands
230,073 --- --- 29,840(11.3)
22,995(10)
287(0.12)
31,624(12)
0 4,567 (2)
114,692(49.8)
45,000(19.6)
1,340(0.6)
13,295(5.8)
5. Caucasus 533,852 --- --- 50,000(10)
143,818(27)
0 --- 0 6,335 (1.2)
281,170(52.7)
90,000(16.7)
22,062 (4.1)j
43,852(8.2)
6. Cerrado 2,036,548
--- --- 356,630(20)
432,814(21.3)
223,620 (11)
--- 2675(0.1)
415,348(20.4)
982,172 (48.2)
366,000
(18)
24,708 (1.2)
403,182(19.8)
7. Chilean Winter
Rainfall and
398,035 --- 46,000
90,000(30)
119,143(30)
64,846 (16.3)
--- 63,088 (15.8)
101,036(25.4)
348,134(87.5)
134,000
61,037(15.3)j
136,185(34.2)
24
Valdivian
Forests
(33)f♦ (33.7)
8. Coastal Forests
of Eastern
Africa
291,905 --- 6000(19)♦
2000(6.7)a♦
29,125(10)
3,720 (1.3)
--- 0 26,614(9.1)
256,345(87.8)
188,000
(64.4)
617 (0.2)
11,142(3.8)
9. East Melanesia
Islands
99,630 --- --- --- 29,815(30)
0 --- 20,507(20.6)
1,505(1.5)
91,231(91.6)
72,000(72.3)
25,163 (25.3)
10,665(10.7)
10. Eastern
Afromontane
1,020,095
--- --- 2000(6.7)a♦
106,870(10.5)
64,764 (6.3)
--- 32,726(3.2)
28,710(2.8)
569,417(55.8)
295,000
(28.9)
52,750(5.2)
92,406(9.0)
11. Forests of
Eastern
Australia
255,328 --- --- --- --- 0 --- 10,573(4.1)
63,469(24.9)
241,065 (94.4)
--- 11,258 (4.4)
89,020 (34.8)
12. Guinean
Forests of
West Africa
621,706 --- 4000(2)♦
126,500(10)
93,047(15)
45,887 (7.4)
75,900(6)
9,299 (1.5)
4,040 (0.6)
236,106 (40)
233,000
(37.5)
19,622 (3.2)
65,971 (10.6)
13. Himalaya 743,371 5300(15)b
--- See Indo-Burmab♦
185,427(25)b
34,852 (4.7)
See Indo-Burm
ab♦
21,293 (2.9)
153,219(20.6)
516,295 (69.4)
211,000
(28.4)
55,751 (7.5)j
130,846(17.6)
14. Horn of Africa 1,663,112
--- --- --- 82,968(5)
233,178 (14)
572,017
(34.4)♦♦
0 321,266(19.3)
1,538,976
(92.5)
2,000(0.12)
0j 395,181(23.8)
15. Indo-Burma 2,378,318
100,000(4.9)b
118,653(5)b
49,878 (2.1)
144,200
(7)b
91,255(3.8)
85,365(3.6)
891,062 (37.5)
742,000
(31.2)
77,047 (3.2)
207,312(8.7)
16. Irano-
Anatolian
901,790 --- --- 134,966(15)
0 --- 0 11,911 (1.3)
643,509 (71.4)
2000(0.22)
0j 32,369(3.6)
17. Japan 374,328 --- --- --- 74,698(20)
0 --- 0 674 (0.2)
258,859 (69.1)
244,000
2,560 (0.7)
30,688(8.2)
25
(65.2)18. Madagascar
and the Indian
Ocean Islands
601,830 10,000
(16)♦
--- 59,038(9.9)
60,046(10)
7,042 (1.2)
89,133(15)
0 60,701(10.1)
465,811 (77.4)
129,000
(21.4)
17,343 (2.9)j
26,464(4.4)
19. Madrean Pine-
Oak
Woodlands
462,300 --- --- ---c 92,253(20)
590 (0.1)
--- 34,541(7.5)
37,975(8.2)
405,906 (87.8)
281,000
(60.8)
5,214 (1.1)
83,600(18.1)
20. Maputaland-
Pondoland-
Albany
273,018 --- --- --- 67,163(24.6)
0 --- 0 33,859(12.4)
234,514(85.9)
124,000
(45.4)
0 17,586(6.4)
21. Mediterranean
Basin
2,089,974
--- --- 110,000(4.7)
98,009(4.7)
257 (<0.1)
118,100
(5)
0 59,777(2.9)
1,261,198
(60.3)
265,000
(12.7)
0j 92,283(4.4)
22. Mesoamerica 1,132,551
--- --- 231,000(20)c
226,004(20)
43,401 (3.8)
67,341 (50.8)
126,488 (11.2)
40,367(3.6)
609,657(53.8)
595,000
(52.4)
47,539 (4.2)
159,980(14.1)
23. Mountains of
Central Asia
865,299 --- --- --- 172,672(20)
57,282 (6.6)
370,595
(42.8)♦♦
0 91,952(10.6)
716,234(82.8)
11,000(1.3)
0j 50,560(5.8)
24. Mountains of
Southwest
China
263,034 --- --- 64,000(8)
20,996(8)
27,932 (10.6)
--- 26,859 (10.2)
38,929 (14.8)
246,809 (93.8)
125,000
(47.5)
28,525 (10.8)
55,920(21.3)
25. New
Caledonia
19,015 1500(10)
--- 5200(28)
5122(5 [27])d
0 0 0 74 (0.4)
18,354 (96.5)
6000(31.5)
0 3,333 (17.5)
26. New Zealand 270,803 --- --- 59,400(22)
59,443(22)
35,879 (13.2)
73,044(27)
20,790 (7.7)
77,465(28.6)
189,902 (70.1)
76,000(28.1)
43,630 (16.1)
81,803(30.2)
27. Philippines 297,846 8000(3)
--- 9023(3)
20,803(7)
0 9,023(3)
0 1,750(0.6)
94,079 (31.6)
83,000(27.9)
5,062 (1.7)
23,730(8.0)
28. Polynesia-
Micronesia
47,361 --- --- 10,024(21.8)
10,015(21)
0 --- 0 3,101(6.6)
43,118(91)
6000(12.7)
692 (1.5)
2,481(5.2)
26
29. Southwest
Australia
357,516 --- 54,700
(49)♦
33,336(10.8)
107,015(30)
22,021 (6.2)
180,302
(50.4)♦♦
0 126,692(35.4)
215,034 (60.1)
73,000(20.4)
11,914(3.3)j
109,451(30.6)
30. Succulent
Karoo
102,922 --- --- 30,000(26.8)
29,780(29)
17,135 (16.7)
--- 0 48,792(47.4)
102,708 (99.8)
100(0.1)
0j 6,690(6.5)
31. Sundaland 1,504,430
64,000
(33)♦
--- 125,000(7.8)
100,571(6.7)
72,077 (4.8)
305,702
(20.3)
300,720 (20)
372,397(24.8)
825,312 (54.8)
766,000
(50.1)
172,107
(11.4)
342,895(22.8)
32. Tropical
