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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

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

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


318,086.8 8,482.2 31.2 9.1


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


Tropical and Subtropical Coniferous Forests

76,254.1 1,969.2 10.3 1.5


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


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



48,504.1 6,241.1 25.5 51.7

Mesoamerica 25.2 14.1Tropical and Subtropical Moist



388,968.5 25,474.7 34.3 15.9


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



4,309.7 114.8 22.7 3.4

Madagascar and the Indian Ocean Islands




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


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


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



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


52,236.3 8,620.8 19.3 10.5


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


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


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




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



632,559.3 220,892.7 40.9 43.3


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


222,263.4 73,752.0 87.1 82.6


32,648.0 15,579.8 12.8 17.4

Coastal Forests of Eastern Africa



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



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


4,428.2 10.2 0.2 0.0


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


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


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


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


East Melanesian Islands

5 10.7Tropical and Subtropical Moist


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


Fynbos Montane fynbos and renosterveld CE 49


Madagascar Forests and Shrublands Madagascar ericoid thickets CE 108


Ethiopian Highlands Ethiopian montane grasslands and woodlands CE 2753


Ethiopian Highlands Ethiopian montane moorlands CE 1006

Temperate Grasslands, Savannas and Shrublands

Arabian Highlands woodlands and shrublands Al Hajar montane woodlands V 25,604


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


Guinean Moist Forests Western Guinean lowland forests CE 205,264


Guinean Moist Forests Guinean montane forests CE 6607


Guinean Moist Forests Eastern Guinean forests CE 637


East African coastal forests Northern Zanzibar-Inhambane coastal forest mosaic

CE 4


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


Statusa

Ecoregion Area(km2)


Southwestern Australia Forests and Scrub Coolgardie woodlands CE 137,681


Southwestern Australia Forests and Scrub Southwest Australia woodlands CE 46,142


Southwestern Australia Forests and Scrub Swan Coastal Plain Scrub and Woodlands CE 15,259


Southwestern Australia Forests and Scrub Jarrah-Karri forest and shrublands CE 10,467


Southwestern Australia Forests and Scrub Southwest Australia savanna CE 630


Southwestern Australia Forests and Scrub Esperance mallee CE 0


New Zealand Temperate Forests North Island temperate forests V 84,580


New Zealand Temperate Forests Nelson Coast temperate forests V 14,608


New Zealand Temperate Forests South Island temperate forests V 11,711


New Zealand Temperate Forests Westland temperate forests V 1504


New Zealand Temperate Forests Richmond temperate forests V 160


New Zealand Temperate Forests Fiordland temperate forests V 20


New Zealand Temperate Forests Northland temperate kauri forests V 9


Nusu Tenggara dry forests Timor and Wetar deciduous forests CE 42


Nusu Tenggara dry forests Lesser Sundas deciduous forests CE 5

40


New Caledonia dry forests New Caledonia dry forests CE 4005


Sulawesi moist forests Sulawesi montane rain forests CE 57,674


Sulawesi moist forests Sulawesi lowland rain forests CE 8


Solomons-Vanuatu-Bismarck moist forests New Britain-New Ireland montane rain forests V 1727


Solomons-Vanuatu-Bismarck moist forests Vanuatu rain forests V 511


Solomons-Vanuatu-Bismarck moist forests Solomon Islands rain forests V 180


Solomons-Vanuatu-Bismarck moist forests New Britain-New Ireland lowland rain forests V 37


Queensland Tropical Forests Queensland tropical rain forests V 117


New Caledonia moist forests New Caledonia rain forests CE 8


Moluccas moist forests Halmahera rain forests V 89


Moluccas moist forests Seram rain forests V 8


41

INDO-MALAYIAN REALM


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


Western Himalayan temperate forests Western Himalayan broadleaf forests CE 203


Eastern Himalayan broadleaf and conifer forests

Eastern Himalayan broadleaf forests V 30,118


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


Sumatran Islands lowland and montane forests Sumatran tropical pine forests CE 900


Philippines moist forests Luzon tropical pine forests CE 7092


Naga-Manapuri-Chin Hills moist forests Northeast India-Myanmar pine forests V 8040


