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WWF Climate Change Campaign

Climate change poses a serious threat to the survival of many species and to the well-being of people around the world.

WWF’s campaign has three main aims: • to ensure that industrialised nations make substantial reductions in their domestic emissions of carbon dioxide—the main global warming gas— by 2010 • to promote the use of clean renewable energy in the developing world • to reduce the vulnerability of nature and economies to the impacts of climate change

WWF Climate Change Campaign Director Jennifer Morgan c/o WWF US 1250 24th Street, NW Washington DC 20037 USA

Tel: +1 202 822 3455 Fax: +1 202 331 2391 Website: www.panda.org/climate

WWF’s mission is to stop the degradation of the planet’s natural environment and to build a future in which humans can live in harmony with nature, by: Global Warming • conserving the world’s biological diversity and Species Loss in • ensuring that the use of renewable resources is sustainable • promoting the reduction of pollution and wasteful consumption Globally Significant Terrestrial at risk

www.davidsuzuki.org Jay R. Malcolm | Canran Liu | Laurie B. Miller | Tom Allnutt | Lara Hansen © 1986, WWF ® WWF Registered Trademark owner Published February 2002 by WWF-World Wide Fund for Nature (Formerly World Wildlife Fund), Gland, Switzerland. Any reproduction in full or in part of this publication must mention the title and credit the above-mentioned publisher as the copyright owner. © text 2002 WWF. All rights reserved.

The material and the geographical designations in this report do not imply the expression of any opinion whatsoever on the part of WWF concerning the legal status of any country, territo- ry, or area, or concerning the delimitation of its frontiers or boundaries. habitats

Global Warming and Species Loss in at risk Globally Significant Terrestrial Ecosystems

Jay R. Malcolm Faculty of Forestry, University of Toronto

Canran Liu Faculty of Forestry, University of Toronto

Laurie B. Miller Faculty of Forestry, University of Toronto

Tom Allnutt WWF

Lara Hansen WWF

Contents

Foreword ...... 5

Executive Summary ...... 7

Introduction ...... 11

Maps ...... 23-29

Tables ...... 30-35

Acknowledgements ...... 36-37

Endnotes ...... 39

Foreword by WWF

ven our most protected wilderness areas may not survive global warming unscathed. As these unique, diverse habitats are rapidly changed by global warming, animals and people who are depend- E ent on them will face a crisis of survival. Immediate action needs to be taken to protect people, animals and habitats from this global threat. Solutions exist that reduce human-induced emissions of carbon dioxide and other gases which blanket the earth, trap in heat and cause global warming.

Key findings of the new WWF report, Habitats at Risk: Global Warming and Species Loss in Terrestrial Ecosystems, underscore the urgent need for immediate action by governments, industry and communities to effectively address this worldwide threat. The report examined the impact of climate change on the terrestrial ecosystems that WWF identified as part of the — areas where the Earth's biological wealth is most distinctive and rich, where its loss will be most severely felt, and where we must fight

the hardest for conservation. If we double the CO2 concentration in the next 100 years — an amount less than current predictions of future CO2 concentra- tions, the following effects are predicted.

More than 80 percent of the tested will suffer extinctions of plant and animal species as a result of global warming. Changes in habitats from global warming will be more severe at high latitudes and altitudes than in lowland tropical areas. Some of the most unique and diverse natural ecosystems may lose more than 70 percent of the habitats upon which their plant and animal species depend. Many habitats will change at a rate approximately ten times faster than the rapid changes during the recent postglacial period, causing extinctions among species unable to migrate or adapt at this fast pace.

To save these valuable habitats and the species that depend on them, emissions must be significantly reduced worldwide. The Kyoto Protocol, the only global effort to address climate change, is a good first step in the right direction. All countries, including the United States, should immediately put in place strong domestic plans to meet or beat their Kyoto Protocol targets.

The solutions are at hand and the risks are high. Action is needed now.

Jennifer Morgan RISK HABITATS AT Director WWF Climate Change Campaign

February 2002

5

Executive Summary

lobal warming is perhaps the most pervasive less than 100 years.1 Previous analyses indicated that of the various threats to the planet's biodiver- most of the variation among the impact scenarios was sity. Unlike other threats also caused by attributable to the particular model used, G human development, it has the potential to hence we provide results separately for the two models. influence all ecosystems, including those that are far The models do not provide information on from human populations and are still classified as wilder- per se, but instead simulate current and future potential ness. Unfortunately, despite this insidious nature, there distributions of major vegetation types () such as have been few efforts to assess the potential effects of and broadleaf tropical rain forest. We were able to greenhouse warming on global ecosystems. Those that use the models to indirectly investigate potential biodi- have been undertaken have focused on flows of energy versity change within the ecoregions in several ways: and matter through ecosystems rather than on the species that make up ecosystems. The implications of global To measure the invasion of new types into the warming for global biodiversity remain largely unstudied. ecoregions, we compared the future biome composi- tion in the ecoregions with their present-day composi- Here, we assess the threats of global warming to tion. The appearance of new biome types signals a ecoregions that were identified as being of particularly decline in the original biome makeup of the ecore- high conservation value from a biodiversity perspective gion, which in turn signals a decline in species rich- (the “Global 200” of WWF). In a real sense, these ecore- ness in the . gions represent the crown jewels of the planet’s biologi- cal diversity. These ecoregions are of special significance To measure the potential loss of existing habitats, we from a global warming perspective for the same reason compared current biome distributions with those project- that they were chosen for their biodiversity value — ed for the future under the various scenarios, and quanti- namely their broad representation of the planet’s biologi- fied the percent of change. A change of biome types sig- cal communities, high species richness, biological dis- nals a potential decrease in local species richness if cli- tinctiveness, and intactness. The potential for extensive mate-induced extinction is not matched by incoming impacts here would signal a key threat to the planet’s bio- migration. A simple conceptual model of local species diversity. Indeed, threats to these ecosystems would pre- richness is presented to illustrate that both extinction and sumably signal the climate-induced “unnatural adapta- migration can influence local species richness. tion” that is to be avoided under the United Nations Framework Convention on Climate Change (Article 2). Losses of existing habitats within the ecoregions were compared against random sets of locations constructed In this study, we used a suite of models of global so that they had the same current biome composition climate and vegetation change to investigate three as the original ecoregions. These analyses indicated important global warming-induced threats to the terres- the extent to which the Global 200 ecoregions were trial Global 200 ecoregions: vulnerable in the context of their biome composition. Equally important, they provided an assessment of the 1) Invasions by new types (and corresponding comparative vulnerability of ecoregions with the same loss of original habitat types) major vegetation types.

2) Local changes of habitat types RISK HABITATS AT 3) High rates of required species migration. To measure the rates of migration that greenhouse warming might impose on species, we calculated the Seven climate models (general circulation models or rates at which major biomes would need to move if GCMs) and two vegetation models (BIOME3 and they were to be able to successfully keep up with cli- MAPSS) were used to produce 14 impact scenarios mate change. The shifts of biome boundaries under the under the climate associated with a doubling of atmos- different climate scenarios were used as proxies for pheric CO2 concentrations, which is expected to occur in shifts in the distributional boundaries of species. More 7 rapid required migration rates (RMRs) signaled an cases. In the broad biome classification scheme, 2% of increased likelihood of local declines in species rich- ecoregions on average showed >70% local change and ness. Required migration rates of greater than 1,000 the possibility of catastrophic species loss. In the nar- m/yr were judged to be “very high” because they are row definition, the percentage increased to 5Ð19%. very rare in the fossil or historical records. We

