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www.sciencemag.org/cgi/content/full/337/6091/228/DC1

Supplementary Materials for

Extinction Debt and Windows of Conservation Opportunity in the Brazilian Amazon

Oliver R. Wearn, Daniel C. Reuman, Robert M. Ewers*

*To whom correspondence should be addressed. E-mail: r.ewers@imperial.ac.uk

Published 13 July 2012, Science 337, 228 (2012)

DOI: 10.1126/science.1219013

This PDF file includes:

Materials and Methods Figs. S1 to S8 Tables S1 and S2 References

Other Supplementary Material for this manuscript includes the following: available at www.sciencemag.org/cgi/content/full/337/6091/228/DC1

Movies S1 to S4

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Extinction Debt and Windows of Conservation Opportunity in the

Brazilian Amazon

Oliver R. WEARN1,2, Daniel C. REUMAN1,3 and Robert M. EWERS1*

1Imperial College London, Silwood Park, Ascot SL5 7PY, UK

2Zoological Society of London, Institute of Zoology, London NW1 4RY, UK

3Rockefeller University, New York 10065, USA

*Corresponding author: Ewers, R. M. (r.ewers@imperial.ac.uk)

Supplementary Material

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Materials and Methods

Range overlay maps for Amazonian forest-dependent vertebrates

Geographic range maps for mammals and amphibians were obtained from the respective IUCN

Global Species Assessments (18). These datasets do not include some species categorised as Data

Deficient which are missing range maps, but the vast majority of species occurring in the region

(>98% for both groups) are included. For birds, we used the NatureServe Birds of the Western

Hemisphere dataset (26). Some species in the bird dataset only had point locality data. For poorly

known taxa, points were buffered with a radius of 100 km. For species with larger numbers of

records, minimum convex polygon range maps were used. In some cases there were inconsistencies

between the IUCN taxonomy for birds (which largely follows BirdLife International) and that of

Ridgely et al. (26). In general, BirdLife International uses a more conservative approach with regard

to sub-species versus species designation, and we here followed this by merging the sub-species

range maps together.

Since SAR-based extinction predictions are undermined by incorporating disturbance-tolerant

species, we filtered our species lists at the outset, leaving only the forest-dependent species. We

obtained habitat data for each species from the IUCN Species Information Service (SIS,

www.iucnredlist.org), a database consisting of information collated from published and grey

literature sources, as well as expert consultation and regional workshops. For mammals (n = 204)

and birds (n = 332), forest-dependents were defined as species that have been recorded only from

natural forest (excluding, for example, species known to occur in savanna, wetland, secondary

growth or agricultural land). In the case of amphibians (n = 214), forest species known to occur in

inland wetlands (including, for example, permanent and seasonal pools and streams) were not

excluded, owing to the two-stage life cycle of most amphibians (27). Whilst every bird species had

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associated habitat info, some amphibians (n = 5) and mammals (n = 7) did not. In these cases, we

assigned a single habitat type based on the species account given or, as necessary, wider literature

sources.

To obtain species richness estimates for each cell, range overlay maps were created in ArcGIS (28),

implementing a Visual Basic for Applications (VBA) script to count only species whose range covered

more than 20% of a grid cell.

Along with the species richness (R

iS ), each grid cell was associated with a value for the area over

which species had been summed ( iA )0( ). Typically, geographic range maps represent the “extent of

occurrence” of a species (18), which may amount to a minimum convex polygon with little regard for

the finer-scale patterns of habitat occurrence. As a result, in most cases iA )0( was simply defined by

the grid spacing (i.e. 50 x 50 km2) but, where all range maps had obviously been clipped to a

geographic feature (such as the Atlantic coastal boundary), iA )0( was lower than the nominal cell

size.

Forest cover time-series data for the Brazilian Amazon

In order to estimate extinction debt and species loss across Brazilian Amazonia, we required high-

resolution maps of forest cover at more or less regular time intervals. Ideally, forest cover time-

series would also extend back to when forests were last in equilibrium.

Forest cover estimates for the recent period (1998 and 2001-2008) were derived from PRODES (the

‘Monitoring Gross Deforestation in the Amazon Project’). As part of PRODES, Instituto Nacional de

Pesquisas Espaciais (INPE) have created a high-resolution, spatially-explicit digitised product on an

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annual basis since 2000 (following a ‘reference’ map established in 1997). The PRODES product

derives from a mosaic of Landsat Thematic Mapper (TM) images, mostly taken during the height of

the dry season when cloud cover is lowest (typically in August), which is then analysed by a

combination of automated classification (29) and manual photo interpretation. It is then made

publicly available as a categorised land cover map in the format of a 120 m resolution raster (20).

All Landsat-based analyses of deforestation suffer from extensive areas with no data, due to cloud

cover present during each satellite pass. Cloud cover is usually extensive over Amazonia, and occurs

throughout the year; it is especially problematic in the northern Brazilian Amazon (30). We took the

conservative approach of excluding cloud-covered pixels from forest cover estimates (other pixels

with no data were dealt with in the same way). This will ultimately lead to a slight underestimation

of absolute quantities of extinction debt and species loss in some cells (mostly in Amapá state, which

has the greatest cloud coverage), but does not affect the percentage estimates of these quantities.