Andes
1,546,119
35,000
(35)♦
--- 314,500(25)
385,661(25)
90,523 (5.9)
123,454
(7.9)♦♦
166,393 (10.8)
332,506(21.5)
1,281,927
(82.9)
426,000
(27.5)
157,513
(10.2)j
515,12433.3)
33. Tumbes-
Chocó-
Magdalena
275,203 74,500
(64)h
63,000(24.2)
65,903(24)
8,441 (3.1)
98,194(35.6)
51,563 (18.7)
18,727(6.8)
158,537 (57.6)
77,000(28)
24,342 (8.6)
81,992 (29.8)
34. Wallacea 339,258 --- --- 52,020(15)
50,774(15)
20,557 (6.1)
31,677(9.3)
50,056 (14.8)
45,794(13.5)
236,736 (69.8)
195,000
(57.5)
42,999 (12.7)
46,794(13.8)
35. Western Ghats
and Sri Lanka
190,037 --- 8700(12)i
12,450(6.8)
43,611(23)
0 0 14,616 (7.7)
220(0.12)
119,072 (62.7)
97,000(51)
0 12,032(6.3)
TOTAL less East
Australia
23,541,137
--- --- --- 3,379,246
(14.3)
1,144,859
(4.8)
--- 1,052,542
(4.4)
2,632,422
(11.1)
14,747,185
(62.6)
6,375,100(27)
911,208
(3.8)
3,456,954
(14.6)
TOTAL 23,796,465
--- --- --- --- 1,144,859
(4.8)
--- 1,063,115
(4.5)
2,695,891
(11.32)
14,988,250 (63)
--- 922,466
(3.9)
3,545,975
(14.9)
Notes: Greyed cells indicate that the study in question delimited the hotspot in question inconsistently with Mittermeier et al. (2004). While absolute aerial
estimates in greyed cells are not directly comparable with those of non-greyed cells, their percentage estimates may still be loosely comparable, except where
denoted with a ♦ symbol. Delimitations for greyed cells were typically more confined than the current delimitations. Slight adjustments to published Expert
27
Estimates and Independent Estimates have been made to enhance comparability, where indicated. Figures for the Independent Estimates were derived by
using the original spatial data, excepting the published estimates of Hannah et al. (1995), Schmitt et al. (2009) and Wright (2010).
♦ As above.
♦♦ Estimate considered inexact (see note (k) below)
Hotspots were delineated according to Conservation International (2004) / Mittermeier et al. (Mittermeier et al., 2004). The recently-designated Forests of
East Australia hotspot was delimited according to Conservation International (2011).
(a) Myers et al. (2000) observe a single east-African hotspot, namely the Eastern Arc and Costal Forests of Tanzania/Kenya, whereas later analyses observe
two hotspots in its place having greater total extent, namely the Eastern Afromontane and the Costal Forest of Eastern Africa. For the sake of comparison, the
estimate of Myers et al. (2000) for the Eastern Arc hotspot are presented for each of these two current hotspots.
(b) Myers et al. (2000) observe a single Indo-Burma hotspot whereas later analyses observe two hotspots in its place, namely the Indo-Burma hotspot and the
relatively much smaller Himalayas hotspot, the latter of which was extended westward as of Mittermeier et al. (2004). Percent-remaining estimates for this
Himalayas hotspot of Mittermeier et al. (2004) may be generally comparable to the Eastern Himalayas hotspot of Myers (1988), although the former extends
considerably further westward into Pakistan and has nearly twice the area of the latter. Estimates by Hannah et al. (1995) are for the single, composite Indo-
Burma hotspot of 2000.
(c) Myers et al. (2000) observe a single Central American hotspot, namely MesoAmerica, whereas later analyses observe two hotspots in its place, namely the
MesoAmerica and the much smaller Madrean Pine-Oak Woodlands. For greater comparability with the single estimate of Myers et al. (2000), later estimates
for these two hotspots should be combined.
28
(d) The 5% estimate of Miettermeier et al. (2004) was erroneous, and is corrected here as 27%.
(e) Myers et al.’s (2000) delimitation encompasses the southern half of Florida, but is otherwise identical to latter delimitations.
(f) Myers’ (1990) delimitation encompasses about half the areas of the presently-defined hotspot. While similar in its oblong north-south shape along the
coast, it does not extend as far into the highlands inland.
(h) Myers (1988) observes a small Western Ecuador hotspot nearby a much larger Colombian Chocó hotspot. These are combined here for greater
comparability with the Tumbes-Chocó-Magdalena hotspot, defined by later analyses. The percentage estimate for Myers (1988) here is an area-weighted
composite of the two constituent hotspots.