assumed that the doubled CO2 climate was reached Amounts of local biome change showed strong latitu- after 100 years. In fact, even relatively optimistic dinal and altitudinal effects, with greatest change at emissions scenarios suggest that CO2 concentrations in high latitudes and altitudes and relatively less change the atmosphere are likely to have doubled from pre- in lowland tropical areas. Ecoregions in Canada, industrial levels around the middle of this century and , and Asia were especially vulnerable. Under will almost triple by 2100. This means that the RMRs the coarse biome definition, seven ecoregions showed reported here are likely to be on the conservative side 70% or more change in at least one vegetation model: and that species may need to move even faster than Ural Mountains (Russia), Canadian Low Arctic reported here. Tundra, Altai-Sayan Montane Forests (Russia/ Mongolia), Muskwa/Slave Lake Boreal Forests In a sensitivity analysis, we varied the biome classifi- (Canada), Kamchatka Taiga and (Russia), cation scheme. A relatively coarse classification Canadian Boreal Taiga, and Southwestern scheme (few biome types) is expected to result in less Forests and Scrub. change under the warming and hence is rel- atively conservative. This is analogous with the possi- In the context of their particular biome types, certain bility that species have relatively large geographic dis- ecoregions were unusually vulnerable, whereas others tributions and/or wide climatic tolerances. We used were relatively stable. For example, despite its large two classification schemes: 1) a relatively coarse 10- size, the Canadian showed 75 and biome-type scheme, and 2) the original (and more nar- 77% local pixel change (for BIOME3 and MAPSS, row) schemes used by the global vegetation models respectively) compared to 44 and 57% respectively for (18 types for BIOME3; 45 for MAPSS). random sets with the same biome composition. Other unusually vulnerable ecoregions included the (, Argentina, and Bolivia), SUMMARY OF KEY FINDINGS Daurian Steppe (Mongolia and Russia), and the Ural Mountains Taiga (Russia). Tundra ecoregions of Specific Conclusions Canada and Russia tended to be relatively vulnerable, whereas those of Fennoscandia and western Alaska Under the broad definition of biome types, many tended to be relatively stable. Within the tropics, the ecoregions had the same collection of biome types South American region tended to be vulnerable, before and after the warming (35% of ecoregions for whereas insular southeast Asia and central Africa MAPSS, 51% for BIOME3). However, when a more tended to be stable. narrow biome definition was used, only 13Ð19% showed no change and 13Ð25% lost greater than 10% Average required migration rates (RMRs) were of their original biome types. Thus, more than 80% of unusually high (often exceeding 1,000 m/yr). Under ecoregions were projected to suffer extinctions as a a broad biome definition, 6Ð11% of ecoregions had result of global warming. Northern and Australian bio- average RMRs above 1,000 m/yr; under a narrow mes tended to be especially hard hit. Ecoregions with definition, the percentages were 19Ð42%. Rates of the most consistent losses of their original biome types change of this magnitude are approximately 10 times included the of Southern Africa and the - faster than the rapid migrations during the recent post- Duar /Grasslands of northeastern India. glacial period and signal the possibility of extinctions as populations fail to reestablish in areas that are Local biome change was much more pervasive, aver- newly climatically suitable. aging 21Ð34% of an ecoregion under the broad biome definition and 32Ð50% under the narrow definition. If Unusually high migration rates were shown in north- new habitat types fail to reestablish because of failed WWF ern areas, especially in Canada and Russia, but also in migration, species loss could be catastrophic in many 8 southwestern Australia and New Zealand. Under a nar- row biome definition, high rates also tended to be subtropical and dry ecosystem types. A narrow biome prevalent in warm temperate and subtropical areas. definition may be more realistic at these sites because Nearly one quarter of the ecoregions, including a of the generally observed decline in geographic range wide diversity of ecosystem types, had consistently size with latitude. high rates (>750 m/yr for both global vegetation models [GVMs]). In nonglaciated regions, where previous selection for high mobility has not occurred, species may suffer dis- proportionately. Therefore, even though high RMRs are General conclusions not as common in the tropics as in colder regions, they may still have strong impacts in terms of species loss. Global warming has the potential to cause extinctions in a great majority of the world’s especially valuable The effects of global warming are influenced signifi- ecosystems. Losses of habitat types are predicted within cantly by species geographic distributions and climatic the ecoregions and, based on speciesÐarea relationships, tolerances. Species with relatively large distributions can be expected to result in losses of biodiversity. and greater climatic tolerances are at lesser risk. Island ecoregions, which were largely excluded from this Depending on species responses to the warming, report because of the small number of grid cells that especially their ability to migrate to new sites, habitat they occupied, may be at special risk because of small change in many ecoregions has the potential to result populations, limited opportunities for migration, and in catastrophic species loss. sea level rise.

The fact that certain ecoregions are of high value Barriers to migration and habitat loss will exacerbate from a biodiversity perspective did little to protect climateÐinduced species loss. Human population them against the effects of global warming. Although growth, landÐuse change, habitat destruction, and pol- some ecoregions were more vulnerable than others, lution stress will therefore exacerbate climate impacts. on the whole they faired little better than random Increased connectivity among natural habitats within regions of the globe. developed landscapes may help organisms to attain their maximum intrinsic rates of migration and help to High required migration rates (RMRs) were not isolat- reduce species loss. Migration is likely to be especial- ed occurrences within the ecoregions. Instead, in many ly problematic for isolated island ecoregions. ecoregions, the average RMR was greater than 1,000 m/yr, which is approximately an order of magnitude Reductions in both the rate and amount of warming higher than migration rates frequently observed in the will reduce species loss. Urgent reductions in carbon historical and fossil record. Future migration rates may dioxide and other greenhouse gas emissions are need to be unprecedented if species are to keep up required to prevent the possibility of widespread, with climate change. It is safe to conclude that and in some cases catastrophic, species loss. although some plants and animals will be able to keep up with the rates reported here, many others will not. In conclusion, this study demonstrates that under a Some species with low dispersal capabilities may fail wide range of assumptions about future global warming to migrate at all. and its effects on major vegetation types, species losses can be expected in most of the planet’s globally signifi- Global warming is likely to have a winnowing effect cant ecoregions. Migration rates required by the warm- on the ecosystems within ecoregions, filtering out ing are unprecedented by historical standards, raising species that are not highly mobile and favoring a less

the possibility of extensive, and in many cases, cata- RISK HABITATS AT diverse, more “weedy” vegetation and ecosystems that strophic, species loss. are dominated by , , and others with high dispersal capabilities.

High required migration rates tended to be common at high latitudes and altitudes; however, when a narrow biome definition was used, they were also prevalent in 9

Introduction

lobal warming resulting from anthropogenic ed under the United Nations Framework Convention on emissions of greenhouse gases is recognized Climate Change (Article 2). as a key threat to biodiversity (Kappelle et G al. 1999, Noss 2001). The direct and indirect Here, we build on previous efforts to assess terrestri- threats of the warming include losses of habitat; shifts in al biodiversity threats at the global scale (Malcolm and climatic conditions and in habitats that surpass migra- Markham 2000, Malcolm et al. in press) by focusing on tional capabilities (Davis 1986, Malcolm and Markham. one of these prioritization efforts: the terrestrial compo- 2000, Malcolm et al. in press); nonsynchronous shifts in nent of the WWF “Global 200” (Olson and Dinerstein habitat conditions (Martin 2001); altered competitive 1998). This approach is representational in that within relationships; invasions by generalist species (Walker major biome (vegetation) types and geographic realms and Steffan 1997); and biodiversity-unfriendly human of the planet, ecoregions were identified that were of adaptive responses (Noss 2001). Evidence suggests that particular value from a biological standpoint based on the warming of the past century already has resulted in such values as species diversity, uniqueness of the biota, marked ecological changes, including changes in grow- and intactness. We were interested not only in the ing seasons (Myneni et al. 1997, Menzel and Fabian absolute threat posed to these biologically valuable 1999), species ranges (Thomas and Lennon 1999, regions by warming, but, following the same logic of Parmesan et al. 1999), and patterns of seasonal representation, in the relative threats to ecoregions with- breeding (Beebee 1995, Brown et al. 1999, Crick in certain major habitat types. and Sparks 1999). To undertake the analysis, we capitalized on efforts Despite the possibility that global warming rivals to assess impacts of global warming on major vegetation other primary threats to biodiversity, there have been few types (biomes) and indirectly investigated several factors efforts to model the implications of global warming for relevant to species diversity. Rather than using species or terrestrial biodiversity, especially at the global level. The habitat distributions themselves, we used the biomes as significant efforts devoted to investigating the ecological proxies. Although indirect, this approach is nonetheless effects of warming instead have been devoted to changes highly relevant to biodiversity. The biomes describe in the functional properties of ecosystems, especially on major habitat types that often have many species in com- the ways in which they process energy and matter (e.g., mon. Equally important, mapping of biomes makes use VEMAP Members 1995, Houghton et al. 1996). of derived climate variables that are relevant to a wide range of organisms (especially plants) and hence, at least This paucity of information on biodiversity is espe- in a heuristic sense, can be thought of as proxies for cially notable given the recent efforts to prioritize con- species climate envelopes. servation efforts at the global scale, for example by focusing on distinctive and representative ecoregions As detailed below, we evaluated the effects of global (Olson and Dinerstein 1998), centers of (Mit- warming with respect to three variables: 1) appearances termeier et al. 1998), or intact forested ecosystems of biome types that were novel to the ecoregion; 2) over- (Bryant et al. 1997). The identified regions represent the all biome change; and 3) migration rates required by the crown jewels of the planet’s biological diversity. They warming. Through a conceptual model of changes in are of special significance from a global warming per- species richness induced by warming, we also show that AIASA RISK HABITATS AT spective for the same reason that they were chosen in the the potential for species loss may be mediated impor- first place — namely, their broad representation of the tantly by migration rates. For biome change, we com- planet’s biological communities, high species richness, pare responses not only among the Global 200 ecore- biological distinctiveness, and intactness. The potential gions, but also in the context of expected responses for a for extensive climate-induced impacts here would signal given biome type. Finally, in sensitivity analyses, as a a key threat to the planet’s biodiversity. Indeed, threats proxy for variation in the sizes of species distributions to these ecosystems would presumably signal the cli- and the breadth of their climatic tolerances, we investi- 11 mate-induced “unnatural adaptation” that is to be avoid- gated two biome classification schemes. METHODS: QUANTIFYING THREATS grids of latitude and longitude. Because of the inherently TO BIODIVERSITY variable responses of regions with small areas, we excluded ecoregions with 10 or less grid cells, reducing Potential distributions of major vegetation types the original 140 terrestrial ecoregions to 113. This were simulated by global vegetation models (GVMs). removed many of the island ecoregions. Based on ecological and hydrological processes and plant physiological properties, these models predict the A Heuristic Approach to potential vegetation on upland, well-drained sites under average seasonal climate conditions. A simulated mix- Modeling Biodiversity Change ture of generalized life forms such as trees, shrubs, and grasses that can coexist at a site is assembled into a An attempt to model the effect of climate change on major vegetation type (or biome) classification (Neilson the myriad species in an ecosystem would be a very et al. 1998). These models are termed “equilibrium” detailed and difficult undertaking. Basic information is models because they model the vegetation that would be often lacking — for example, information on where expected to occur at a site once both climate and vegeta- species occur and how quickly they might respond to tion at the site have stabilized. change. Equally problematic are the complex sets of fac- tors and interactions among species that may influence To analyze the potential effects of global warming, the responses of species to climate change. Predicting the biomes were mapped both under recent (1961Ð1990) cli- effect of global warming on physical conditions is rela- mates and under future climates that were simulated by tively straightforward, but disentangling the interaction of general circulation models (GCMs) under a doubling of both physical and biotic changes is enormously difficult.