Moreover, cells with less than 20 % observed forest cover in their pristine state were excluded from

all further analyses. This also removed the large areas of cerrado (and other natural, non-forest

habitats) that the Brazilian Legal Amazon encompasses.

We used the PRODES pixel classes to construct maps of forest cover for 1998 and each year between

2001 and 2008, as well as a “pristine” forest cover map. The “pristine” forest cover map was

ascribed to the year 1970; this year is generally agreed to be the beginning of the post-Columbian,

“modern era” of major deforestation (31, 32) and is therefore suitable as our baseline datum for

when forests are assumed to be at equilibrium. Furthermore, we were able to add intervening data

points using layers of “old” (pre-PRODES) deforestation apportioned to three broad time periods

(1970-1977, 1978-1987 and 1988-1991) (31). Since exact dates could not be attributed to this early

deforestation data, we considered three scenarios in order to capture the resulting uncertainty: (1)

deforestation within each cell was apportioned equally throughout the period considered

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(“Interpolated”); (2) forest was lost in a single episode at the beginning of the period (“Immediate”),

or (3) all deforestation occurred in a single episode at the very end of the period (“Delayed”). We

consider the first scenario to be the most reasonable. All estimates of extinction debt will be

bounded from above by the Delayed scenario and from below by the Immediate scenario (vice versa

for estimates of species loss).

Scenarios of future deforestation – simulation models and policy targets

‘Business as Usual’ and ‘Governance’ scenarios from SimAmazonia

We used two spatially-explicit scenarios of future deforestation in the Brazilian Amazon – “business

as usual” (BAU) and “governance” (GOV) – as modelled by Soares-Filho et al. (8). These simulations,

collectively called “SimAmazonia”, were originally run beginning with observed forest cover data for

July-September 2001 and predicting forest cover through to 2050. In our models, we used observed

forest cover data up until 2008 and the SimAmazonia predictions from 2009-2050. Since real

deforestation rates underwent an unexpectedly rapid decline in the second half of this decade,

SimAmazonia had overestimated absolute amounts of deforestation in the period 2002-2008 (less so

in the GOV than in the BAU scenario). This discrepancy causes an increase in extinction debt at the

transition between observed and modelled datasets which is artificially rapid.

Under the BAU scenario, deforestation in a sub-region could not exceed a designated 85 % of the

area outside protected areas, and 40 % within protected areas (8). In order to model an

improvement in frontier and protected area governance, as well as increased compliance with

environmental law, maximum deforestation in the GOV scenario was set to 50 % and 0 %,

respectively, outside and inside protected areas and sub-region deforestation rates were

constrained to decrease in an arbitrary logistic manner. Note, even in the GOV scenario, the

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minimum forest cover outside protected areas still exceeds the 80% that is actually required of

private landowners in the Brazilian Forest Code (8). In the GOV scenario, the protected area network

was also expanded in accordance with plans outlined under ARPA (the “Protected Areas of the

Amazon” programme).

In both scenarios the road network is improved and extended, which is in line with current

government plans under PAC and PAC-2 (the two phases of the “Growth Acceleration Program”).

This includes: (1) the re-paving of the BR-319 to Manaus by 2018; (2) the paving of the Trans-

Amazon (BR-230) between Itaituba and Humaitá by 2025; (3) the completion of the paving of the BR-

364 in Acre; and (4) the construction of a continuously-paved link road between the BR-163 and BR-

364 across northern Mato Grosso by 2025 (Fig. S2).

Targeted deforestation reduction scenarios

On the basis of recent political, societal and economic trends in the Brazilian Amazon, some

commentators have tentatively concluded that the prospects for forest cover, and therefore

biodiversity, in the region may be brighter than they have perhaps ever been since the beginning of

the “modern era” of deforestation (9, 33). This poses a stark contrast to the dramatic scenarios of

future tropical forest loss that have prevailed in the literature for the last three decades (34-36). In

order to model these newly optimistic scenarios of forest loss, we constructed two additional

scenarios to those outlined above. These were implemented quasi-spatially, taking each 50 x 50 km2

grid cell as a homogenous unit of simulation (echoing the scale at which calculations of species loss

and extinction debt would be made).

The Strong Reduction scenario is based on the recent goal – set out by the Brazilian Government – to

achieve an 80% reduction in the deforestation rate by 2020, relative to a ten-year baseline period

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ending in 2005 (9). For each cell, we calculated this baseline rate, as well as the 80% reduced target

rate for 2020, and interpolated the annual rates that would need to be achieved in each year from

2009 to reach the target in 2020. Note that the basin-wide deforestation rate by 2008 was already

reduced to 66% of the baseline (20), so the reductions from 2009 to 2020 are more modest than the

80% target might suggest. The Brazilian National Plan on Climate Change does not indicate any long-

term commitment to eliminating deforestation; we therefore allowed deforestation after 2020 to

continue in each cell according to the reduced rate that had already been achieved.