(i) Myers (1990) observes a Western Ghats in India hotspot separately to a Southwestern Sri Lankan hotspot. These are combined here for greater
comparability with the estimates for a somewhat larger Western Ghats and Sri Lanka hotspot, defined by later analyses. The percentage estimate for Myers
(1990) here is an area-weighted composite of the two separate hotspots.
(j) Independent Estimate has partial or possibly partial spatial coverage of this hotspot.
(k) As the spatial data of Hannah et al. (1995) are lost, estimates here are compiled from two sources: (i) ‘Percent-undisturbed’ estimates presented for select
hotspots of 2000 by Hannah (2001). These are exact and were rendered as areas with reference to the hotspot areas of Mittermeier et al. (1999); (ii) ‘Percent-
undisturbed’ estimates for the biogeographic provinces of Udvardy (1975), as presented by Hannah et al. (1995). Only estimates for province largely
concordant with the hotspots of Conservation International (2011) were considered, and only data for hotspots with a majority of their extents thus covered
are presented here. Provincial figures were area-weighted according to their overlap with a hotspot. Estimates are designated with a ♦♦ symbol where
considered appreciably inexact.
29
30
Table A2 – The Coefficient of Concentration Describing the Degree to which NIV is Unevenly Distributed Across Biomes, by Hotspot.
HotspotCoefficient of Concentratio
na
NIV Area of Hotspot
as % of Hotspot
Area
Biome
Area ofHotspot-by-
Biome Combination
(km2)b
NIV Area(km2)b
Area of Hotspot-by-
Biome Combination as % of Total Hotspot Area
NIV Area ofHotspot-by-
Biome Combination as % Total NIV Area of
HotspotMaputaland-
Pondoland-Albany45.3 6.4
Montane Grasslands and Shrublands
146,145.4 3,856.5 53.5 21.9
Tropical and Subtropical Moist Broadleaf Forests
48,573.4 2,376.3 17.8 13.5
Tropical and Subtropical Grasslands, Savannas and Srublands
40,173.4 1,026.0 14.7 5.8
Deserts and Xeric Shrublands 24,509.7 9,239.5 9.0 52.4Mediterranean Forests, Woodlands,
and Scrub12,416.3 1,139.4 4.5 6.5
Mangrove 992.5 10.1 0.4 0.1
Eastern Afromontane 37.2 9.1Tropical and Subtropical Moist
Broadleaf Forests500,928.8 80,076.2 49.1 86.4
Montane Grasslands and Shrublands
318,086.8 8,482.2 31.2 9.1
Tropical and Subtropical Grasslands, Savannas and Srublands
111,554.0 3,981.9 10.9 4.3
Deserts and Xeric Shrublands 87,115.3 158.7 8.5 0.2Flooded Grasslands and Savannas 2,382.0 30.6 0.2 0.0
California Floristic Province
35.4 34.8Mediterranean Forests, Woodlands,
and Scrub120,833.3 24,615.3 41.0 23.9
31
Temperate Coniferous Forests 116,697.9 77,223.6 39.6 75.0Temperate Grasslands, Savannas
and Srublands55,191.7 1,091.2 18.7 1.1
Caucasus 34.0 8.2Temperate Broadleaf and Mixed
Forests266,444.1 36,843.8 49.9 83.9
Temperate Grasslands, Savannas and Srublands
191,794.7 1,422.4 35.9 3.2
Deserts and Xeric Shrublands 63,692.4 4,935.0 11.9 11.2Temperate Coniferous Forests 10,724.1 705.1 2.0 1.6
Horn of Africa 33.7 23.8Tropical and Subtropical
Grasslands, Savannas and Srublands
1,054,265.3 383,395.9 63.4 97.1
Deserts and Xeric Shrublands 606,658.1 11,408.3 36.5 2.9
Himalaya 29.0 17.6Montane Grasslands and
Shrublands241,873.9 41,084.4 32.5 31.4
Temperate Broadleaf and Mixed Forests
149,879.9 48,489.4 20.2 37.1
Temperate Coniferous Forests 113,444.9 35,819.3 15.3 27.4Tropical and Subtropical Moist
Broadleaf Forests94,974.9 1,886.0 12.8 1.4
Tropical and Subtropical Coniferous Forests
76,254.1 1,969.2 10.3 1.5
Tropical and Subtropical Grasslands, Savannas and Srublands
34,503.2 43.5 4.6 0.0
Rock and Ice 32,387.1 1,570.9 4.4 1.2
Polynesia-Micronesia 28.2 5.2Tropical and Subtropical Moist
Broadleaf Forests27,909.9 2,170.0 58.9 87.2
Tropical and Subtropical Dry Broadleaf Forests
14,617.7 268.8 30.9 10.8
Tropical and Subtropical 3,354.9 50.6 7.1 2.0
32
Grasslands, Savannas and Srublands
Western Ghats and Sri Lanka
26.2 6.3Tropical and Subtropical Moist
Broadleaf Forests141,405.5 5,832.6 74.4 48.3
Tropical and Subtropical Dry Broadleaf Forests
48,504.1 6,241.1 25.5 51.7
Mesoamerica 25.2 14.1Tropical and Subtropical Moist
Broadleaf Forests559,781.1 119,692.1 49.4 74.6
Tropical and Subtropical Dry Broadleaf Forests
388,968.5 25,474.7 34.3 15.9
Tropical and Subtropical Coniferous Forests
134,847.8 10,725.3 11.9 6.7
Mangrove 35,479.7 4,418.2 3.1 2.8Lacustrine 8,032.4 6.3 0.7 0.0Deserts and Xeric Shrublands 2,456.2 63.3 0.2 0.0
New Caledonia 23.1 17.5Tropical and Subtropical Moist
Broadleaf Forests13,977.8 3,229.5 73.5 96.6
Tropical and Subtropical Dry Broadleaf Forests
4,309.7 114.8 22.7 3.4
Madagascar and the Indian Ocean Islands
22.9 4.4Tropical and Subtropical Moist
Broadleaf Forests318,704.6 19,881.6 53.0 74.9
Tropical and Subtropical Dry Broadleaf Forests
152,280.9 1,854.9 25.3 7.0
Deserts and Xeric Shrublands 123,509.4 4,366.1 20.5 16.4Mangrove 5,172.1 134.5 0.9 0.5Montane Grasslands and
Shrublands1,280.4 319.1 0.2 1.2
Caribbean Islands 21.5 5.8Tropical and Subtropical Moist
Broadleaf Forests86,271.1 6,103.1 37.5 45.7
Tropical and Subtropical Dry 85,633.2 2,610.5 37.2 19.6
33
Broadleaf ForestsTropical and Subtropical
Coniferous Forests24,796.8 2,756.9 10.8 20.7
Mangrove 18,007.6 951.1 7.8 7.1Flooded Grasslands and Savannas 6,302.9 812.8 2.7 6.1Deserts and Xeric Shrublands 6,268.5 106.7 2.7 0.8