atmospheric CO2 concentrations. The GCMs are detailed computer simulations that model three-dimensional repre- Nevertheless, we can apply general ecological prin- sentations of the Earth's surface and solve the systems of ciples to investigate possible biodiversity change. In this equations that govern mass and energy dynamics. They paper, rather than attempting to model each species, we suffer from coarse geographic scales and numerous simpli- apply a broader brush and, as detailed below, take a fying assumptions; however, they have met with consider- heuristic approach. able success in modeling global climatic patterns (e.g., Hasselmann 1997, Houghton et al. 1996, Kerr 1996). The Appearance of Novel Biome Types We reasoned that ecoregions would be at particular risk Altogether, we used 14 combinations of GVMs and if global warming resulted in replacement of the origi- GCMs (the same set used by Malcolm and Markham. nal biome types with new biome types (figure 1). Bio- 2000; see also Neilson et al. 1998). These included two mes are characterized by a particular suite of climatic GVMs (MAPSS [Neilson 1995] and BIOME3 [Haxel- conditions and, at least within a , tine and Prentice 1996])1 and seven GCMs, including often have many species in common. Replacement of both “older” and “newer” generation models.2 These the original biomes with new ones would signal major model outcomes cannot be viewed as predictions ecological change. In particular, it would signal a reduc- (VEMAP Members 1995); rather, they represent a range tion in area of the original biomes that made the ecore- of possible future outcomes as envisioned by different gion so valuable in the first place. A reduction in the groups of scientists. Uncertainties concerning the best area of a biome in turn would mean a loss of the some ways in which to model climate and vegetation are con- of the original species from the ecoregion, as predicted siderable, hence our use of this range of possibilities. from species-area relationships.

Previous analyses have shown that the greatest Notice that the condition that the biomes be “new” source of variation among these 14 models was the veg- to the ecoregion makes this a conservative assessment. etation model used (Malcolm et al. in press, Malcolm et Inevitably, many ecoregions will contain many biome al. in prep.). Accordingly, to span the approximate range types (although they may be dominated by one or a of model outcomes in the analyses presented here, we few). Even if an ecoregion contains only one small area provide results separately for the two vegetation models. of a biome type, a broad future spread of that biome type

WWF The Global 200 ecoregions were converted to the same in the ecoregion would not be judged to be new. A better 12 resolution as the 14 model combinations: 0.5 degree measure might be an index of similarity of the biome collection before and after the global warming; however, (figure 2). We envisioned a latitudinally arrayed the approach taken here has the virtue of being simple sequence of biomes, with each having a unique set of and transparent. Although conservative, the sensitivity of species. To represent increasing species richness with an ecoregion based on its current makeup makes biologi- latitude, the number of species per biome is assumed to cal sense. If future biome types already tend to be repre- increase arithmetically through the sequence. Mean sented in the area, then source populations will be near- annual temperature, which serves to define species cli- by. It also implicitly assumes that ecoregions that have a mate envelopes, is assumed to increase smoothly and relatively diverse biome makeup will be more robust in linearly from one edge of the sequence (the most the face of climate change, an observation that is made species-poor edge) to the other. All species were frequently (e.g., Markham 1996). assumed to be able to migrate the same distance x during a time step and to establish and survive at a locality if Local Biome Change Of course, the appearance the locality's temperature was within their original of novel biome types will identify only a fraction of the biome's temperature range (i.e., the temperature range of local biome change in an ecoregion (figure 1). For their original biome defined the species climate enve- example, it is possible to imagine an ecoregion in which lope). The model did not include extinction lags — it was the set of types is identical under current and future con- assumed that species outside of their climate envelopes ditions, but where the locations of the biomes have com- would disappear immediately. Note also that the model pletely changed. This resorting of biomes also has is “neutral” in the sense that species did not interact with important implications for biodiversity. Provided that each other and had equal migration capabilities and cli- organisms are unable to tolerate the new climatic condi- mate envelope breadths. tions, extinctions will occur. These extinctions may be offset by migrations of species that are suited to the new To use the model to investigate the effect of different climate, but only if the populations are able to migrate rates of warming on local extinction and migration (and fast enough to keep up with the warming. the resulting change in net species richness), we varied the amount of warming in a given time step. The impor- We illustrate this interplay between local extinction tance of migration in mitigating change at a location is and incoming migration in a simple conceptual model evident (figure 3). Provided that migrational capabilities

FIGURE 1.

Temperate Evergreen Forest An example of biome change in /Woodland a Global 200 ecoregion ( Shrub/Woodland and Woodlands) that illustrates calculation of: 1) percent new biome types, and 2) percent local biome change. In the first measure- ment, future biome types that were not originally represented in the ecoregion (lower left figure) are counted and expressed as a percentage of the 1xCO2 2xCO 2 total number of grid cells in the ecoregion. In the second, grid cells that changed biome type (lower right figure) are counted and expressed as a per- centage of the total number of grid cells in the ecoregion. This example is based on simulations that used 10 global biome types, the MAPSS AIASA RISK HABITATS AT vegetation model (with increased water use efficien- cy), and the Hadley Centre climate model (with sulphate cooling). Grid cell dimensions are one-half New Changed degree of latitude by one-half degree of longitude. biomes biomes

13 FIGURE 2.

Schematic illustration of a model of changes in local species richness in response to global warming. Biomes were assumed to have unique sets of Migration Species capabilities species, with species richness increas- Biome x ing regularly through the biome a,b,c,d sequence. The sequence was under- laid by a smoothly and linearly varying Species gradient of mean annual temperatures. Biome y e,f,g,h,i,j,k,l Species were assumed to have identi- cal migrational capabilities and to be

Decreasing temperature

Decreasing speciese richness able to persist at a locality only if the annual temperature there was within the range of annual temperatures in their original biome.

FIGURE 3.

Rate within Rate exceeds migration capabilities migration capabilities Local species richness

0 0 Rate of temperature increase

Local species richness at a constant time into the future as a function of the rate of warming. Provided that species are able to accompany the shifting climate, more rapid warming leads to greater species richness as species characteristic of warmer biomes invade the community. When the rate of warming exceeds migrational capabilities, a net loss of species occurs, and the loss is greater at higher rates of warming.

exceeded or equaled the movement of climatic enve- ed from the replacement of local species by those char- lopes, the original relationship between species richness acteristic of warmer biomes). However, once the shifts in