The End of Deforestation scenario, on the other hand, ambitiously sets out to end deforestation by

2020, based on a programme of compensation (both direct and indirect) for forest conservation and

increased investment in protected areas (9). This programme, which builds upon the record low

deforestation rate of 7,000 km2 observed for 2009, plans to reduce the baseline in successive stages:

to 5,000 km2 by 2011, 1,500 km2 by 2016 and zero by 2020. We translated these basin-wide absolute

rates into proportional reductions and applied them on a cell-by-cell basis. The 2009 baseline for

each cell, in this case, was derived from the PRODES product for 2009 (20). Thereafter, the transition

to a zero-deforestation Brazilian Amazon was considered complete, and deforestation rates

remained at zero until 2050.

Quantifying extinction debt

Modelling approach

The most common approach taken to identify, and indeed quantify, extinction debt has been to use

SAR-based predictions of equilibrium species richness (4, 37-43). Across systems presumed to be in

equilibrium, SARs are exceptionally well validated and have high generality across taxa, spatial scale

and ecological system (12, 13, 44, 45). In combination with estimates of current species richness,

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SARs therefore offer a highly practical means of identifying extinction debt at large spatial and

ecological scales. This developed from the early realisation that SARs consistently over-predicted the

observed species losses in the short-term (14, 46, 47), and that this discrepancy could be interpreted

by the persistence of species that were nonetheless committed to extinction (15, 46). Empirical

support for this interpretation has been given by a number of studies (15, 37, 38, 48-50) noting a

correspondence between SAR-based extinction predictions and regional or global threatened

species lists, such as the IUCN Red List.

To model extinction debt through time, we can extend the SAR to incorporate a process of

community relaxation. We know that in the immediate term following habitat loss, individuals of

mobile species concentrate in the remaining habitat fragments, giving rise to so-called “crowding

effects” and species “supersaturation” (51-54). Following this, species richness relaxes to a new

equilibrium over a period of decades to centuries (5, 55). We also know that the shape of the

relaxation curve is much steeper in the initial stages of relaxation and shallower as the system

approaches equilibrium (5, 54, 56-60). These empirical and theoretical findings can be included in a

model which provides instantaneous estimates of species loss and extinction debt, with dynamically

changing habitat area.

Assumptions of the model

We used the familiar power-law form of the SAR (12). We recognise that, though this is by no means

the only model to describe the relationship, and much debate has ensued over this (44, 61-63), it is

the most tested and generally most robust (13,44). Provided it is not used at either very small or

very large scales (less than 1 ha or greater than 107 km2), it will most often provide a good fit to data

(12, 61, 64). We did not favour the endemics-area relationship (EAR) (65), because we were

modelling a community-level process of relaxation in remnant habitat, rather than a sampling

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process within continuous habitat. Most obviously, these approaches differ in their requirements for

extinction (66): the EAR requires every individual of a species to lose its habitat for extinction to

occur, whilst the SAR incorporates the effects of increased isolation and density-compensation in

smaller habitat patches, such that a species may be driven to extinction before all of its habitat has

been destroyed.

We took a cell-by-cell approach to account for spatial variability in species richness and intensity of

habitat loss (67). Each cell was treated as an independent habitat isolate, akin to the idealised patch

considered in the model’s formulation. We used a relatively coarse-scale grid (2500 km2 cells) to

minimise neighbourhood effects, common to all analyses of this type, but this reduces our ability to

capture effects of within-cell habitat heterogeneity. The cell-by-cell application of SARs to predict

extinction has precedent; for example, it has previously been used to predict losses of primate

species from African countries (38) and plant species from regions of California (67).

Empirical z-values

The exponent z is the slope of the log-log plot of the power-law SAR. Simply, it describes how rapidly

species are lost or gained as, respectively, habitat is lost or sampling size is increased. Employing the

“island analogy” for isolated remnant habitats, a z-value of 0.25 has commonly been used to

predict extinction following habitat loss (2, 4, 14, 15, 23, 33, 37). Rather than using a single mean

value for z, or even an upper and lower estimate, we instead estimated a probability distribution for

z from empirical data (13, 38, 68-72). In turn, this enabled us to capture the effect of parameter

uncertainty on the outputs of our models using Monte Carlo randomisations (see Section Capturing

uncertainty: Monte Carlo simulations).

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We assembled most of our data for z from a database established for a meta-analysis of SARs (13);

this is the most comprehensive analysis of z-values thus far conducted, using nearly 800 SARs from a

broad diversity of locations, habitats and taxonomic groups. Since we know that at least some of the

wide variation in observed z is systematic (13), we filtered the dataset to leave only those SARs that

were strictly for tropical mammals, birds or amphibians, leaving 34 SARs. Unfortunately, few

amphibian studies remained after this filtering, so we supplemented the data with additional

published SARs located using the ISI Web of Science (www. isiknowledge.com, using the search

term: ["species-area" SAME relationship*] AND [amphibian*]) and other studies that we were aware

of. In total, we used 42 SARs (17, 19 and 6 SARs from mammalian, avian and amphibian taxa,

respectively). Due to the relatively low number of estimates, we could not justify modelling separate

distributions of z for each vertebrate group. The overall bootstrap mean (stratified by study and

taxon) for z was 0.269 (95% CI: 0.224-0.315), and the random draws of z used in Monte Carlo

simulations (Fig. S1) had a mean value of 0.270.