Indochina dry forests Southeastern Indochina dry evergreen forests CE 124,530


Indochina dry forests Central Indochina dry forests CE 18,364


Terai-Duar savannas and grasslands Terai-Duar savanna and grasslands CE 12,151


Western Java montane forests Western Java montane rain forests CE 26,342


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


Sri Lankan moist forest Sri Lanka lowland rain forests CE 12,582


Sri Lankan moist forest Sri Lanka montane rain forests CE 335


Southwestern Ghats moist forest South Western Ghats moist deciduous forests CE 23,824


Southwestern Ghats moist forest South Western Ghats montane rain forests CE 22,686


Philippines moist forests Mindoro rain forests CE 9932


Philippines moist forests Mindanao montane rain forests CE 2366


Philippines moist forests Luzon montane rain forests CE 453


Philippines moist forests Mindanao-Eastern Visayas rain forests CE 100


Philippines moist forests Greater Negros-Panay rain forests CE 11


Philippines moist forests Luzon rain forests CE 5


Peninsular Malaysia lowland and montane forests

Peninsular Malaysian montane rain forests V 13,724


Peninsular Malaysia lowland and montane forests

Peninsular Malaysian rain forests V 5


Palawan moist forests Palawan rain forests CE 5


Naga-Manapuri-Chin Hills moist forests Mizoram-Manipur-Kachin rain forests V 135,855


Naga-Manapuri-Chin Hills moist forests Northern Triangle subtropical forests V 53,993


Naga-Manapuri-Chin Hills moist forests Meghalaya subtropical forests V 41,804


Naga-Manapuri-Chin Hills moist forests Chin Hills-Arakan Yoma montane forests V 29,764

43


Kayah-Karen/Tenasserim moist forests Kayah-Karen montane rain forests V 119,802


Kayah-Karen/Tenasserim moist forests Tenasserim-South Thailand semi-evergreen rain forests

V 6


Cardamom Mountains moist forests Cardamom Mountains rain forests RS 5


Borneo lowland and montane forests Borneo montane rain forests CE 3337


Borneo lowland and montane forests Borneo lowland rain forests CE 4


Annamite Range moist forests Northern Annamites rain forests V 47,311


Annamite Range moist forests Southern Annamites montane rain forests V 1


44

NEARTIC REALM


Statusa

Ecoregion Area(km2)


California chaparral and woodlands California montane chaparral and woodlands CE 15,867


California chaparral and woodlands California coastal sage and chaparral CE 200


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



Sierra Madre Oriental pine-oak forests CE 85


45

NEOTROPICAL REALM


Statusa

Ecoregion Area(km2)


Chilean Matorral Chilean matorral CE 148,835


Central Andean Dry Puna Central Andean dry puna V 254,089


Valdivian Temperate Rain Forests / Juan Fernández Islands

Valdivian temperate forests CE 10



Sierra de la Laguna pine-oak forests CE 1066


Mesoamerican Pine-Oak Forests Central American pine-oak forests CE 125


Mesoamerican Pine-Oak Forests Trans-Mexican Volcanic Belt pine-oak forests CE 102