WWF and temperature was maintained and higher rates of temperature envelopes outpaced migrational capabilities, 14 warming led to higher local species richness (this result- local extinction began to outweigh immigration and higher rates of warming resulted in increasingly large understood, even for relatively well-known organisms declines in species richness. such as trees (see Clark 1998, Clark et al. 1998). There- fore, instead of attempting to predict how fast species In this sense, local change can be viewed as a worst- and biomes might be able to move, we asked how fast case scenario under climate change if it is assumed that species and biomes might be required to move in order populations go extinct and are not replaced through to keep up with the projected warming. migration (or, more realistically, are replaced only by climatically tolerant and fast migrating taxa). Our two As noted above, the climate/vegetation models pro- biome change calculations (appearances of new biome vided information on the current and future distribu- types and local biome change) thus span a spectrum of tions of biomes. We could use this information to cal- possible biodiversity outcomes, from decreases in culate the speeds that biomes might have to achieve in species richness in an ecoregion because of absolute order to keep up with the warming. Of course, our pri- reductions in the amounts of the original habitats, to mary interest was not in the biomes themselves (a widespread species loss through failures of populations biome is, after all, an abstract entity), but rather in the to keep pace with the warming. species within them. At least in a heuristic sense, the movement of the biomes provides information on the Local Change: Controlling for Biome movements of species. Species distributions in many Types A global-scale look at local biome change cases are strongly associated with particular biome (Malcolm and Markham 2000) showed a strong latitudi- types — for example, the many plants and animals that nal trend, a result that is verified in the analyses present- can only survive in arctic conditions. In a more general ed here. In part, this is due to the greater projected sense, the “biome climate envelopes” that the vegeta- warming in northern regions, and hence the more exten- tion models simulate can be thought of as proxies for sive ecological change expected in those regions. To “species climate envelopes.” control for this effect, we also examined local biome change in the context of the particular biomes found in Our calculations of RMRs followed Malcolm and an ecoregion. Markham 2000 (core calculations; see also Malcolm et al. in press). To measure required migration distances, This approach allowed us to compare climate we reasoned that the nearest possible immigration source change vulnerability among ecoregions with similar for a locality with future biome type x would be the biome types (such as among the lowland tropical areas) nearest locality of the same biome type under the current and to examine a very fundamental question: Are the climate. Thus, the migration distance was calculated as Global 200 more (or less) sensitive to the effects of the straight-line distance between a future locality and global warming than other comparable ecoregions? For the nearest sameÐbiomeÐtype locality in the current cli- example, if the biological value and uniqueness of the mate.4 To calculate a migration rate, we divided the Global 200 is partly attributable to historical long-term migration distance by the time period over which the climatic stability, and that stability carries forward under migration occurred. Based on Intergovernmental Panel a doubling of CO2 concentrations, then lower vulnerabil- on Climate Change (IPCC) estimates (Houghton et al. ity to global warming might be observed. Specifically, 1996), we assumed that the doubled CO2 climate would we used a bootstrap approach and compared percent occur in 100 years. This assumption is based on an IPCC change in the ecoregions against percent change in ran- midrange emission scenario, “medium” climate sensitivi- domly selected collections of grid cells of the same ty (2.5 oC), and sulphate aerosol cooling. Some transient biome composition. For each of the 14 model combina- model runs suggest that 2 times CO2 forcing may be tions and 113 ecoregions, we computed 10,000 random reached over a considerably shorter time period (see ref- collections and tested the null hypothesis that the erences in Solomon and Kirilenko 1997); hence, our AIASA RISK HABITATS AT observed ecoregion percentage could have come from migration rates may be conservative. the random collection.3 Sensitivity Analyses: Biome Breadths The Migration Rates As noted above, migration capa- above measurements, especially local biome change and bilities are an important aspect of responses to global RMRs, are influenced by the biome classification warming. Unfortunately, the abilities of organisms to scheme used, especially the number of biomes that occur in an area (equivalently, the “breadth” of the definitions). migrate in response to climate changes are not well 15 In general, the use of fewer, more broadly defined cli- Local Biome Change mate envelopes can be expected to result in lower biome change and migration rates because existing and future Although ecoregions often maintained the same set distributions of a biome will show larger areas of overlap of biome types under the doubled-CO climate, most and hence larger areas of no biome change and zero 2 showed high levels of local ecosystem change (figure 5). migration. Unfortunately for our purposes, species distri- That is, there was strong spatial re-sorting of the biomes butions vary enormously in size (as do their climatic tol- within the ecoregions. For example, under a broad biome erances); hence, the utility of using more than one biome definition, absolute change averaged only 1 and 3% for classification scheme. We used two: 1) a broadly defined the two GVMs (BIOME3 and MAPSS respectively), scheme in which 10 global biome types were defined; whereas local biome change averaged 21 and 34% and 2) the original (and more narrowly defined) schemes respectively. Similarly, under a narrow biome definition, of the GVMs (18 biome types for BIOME3 and 45 for GVM-specific absolute changes averaged only 5 and 7% MAPSS) (table 1). respectively, whereas local change averaged 32 and 50%, respectively. If new habitat types fail to reestablish through migration, these changes in many cases have the RESULTS potential to result in catastrophic species loss (~70% Appearances of Novel local change)(see Andre 1994, Fahrig 1997, in press). For the broad biome classification scheme, proportions Biome Types of ecoregions that showed >70% local change averaged only approximately 2% for the two GVMs, but increased Provided that biomes were defined broadly (10 to 5 and 19% for the narrow biome definition (figure 6). biome types), in many cases the same set of biomes types was present before and after the warming. The Amounts of local change at the global scale showed proportion of ecoregions that underwent no change (i.e., a strong latitudinal/altitudinal effect, with greatest that included the same set of biome types before and change at high altitudes and latitudes and relatively less after the warming) was relatively high, at 51 and 35% change in tropical lowland areas (map 2). Ecoregions in respectively on average for the two global vegetation Asia, Russia, and Canada showed relatively high models (BIOME3 and MAPSS) (figure 4). Only a few amounts of change and covered large areas. A primary ecoregions underwent more than 10% loss of existing effect of a narrow definition of biome change was to biomes (3 and 7% respectively), and only five under increase habitat change in subtropical areas. In the list- went 15% or more change under one or the other of the ings of local change under either GVM, tundra/taiga, GVMs. For the five, the percent loss tended to be incon- montane, and temperate forests were prominent (tables sistent among GVMs (table 2). MAPSS was more sensi- 4, 5). For many, high amounts of change were predicted tive to the warming than BIOME3, a difference between by both GVMs. For the coarse classification scheme, the GVMs that was evident in all analyses. seven ecoregions showed 70% or more change in at least one GVM (Ural Mountains Taiga, Canadian Low Arctic When biomes were defined more narrowly (18 types Tundra, Altai-Sayan Montane Forests, Muskwa/Slave for BIOME3 and 45 for MAPSS), loss of existing biome Lake Boreal Forests, Kamchatka Taiga and Grasslands, types was more common. Only 19% of ecoregions for Canadian Boreal Taiga, and Southwestern Australia BIOME3 and 13% for MAPSS showed no change, and Forests and Scrub). Drier ecosystems tended to be more 13 and 25% respectively showed greater than 10% loss prominent under the more narrowly delimited classifica- of existing biomes (figure 4). This time, 22 ecoregions tion scheme, especially for MAPSS (table 5). showed 15% or more loss under one or the other GVM; however, the results again tended to be inconsistent A comparison of the amounts of local change between the GVMs (table 3). Changes in several north- against randomly chosen sets of pixels of the same ern areas were notable because of their large size; how- biome types showed that some ecoregions were especial- ever, Australian ecoregions also tended to be hard hit ly vulnerable to climate change in a comparative con- (map 1, table 3). Ecoregions with the most consistent text, whereas others were relatively stable (tables 6, 7; loss of the original biomes under both GVMs included map 3). Unusually vulnerable ecoregions included the

WWF the Fynbos of Southern Africa and the Terai-Duar Central Andean Dry Puna, the Daurian Steppe, the Ural 16 Savannas/Grasslands of northeastern India. Mountains Taiga, and the Canadian Low Arctic Tundra. HABITATS AT RISK 17 plotted against that novel became proportion of biome novel types. the As indicated by arrow, local change may indicate substantially more habitat loss if exist- ing habitats fail to reestablish elsewhere. Proportional ofchange biome types BIOME3 MAPSS Percent new biome types MAPSS MAPSS Narrow biome definition Narrow 0 102030405060708090100 0

80 20

100 Percent of ecoregions of Percent 0.0 0.2 0.4 0.6 0.8 concentrations. calculated each as ecoregion was The area for percentage 2

Proportion new biomes Y=X BIOME3 BIOME3 BIOME3 MAPSS Percent new biome types Broad biome definition Broad biome Broadly-defined biomes Narrowly-defined biomes 0 102030405060708090100 0.0 0.2 0.4 0.6 0.8

0

20 80

Percent of ecoregions of Percent 0.0 0.2 0.4 0.6 0.0 0.8 0.2 1.0 0.4 0.6 0.8 1.0 100 biome types under scenarios of doubled CO change biome local Proportion the average across multiple climate scenarios for each global vegetation model (BIOME3 or MAPSS). across multiple climate each global vegetation scenarios for the average The left figure shows types); under a broad biome definition (10 biome the distribution is under a narrow the right figure biome definition (18 types MAPSS). BIOME3 and 45 for for Percent ofPercent of percent to average ecoregions plotted according area FIGURE 5. FIGURE 4. FIGURE 18 WWF FIGURE 7. FIGURE 6. ne cnro fdoubledCO under scenariosof ecn fecoregionsplottedaccording toaverage required rates migration Percent of ecoregionsplottedaccording toaverage percent change Percent of and 45for MAPSS). biomedefinition(18typesfor BIOME3 results areshownfor abroadbiomedefinition (10biometypes)andanarrow figure, Ineach scenariosforglobalvegetation each across multipleclimate model(BIOME3[leftfigure]orMAPSS[rightfigure]). BIOME3 and45for MAPSS). biomedefinition (18typesfor theright figure isunderanarrow distribution underabroadbiomedefinition(10types); The leftfigureshowsthe scenariosforglobalvegetation each across multipleclimate model(BIOME3orMAPSS). ne cnro fdoubledCO under scenariosof Percent of ecoregions Percent of ecoregions 10 15 20 25 30 35 20 40 60 80 0 5 0 1020304050607080901000 0040 008000 6000 4000 2000 0 Broad biomedefinition BIOME3 Mean percentlocalchange observed inthepaleorecord Migration ratesrarely Average migrationrate(m/yr) Narrow biomedefinitions Broad biomedefinitions 2 2 concentrations. The percentage The areaforecoregionwas asthe average each calculated concentrations. concentrations. The migration rate for ecoregionwas astheaverage rate each calculated migration The concentrations. MAPSS (n=8) BIOME3 (n=6) precipitous biodiversity declinein Possible