Estimating values of k from the literature

The relaxation constant k is the most important parameter in our model, as it determines the

relative speed at which species losses occur. It can be seen simply as the reduction in absolute

species loss rate that occurs with each extinction. Unlike the case for z, no theory currently exists to

indicate what value it might take, and empirical attempts at its quantification have been rare.

Perhaps prematurely, the “ball-park” findings of a study of bird community relaxation in Kenyan

forest fragments (4) – about 50% species loss in 50 years – have already been adopted in the

literature (73, 74). We attempted to improve upon this rough estimate, ultimately aiming to input a

probability distribution for k into Monte Carlo randomisations, as we did for z (see Section Capturing

uncertainty: Monte Carlo simulations).

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Ideally, the best data for these purposes would come from contemporary observations of the

relaxation process, from beginning to end. Then, given an assumption of single-episode habitat loss,

solving Eq. 1 is straightforward. After re-arranging terms, we are able to estimate k:

eq

eq

StS

SStk

)(

)0(ln1 (S1)

where t is the time since habitat loss or fragment isolation. In practice, values of S(0) and Seq are

often not observed directly. S(0) can be inferred, however, from suitable historical records, or by

using a space-for-time substitution (i.e. from “control” sites that have not undergone habitat loss).

We can use a suitably parameterised SAR to obtain Seq, so long as data on the areal extent of habitat

are available both before and after habitat loss.

A related parameter used to express the speed of relaxation is the half-life of community species

richness (3, 4). The half-life, th is interchangeable with k via:

2ln1 kth (S2)

Despite their more intuitive nature, we do not use half-lives because the clarity of their definition

becomes lost in the context of a model with continual habitat loss and shifting equilibrium.

We conducted an extensive literature search for suitable studies from which we could calculate k,

using various searches on the ISI Web of Science (www.isiknowledge.com) for articles pertaining to

relaxation, species loss, extinction from fragments or regional extirpations, and following the

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reference trail where relevant. We supplemented these studies with other published studies we

were aware of.

Acceptable sources of S(0) included: survey work; species checklists (assuming they were sufficiently

specific to the area in question); the application of expert knowledge (often in combination with

other historical evidence), or inferences from present-day “control” sites. A few studies used SARs

(appropriately parameterised) to infer S(0) and, in these cases, a type of “founder effect” on species

richness had been presumed (i.e. instantaneous sampling following habitat loss). However, in most

cases, fragments or landscapes were assumed to be “supersaturated” immediately following habitat

loss, or S(0) had been directly observed. Estimates of current species richness S(t) had to be derived

from survey work of comparable effort and scale; in general, this was often explicitly accounted for,

since most studies had expressly set out to compare S(0) and S(t). Some studies provided two or

more estimates of S(t) at different time points, in which case we took the most recent provided.

Where studies had offered evidence of absent or extremely limited immigration, we followed any

assertions that equilibrial species richness was zero. If the area of original habitat (or the area of the

control fragment) was not given precisely, we used maps or written descriptions to estimate it;

where this was not possible, or fragments were isolated from purportedly “continuous” forest (i.e.

habitat with an area > 1 million ha), we took the initial area to be an arbitrary 100 km2 (75). In such

cases, calculated k-values were not especially sensitive to the initial area estimate, as they almost all

involved drastic reductions in habitat area down to less than 1 km2, meaning Seq was usually close to

zero. The time since habitat isolation, t, was often not equivalent to the time elapsed between

species richness estimates (e.g. the period between surveys), though the latter was more often

provided; where suitable information could be found, we used the former (and the mid-point if t

could only be estimated within bounds). Finally, if the data allowed it, we considered only the subset

of taxa that were forest-dependent and native to the region. We found 53 studies suitable for

estimating values of k (3, 4, 14, 42, 43, 51, 54, 56, 57, 59, 69, 70, 75-115). This included 329 patch-

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level observations and 45 landscape-level observations (making 309 observations which were

independent in the sense that no two of them used the same taxa from the same location). All told,

238 different habitat isolates (i.e. habitat islands, real islands or habitat-denuded landscapes) were

included, across a diversity of plant, invertebrate and vertebrate taxa. 44% of observations were for

avian taxa.

It has previously been asserted that relaxation rates are dependent on the absolute area of remnant

habitat (4) and, in this way, might be scale-dependent. Since we applied our model of species loss

and extinction debt in the Brazilian Amazon at large scales (in grid cells of 250,000 ha), for the

purposes of the Monte Carlo simulations we excluded all observations of k from small habitat

isolates (< 100 ha). Mean k in this reduced dataset, using an empirically-derived z-value of 0.269 (see

Section Empirical z-values), was 0.0122 (95% CI: 0.0076-0.0194), which corresponds to a half-life of

57 years (from Eq. S2). This is only slightly higher than the “ball-park” half-life often repeated to be

50 years (4, 73, 74), though it is more robustly supported.

