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Article https://doi.org/10.1038/s41467-024-47872-7 Biodiversity loss reduces global terrestrial carbon storage Sarah R. Weiskopf 1,2 , Forest Isbell 3, Maria Isabel Arce-Plata4, Moreno Di Marco 5, Mike Harfoot6, Justin Johnson 7,S u s a n n a hB .L e r m a n8, Brian W. Miller 9, Toni Lyn Morelli 2,10, Akira S. Mori 11, Ensheng Weng12 & Simon Ferrier13 Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow- decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. Wefind that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and10.87- 145.95 PgC (fossil-fueled development scenario). This indicates a self- reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals. Climate change and biodiversity loss have been increasingly recog- nized as related crises that are most effectively addressed together1–5. Hundreds of experimental studies have consistently found that within a place, more diverse assemblages, and in particular more diverse plant assemblages, have higher standing biomass production and carbon sequestration 6–9. There are several possible mechanisms for this phenomenon. Species with different traits and resource require- ments may be able to utilize more resources in an ecosystem through reduced competition, increased facilitation, or both, which leads to overall more efficient resource use 10–12. At the same time, more diverse assemblages are more likely to contain the most productive species, which can increase overall functioning 13,14. Indeed, biodiversity loss can be one of the major drivers of productivity loss within ecosystems, on par with elevated carbon dioxide or effects of drought 15. Thus, while climate change can affect biodiversity, biodiversity loss can also affect climate change by altering carbon sequestration and storage 4,16. Received: 4 August 2023 Accepted: 11 April 2024 Check for updates 1U.S. Geological Survey National Climate Adaptation Science Center, Reston, VA, USA.2Department of Environmental Conservation, University of Massa- chusetts, Amherst, MA, USA. 3Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN, USA.4Département de Sciences Biologiques, Université de Montréal, Montréal, QC H3T 1J4, Canada.5Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy. 6Vizzuality, 123 Calle de Fuencarral, 28010 Madrid, Spain.7Department of Applied Economics, University of Minnesota, 1994 Buford Ave, Saint Paul, MN 55105, USA. 8USDA Forest Service Northern Research Station, Amherst, MA, USA.9U.S. Geological Survey North Central Climate Adaptation Science Center, Boulder, CO, USA.10U.S. Geological Survey Northeast Climate Adaptation Science Center, Amherst, MA, USA.11Research Center for Advanced Science and Technology, the University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 153-8904, Japan.12Columbia University/NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA. 13CSIRO Environment, Canberra, ACT 2601, Australia. e-mail: [email protected] Nature Communications| (2024) 15:4354 1 1234567890():,; 1234567890():,; Despite the contribution that biodiversity itself makes to carbon sequestration, high-level nature-based solution initiatives often focus on increasing the spatial extent of natural ecosystems, particularly forests, and not on their diversity or composition 17. Similarly, ecosystem service models do not always account for the effects of biodiversity. Models projecting changes in biodiversity, ecosystem functioning, and ecosystem services typically operate independently and do not account for interactions or feedbacks 18,19. For example, Earth System Models (ESMs) typically model terrestrial ecosystems using a small number of plant functional types and do not include biodiversity-productivity mechanisms 20,21. Not accounting for biodiversity may lead to inaccurate projections of ecosystem function and ecosystem services. For instance, these estimates assume that remnant habitat patches will provide the same level of function even in the face of signi ficant losses of species diversity 22. Incorporating biodiversity-ecosystem function relationships could improve model accuracy, especially over long timescales as biodiversity effects become stronger over time 23. For example, an Australian ecosystem modeling exercise found that including species turnover in marine ecosystem models led to very different outcomes for marinefisheries under different climate change scenarios 24. Multiple pathways to integrate biodiversity and ecosystem func- tion models have been proposed25. One approach that can be applied at the global scale is to connect biodiversity to ecosystem function and ecosystem service models using empirical, observational, or experi- mental biodiversity-ecosystem function data. Because of the extensive experimental data on the increase in biomass associated with increasing plant species richness 9,26, assessing how loss of plant diversity will affect carbon storage offers a feasible and useful case study to demonstrate the utility of this modeling approach. Moreover, assessments of plant species diversity and carbon storage are relevant for monitoring biodiversity and climate change mitigation goals. Early analyses have been conducted that illustrate this method 4,22.F o r example, Isbell et al.22 linked regional estimates of species loss (using species-area relationships) with biodiversity-ecosystem function rela- tionships derived from local-scale