Mesoamerican Pine-Oak Forests Sierra Madre del Sur pine-oak forests CE 38


Mesoamerican Pine-Oak Forests Sierra Madre de Oaxaca pine-oak forests CE 26


Greater Antillean Pine Forests Hispaniolan pine forests CE 2199


Greater Antillean Pine Forests Cuban pine forests CE 70


Tumbesian-Andean Valleys Dry Forests Tumbes-Piura dry forests CE 41,372


Tumbesian-Andean Valleys Dry Forests Magdalena Valley dry forests CE 19,679


Tumbesian-Andean Valleys Dry Forests Ecuadorian dry forests CE 15,552


Tumbesian-Andean Valleys Dry Forests Marañón dry forests CE 11,397


Tumbesian-Andean Valleys Dry Forests Cauca Valley dry forests CE 7361

46


Tumbesian-Andean Valleys Dry Forests Patía Valley dry forests CE 2276


Southern Mexican Dry Forests Sinaloan dry forests CE 77,646


Southern Mexican Dry Forests Southern Pacific dry forests CE 42,520


Southern Mexican Dry Forests Bajío dry forests CE 37,574


Southern Mexican Dry Forests Chiapas Depression dry forests CE 14,053


Southern Mexican Dry Forests Sierra de la Laguna dry forests CE 3429


Southern Mexican Dry Forests Balsas dry forests CE 65


Southern Mexican Dry Forests Jalisco dry forests CE 19


Atlantic Dry Forests Atlantic dry forests CE 8727


Cerrado Woodlands and Savannas Cerrado V 858


Atlantic Forests Campos Rupestres montane savanna CE 1012


Talamancan-Isthmian Pacific Forests Talamancan montane forests RS 1447


Northern Andean Montane Forests Magdalena Valley montane forests CE 105,289


Northern Andean Montane Forests Eastern Cordillera real montane forests CE 102,745


Northern Andean Montane Forests Northwestern Andean montane forests CE 81,346


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


Northern Andean Montane Forests Venezuelan Andes montane forests CE 29,457


Northern Andean Montane Forests Santa Marta montane forests CE 4795


Mesoamerican Pine-Oak Forests Chimalapas montane forests CE 2088


Mesoamerican Pine-Oak Forests Central American montane forests CE 48


Coastal Venezuela Montane Forests Cordillera La Costa montane forests V 1230


Chocó-Darién Moist Forests Magdalena-Urabá moist forests RS 76,934


Chocó-Darién Moist Forests Western Ecuador moist forests RS 34,183


Chocó-Darién Moist Forests Eastern Panamanian montane forests RS 233


Chocó-Darién Moist Forests Chocó-Darién moist forests RS 15


Central Andean Yungas Bolivian Yungas CE 90,748


Central Andean Yungas Peruvian Yungas CE 33,887


Central Andean Yungas Southern Andean Yungas CE 13,591


Atlantic Forests Araucaria moist forests CE 211,731


Atlantic Forests Bahia coastal forests CE 106,926


Atlantic Forests Serra do Mar coastal forests CE 102,163


Atlantic Forests Alto Paraná Atlantic forests CE 31,703


Atlantic Forests Pernambuco interior forests CE 8799

48


Atlantic Forests Bahia interior forests CE 1143


Atlantic Forests Caatinga Enclaves moist forests CE 872


Atlantic Forests Pernambuco coastal forests CE 852


Atlantic Forests Atlantic Coast restingas CE 44


49

OCEANIC REALM


Statusa

Ecoregion Area(km2)


Southern Pacific Islands forests Fiji tropical dry forests CE 2339


Hawaii dry forest Hawaii tropical dry forests CE 4459


Hawaii dry forest Hawaii tropical low shrublands CE 374


Hawaii dry forest Hawaii tropical high shrublands CE 159


Southern Pacific Islands forests Samoan tropical moist forests CE 1746


Southern Pacific Islands forests Marquesas tropical moist forests CE 83


Southern Pacific Islands forests Society Islands tropical moist forests CE 28


Southern Pacific Islands forests Cook Islands tropical moist forests CE 27


Southern Pacific Islands forests Tuamotu tropical moist forests CE 9


Southern Pacific Islands forests Tongan tropical moist forests CE 7


Southern Pacific Islands forests Fiji tropical moist forests CE 1


Hawaii moist forest Hawaii tropical moist forests CE 3225


50

PALEARTIC REALM


Statusa

Ecoregion Area(km2)


Middle Asian montane woodlands and steppe Tian Shan montane steppe and meadows V 7654


Middle Asian montane woodlands and steppe Pamir alpine desert and tundra V 834


Middle Asian montane woodlands and steppe Hindu Kush alpine meadow V 604


Eastern Himalayan alpine meadows Eastern Himalayan alpine shrub and meadows RS 54


Caucasus-Anatolian-Hyrcanian temperate forests

Kopet Dag woodlands and forest steppe CE 55,799



Caucasus mixed forests CE 170,663



Caspian Hyrcanian mixed forests CE 53,356



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


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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