Irano-Anatolian 20.5 3.6Temperate Broadleaf and Mixed
Forests581,561.5 25,481.4 64.5 78.4
Temperate Grasslands, Savannas and Srublands
193,486.9 2,298.2 21.5 7.1
Temperate Coniferous Forests 63,392.7 4,394.1 7.0 13.5Montane Grasslands and
Shrublands58,271.9 308.0 6.5 0.9
Lacustrine 4,334.4 - 0.5 0.0
Wallacea 17.9 13.8Tropical and Subtropical Moist
Broadleaf Forests251,159.7 43,190.2 74.0 92.0
Tropical and Subtropical Dry Broadleaf Forests
82,792.7 3,767.7 24.4 8.0
Chilean Winter Rainfall and Valdivian Forests
14.7 34.2Temperate Broadleaf and Mixed
Forests247,832.4 105,130.7 62.3 77.0
Mediterranean Forests, Woodlands, and Scrub
148,679.7 31,444.0 37.4 23.0
New Zealand 12.7 30.2Temperate Broadleaf and Mixed
Forests168,013.8 51,782.5 62.0 63.1
Temperate Grasslands, Savannas and Srublands
52,236.3 8,620.8 19.3 10.5
Montane Grasslands and Shrublands
40,019.1 21,686.1 14.8 26.4
Tundra 624.4 - 0.2 0.0Indo-Burma 12.6 8.7 Tropical and Subtropical Moist 1,797,972.3 182,357.3 75.6 88.2
34
Broadleaf ForestsTropical and Subtropical Dry
Broadleaf Forests515,428.4 22,848.3 21.7 11.1
Mangrove 46,847.8 1,076.2 2.0 0.5Tropical and Subtropical
Coniferous Forests9,726.6 482.5 0.4 0.2
Japan 11.1 8.2Temperate Broadleaf and Mixed
Forests305,651.2 22,385.5 81.7 72.7
Temperate Coniferous Forests 57,050.1 7,834.2 15.2 25.4Tropical and Subtropical Moist
Broadleaf Forests3,693.3 575.5 1.0 1.9
Mountains of Central Asia
7.6 5.8Temperate Grasslands, Savannas
and Srublands425,272.5 22,617.9 49.1 44.6
Montane Grasslands and Shrublands
398,414.6 27,177.4 46.0 53.6
Temperate Coniferous Forests 27,581.7 798.7 3.2 1.6Rock and Ice 7,057.1 80.3 0.8 0.2Lacustrine 6,273.1 - 0.7 0.0
Tumbes-Choco-Magdalena
6.8 29.7Tropical and Subtropical Moist
Broadleaf Forests187,936.2 61,750.9 68.3 75.1
Tropical and Subtropical Dry Broadleaf Forests
62,688.3 14,631.4 22.8 17.8
Mangrove 13,379.8 3,563.0 4.9 4.3Deserts and Xeric Shrublands 8,054.9 2,323.6 2.9 2.8Flooded Grasslands and Savannas 2,943.4 10.6 1.1 0.0
Tropical Andes 6.1 33.3Tropical and Subtropical Moist
Broadleaf Forests791,065.2 280,287.8 51.2 54.9
Montane Grasslands and Shrublands
632,559.3 220,892.7 40.9 43.3
Tropical and Subtropical Dry Broadleaf Forests
113,995.3 9,194.5 7.4 1.8
35
Lacustrine 8,033.4 148.8 0.5 0.0Forests of East
Australia4.7 34.8
Temperate Broadleaf and Mixed Forests
222,263.4 73,752.0 87.1 82.6
Tropical and Subtropical Moist Broadleaf Forests
32,648.0 15,579.8 12.8 17.4
Coastal Forests of Eastern Africa
3.7 3.8Tropical and Subtropical Moist
Broadleaf Forests255,923.5 10,133.9 87.7 90.6
Flooded Grasslands and Savannas 19,554.5 348.0 6.7 3.1Mangrove 16,028.5 698.8 5.5 6.3
Philippines 3.3 8.0Tropical and Subtropical Moist
Broadleaf Forests287,193.0 22,462.9 96.4 94.3
Tropical and Subtropical Coniferous Forests
7,091.8 1,349.7 2.4 5.7
Mediterranean Basin 1.7 4.4Mediterranean Forests, Woodlands,
and Scrub2,040,147.8 91,927.1 97.6 99.3
Temperate Coniferous Forests 23,274.0 464.6 1.1 0.5Montane Grasslands and
Shrublands6,346.7 204.0 0.3 0.2
Temperate Broadleaf and Mixed Forests
4,428.2 10.2 0.2 0.0
Tropical and Subtropical Dry Broadleaf Forests
3,681.7 - 0.2 0.0
Sundaland 1.6 22.8Tropical and Subtropical Moist
Broadleaf Forests1,453,112.8 336,540.5 96.6 97.8
Mangrove 35,900.6 4,714.3 2.4 1.4Montane Grasslands and
Shrublands4,348.6 1,517.5 0.3 0.4
Tropical and Subtropical Coniferous Forests
2,766.4 1,322.4 0.2 0.4
Cerrado 1.5 19.8 Tropical and Subtropical Grasslands, Savannas and
1,919,947.3 380,511.6 94.3 95.8
36
SrublandsTropical and Subtropical Dry
Broadleaf Forests115,366.1 16,784.1 5.7 4.2
Madrean Pine-Oak Woodlands
1.4 18.1Tropical and Subtropical
Coniferous Forests455,350.9 81,941.6 98.5 97.7
Temperate Coniferous Forests 3,984.6 1,928.6 0.9 2.3
Atlantic Forest 0.5 3.5Tropical and Subtropical Moist
Broadleaf Forests1,198,304.3 41,271.3 96.9 97.1
Tropical and Subtropical Grasslands, Savannas and Srublands
27,306.5 765.6 2.2 1.8
Mangrove 10,030.3 464.5 0.8 1.1
Southwest Australia 0 30.6Mediterranean Forests, Woodlands,
and Scrub356,198.8 99,224.3 100.0 100.0
Mountains of Southwest China
0 21.3 Temperate Coniferous Forests 262,377.9 55,629.5 100.0 100.0
Guinean Forests of West Africa
0 10.6Tropical and Subtropical Moist
Broadleaf Forests621,402.5 66,202.2 100.0 100.0
East Melanesian Islands
5 10.7Tropical and Subtropical Moist
Broadleaf Forests94,385.2 10,701.8 100.0 100.0
Cape Floristic Region 0 32.9Mediterranean Forests, Woodlands,
and Scrub78,144.6 25,986.0 100.0 100.0
Succulent Karoo 0 6.5 Deserts and Xeric Shrublands 102,407.0 6,712.8 100.0 100.0
Notes: (a) The coefficient of concentration is a non-denominational index of the degree to which NIV area in a hotspot is spatially concentrated within the one more of the biomes within the hotspot, i.e., distributed across the constituent biomes of the hotspot disproportionately to their relative area within the hotspot (Joseph, 1982). The coefficient ranges from 0 to 100, with higher values indicating greater degrees to which NIV concentrates in a relative few biomes within a hotspot. The coefficient is calculated per hotspot, and only with respect to NIV and biome areas within the hotspot. The coefficient is insensitive to the absolute areas of NIV or the hotspot. (b) Areas here may differ slightly to those in Table 1 due to rounding and the intersections of various spatial data. Aerial discrepancies are of no consequence to the coefficient of concentration.