Capturing uncertainty: Monte Carlo simulations

In order to explicitly incorporate the uncertainty associated with our parameter estimates into our

models of species loss and extinction debt, we used a Monte Carlo simulation framework. This

framework better reflects the primacy of empirical data than alternative approaches, and uses

available data more effectively than simply inputting an upper and lower estimate for each

parameter.

Using the empirical z-values, we defined a probability density function for mean z by taking stratified

bootstrap samples of the data (n = 10,000) and then fitting a Gaussian curve. Similarly, we fitted a

probability density function to the stratified bootstrap samples of the k-value data: in this case a

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lognormal density function was used owing to the skew in the distribution. Note that since the

calculation of k usually involves z (in order to calculate Seq; see Eq. S1), we incorporated this

additional uncertainty by taking a random draw of z and re-calculating k before each bootstrap was

taken. We decided to decouple the parameters z and k during Monte Carlo simulations in order to

fully capture the range of uncertainty (otherwise low z-values would always be entered into

randomisations with high k-values, and vice versa). This resulted in a distribution for k (Fig. S1) with

marginally wider confidence intervals than if just a mean value for z had been used.

Random draws from the fitted parameter distributions were then taken in each Monte Carlo

simulation (n = 1000) and used in our model to calculate annual estimates, for each 50 x 50 km2 grid

cell, of species loss and extinction debt for the three vertebrate groups, across the various scenarios

of forest loss. All reported confidence intervals for a given statistic are based on the quantiles of its

distribution, taken from the results across Monte Carlo randomisations.

Model validations

We attempted to validate our model predictions at both the scale of the Brazilian Amazon and at the

scale of individual grid cells. First, we compared our Brazilian Amazon-scale predictions of extinction

debt to the number of species listed as threatened on the Red List (Critical, Endangered or

Vulnerable categories) (18). There are surprisingly few Brazilian Amazon forest-dependent species

listed as threatened – just 35. However, because of the 76 Data Deficient species in the mammals

and amphibians, the ultimate number of threatened species could lie somewhere between 35 and

111. Assuming Data Deficient species will be found to be as threatened as species that have already

been assessed, there will be 38.9 species threatened. This compares with a predicted debt of 35.0

(95% CI: 28.8 - 41.8), which is very close (Fig. 2A).

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We also attempted to validate our models at the cell level. We obtained bird lists in four gridsquares

in the Brazilian Amazon: Jari (116, 117), Paragominas, Santarem and Alta Floresta (118). Making the

assumption that species range maps gives us an estimate of the species that were present in a given

grid square pre-deforestation, the difference between that estimate and the observed number of

forest-dependent species within those gridsquares in the present day should give an empirical

estimate of local extinction. When we conducted this analysis, we found a positive but non-

significant correlation between predicted and ‘observed’ local extinction across the four grid squares

(r = 0.098, df = 2, p = 0.90). The slope of the relationship is less than 1 suggesting that the validation

exercise shows our model to under-predict extinction relative to ‘observation’. This likely arises

because none of the field data were collected at spatial scales that come close to representing

exhaustive censuses of 50 x 50 km grids at which scale we were modelling. It follows that any species

list is biased towards underestimating the number of species present in a grid square, so this type of

validation is instantly biased towards making it look as though our model under-predicts extinction.

Moreover, all of the gridsquares appeared to gain forest-dependent species, highlighting a problem

in matching the data from the Red List species distribution maps and the field data to estimate

‘observed’ extinction. Part of the problem arises from the two data sources using different

taxonomies (species that are recorded in the field don’t have a species distribution in the Red List

data), and part of the problem arises from the inevitable inaccuracies involved in mapping species

distributions at global scales (some of the ‘new’ species occur in a gridsquare that is outside of their

range according to the distribution maps). Because of these issues with the comparability of the two

datasets, we consider this validation a particularly weak test of the model predictions.

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Supplementary Movies

Movie S1: Historical trends of absolute species loss and extinction debt between 1970 and 2008

(using the “interpolated clearance” scheme of Table 1) and predicted future trends through to 2050

for the Brazilian Amazon under the Business as Usual (BAU) scenario. Spatial patterns of absolute

species loss are represented by the size of squares, with squares reducing in size through time

according to the number of local extinctions predicted to occur. Spatial patterns of absolute

extinction debt are represented by colour, with squares changing from green through to red

according to the number of species committed to local extinction. Large and red squares represent

faunas that are intact but imperilled, and are where the greatest gains could be expected from

conservation actions. Values are cell-wise means across Monte Carlo simulations, and were summed

across birds, mammals and amphibians.

Movie S2: Historical trends of absolute species loss and extinction debt between 1970 and 2008

(using the “interpolated clearance” scheme of Table 1) and predicted future trends through to 2050

for the Brazilian Amazon under the Governance (GOV) scenario. Spatial patterns of absolute species

loss are represented by the size of squares, with squares reducing in size through time according to

the number of local extinctions predicted to occur. Spatial patterns of absolute extinction debt are

represented by colour, with squares changing from green through to red according to the number of

species committed to local extinction. Large and red squares represent faunas that are intact but

imperilled, and are where the greatest gains could be expected from conservation actions. Values

are cell-wise means across Monte Carlo simulations, and were summed across birds, mammals and

amphibians.