Percent of ecoregions Percent of ecoregions 10 15 20 25 20 40 60 0 5 0 1020304050607080901000 0040 008000 6000 4000 2000 0 Narrow biomedefinition MAPSS Mean percentlocalchange Average migrationrate(m/yr) fbiometypes of (RMRs) Despite its large size, the last showed usually high DISCUSSION change, at 75 and 77% for the two GVMs (BIOME3 and MAPSS, respectively) compared to 47 and 57% respec- These results indicate that global warming has the tively for the random sets. Especially stable regions potential to strongly affect species richness in the Global included the Central and Eastern Woodlands, 200 ecoregions. Even if ecosystems are assumed to be the Valdivian Temperate Rain Forests/Juan Fernandez able to perfectly keep up with the warming, an outright Islands, and the Appalachian and Mixed Mesophytic loss of species is expected in many ecoregions because Forests. Tundra ecoregions of Canada and Russia tended of reductions in the area of the original habitat types. to be vulnerable, whereas those of Fennoscandia and western Alaska tended to be more stable. Strong con- Under our broad biome definition, which was very trasts were shown within the tropics. Within the South conservative because it divided the planet's ecosystems American region, most areas showed vulnerability (with into only 10 vegetation types, the areas lost tended to be the exception of eastern Columbia and the northwestern small, with only 3Ð7% of ecoregions on average losing Amazon). In southeast Asia, insular areas tended to 10% or more of their habitat. However, under a narrow show stability, whereas mainland areas tended to be biome definition, 22 ecoregions lost 15% or more of more vulnerable. In tropical Africa, the central region their original biome types in at least one of the vegeta- tended to be relatively stable, whereas western, southern, tion models. Because of the strong relationship between and eastern Africa tended to be vulnerable. the area of a habitat and the number of species that it contains, this loss of habitat can be expected to result in Required Migration Rates species extinctions. This species loss will arise from a combination of factors, including reduced population sizes of the various species that dwell in the habitat and Required migration rates (RMRs) were strongly reduced numbers of micro-habitats in the habitat. affected by the GVM type and the breadth of biome def- initions. As was true of the other measurements, BIOME3 was less sensitive than MAPSS, and the In the present analysis, it is difficult to calculate the assumption of narrow biomes increased vulnerability number of species extinctions that can be expected as a result of the area loss, in part because the various bio- (figure 7). For example, under the 10-class biome defini- tion, average ecoregion RMRs for the two GVMs mes within an ecoregion differ with respect to their (BIOME3 and MAPSS, respectively) were 313 and 502 species richness, and in part because the relationship m/yr, whereas under the narrow definition they were 766 between area and species richness within a biome type and 1,075 m/yr. Percentages of ecoregions that showed varies according to the spatial scale examined (e.g., average rates above 1,000 m/yr (unusual in the paleo Hubbell 2001). A very rough figure assuming a conti- record) were 6, 11, 19, and 42%, respectively. nental species-area exponent of 0.15 would mean species loss of some 2Ð3% of the biota under 15% habi- tat loss.5 In a species-rich ecoregion such as the Fynbos Examination of the spatial distribution of RMRs of southern Africa, which averaged 32% loss, the showed strong similarities to the pattern of local biome expected 5Ð6% species loss would imply the eventual change. Under a coarse biome classification, unusually loss of thousands of species. high rates were shown in northern areas, especially in Canada and Russia, but also in southwestern Australia As indicated by the conceptual model, however, and New Zealand (map 4). Consistently high rates (>750 m/yr for both GVMs) were dominated by tundra and species loss could be much higher if the rate of warming exceeds migrational capabilities. Under the worst-case taiga areas (table 8). Finer subdivision of biomes led to much higher rates in warm temperate and subtropical scenario that populations fail to move altogether due to areas; only the lowland tropics still had relatively low migration limitation, species loss could increase marked- RISK HABITATS AT ly. In the Fynbos, for example, loss of habitat would rates (map 4). High rates were especially prevalent in North America, Europe, Asia, Australia, eastern and increase from 32% to 58%. In the Canadian Low Arctic southern Africa, and southern South America. Nearly Tundra, the average percent biome change was 76%, one quarter of the ecoregions, including a wide diversity which, using an exponent of 0.15, would translate into of ecosystem types, had consistently high rates (>750 species loss of 19%. However, use of a species area rela- tionship for habitat loss of this magnitude may seriously m/yr for both GVMs) (table 9). underestimate species loss. For example, a variety of 19 spatially explicit habitat loss models suggest threshold effect will be a filtering effect, with subsequent ecosys- effects (Fahrig in press) wherein sufficiently high habitat tems consisting of the fastest migrating (often weedy) loss (typically in the order of 70Ð80%) may lead to a species. Surprisingly, even the migration capabilities of precipitous increase in extinction probabilities (Andrén temperate trees (a well studied group) are poorly under- 1994, Fahrig 1997). stood. They appear to have been able to keep up with retreating glaciers (Prentice et al. 1991), but plant In the present analysis, 2% of ecoregions showed demographers have a hard time understanding how they habitat loss of this magnitude under the coarse biome def- could migrate even that fast (Clark 1998). inition, whereas the average figures for the narrow biome definition were 5 and 19% (for BIOME3 and MAPSS, The importance of dispersal rates in determining respectively) (table 5). The mere possibility of catastroph- migration capabilities raises the specter of low migration ic species loss in 19% of the world's most valuable ecore- capabilities in some ecoregions. For example, the high gions (22 of the 113 considered here) indicates the poten- species richness in the Cape Floristic Province of south- tially serious consequences of the projected warming. ern Africa may be related to low dispersal capabilities (nearly 3,000 species are ant-dispersed [Mittermeier et A relevant issue is the overall vulnerability of the al. 1999]), with the possibility of low migration capabili- Global 200 to global warming compared to random loca- ties. Matlack (1994) obtained evidence that forest under- tions. Apparently, the factors that give rise to the biologi- story plants reinvaded secondary forests at very low rates cal value of these regions do little to protect them (less than 1 m/yr typically), with ant-dispersed species against the ravages of climate change. Indeed, in some performing especially poorly. cases, as discussed below, factors such as low dispersal capabilities and high endemism may make them more A related issue is geographic range size. For virtu- vulnerable to climate change. ally all of the variables investigated here, the breadth of the biome definitions influenced the results. Again, it is Of course, the possibility of such radical species loss difficult to make any definitive statements as to what depends both on migration rates and extinction rates. definition might be more relevant. The relationship Although the invasion of new biome types implies a loss between average biome area and average range size of some of the original biota of an ecoregion, it is possi- shows considerable variation. For example, for common ble in some cases that the original habitats could reestab- U.S. tree species east of the 100th meridian, even the lish elsewhere. The exception is for many tundra and coarsest (10-type) biome classification underestimated taiga habitats (including those at high elevations), which average range sizes (Malcolm et al. in press). From are projected to show a net decline globally. These habi- Iverson et al. (1999), 75 tree taxa mapped by Little tats literally have nowhere to go in a warmer world. The (1971, 1977; cited by Iverson et al. [1999]) had average same may also apply to arid ecosystems (Neilson et al. range sizes of 1.59 million km2, which was larger than 1998, Malcolm et al. in prep). the average 10-class biome sizes for the same region (0.67 million km2 for both BIOME3 and MAPSS). By This reestablishment of new populations will depend contrast, for 819 species in the genus Eucalyptus in on successful migration, which makes the high required Australia, average range size was 0.11 million km2 migration rates reported here of concern. Typical migra- (Hughes et al. 1996). This was smaller than the average tion rates observed during the glacial retreat were 100- areas of our narrowest biome definitions in the same 200 m/yr; the average rates of many of the ecoregions in region (0.85 million km2 for BIOME3 [18 biome types] this analysis were an order of magnitude higher. Incredi- and 0.45 million km2 for MAPSS [45 biome types]). bly, nearly one quarter of ecoregions had average rates of greater than 750 m/yr. Added to this is the possibility that This emphasizes that the use of biome distributions our rates are underestimates because of a conservative as proxies for species climate envelopes, even in a estimate of the time period of the warming (100 years). heuristic sense, must be treated with caution. Given the average increase in geographic range size with latitude Unfortunately, the significance of these high rates is (Rapoport’s rule), it seems likely that a coarse biome difficult to determine with certainty, and little can be definition makes more sense at high latitudes, whereas