37
Table A3 – The Global 200 Ecoregions of Olson and Dinerstein (1998, 2002) and Corresponding Ecoregions of Olson et al. (2001) Selected for Analysis
in Table 3, by Biogeographical Realm and Biome.
AFROTROPICAL REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Deserts and Xeric Shrublands Socotra Island Desert Socotra Island xeric shrublands CE 3644Deserts and Xeric Shrublands Madagascar Spiny Thicket Madagascar succulent woodlands CE 79,870Deserts and Xeric Shrublands Madagascar Spiny Thicket Madagascar spiny thickets CE 4Deserts and Xeric Shrublands Arabian Highlands woodlands and shrublands Southwestern Arabian foothills savanna V 275,270Deserts and Xeric Shrublands Arabian Highlands woodlands and shrublands Southwestern Arabian montane woodlands V 87,102Deserts and Xeric Shrublands Arabian Highlands woodlands and shrublands Arabian Peninsula coastal fog desert V 185Mangroves Madagascar Mangroves Madagascar mangroves CE 1Mediterranean Forests, Woodlands, and Scrub
Fynbos Lowland fynbos and renosterveld CE 66
Mediterranean Forests, Woodlands, and Scrub
Fynbos Montane fynbos and renosterveld CE 49
Montane Grasslands and Shrublands
Madagascar Forests and Shrublands Madagascar ericoid thickets CE 108
Montane Grasslands and Shrublands
Ethiopian Highlands Ethiopian montane grasslands and woodlands CE 2753
Montane Grasslands and Shrublands
Ethiopian Highlands Ethiopian montane moorlands CE 1006
Temperate Grasslands, Savannas and Shrublands
Arabian Highlands woodlands and shrublands Al Hajar montane woodlands V 25,604
Tropical and Subtropical Dry Broadleaf Forests
Madagascar Dry Forests Madagascar dry deciduous forests CE 4
Tropical and Subtropical Grasslands, Savannas and Shrublands
Horn of Africa Acacia Savannas Somali Acacia-Commiphora bushlands and thickets
V 1,054,393
Tropical and Subtropical Moist Madagascar Forests and Shrublands Madagascar subhumid forests CE 592
38
Broadleaf ForestsTropical and Subtropical Moist Broadleaf Forests
Madagascar Forests and Shrublands Madagascar lowland forests CE 169
Tropical and Subtropical Moist Broadleaf Forests
Guinean Moist Forests Western Guinean lowland forests CE 205,264
Tropical and Subtropical Moist Broadleaf Forests
Guinean Moist Forests Guinean montane forests CE 6607
Tropical and Subtropical Moist Broadleaf Forests
Guinean Moist Forests Eastern Guinean forests CE 637
Tropical and Subtropical Moist Broadleaf Forests
East African coastal forests Northern Zanzibar-Inhambane coastal forest mosaic
CE 4
Tropical and Subtropical Moist Broadleaf Forests
Albertine Rift Montane Forests Albertine Rift montane forests CE 6016
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
39
AUSTRALASIAN REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Coolgardie woodlands CE 137,681
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Southwest Australia woodlands CE 46,142
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Swan Coastal Plain Scrub and Woodlands CE 15,259
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Jarrah-Karri forest and shrublands CE 10,467
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Southwest Australia savanna CE 630
Mediterranean Forests, Woodlands, and Scrub
Southwestern Australia Forests and Scrub Esperance mallee CE 0
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests North Island temperate forests V 84,580
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests Nelson Coast temperate forests V 14,608
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests South Island temperate forests V 11,711
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests Westland temperate forests V 1504
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests Richmond temperate forests V 160
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests Fiordland temperate forests V 20
Temperate Broadleaf and Mixed Forests
New Zealand Temperate Forests Northland temperate kauri forests V 9
Tropical and Subtropical Dry Broadleaf Forests
Nusu Tenggara dry forests Timor and Wetar deciduous forests CE 42
Tropical and Subtropical Dry Broadleaf Forests
Nusu Tenggara dry forests Lesser Sundas deciduous forests CE 5
40
Tropical and Subtropical Dry Broadleaf Forests
New Caledonia dry forests New Caledonia dry forests CE 4005
Tropical and Subtropical Moist Broadleaf Forests
Sulawesi moist forests Sulawesi montane rain forests CE 57,674
Tropical and Subtropical Moist Broadleaf Forests
Sulawesi moist forests Sulawesi lowland rain forests CE 8
Tropical and Subtropical Moist Broadleaf Forests
Solomons-Vanuatu-Bismarck moist forests New Britain-New Ireland montane rain forests V 1727
Tropical and Subtropical Moist Broadleaf Forests
Solomons-Vanuatu-Bismarck moist forests Vanuatu rain forests V 511
Tropical and Subtropical Moist Broadleaf Forests
Solomons-Vanuatu-Bismarck moist forests Solomon Islands rain forests V 180
Tropical and Subtropical Moist Broadleaf Forests
Solomons-Vanuatu-Bismarck moist forests New Britain-New Ireland lowland rain forests V 37
Tropical and Subtropical Moist Broadleaf Forests
Queensland Tropical Forests Queensland tropical rain forests V 117
Tropical and Subtropical Moist Broadleaf Forests
New Caledonia moist forests New Caledonia rain forests CE 8