Movie S3: Historical trends of absolute species loss and extinction debt between 1970 and 2008

(using the “interpolated clearance” scheme of Table 1) and predicted future trends through to 2050

for the Brazilian Amazon under the Strong Reduction (SR) scenario. Spatial patterns of absolute

species loss are represented by the size of squares, with squares reducing in size through time

according to the number of local extinctions predicted to occur. Spatial patterns of absolute

extinction debt are represented by colour, with squares changing from green through to red

according to the number of species committed to local extinction. Large and red squares represent

faunas that are intact but imperilled, and are where the greatest gains could be expected from

conservation actions. Values are cell-wise means across Monte Carlo simulations, and were summed

across birds, mammals and amphibians.

17

Movie S4: Historical trends of absolute species loss and extinction debt between 1970 and 2008

(using the “interpolated clearance” scheme of Table 1) and predicted future trends through to 2050

for the Brazilian Amazon under the End of Deforestation (EOD) scenario. Spatial patterns of absolute

species loss are represented by the size of squares, with squares reducing in size through time

according to the number of local extinctions predicted to occur. Spatial patterns of absolute

extinction debt are represented by colour, with squares changing from green through to red

according to the number of species committed to local extinction. Large and red squares represent

faunas that are intact but imperilled, and are where the greatest gains could be expected from

conservation actions. Values are cell-wise means across Monte Carlo simulations, and were summed

across birds, mammals and amphibians.

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Supplementary Tables

Table S1. Absolute species losses per 2500 km2 cell for three forest-dependent vertebrate groups in the

Brazilian Amazon, according to various scenarios of forest loss (see Section Scenarios of future deforestation).

For estimating species loss to 2050, the middle range 1970-2008 scenario (‘Interpolated Clearance’) was

combined with one of the four scenarios of future forest loss. All quantities are summary statistics from Monte

Carlo randomisations (n = 1000). Ranges are the mean minimum and maximum values obtained across

simulations. Median species losses were calculated after rounding estimates for each grid cell to the nearest

species.