WWF said other than that some species presumably will be finer definitions make more sense at low latitudes. The prevalence of endemic species (which tend to have small 20 able to attain them, whereas others will not. The net geographic ranges) in the ecoregions raises the possibili- Thus, although wide climatic tolerances may reduce ty that even our narrow-definition estimates of habitat the effects of the warming in the short term, they may change and required migration rates are underestimates. exacerbate future species loss by further reducing possi- bilities for migration. If distributions are determined by The expectation of systematic variation in relevant biotic interactions, the net effect of a changing climate life history traits among ecoregions outlines the impor- becomes very difficult to predict, with the possibility tance of investigating vulnerability within the context of that some taxa will maintain populations outside of their the major habitat types (which vary systematically with normally observed climate envelopes. In fact, the validi- latitude). Generally lower levels of habitat change and ty of the climate envelope modeling approach in a pre- required migration rates in tropical compared to temper- dictive capacity remains to be established, although ate regions may not be particularly relevant if tropical recent success using the approach has been obtained in species have lower migration capabilities, for example. predicting recent changes in distributions of European Of course, the enormously high species richness in tropi- birds (R. Green pers. comm.). cal regions makes these ecosystems of special concern regardless. There, losses of just a few percentages of the Finally, the analysis here fails to consider preexisting biota potentially translate into tens of thousands of anthropogenic influences. Many of the species in the species or more. Global 200 ecoregions are already threatened by human activities (Olson and Dinerstein 1998). Schwartz (1992, Although these calculations indicate the potential see also Davis 1989) noted that climate warming could seriousness of the problem posed by climate change for especially threaten species with geographically restricted the planet's species and ecosystems, they also outline ranges (such as narrow endemics), those restricted to some of the difficulties in obtaining realistic projections habitat islands, and those that are specialists in uncom- of the possible impacts of climate change on species mon habitats. This is an important concern for many richness. Perhaps most surprising are the strong differ- plant species; for example, The Nature Conservancy esti- ences in vegetation change between the GVMs. These mates that one-half of the endangered plant taxa in the differences tend to greatly exceed those attributable to United States is restricted to five or fewer populations differences among the GCMs (Malcolm et al. in press, (Pitelka et al. 1997). Rarity can be expected not only to Malcolm et al. in prep). However, in a relative sense, the increase extinction rates (MacArthur and Wilson 1967) two models indicate similar effects of the warming, sug- but, along with losses of natural habitats, to reduce the gesting that the overall effect of the warming could be potential for migration (see Schwartz 1992). Similar reasonably assessed despite these differences. arguments apply to island ecoregions, which were largely excluded from this report because of the small number of The conceptual model presented here illustrates the grid cells that they occupied. The biodiversity of islands potentially important roles of migration and extinction may be at special risk because of small populations, lim- in determining species loss; however, it does not include ited opportunities for migration, and sea level rise. several factors that can be expected to importantly affect overall responses to climate change. These factors In conclusion, a relatively large collection of global in turn significantly complicate attempts to equate vege- climate and vegetation models all indicate the potential tation change with species loss (or gain). Perhaps most for massive vegetation change in globally significant significant among these is the degree to which climate ecoregions under projected climates associated with a change increases local extinction rates. Any such extinc- doubling of CO2 concentrations. This vegetation change tions will depend on a number of factors, including the has the potential to result in substantial species loss longevity of the organisms involved (which is highly through reductions in the overall amounts of habitat; and relevant over the short time scale during which a dou- in some cases, it may result in catastrophic species loss, AIASA RISK HABITATS AT bling of CO2 concentrations are expected [<100 years]) especially if the rate of warming exceeds the capabilities and the extent to which distributions are determined by of species to migrate. However, the relationship between climate. However, although wide tolerances may reduce species loss and climate-induced vegetation change is extinction rates, they also may reduce migration rates poorly understood, as are species migration capabilities. because of fewer opportunities for species newly Efforts to test the validity of the climate-envelope migrating into an area (the “zero sum” dynamics of approach are needed, as are further efforts to model the Hubbell [2001]). interplay between local extinction and migration. 21

Percent of cells 28 - 44 20 - 27 16 - 19 14 - 15 12 - 13 10 - 11 8-9 5-7 2-4 0-1

MAP 1.

Percentages of ecoregion grid cells that became novel biome types under scenarios

of doubled CO2 concentrations, averaged across 14 combinations of global vegetation and climate models. Biomes were classified into 18 types for the vegetation model BIOME3 and 45 types for the vegetation model MAPSS.

23 AIASA RISK AT HABITATS 24 WWF

MAP 2A

Percent of cells 70 - 77 62 - 69 54 - 61 47 - 53 39 - 46 31 - 38 24 - 30 16 - 23 8-15 0-7 MAP 2B

Percent of cells 71 - 78 63 - 70 55 - 62 47 - 54 40 - 46 32 - 39 24 - 31 16 - 23 8-15 0-7

MAP 2.

Percentages of ecoregion grid cells that changed biome types under scenarios of doubled

CO2 concentrations, averaged across 14 combinations of global vegetation and climate models. In the upper map, biomes were classified into 10 types; in the lower map, they were classified into 18 types (BIOME3) or 45 types (MAPSS).

25 AIASA RISK AT HABITATS 26 WWF

MAP 3A

Average 0-10 0-10 11-20 11-20 21-30 21-30 31-40 31-40 41-50 41-50 51-60 51-60 61- 70 61- 70 71-80 71-80 81-90 81-90 91 - 100 91 - 100 MAP 3B

Average 0-10 0-10 11 - 20 11 - 20 21 - 30 21 - 30 31 - 40 31 - 40 41 - 50 41 - 50 51 - 60 51 - 60 61- 70 61- 70 71 - 80 71 - 80 81 - 90 81 - 90 91 - 100 91 - 100

MAP 3.

Comparisons of local biome change between ecoregions and randomly drawn collections of grid cells with the same biome composition. Numbers represent tests of significance between the two (two- sided, type I probabilities), averaged across 14 combinations of global vegetation and climate models. Yellow shades symbolize ecoregions that were relatively stable; that is, they underwent less change than the random collections. Red shades symbolize ecoregions that were relatively vulnerable; that is, they underwent more change than the random collections. For both, smaller numbers indicate a stronger difference between the ecoregion and the random set. In the upper map, biomes were classified into 10 types; in the lower map, they were classified into 18 types (BIOME3) or 45 types (MAPSS).

27 AIASA RISK AT HABITATS 28 WWF

MAP 4A

Average rate 1500 - 1932 1250 - 1499 1000 - 1249 750 - 999 500 - 749 250 - 499 0- 249 MAP 4B

Average rate 1500 - 4317 1250 - 1499 1000 - 1249 750 - 999 500 - 749 250 - 499 0- 249

MAP 4.

Average RMRs (required migration rates) within ecoregions, averaged across 14 combinations of global vegetation and climate models. In the upper map, biomes were classified into 10 types; in the lower map, they were classified into 18 types (BIOME3) or 45 types (MAPSS).