Tropical and Subtropical Moist Broadleaf Forests
Moluccas moist forests Halmahera rain forests V 89
Tropical and Subtropical Moist Broadleaf Forests
Moluccas moist forests Seram rain forests V 8
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
41
INDO-MALAYIAN REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Mangroves Greater Sundas mangroves Sunda Shelf mangroves CE 36Montane Grasslands and Shrublands
Kinabalu montane shrublands Kinabalu montane alpine meadows RS 4349
Temperate Broadleaf and Mixed Forests
Western Himalayan temperate forests Western Himalayan broadleaf forests CE 203
Temperate Broadleaf and Mixed Forests
Eastern Himalayan broadleaf and conifer forests
Eastern Himalayan broadleaf forests V 30,118
Temperate Broadleaf and Mixed Forests
Eastern Himalayan broadleaf and conifer forests
Northern Triangle temperate forests V 10,750
Temperate Coniferous Forests Western Himalayan temperate forests Western Himalayan subalpine conifer forests CE 57Temperate Coniferous Forests Eastern Himalayan broadleaf and conifer
forestsEastern Himalayan subalpine conifer forests V 338
Tropical and Subtropical Coniferous Forests
Sumatran Islands lowland and montane forests Sumatran tropical pine forests CE 900
Tropical and Subtropical Coniferous Forests
Philippines moist forests Luzon tropical pine forests CE 7092
Tropical and Subtropical Coniferous Forests
Naga-Manapuri-Chin Hills moist forests Northeast India-Myanmar pine forests V 8040
Tropical and Subtropical Dry Broadleaf Forests
Indochina dry forests Southeastern Indochina dry evergreen forests CE 124,530
Tropical and Subtropical Dry Broadleaf Forests
Indochina dry forests Central Indochina dry forests CE 18,364
Tropical and Subtropical Grasslands, Savannas and Shrublands
Terai-Duar savannas and grasslands Terai-Duar savanna and grasslands CE 12,151
Tropical and Subtropical Moist Broadleaf Forests
Western Java montane forests Western Java montane rain forests CE 26,342
Tropical and Subtropical Moist Broadleaf Forests
Sumatran Islands lowland and montane forests Sumatran montane rain forests CE 33,536
Tropical and Subtropical Moist Sumatran Islands lowland and montane forests Sumatran lowland rain forests CE 29
42
Broadleaf ForestsTropical and Subtropical Moist Broadleaf Forests
Sri Lankan moist forest Sri Lanka lowland rain forests CE 12,582
Tropical and Subtropical Moist Broadleaf Forests
Sri Lankan moist forest Sri Lanka montane rain forests CE 335
Tropical and Subtropical Moist Broadleaf Forests
Southwestern Ghats moist forest South Western Ghats moist deciduous forests CE 23,824
Tropical and Subtropical Moist Broadleaf Forests
Southwestern Ghats moist forest South Western Ghats montane rain forests CE 22,686
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Mindoro rain forests CE 9932
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Mindanao montane rain forests CE 2366
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Luzon montane rain forests CE 453
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Mindanao-Eastern Visayas rain forests CE 100
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Greater Negros-Panay rain forests CE 11
Tropical and Subtropical Moist Broadleaf Forests
Philippines moist forests Luzon rain forests CE 5
Tropical and Subtropical Moist Broadleaf Forests
Peninsular Malaysia lowland and montane forests
Peninsular Malaysian montane rain forests V 13,724
Tropical and Subtropical Moist Broadleaf Forests
Peninsular Malaysia lowland and montane forests
Peninsular Malaysian rain forests V 5
Tropical and Subtropical Moist Broadleaf Forests
Palawan moist forests Palawan rain forests CE 5
Tropical and Subtropical Moist Broadleaf Forests
Naga-Manapuri-Chin Hills moist forests Mizoram-Manipur-Kachin rain forests V 135,855
Tropical and Subtropical Moist Broadleaf Forests
Naga-Manapuri-Chin Hills moist forests Northern Triangle subtropical forests V 53,993
Tropical and Subtropical Moist Broadleaf Forests
Naga-Manapuri-Chin Hills moist forests Meghalaya subtropical forests V 41,804
Tropical and Subtropical Moist Broadleaf Forests
Naga-Manapuri-Chin Hills moist forests Chin Hills-Arakan Yoma montane forests V 29,764
43
Tropical and Subtropical Moist Broadleaf Forests
Kayah-Karen/Tenasserim moist forests Kayah-Karen montane rain forests V 119,802
Tropical and Subtropical Moist Broadleaf Forests
Kayah-Karen/Tenasserim moist forests Tenasserim-South Thailand semi-evergreen rain forests
V 6
Tropical and Subtropical Moist Broadleaf Forests
Cardamom Mountains moist forests Cardamom Mountains rain forests RS 5
Tropical and Subtropical Moist Broadleaf Forests
Borneo lowland and montane forests Borneo montane rain forests CE 3337
Tropical and Subtropical Moist Broadleaf Forests
Borneo lowland and montane forests Borneo lowland rain forests CE 4
Tropical and Subtropical Moist Broadleaf Forests
Annamite Range moist forests Northern Annamites rain forests V 47,311
Tropical and Subtropical Moist Broadleaf Forests
Annamite Range moist forests Southern Annamites montane rain forests V 1
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
44
NEARTIC REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Mediterranean Forests, Woodlands, and Scrub
California chaparral and woodlands California montane chaparral and woodlands CE 15,867