Taxon Year Scenario Mean (95% CI) Median (95% CI) Range

Mammals 2008 Immediate Clearance 0.47 (0.30 – 0.71) 0 (0 – 0) 0 – 9.88

Interpolated Clearance 0.42 (0.26 – 0.63) 0 (0 – 0) 0 – 8.34

Delayed Clearance 0.37 (0.23 – 0.57) 0 (0 – 0) 0 – 7.76

2020 Business-as-usual 1.01 (0.63 – 1.51) 0 (0 – 0) 0 – 11.48

Governance 0.93 (0.58 – 1.38) 0 (0 – 0) 0 – 11.32

Strong Reduction 0.82 (0.52 – 1.21) 0 (0 – 0) 0 – 11.28

End of Deforestation 0.78 (0.49 – 1.15) 0 (0 – 0) 0 – 11.28

2050 Business-as-usual 4.00 (2.63 – 5.77) 2 (2 – 4) 0 – 21.26

Governance 2.47 (1.64 – 3.47) 1 (0 – 1) 0 – 19.30

Strong Reduction 2.02 (1.36 – 2.83) 0 (0 – 0) 0 – 17.64

End of Deforestation 1.53 (1.04 – 2.11) 0 (0 – 0) 0 – 17.46

Birds 2008 Immediate Clearance 0.93 (0.58 – 1.39) 0 (0 – 0) 0 – 15.95

Interpolated Clearance 0.83 (0.51 – 1.24) 0 (0 – 0) 0 – 13.73

Delayed Clearance 0.73 (0.45 – 1.11) 0 (0 – 0) 0 – 12.58

2020 Business-as-usual 2.04 (1.27 – 3.06) 0 (0 – 1) 0 – 24.00

Governance 1.87 (1.17 – 2.79) 0 (0 – 1) 0 – 22.33

Strong Reduction 1.63 (1.03 – 2.42) 0 (0 – 1) 0 – 22.30

End of Deforestation 1.56 (0.98 – 2.29) 0 (0 – 0) 0 – 20.62

2050 Business-as-usual 8.55 (5.62 – 12.38) 5 (3 – 7) 0 – 51.82

Governance 5.15 (3.40 – 7.25) 2 (1 – 2) 0 – 43.98

Strong Reduction 4.06 (2.73 – 5.68) 0 (0 – 1) 0 – 51.39

End of Deforestation 3.08 (2.08 – 4.25) 0 (0 – 1) 0 – 36.88

Amphibians 2008 Immediate Clearance 0.20 (0.13 – 0.30) 0 (0 – 0) 0 – 4.07

Interpolated Clearance 0.18 (0.11 – 0.27) 0 (0 – 0) 0 – 3.50 Delayed Clearance 0.16 (0.10 – 0.24) 0 (0 – 0) 0 – 3.07 2020 Business-as-usual 0.46 (0.29 – 0.70) 0 (0 – 0) 0 – 6.56 Governance 0.42 (0.26 – 0.63) 0 (0 – 0) 0 – 6.38 Strong Reduction 0.36 (0.23 – 0.53) 0 (0 – 0) 0 – 6.29 End of Deforestation 0.34 (0.22 – 0.51) 0 (0 – 0) 0 – 5.64 2050 Business-as-usual 2.09 (1.37 – 3.04) 1 (1 – 1) 0 – 16.29 Governance 1.22 (0.81 – 1.73) 0 (0 – 0) 0 – 15.96 Strong Reduction 0.91 (0.61 – 1.27) 0 (0 – 0) 0 – 14.85 End of Deforestation 0.67 (0.45 – 0.94) 0 (0 – 0) 0 – 10.44

19

Table S2. Absolute extinction debt per 2500 km2 cell for three forest-dependent vertebrate groups in the

Brazilian Amazon, according to various scenarios of forest loss (see Section Scenarios of future deforestation).

For estimating extinction debt to 2050, the middle range 1970-2008 scenario (‘Interpolated Clearance’) was

combined with one of the four scenarios of future forest loss. All quantities are summary statistics from Monte

Carlo randomisations (n = 1000). Ranges are the mean minimum and maximum values obtained across

simulations. Median extinction debts were calculated after rounding estimates for each grid cell to the nearest

species.

Taxon Year Scenario Mean (95% CI) Median (95% CI) Range

Mammals 2008 Immediate Clearance 2.22 (1.85 – 2.55) 0 (0 – 0) 0 – 20.81

Interpolated Clearance 2.27 (1.91 – 2.59) 0 (0 – 0) 0 – 21.70

Delayed Clearance 2.32 (1.97 – 2.64) 0 (0 – 0) 0 – 22.16

2020 Business-as-usual 4.92 (4.18 – 5.55) 1 (1 – 2) 0 – 31.03

Governance 3.81 (3.17 – 4.34) 1 (1 – 1) 0 – 30.53

Strong Reduction 2.99 (2.52 – 3.39) 0 (0 – 0) 0 – 40.65

End of Deforestation 2.45 (1.99 – 2.83) 0 (0 – 0) 0 – 32.58

2050 Business-as-usual 11.33 (9.40 – 12.89) 9 (7 – 11) 0 – 48.42

Governance 4.31 (3.21 – 5.24) 2 (1 – 2) 0 – 44.38

Strong Reduction 3.51 (2.73 – 4.17) 0 (0 – 0) 0 – 35.50

End of Deforestation 1.70 (1.12 – 2.22) 0 (0 – 0) 0 – 22.55

Birds 2008 Immediate Clearance 4.51 (3.75 – 5.18) 1 (1 – 1) 0 – 49.06

Interpolated Clearance 4.62 (3.89 – 5.28) 1 (1 – 1) 0 – 51.11

Delayed Clearance 4.71 (3.99 – 5.38) 1 (1 – 1) 0 – 52.72

2020 Business-as-usual 10.34 (8.77 – 11.67) 3 (3 – 4) 0 – 80.41

Governance 7.92 (6.60 – 9.04) 2 (2 – 3) 0 – 69.68

Strong Reduction 6.06 (5.08 – 6.89) 1 (1 – 1) 0 – 104.79

End of Deforestation 4.95 (3.89 – 5.28) 1 (1 – 1) 0 – 55.56

2050 Business-as-usual 25.12 (20.90 – 28.52) 19 (15 – 22) 0 – 117.74

Governance 9.27 (6.94 – 11.24) 3 (2 – 4) 0 – 91.57

Strong Reduction 7.18 (5.58 – 8.54) 1 (0 – 1) 0 – 95.40

End of Deforestation 3.43 (2.24 – 4.49) 0 (0 – 1) 0 – 38.49

Amphibians 2008 Immediate Clearance 1.00 (0.83 – 1.16) 0 (0 – 0) 0 – 13.88

Interpolated Clearance 1.03 (0.86 – 1.18) 0 (0 – 0) 0 – 14.41 Delayed Clearance 1.05 (0.89 – 1.20) 0 (0 – 0) 0 – 14.82 2020 Business-as-usual 2.46 (2.08 – 2.78) 1 (1 – 1) 0 – 25.68 Governance 1.86 (1.55 – 2.13) 0 (0 – 1) 0 – 25.27 Strong Reduction 1.34 (1.12 – 1.53) 0 (0 – 0) 0 – 35.23 End of Deforestation 1.08 (0.86 – 1.18) 0 (0 – 0) 0 – 15.99 2050 Business-as-usual 6.47 (5.43 – 7.33) 4 (3 – 4) 0 – 33.57 Governance 2.33 (1.77 – 2.81) 1 (0 – 1) 0 – 26.88 Strong Reduction 1.63 (1.27 – 1.94) 0 (0 – 0) 0 – 30.14 End of Deforestation 0.75 (0.49 – 0.99) 0 (0 – 0) 0 – 11.09

20

Supplementary Figures

Fig. S1. Empirically-derived input distributions of z and k and the random draws actually used in the Monte

Carlo simulations (n = 1000). In the main plot: the density of points is represented by colour saturation; grey

dashed lines indicate the respective 95% confidence intervals for the empirical z- and k-value data (calculated

by stratified bootstrapping), and axis tick marks correspond to the random draws. The horizontal and vertical

marginal plots show histograms and fitted probability density functions (red dashed lines) for the distributions

of mean z and k.