29 AIASA RISK AT HABITATS 30 WWF AL 1. TABLE types usedintwo models(BIOME3andMAPSS). globalvegetation scheme typesfrom usedtodefine10majorvegetation theoriginal Classification 0 AridLands 10. 9. Shrub/Woodland 8. Savanna/Woodland 7. Forest Tropical Broadleaf 6. Temperate Mixed Forest 5. Temperate Evergreen Forest 4. Boreal Conifer Forest 3. Taiga/Tundra 2. Tundra 1. Shrub Savanna Tropical Shrub Desert Grassland SemiDesert Mixed Tall Grass Northern C3 Arid /steppe NoGrass Open Shrubland savannas Dry Chaparral, Tree Savanna Mixed Warm Tree Savanna DeciduousBroadleaf Tree Savanna Mixed Cool grassland Short Forest Tropical Savanna Dry Evergreen Broadleaf Xeric woodlands/scrub Tall grassland Forest SeasonalTropical Moist savannas Tropical deciduousforest Forest Broadleaf Forest Mixed Warm Temperate broad-leaved evergreen forest Tropical rain forest Tropical seasonalforest Ice Forest Mixed Warm Temperate deciduousforest MAPSS Temperate conifer forest Tundra Forest Evergreen NeedleTaiga Taiga/Tundra Temperate/boreal mixed forest Boreal evergreen forest/woodland Boreal deciduousforest/woodland Polar desert Arctic/alpine tundra BIOME3 Desert Tropical, Desert Extreme Desert Tropical, Desert Subtropical Desert Temperate, Desert Boreal Desert C4, Grass Semi-desert Savanna MixedShrub Warm C4 Grass Short Grass MidC4 Grass Tall C4, C3/C4 Grass Short Grass MidC3/C4, Grass Tall C3/C4 C3, Grass Short Grass MidC3 Grass Tall C3, C3 Short Grass Dry MidC3, Grass Northern TallGrass Northern C3 C4, Grass Short C3 Mixed Short Grass Dry Mixed MidC4 Grass Southern Mixed MidC3 Grass Northern Grass Prairie Tall C4 C3/C4 Grass Semi-desert C3 Grass Semi-desert TemperateShrubland Conifer Xeromorphic TemperateShrubland Conifer SubTropicalShrubland Mediterranean SubTropicalShrubland Xeromorphic Savanna SubTropicalShrub Mixed Savanna EvergreenShrub Micro Savanna MixedShrub Cool Savanna Mixed Shrub Warm Broadleaf, Tree Savanna PJXericContinental Tree Savanna PJMaritime Tree Savanna PJContinental Tree Savanna Evergreen NeedleContinental Tree Savanna Evergreen NeedleMaritime Tree Savanna Mixed Warm Forest Hardwood Cool Forest Mixed Cool Forest Evergreen NeedleContinental Forest Evergreen NeedleMaritime HABITATS AT RISK 31 PERCENT NEW BIOME TYPES PERCENT PERCENT NEW BIOME TYPES NEW BIOME PERCENT 1294 18.05 0 20.83 29.25 19.639 16.714 3112 15.5912 36.1141 44.35 6.94 20.8355 50 32.024 32 38.54 27.379 9423 6.97 3.3536 8.86 24.997 72 26.59 34.06 0 23.343 23.44 16.2 8.57 18.181 29.25 1.0915 17.191 14.2434 17.88 16.714 15.22 21 4.44 15.08 19.1215 13.89 18.33 4.41 17.78 0 12.377 10.714 15.48 0 8.846 7.62 339 18.58 4.57 10.574 114117 1.03 76.32222 2.28119 44.053 43.38 0 40.2 25.766 33.95 3.04339385 18.966 19.4 25.37 29.13204 4.57 0.39 0.33 13.484 12.707 17.1 9.913 114 1.03 73.25 42.299 2656 27.81 1.34 12.684 2656 27.81 1.34 12.684 81 Forests Boreal Lake Muskwa/Slave 118 Fynbos 103 Southern Rift Montane Woodlands 33121 Eastern Moist Forests California Chaparral and Woodlands 908435 Savannas Northern & Trans-Fly Australia 65 Mountains Moist Forests Cardamom Rain Forests Temperate Tasmanian 83 Taiga Mountains Ural 7184 Forests East Temperate Far Russian East Siberian Taiga Ecoregion Ecoregion ID name91120 & Grasslands Savannas 55 Terai-Duar & Woodlands Mallee Southern Australia 75 Number BIOME3 Chhota-Nagpur Dry Forests 68 Southeastern & Broadleaf Coniferous Forests MAPPS7152 Weighted Forests 57 Temperate Western Himalayan of pixels Forests East Temperate 105 Far Russian Dry Forests 81 Nusa Tenggara (n = 6) Dry Forests Valleys Tumbesian-Andean Montane Shrub & Woodlands (n = 8) Forests Boreal Lake Muskwa/Slave 864 average 3 African Coastal Forests East Forests Temperate Eastern Australia Forests Highlands 91 & Grasslands Savannas Terai-Duar 83 Taiga Mountains Ural Ecoregion Ecoregion Ecoregion Ecoregion ID name Number BIOME3 MAPPS Weighted of pixels (n = 6) (n = 8) average Ecoregions with relatively large percent increases in the appearance of increases large percent Ecoregions with relatively biome new types (>15% of under a narrow either BIOME3 or MAPSS) for on average pixels BIOME3;types for biome definition (18 biome MAPSS). 45 for Ecoregions with relatively large percent increases in the appearance in the of increases percent large Ecoregions with relatively new types (>15% ofbiome under BIOME3 or MAPSS) either for on average pixels types). (10 biome biome definition a broad TABLE 3. TABLE 2. 32 WWF AL 4. TABLE a broad biomedefinition(10types). pixels on average for eitherBIOME3orMAPSS)under of (>0.50 proportion changes withrelativelyEcoregions inlocalbiometypes largeproportional 2 Carnavon XericScrub MoistForests Deccan Plateau Eastern Chukote CoastalTundra Cardamom MountainsMoistForests128 Nevada Sierra Coniferous33 Forests Australia Eastern Temperate117 Forests Pacific Temperate35 RainForests 74 Fynbos Klamath-Siskiyou Coniferous Forests64 AntilleanMoistForests Greater 72 118 Australia Southern Mallee&Woodlands Himalayan Western Temperate73 Forests 37 Drakensberg MontaneShrublands&Woodlands average MontaneMixed European-Mediterranean Forests120 68 AndeanParamo Northern (n=8) 105 Australia Southwestern Forests &Scrub 77 AsianMontaneSteppe&Woodlands Middle CanadianBoreal Taiga108 (n=6) 119 Fenno-Scandia AlpineTundra &Taiga pixels DaurianSteppe 111 of 82 Taimyr &Russian CoastalTundra Weighted 115 Steppe TibetanPlateau EastSiberianTaiga96 MAPPS Kamchatka Taiga116 &Grasslands 110 Himalayan Eastern AlpineMeadows BIOME3 84 Number Puna Central AndeanDry Muskwa/Slave Lake85 Boreal Forests Altai-Sayan MontaneForests112 109 CanadianLowArctic Tundra Ural MountainsTaiga81 79 name 114 83 ID Ecoregion Ecoregion 07029 .4 0.52416 0.745 0.54774 0.2297 0.56103 0.5792 1097 0.4763 0.5058 0.674 1227 2656 1 .24012 0.30249 0.32293 0.1323 0.513 0.38304 0.5294 0.38616 0.0695 0.5888 119 0.193 264 0.4127 0.1087 0.6437 204 0.578 138 0.44867 0.1923 0.4876 0.5484 0.50703 117 0.7132 0.3157 0.52731 0.4754 0.53423 0.1868 217 0.6663 0.5492 0.5527 0.5182 190 0.342 400 0.57807 0.6445 0.5556 248 0.60523 0.8905 0.4303 515 0.6111 0.62276 0.6471 0.1615 586 0.75997 0.5241 0.77194 0.723 0.5494 161 0.7688 0.7271 0.9024 0.4891 108 0.7482 339 0.598 441 607 114 5009 .070.30003 0.5027 0.34287 0.3821 0.0298 0.5167 0.2687 95 0.39173 0.1111 0.39284 0.5333 0.39653 0.5686 15 0.2125 20 0.42416 0.5689 0.1559 0.4266 0.6333 0.3672 0.1667 31 0.47959 0.5955 20 0.5001 29 0.625 0.2014 32 0.2857 72 14 0.5812 0.6949 0.4296 45 RPRINLCLBOECHANGE BIOME LOCAL PROPORTION HABITATS AT RISK 33 PROPORTION LOCAL BIOME CHANGE 45 0.5408 0.844472 0.71429 31 0.419 0.3763 0.7222 0.737934 0.59226 0.58293 54 0.3431 0.3056 0.72792915 0.7153 0.56299 0.1954 0.1111 0.53971 0.7457 0.7 0.50986 0.44761 114607108 0.6024 0.7482441 0.591 0.909190 0.7688146 0.5011 0.8102339 0.7776 0.3772 0.75997 325 0.516 0.801 0.71626 0.7271 0.8454 0.2892 0.7012 0.67247 117 0.5241 0.64474 128 0.8254 0.62183 161 0.3162 0.6111 248 0.2266 0.5956 0.1615 0.781290 0.8467 0.342 0.8936204 0.5818 0.3316 0.58094 0.7334 0.57984 0.2492 0.722 0.56566 0.55469 0.75 0.53537 1097 0.232 0.745 0.52514 83 Mountains Taiga Ural Ecoregion Ecoregion Ecoregion Ecoregion ID114 name109112 Tundra Canadian Low Arctic 79 Andean Dry Central Puna 119 Alpine Meadows Eastern Himalayan 54 Number 81 Montane Forests Altai-Sayan BIOME3 & Scrub Forests Southwestern Australia 94105 Dry Indochina Forests MAPPS Forests Boreal 118 Lake Muskwa/Slave 120 Northern Prairie Weighted Montane & Woodlands Drakensberg 130 Fynbos of85 pixels & Woodlands Mallee Southern Australia 115 (n = 6) Sonoran-Baja 8 & Grasslands 124 Taiga Kamchatka & Taiga Alpine Tundra Fenno-Scandia 101 (n = 8)64 East African Coastal Forests Deserts -Karoo- 82 average Savannas Flooded 37 Forests 35 Temperate Eastern Australia Taiga Boreal Canadian Greater Antillean Moist Forests Mountains Moist Forests Cardamom Ecoregions with relatively large proportional large local biome types in Ecoregions with relatively changes (>0.70 proportion of under BIOME3 or MAPSS) either for on average pixels a narrow BIOME3; types for (18 biome biome definition MAPSS). for 45 TABLE 5. 