Mediterranean Forests, Woodlands, and Scrub
California chaparral and woodlands California coastal sage and chaparral CE 200
Mediterranean Forests, Woodlands, and Scrub
California chaparral and woodlands California interior chaparral and woodlands CE 6
Temperate Coniferous Forests Sierra Nevada Coniferous Forests Sierra Nevada forests CE 52,951Temperate Coniferous Forests Klamath-Siskiyou coniferous forests Klamath-Siskiyou forests CE 50,412Tropical and Subtropical Coniferous Forests
Sierra Madre Oriental and Occidental pine-oak forests
Sierra Madre Occidental pine-oak forests CE 2028
Tropical and Subtropical Coniferous Forests
Sierra Madre Oriental and Occidental pine-oak forests
Sierra Madre Oriental pine-oak forests CE 85
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
45
NEOTROPICAL REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Mediterranean Forests, Woodlands, and Scrub
Chilean Matorral Chilean matorral CE 148,835
Montane Grasslands and Shrublands
Central Andean Dry Puna Central Andean dry puna V 254,089
Temperate Broadleaf and Mixed Forests
Valdivian Temperate Rain Forests / Juan Fernández Islands
Valdivian temperate forests CE 10
Tropical and Subtropical Coniferous Forests
Sierra Madre Oriental and Occidental pine-oak forests
Sierra de la Laguna pine-oak forests CE 1066
Tropical and Subtropical Coniferous Forests
Mesoamerican Pine-Oak Forests Central American pine-oak forests CE 125
Tropical and Subtropical Coniferous Forests
Mesoamerican Pine-Oak Forests Trans-Mexican Volcanic Belt pine-oak forests CE 102
Tropical and Subtropical Coniferous Forests
Mesoamerican Pine-Oak Forests Sierra Madre del Sur pine-oak forests CE 38
Tropical and Subtropical Coniferous Forests
Mesoamerican Pine-Oak Forests Sierra Madre de Oaxaca pine-oak forests CE 26
Tropical and Subtropical Coniferous Forests
Greater Antillean Pine Forests Hispaniolan pine forests CE 2199
Tropical and Subtropical Coniferous Forests
Greater Antillean Pine Forests Cuban pine forests CE 70
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Tumbes-Piura dry forests CE 41,372
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Magdalena Valley dry forests CE 19,679
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Ecuadorian dry forests CE 15,552
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Marañón dry forests CE 11,397
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Cauca Valley dry forests CE 7361
46
Tropical and Subtropical Dry Broadleaf Forests
Tumbesian-Andean Valleys Dry Forests Patía Valley dry forests CE 2276
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Sinaloan dry forests CE 77,646
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Southern Pacific dry forests CE 42,520
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Bajío dry forests CE 37,574
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Chiapas Depression dry forests CE 14,053
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Sierra de la Laguna dry forests CE 3429
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Balsas dry forests CE 65
Tropical and Subtropical Dry Broadleaf Forests
Southern Mexican Dry Forests Jalisco dry forests CE 19
Tropical and Subtropical Dry Broadleaf Forests
Atlantic Dry Forests Atlantic dry forests CE 8727
Tropical and Subtropical Grasslands, Savannas and Shrublands
Cerrado Woodlands and Savannas Cerrado V 858
Tropical and Subtropical Grasslands, Savannas and Shrublands
Atlantic Forests Campos Rupestres montane savanna CE 1012
Tropical and Subtropical Moist Broadleaf Forests
Talamancan-Isthmian Pacific Forests Talamancan montane forests RS 1447
Tropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Magdalena Valley montane forests CE 105,289
Tropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Eastern Cordillera real montane forests CE 102,745
Tropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Northwestern Andean montane forests CE 81,346
Tropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Cordillera Oriental montane forests CE 68,021
Tropical and Subtropical Moist Northern Andean Montane Forests Cauca Valley montane forests CE 32,127
47
Broadleaf ForestsTropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Venezuelan Andes montane forests CE 29,457
Tropical and Subtropical Moist Broadleaf Forests
Northern Andean Montane Forests Santa Marta montane forests CE 4795
Tropical and Subtropical Moist Broadleaf Forests
Mesoamerican Pine-Oak Forests Chimalapas montane forests CE 2088
Tropical and Subtropical Moist Broadleaf Forests
Mesoamerican Pine-Oak Forests Central American montane forests CE 48
Tropical and Subtropical Moist Broadleaf Forests
Coastal Venezuela Montane Forests Cordillera La Costa montane forests V 1230
Tropical and Subtropical Moist Broadleaf Forests
Chocó-Darién Moist Forests Magdalena-Urabá moist forests RS 76,934
Tropical and Subtropical Moist Broadleaf Forests
Chocó-Darién Moist Forests Western Ecuador moist forests RS 34,183
Tropical and Subtropical Moist Broadleaf Forests
Chocó-Darién Moist Forests Eastern Panamanian montane forests RS 233
Tropical and Subtropical Moist Broadleaf Forests
Chocó-Darién Moist Forests Chocó-Darién moist forests RS 15
Tropical and Subtropical Moist Broadleaf Forests
Central Andean Yungas Bolivian Yungas CE 90,748
Tropical and Subtropical Moist Broadleaf Forests