21

Fig. S2. Significant geographic features mentioned in the text. The base map shows land cover for the region in

2004 (NASA Earth Observatory). The grey outlines show the nine Legal Amazon state borders (AP: Amapá; PA:

Pará; MA: Maranhao; TO: Tocantins; MG: Mato Grosso; RO: Rondônia; AC: Acre; AM: Amazonas; RR: Roraima).

22

Fig. S4. Temporal trajectories of relative species loss and extinction debt under BAU and GOV scenarios of future forest loss (2009-2050), stratified across the nine different

states of the Brazilian Amazon (top panel) and absolute species loss and extinction debt (95% CIs, with medians in parentheses) across forest-dependent mammals, birds and

amphibians (table below). Relative quantities were calculated as a percentage of initial species richness and averaged across 50 x 50 km2 grid cells. Uncertainties arising from

model parameterisation were incorporated into Monte Carlo simulations, with background colour saturation of plots corresponding to the density of simulation runs.

Fig. S3. Temporal trajectories of relative species loss and extinction debt during the modern-era of deforestation in the Brazilian Amazon (1970-2008), stratified across the

nine different state territories (AP: Amapá; PA: Pará; MA: Maranhao; TO: Tocantins; MG: Mato Grosso; RO: Rondônia; AC: Acre; AM: Amazonas; RR: Roraima) and a

landcover map for 2008 (centre). Relative quantities were calculated as a percentage of initial species richness and averaged across 50 x 50 km2 grid cells. Uncertainties

arising from model parameterisation were incorporated into Monte Carlo simulations, with background colour saturation corresponding to the density of simulation runs.

The apparent disjunction in the 2001-2002 extinction debt for Maranhão is an artefact caused by cloud cover (causing an overestimate of 2001 deforestation and an

underestimate for the years prior).

23

Fig. S4. Temporal trajectories of relative species loss and extinction debt under BAU and GOV scenarios of future forest loss (2009-2050), stratified across the nine different

states of the Brazilian Amazon (top panel) and absolute species loss and extinction debt (95% CIs, with medians in parentheses) across forest-dependent mammals, birds and

amphibians (table below). Relative quantities were calculated as a percentage of initial species richness and averaged across 50 x 50 km2 grid cells. Uncertainties arising from

model parameterisation were incorporated into Monte Carlo simulations, with background colour saturation of plots corresponding to the density of simulation runs.

24

Fig. S5. Hotspots of

extinction debt (top 5%

of grid cells) for forest-

dependent mammals,

birds and amphibians in

the Brazilian Amazon

for 2008 (A) and for

2050 under scenarios of

Business as Usual (B)

and improved forest

frontier governance (C).

Current priority areas

are mostly in regions of

lower species richness,

but newly-paved

highways running

through regions of high

species richness will be

the areas of future

extinction in the

Amazon.

25

Fig. S6. Spatial patterns in historical “forest-dependent” (n = 201) mammal species richness (top), and extinction debt (bottom) in the Brazilian Amazon. Current extinction

debt (left) is highest in Maranhão, especially along the BR-316 highway and its intersection with the BR-222. Under the BAU scenario (right), debt accumulation is highest

along the paved BR-319 and Trans-Amazon west of Itaituba, and also in the species-rich state of Roraima. The extensification of Manaus is a focus of debt across both BAU

(right) and GOV (middle) scenarios.

26

Fig. S7. Spatial patterns in historical “forest-dependent” (n = 329) bird species richness (top), and extinction debt (bottom) in the Brazilian Amazon. Current extinction debt

(left) is highest along the BR-316 in Maranhão and in central Rondônia. Under the BAU scenario (right), extinction debt in 2050 is highest along the two major highways

due to be paved in the central Amazon: the BR-319 and Trans-Amazon (BR-230) west of Itaituba. Under the GOV scenario (middle), extinction debt mostly accumulates

around Manaus, but the intersection of the BR-364 and a newly-paved highway across northern Mato Grosso at Ariquemes also becomes a focus of debt.

27

Fig. S8. Spatial patterns in historical “forest-dependent” (n = 176) amphibian species richness (top), and extinction debt (bottom) in the Brazilian Amazon. Current

extinction debt (left) is highest in the western Brazilian Amazon, especially in central Rondônia and around Rio Branco. Under the BAU scenario (right), Rio Branco

continues to be an important focus of debt but, by 2050, the impacts of paving the western section of the BR-364 have caused widespread debt across Acre. Under the

GOV scenario (middle), Manaus is a centre of debt, but Rio Branco and Ariquemes (at the intersection of the BR-364 and a newly-paved highway joining it to the BR-163)

are also peak areas of debt.

2

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