34 WWF AL 7. TABLE TABLE 6. (10 biometypes). were Calculations underabroad biomedefinition set was P<0.1for bothmodels. significancebetween andrandom theecoregion and theaverage (two-tailed) testof was orvulnerable) thesamefor(either stable thetwo models, globalvegetation change direction The of biometypes. hadthesamecollectionof selections that wereEcoregions that incomparisontorandom unusually orstable vulnerable eiiin(8boetpsfrBOE;45for MAPSS). definition (18biometypesfor BIOME3; were biome Calculations underanarrow random setwas P<0.1for bothmodels. significancebetween and theecoregion models andtheaverage (two-tailed) testof change was orvulnerable) thesamefor (eitherstable thetwo globalvegetation average The direction of biometypes. hadthesamecollectionof selections that wereEcoregions that incomparisontorandom unusually orstable vulnerable Dnm hnecag hnechange 0 0 0 change 0.503 0.5738 0.6132 0.7688 0.8102 0.909 change 0 0 0.1 change 0.4515 0.4745 0.4564 0.6024 0.7482 0.591 114 Rain Forests 607 Valdivian / Temp. 108 CanadianLow ArcticTundra 76 Puna Central Andean Dry Stable Ural MountainsTaiga 114 109 name 83 Vulnerable ID 0 0 0 0.4091 0.4979 change 0.5182 change 0.9024 0.5741 0.1 0.7688 0 0 0.4969 0 0.3408 0.4116 change 0.6471 andMixed Appalachian change 0.4445 0.5556 1 0.598 0 Rain Forests Valdivian / Temp. 515 0.7482 114 0.4145 69 607 Central andEastern 0.5494 76 108 CanadianLowArcticTundra 88 Ural MountainsTaiga Stable DaurianSteppe 114 Puna Central AndeanDry 83 96 name 109 Vulnerable ID crgo crgo fpxl im im P biome biome pixels Ecoregion of Ecoregion P biome biome pixels Ecoregion of Ecoregion 1 Probability fromtwo-tailedProbability bootstrap test. Probability fromtwo-tailedProbability bootstrap test. unFr.I.12025 .540032 .980 0.5998 0.3822 0 0.4584 0.2255 102 Is. Juan Fern. 0 0 0.362 0.402 0.0428 0.1154 0 0 0 0.1252 0.3004 0 0.1659 0.4397 0.2108 0 146 102 0.1991 0.0831 620 Mesophytic Forests Is. Juan Fern. Miombo Woodlands ubrpo.po.po.prop. prop. prop. Number prop. ubrpo.po.po.prop. prop. prop. Number prop. b.Ep b.Exp. Obs. Exp. Obs. b.Ep b.Exp. Obs. Exp. Obs. IM3(=)MAPSS(N=8) BIOME3 (N=6) IM3(=)MAPSS(N=8) BIOME3 (N=6) 1 1 im im P biome biome im im P biome biome 1 1 HABITATS AT RISK 35 AVERAGE MIGRATIONAVERAGE (M/YR) RATE AVERAGE MIGRATIONAVERAGE (M/YR) RATE 55 1092.33 4001.1331 2754.5 1038.2941 2121.06 2648.5332 1657.02 72 790.9 998.7234 1258.6 1587.03 1392.7874 834.5395 1101.09 1380.77 1223.9 1401.34 758.47 1168.59 864.7941 1158.42 1241.358 1085.92 794.74 1034.37 799.65 914.51 770.42 863.18 782.95 138190 2190.18119 1693.11114 2806.32 3026.07 2811.06607 794.85339 2542.26 1020.89 2331.94 1642.44 2534.12515 2279.66 1880.25 1609.18 1517.29 1788.72 1084.05146 1623.43 1330.54 1596.45 1604.18 1410.58 880.25620441 1190.51 130 812.17 808.51645 992.63 1152.75 1129.47 905.9 1006.79 782.7 991.92 822.64 872.67 858.32 385 5959.67 929.28 3085.16 138607339 1663.1114 1638.93190 1586.7 1929.62 1609.18 793.05 1290.39 1067.71 1815.4 1554.74 1621.93 1052.61 1290.13 1228.3 1154.52 2656 3310.31 900.22 1933.12 1097 848.25 1540.181227 1288.07 1243.64 995.08 1120.65 2656 3310.3 898.641227 1932.21 1281.8 995.08 1117.96 1198433 & Scrub Forests 83 Southwestern Australia 118 Siberian Taiga East Eastern Deccan Plateau Moist Forests 81 Mountains Taiga Ural 55 Fynbos 82 Forests Boreal Lake Muskwa/Slave 68 Chhota-Nagpur Dry Forests 105 Taiga Canadian Boreal Forests Temperate Western Himalayan Montane Shrub.29 Drakensberg & Woodlands 79 Moist Forests Kayah-Karen/Tenasserim 92125 Montane Forests Altai-Sayan 98 Savannas Spiny Thicket Savannas Flooded Zambezian 72 Rain Forests Temperate Pacific 11496 Tundra Low Arctic Canadian 54 Daurian Steppe 8116 Dry Indochina Forests 128 African Coastal Forests East Coastal Tundra & Russian 88 Taimyr Xeric Scrub Carnavon & Eastern Central Miombo Woodlands 93 & Savannas Woodlands 121 California Chaparral & Woodlands 90 Savannas Northern and Trans-Fly Australia Ecoregion Ecoregion Ecoregion Ecoregion ID name Number BIOME3 MAPPS Weighted of pixels (n = 6) (n = 8) average 721148183 Rain Forests Temperate Pacific 119 Tundra Canadian Low Arctic 116 Forests Boreal Lake Muskwa/Slave Taiga Mountains Ural and Scrub Forests Southwestern Australia Coastal Tundra and Russian Taimyr 84 Taiga East Siberian Ecoregion Ecoregion Ecoregion Ecoregion ID name Number BIOME3 MAPPS Weighted of pixels (n = 6) (n = 8) average Ecoregions that had unusually high average migration rates (> 750 m/yr for both migration rates (> 750 m/yr for Ecoregions that high average had unusually a narrowBIOME3 and MAPSS) under BIOME3; for biome definition (18 biome types MAPSS). 45 for Ecoregions that had unusually high average migration rates (> 750 m/yr for both migration rates for (> 750 m/yr thatEcoregions average high had unusually types). (10 biome biome definition under a broad and MAPSS) BIOME3 TABLE 9. TABLE 8. 36 WWF David SuzukiFoundation. fundingfromwith additional the This research was fundedbyWWF, Alice Taylor andRonald Neilson. Freda Colbert, Sullivan, Kathleen Jennifer Morgan, Adam Markham, We would like tothankMike Garaci, Acknowledgements LITERATURE CITED Haxeltine A. andPrentice,I.C.1996. BIOME3: An equilibrium Hasselmann, K.1997. Are weseeingglobalwarming? Science Fahrig, L.inpress.Effect ofhabitatfragmentationontheextinction Fahrig, L.1997.Relative effects ofhabitatlossandfragmentation Davis, M.B.1989.Lagsinvegetation responsetogreenhouse Davis, M.B. 1986.Climaticinstability, timelags,andcommunity Crick, H.Q.P., andSparks, T. H.1999.Climatechangerelatedto Clark, J.S,Fastie, C.,Hurtt,G.,Jackson,S. T., Johnson,C.,King,G. Clark, J.S.1998. Why treesmigratesofast: confrontingtheorywith Bryant, D.,Nielsen,and Tangley, L.1997. The LastFrontier Brown, J.L.,Li,S.H.,andBhagabati,N.1999.Long-termtrend Beebee, T. 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HABITATS AT RISK 39 — raised to the power of the slope raised to the power p of habitat remaining, the expected pro- of habitat remaining, the expected p Endnotes for example, slopes in island systems typically range from 0.24 for example, range from about to 0.33, whereas in continental situations they conservative took a relatively We 0.12 to 0.17 (Pianka 1978). that to show of 0.15. It is not difficult approach and used a value proportion some given portion of species remaining is (0.15 in this case). For example, an 85% reduction in the area an 85% example, (0.15 in this case). For to result in a 25% reduction in species richness. can be expected ecoregion value would be larger or smaller than the random be larger would value ecoregion tests of the hypothesis. calculated two-tailed we value, from the United States software ters were calculated using sub- Association (FORTRAN Atmospheric National Oceanic and by J. G. by L. Pfeifer and modified routine INVER1, written System reference ellip- Geodetic World using the 1984 Gergen) soid (WGS84). linear relationship is typically rithm of area, a more-or-less to The slope of the relationship has been observed observed. of factors systematically under the influence of a variety vary 3 the as to whether prior expectation any Because we did not have 4 methods, distances between cell cen- In both distance calculation 5 If the logarithm of species richness is plotted against the loga- forc- 2 simulations 2 and 2 x CO 2 forcing (2070-2099). Neilson et al. 2 effects, whereas in keeping with the whereas in keeping effects, 2 effects (Neilson et al. 1998). MAPSS and BIOME2 effects 2 , particularly the ability of plants to use water more effi- , particularly the ability of plants to use water 2 back to a baseline monthly climate dataset (see Neilson et al. back to a baseline monthly climate dataset (see calculate future climate from the transient GCMs, a To 1998). was Centre) or 10-year (MPI) climate average 30-year (Hadley from the current period (1961-1990) and the period extracted approximating 2 x CO ing and included models from GISS, GFDL-R30, OSU, and ing and included models from GISS, GFDL-R30, were from (transient) models included, two UKMO. Of newer Centre (HADCM2GHG and HADGCM2SUL) the U.K. Hadley (MPI- and one from the Max Plank Institute for Meteorology models made use of coupled atmospheric-ocean These T106). incorporated the dynamics and in one case (HADGCM2SUL) The course of atmospheric aerosols (sulfates). cooling effect latitude/longi- grids of the GCMs were interpolated to 0.5 degree by applying tude grids. Climate change scenarios were created from 1 x CO ratios and differences Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Analysis Modeling and Vegetation/Ecosystem run only with analyses, the older climate change scenarios were direct CO (a precursor to BIOME3) produced generally similar results for (a precursor to BIOME3) produced generally compared to BIOME2, the coterminous United States. However, more sensi- consistently in MAPSS was the modelled vegetation stress, producing drier future outcomes, and had a water to tive of increased the direct physiological effects from benefit larger CO oceans to simulate equilibrium climate under 2 times CO oceans to simulate equilibrium climate under ciently (VEMAP Members 1995). (1998) used a similar set of models to investigate global changes (1998) used a similar set of models to investigate and runoff. in biome area, leaf area index, scenarios, whereas only BIOME3 was run under the Max Plank BIOME3 was scenarios, whereas only (MPI) scenario and only MAPSS was Institute for Meteorology Fluid Dynamics Laboratory (GFDL), run under the Geophyical State Uni- Studies (GISS), Goddard Institute for Space United Kingdom Meteorological Office (OSU), and the versity that made use of models vegetation (UKMO) scenarios. Global MPI climate were run both with and with- Centre and the Hadley out direct CO 2 layer mixed The older general circulation models used simple 1 Centre Hadley were run under the two MAPSS and BIOME3

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