Central Andean Yungas Peruvian Yungas CE 33,887
Tropical and Subtropical Moist Broadleaf Forests
Central Andean Yungas Southern Andean Yungas CE 13,591
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Araucaria moist forests CE 211,731
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Bahia coastal forests CE 106,926
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Serra do Mar coastal forests CE 102,163
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Alto Paraná Atlantic forests CE 31,703
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Pernambuco interior forests CE 8799
48
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Bahia interior forests CE 1143
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Caatinga Enclaves moist forests CE 872
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Pernambuco coastal forests CE 852
Tropical and Subtropical Moist Broadleaf Forests
Atlantic Forests Atlantic Coast restingas CE 44
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
49
OCEANIC REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Tropical and Subtropical Dry Broadleaf Forests
Southern Pacific Islands forests Fiji tropical dry forests CE 2339
Tropical and Subtropical Dry Broadleaf Forests
Hawaii dry forest Hawaii tropical dry forests CE 4459
Tropical and Subtropical Grasslands, Savannas and Shrublands
Hawaii dry forest Hawaii tropical low shrublands CE 374
Tropical and Subtropical Grasslands, Savannas and Shrublands
Hawaii dry forest Hawaii tropical high shrublands CE 159
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Samoan tropical moist forests CE 1746
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Marquesas tropical moist forests CE 83
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Society Islands tropical moist forests CE 28
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Cook Islands tropical moist forests CE 27
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Tuamotu tropical moist forests CE 9
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Tongan tropical moist forests CE 7
Tropical and Subtropical Moist Broadleaf Forests
Southern Pacific Islands forests Fiji tropical moist forests CE 1
Tropical and Subtropical Moist Broadleaf Forests
Hawaii moist forest Hawaii tropical moist forests CE 3225
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
50
PALEARTIC REALM
Biome Global 200 Ecoregion EcoregionGlobal 200 Ecoregion
Statusa
Ecoregion Area(km2)
Montane Grasslands and Shrublands
Middle Asian montane woodlands and steppe Tian Shan montane steppe and meadows V 7654
Montane Grasslands and Shrublands
Middle Asian montane woodlands and steppe Pamir alpine desert and tundra V 834
Montane Grasslands and Shrublands
Middle Asian montane woodlands and steppe Hindu Kush alpine meadow V 604
Montane Grasslands and Shrublands
Eastern Himalayan alpine meadows Eastern Himalayan alpine shrub and meadows RS 54
Montane Grasslands and Shrublands
Caucasus-Anatolian-Hyrcanian temperate forests
Kopet Dag woodlands and forest steppe CE 55,799
Temperate Broadleaf and Mixed Forests
Caucasus-Anatolian-Hyrcanian temperate forests
Caucasus mixed forests CE 170,663
Temperate Broadleaf and Mixed Forests
Caucasus-Anatolian-Hyrcanian temperate forests
Caspian Hyrcanian mixed forests CE 53,356
Temperate Broadleaf and Mixed Forests
Caucasus-Anatolian-Hyrcanian temperate forests
Euxine-Colchic broadleaf forests CE 3126
Temperate Coniferous Forests Middle Asian montane woodlands and steppe Tian Shan montane conifer forests V 573Temperate Coniferous Forests Hengduan Shan conifer forests Hengduan Mountains subalpine conifer forests RS 99,641Temperate Coniferous Forests Hengduan Shan conifer forests Nujiang Langcang Gorge alpine conifer and
mixed forestsRS 81,225
Temperate Coniferous Forests Hengduan Shan conifer forests Qionglai-Minshan conifer forests RS 1513Temperate Coniferous Forests Eastern Himalayan broadleaf and conifer
forestsNortheastern Himalayan subalpine conifer forests
V 37,847
Temperate Coniferous Forests Caucasus-Anatolian-Hyrcanian temperate forests
Northern Anatolian conifer and deciduous forests
CE 98,528
Temperate Coniferous Forests Caucasus-Anatolian-Hyrcanian temperate forests
Elburz Range forest steppe CE 50,629
Temperate Grasslands, Savannas and Shrublands
Middle Asian montane woodlands and steppe Gissaro-Alai open woodlands V 168,399
Temperate Grasslands, Middle Asian montane woodlands and steppe Tian Shan foothill arid steppe V 129,285
51
Savannas and ShrublandsTemperate Grasslands, Savannas and Shrublands
Middle Asian montane woodlands and steppe Alai-Western Tian Shan steppe V 127,805
Notes: (a) CE = ‘Critically Endangered’, V = ‘Vulnerable’, and RS = ‘Relatively Stable / Intact’, after Olson and Dinerstein (2002).
52
Table A4 – Examination of Natural Intact Vegetation for the Atlantic Forest Hotspot.
Points Sampled within
Reference Dataset
Coincident Points
Non-Coincident PointsPossibility i:
NIV Disturbance
Filters
Possibility ii: Discrepant Geometries
Possibility iii:In Smaller Fragments (≤100 ha)
Possibility iv: In Larger Fragments (>100 ha)
5778822
(14.2%)2347
(40.6%)551
(9.5%)821
(14.2%)1237
(21.4%)
Notes: Percentages are with respect to the total number of sample points.
53
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