experiments. Yet, that work did not consider how climate change or land-use change might affect spatial patterns of species compositional turnover. Species turnover and regional species richness likely have important effects on functioning and stability 27–29. We build on this previous analysis by accounting for compositional turnover while estimating regional diversity loss. We use the Biogeographic Infrastructure for Large ‐scaled Biodi- versity Indicators (BILBI) model to project changes in plant species richness 30, going beyond the species-area 22 or endemics-area4 rela- tionships considered in previous studies by also accounting for var- iation in the species composition of communities (beta diversity) at fine spatial scales. This allows BILBI to be used to assess species per- sistence/loss over the long term under different scenarios of land-use and climate change 30,31. We link our species-loss estimates with empirical biodiversity-biomass stock relationships9 to assess the bio- mass loss, and ultimately carbon storage loss, associated with loss of vascular plant diversity. Although carbon stocks are affected by many global change drivers (e.g., climate, land use), this approach allows us to estimate carbon loss driven speci fically by biodiversity loss. Like previous analyses 22, we use data from local-scale biodiversity experi- ments to estimate biodiversity-biomass relationships at regional scales. The advantage of using the local experimental data is that by strictly controlling for species richness, composition, and other con- founding factors, local experiments can disentangle the causal effects of species richness on biomass production. This assumes that (1) local loss of species diversity is similar to regional scale biodiversity loss, and (2) species loss occurring at the regional scale has consequences for ecosystem functioning. Although the first assumption may not a l w a y sb em e t ,t h e r ei sc o n s i d e r a b l ee v i d e n c ef o rt h es e c o n d assumption 28. BILBI produces estimates of plant species loss, based on the local-scale effect of land-use change on species persistence and the regional-scale effect of climate change on species composition. Therefore, we followed previous analyses 22 and used estimates of plant species loss at the ecoregion scale. Our study explores how projected future climate and land-use change scenarios will affect biodiversity loss. We estimate the additional long-term loss of carbon storage resulting indirectly from biodiversity loss that is expected in addition to the direct emissions from land-use change or other climate change i m p a c t so nc a r b o ns t o c k s( F i g .1). We used BILBI model projections of the proportion of vascular- plant species expected to persist under “global sustainability” and “fossil-fueled development” scenarios that were produced for a recent model intercomparison project 32. The BILBI model uses generalized dissimilarity modelsfitted with more than 52 million records from over 254,000 plant species to map beta diversity at ~1 km 2 scale globally (see refs. 30,31 for complete model fitting information). Following Weiskopf et al. 25 (pathway A), we combined beta-diversity estimates with species-area relationships to assess plant species losses in each ecoregion globally, and then used empirical estimates of biodiversity- biomass stock relationships from O ’Connor et al. 9 to assess propor- tional changes in plant biomass. Finally, we used projected terrestrial carbon stock maps (which do not include biodiversity losses) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) IPSL-CM5A- MR model to estimate aboveground plant and soil carbon storage losses associated with projected biodiversity loss in each ecoregion 33. Results Under the global sustainability scenario, the 818 ecoregions lost an average of 16.0% of plant species using a z-value (species-area rela- tionship) of 0.25, ranging from −14.6% to 45.9% for individual ecor- egions (Fig.2; see Supplementary Fig. 2 for full range ofz-values). This led to an average biomass loss of 4.4% using a b-value (the power Fig. 1 | Modeling framework for this analysis. Coupled Model Intercomparison Project (CMIP) and Biogeographic Infrastructure for Large‐scaled Biodiversity Indicators (BILBI) models were used to estimate the effects of land-use and climate change (red boxes) on vegetation/soil carbon and biodiversity, respectively. We used BILBI model output of proportional species loss and empirical biodiversity- biomass relationships to estimate the proportional biomass loss from biodiversity loss (blue boxes). We then applied this proportional biomass loss to soil and vegetation carbon estimates from CMIP5 (green box) using similar climate change and land-use change scenarios as the BILBI model to estimate carbon emissions due to biodiversity loss (green-dashed line). Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 2 relationship between a change in species richness and biomass) of 0.26, ranging from −3.6% to 14.8% (Supplementary Fig. 1, see Supple- mentary Fig. 4 for full range of z and b values). This biomass loss was from within remaining vegetation as a result of biodiversity loss, over and above any biomass loss resulting from the direct impact of land- use change under a given scenario. Biodiversity and biomass losses were higher under the fossil-fueled development scenario, with ecor- egions losing an average of 20.8% of plant species (ranging from −36.9% to 46.2% across individual ecoregions; Fig. 2, see Supplemen- tary Fig. 3 for full range ofz-values), leading to an average biomass loss of 5.9% (ranging from −8.5% to 14.9%; Supplementary Fig. 1, see Sup- plementary Fig. 4 full range ofz and b values). In both scenarios, plant species loss, and therefore proportional biomass loss driven by plant species loss were especially high in the tropics. Southern Australia, eastern Europe, and some regions of South America also had high losses. When we combined the biomass loss values with projected carbon storage maps, we found that overall vegetation carbon loss was greatest in the tropical regions of South America, central Africa, and Southeast Asia, which was driven by which regions store the greatest amount of vegetation carbon and by the level of biodiversity loss (Fig. 3). For example, biodiversity loss projections and vegetation carbon were both high in the Amazon, making this a hotspot of biodiversity-driven carbon loss. In contrast, biodiversity loss projec- t i o n sw e r ea l s oh i g hi ns o u t h e r nA u s t r a l i a ,b u tb e c a u s et h i sr e g i o nh a s lower vegetation carbon, it was not an area with high biodiversity- driven carbon loss. When summed across all terrestrial ecoregions, biodiversity declines led to loss of 7.40 –102.68 (29.55 using b =0 . 2 6a n dz =0 . 2 5 ) A −150 −120 −90 −60 −30 0 30 60 90 120 150 −90 −60 −30 0 30 60 90 120 Plant species loss (%) −10 −5 0 5 10 15 20 25 30 35 40 45 50 B −150 −120 −90 −60 −30 0 30 60 90 120 150 −90 −60 −30 0 30 60 90 120 Plant species loss (%) −10 −5 0 5 10 15 20 25 30 35 40 45 50 Fig. 2 | Species loss by ecoregion.Plant species loss by ecoregion projected by the Biogeographic Infrastructure for Large‐scaled Biodiversity Indicators (BILBI) model under a global sustainability (SSP1/RCP2.6,A) and fossil-fueled development (SSP5/ RCP8.5, B) scenario using a species-area relationship of z = 0.25. Darker areas indicate greater plant species loss. Species-loss estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050. Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 3 PgC of vegetation carbon in the long term under global sustainability and 10.83–145.32 (42.89 using b =0 . 2 6 a n dz = 0.25) PgC under fossil- fueled development. Again, this refers to losses within remaining habitat, above those resulting from direct impacts of land-use change on vegetation extent. The range of carbon-loss values was estimated from the full range of species-area relationships and biodiversity- biomass stock estimates, thus capturing a large range of uncertainty in the strengths of these relationships within and among sites (Supple- mentary Figs. 5 and 6). These carbon losses per ecoregion depended not only on how much plant diversity was lost from the ecoregion, but also the remaining area of the ecoregion, given that they are summed across all remaining habitat (Fig. 4). For example, under the global sustainability scenario, the overall loss of carbon was higher from the ecoregions that have lost 10–20% of plant species diversity compared to ecoregions that lost >20% of their diversity because the former c o v e rl a r g e ra r e a s( F i g .4). Although our uncertainty range was high, projected carbon emissions from biodiversity loss have the potential to rival emissions expected from other sources such as land-use change or melting permafrost (Supplementary Table 1). Our models predicted long-term vegetation carbon emissions from long-term biodiversity loss (i.e., over the coming decades as the system moves toward a new equili- brium state) driven by climate and land-use change projections for 2050. These long-term biodiversity-driven carbon emissions were equivalent to about 12 –169% of the total emissions expected from land-use change by 2100 for the global sustainability scenario, and A −150 −120 −90 −60 −30 0 30 60 90 120 150 −90 −60 −30 0 30 60 90 120 Carbon loss (kg/m2) 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 B −150 −120 −90 −60 −30 0 30 60 90 120 150 −90 −60 −30 0 30 60 90 120 Carbon loss (kg/m2) 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 Fig. 3 | Carbon loss by ecoregion.Carbon loss (kg/m2) driven by long-term loss of plant biodiversity by ecoregion under a global sustainability (SSP1/RCP2.6,A)a n d fossil-fueled development (SSP5/RCP8.5,B) scenario using the mean biodiversity- ecosystem functioning slopeb = 0.26 and a species-area relationshipz =0 . 2 5 . Darker areas indicate greater carbon loss. This carbon loss is from within remaining vegetation as a result of biodiversity loss, over and above any carbon loss resulting from the direct impact of land-use change (e.g., deforestation) under a given sce- nario. Carbon-loss estimates are what is expected over the long term, when eco- systems approach their new equilibrium states, based on climate and land-use changes projected for 2050. Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 4 about 20 –271% for the fossil-fueled development scenario (Supple- mentary Table 1) using the full range of uncertainty from our analysis. Discussion We used a macroecological model to predict changes in ecoregion- level plant species richness and linked it with empirical estimates of the plant biodiversity-biomass stock relationship based on experimental data. We found that biodiversity loss can reduce global carbon storage potential, and although our uncertainty range was large, it could lead to high loss of vegetation carbon. Substantially greater loss is pro- jected under the more intense climate change and land-use change scenario, but even a sustainability scenario (compliant with the Paris target of 2 °C) carries high risks, similar to findings for mammals and wilderness areas 34,35. This engenders a positive feedback wherein higher levels of climate change leads to greater biodiversity loss, which in turn leads to even greater carbon emissions. Our study builds on a previous analysis that found that land-use change to date could result in the gradual loss of 2 –21 PgC as plant species are lost from remaining habitats 22. We found that projected emissions under future climate and land-use change have the potential to be much higher. In our analysis , we modeled aboveground carbon loss resulting from plant biodiversity loss. Soil carbon may also strongly depend on plant diversity36–39. Carbon loss could increase dramatically if the relationship is on a similar scale to aboveground biomass (18.71–259.72 PgC under the global sustainability scenario and 26.25–353.47 PgC under the fossil-fueled development scenario, Sup- plementary Figs. 7 and 8). A recent analysis using a different approach— linking species distribution model s and other ecological modeling approaches with biodiversity-productivity relationships—found that mitigation activities that maintain tree diversity could avoid a 9–39% loss of productivity across terrestrial biomes 4. Similarly, our estimated carbon loss from biodiversity loss was about 30% lower in the global sustainability scenario compared to the fossil-fueled development scenario. Our analyses used different biodiversity models and climate models, and Mori et al. 4 used estimates of local species loss rather than ecoregion losses as we did here. Together, thesefindings indicate that biodiversity loss can be a strong driver of carbon emissions. Priority areas for biodiversity conservation and climate change mitigation could change by accounting for the role of biodiversity in promoting carbon storage. For example, Soto-Navarro et al. 2 identified few areas in central Africa that overlapped as being in the top 20% for both biodiversity and carbon importance. However, we found that carbon losses due to biodiversity loss were high in this area (Fig.3), and therefore that biodiversity protection and restoration here could be highly valuable for climate change mitigation 40. Several other factors that could contribute to this difference are that we used projected changes in biodiversity and carbon compared to current maps used by Soto-Navarro et al.2, and also that we considered only plant diversity while Soto-Navarro et al.2 looked at mammals, birds, and amphibians. Projected biodiversity loss and associated proportional biomass loss were higher in the Amazon and central Africa under the fossil-fueled development scenario compared to the global sustainability scenario. Interactions between biodiversity loss and ecosystem-level carbon storage led to consistently high losses of carbon in the tropics under both scenarios, specifically in the Amazon, central Africa, and South- east Asia, and moderately high losses in boreal forests. In other words, because these places store large amounts of carbon, even smaller biodiversity losses under the global sustainability scenario can lead to significant overall loss of carbon. These places thus represent potential hotspots in terms of biodiversity and carbon storage loss. Earth system models generally project increasing terrestrial car- bon accumulation in high latitudes and decreasing accumulation in the tropics 41. In our analysis, we found that northern latitudes may also experience carbon losses due to biodiversity loss. This could happen in part because our biodiversity model provides a more conservative estimate of potential species gains in these areas (discussed more below). Similar to Mori et al. 4, we found that total carbon loss from biodiversity loss was also greatest in the tropics (driven by the inter- action between biodiversity loss and the location of high carbon stores), which may represent additional losses not captured in current models. Moreover, when proportional loss is considered, other areas such as southern Australia and the European Alps become hotspots of biodiversity and carbon loss (Supplementary Fig. 1). A recent inter- comparison of ecosystem function models, including dynamic global 0 5 10 15 20 0 25000 50000 75000 100000 Cumulative ecoregion area/g111000Km2/g12 Cumulative carbon loss (PgC) Plant species loss (%) (0,10] (10,20] (20,30] (30,Inf] Scenario Fossil−fueled development Global sustainabilityFig. 4 | Carbon loss by ecoregion area. Cumulative carbon loss by cumulative ecoregion area (added from smallest to largest ecoregion size) grouped by pro- portion of plant species diversity lost in the ecoregion (depicted as different colors) for a global sustainability scenario (SSP1/RCP2.6, dashed line) and fossil-fueled development scenario (SSP5/RCP8.5, solid line). Because carbon losses are sum- med across all remaining habitat, places with moderate biodiversity loss, collectively, can contribute more to overall carbon loss than areas of high biodi- versity loss. For the global sustainability scenario, carbon loss from ecoregions that lost more than 30% of plant species is <0.015 PgC and thus does not show up on the graph. Carbon loss estimates are what is expected over the long term, when eco- systems approach their new equilibrium states, based on climate and land-use changes projected for 2050. Source data are provided as a Source Data file. Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 5 vegetation models, found similar patterns of carbon loss across South America and central Africa42. These models also found high losses in northern Africa, but northern Africa did not come out from our models as a hotspot of biodiversity-driven carbon loss. Interestingly, the model intercomparison found little difference in total ecosystem car- bon between global sustainability and fossil-fueled development sce- narios, likely due to CO 2 fertilization with higher levels of climate change42. Dynamic global vegetation models represent global plant diversity as a small set of plant functional types and simulate their distribution and biogeochemical cycles across the world under dif- ferent climate and land-use change scenarios. Thus, these models are not accounting for how changes in species diversity within an area will affect biomass. Incorporating biodiversity-biomass relationships could be a useful way to improve such models in the future. The IPCC estimates that the remaining carbon budgets —the amount of carbon that can be emitted by human activities while still limiting global warming to speci fied levels —is 140 PgC for limiting warming to 1.5 °C, and 310 PgC for limiting warming to 2 °C, although there is substantial uncertainty around these estimates 41. While the uncertainty range for biodiversity-driven carbon loss is large, our high- end estimates for carbon loss from biodiversity loss constitute a large proportion of these limits (102.68 PgC under the global sustainability scenario and 145.32 PgC under the fossil-fueled development sce- nario). Not considering biodiversity loss in emissions scenarios could lead to severe overestimates of terrestrial carbon stocks and remaining carbon budgets. Overall, our analysis points to the important role that maintaining and/or enhancing the diversity of plant species within areas of natural vegetation, in addition to increasingthe extent of these areas, can play in addressing climate change. Alongside increasing the global extent of conservation areas (to prevent rapid carbon loss from ecosystem degradation), increasing plant species diversity in degraded ecosys- tems can increase carbon storage potential 3. However, existing inter- national initiatives like the Bonn Challenge and the Paris Agreement focus on forest extent rather than forest quality and composition for protection, afforestation, and reforestation 17,43. Further, initiatives that include biodiversity goals do not always provide clear de finitions of what constitutes a biodiverse restoration44. This can lead to planting monocultures with non-native species, which could be detrimental to biodiversity and carbon storage over the long-term 17.H i g h e rb i o d i - versity, with the right species in the right places 45, could even help reduce the impacts of climate change on biodiversity, and therefore indirectly help maintain carbon storage potential of ecosystems 46. Although informative, there are a number of uncertainties and limitations in our analysis that should be refined in future assessments. First, our empirical relationship between biodiversity and biomass stock comes from a meta-analysis of hundreds of experiments con- ducted at the local scale 9. Experiments can disentangle the causal effects of species richness on biomass production. However, experi- ments generally take place over small spatial and temporal scales and may miss important processes like dispersal, evolution, and natural patterns of species assembly and loss 47.T h i sm a k e sr e s u l t sm o r ed i f - ficult to generalize to natural ecosystems, and additional work is needed to do so. See the Pathway A assumptions and challenges sec- tion in 25 for more discussion on this limitation. Moreover, the local scale of experimental data does not directly match the ecoregion scale of the BILBI model analysis. As discussed above, this assumes that (1) local loss of species diversity is similar to regional scale biodiversity loss, and (2) species loss occurring at the regional scale has con- sequences for ecosystem functioning of a similar magnitude to those for species loss at a local scale. There are several theoretical reasons why we expect biodiversity-ecosys tem functioning relationships observed at a local level to be equally strong, and perhaps even stronger, across larger spatial extents. Larger spatial and temporal extents will encompass a greater range of environmental conditions. This provides greater opportunity for niche partitioning, and thus positive biodiversity-ecosystem functioning relationships 28,48. Addi- tionally, whole landscapes require more species to maintain ecosystem functioning than do individual locations, with more diversity needed at broader spatial and temporal scales 49. We presented estimated carbon losses over a large range of potential biodiversity-ecosystem func- tioning relationship values to capture some of the uncertainties introduced by these assumptions. Second, the BILBI model assumes that if changes result in non- analog climatic conditions, species will not persist (and thus does not allow for adaptation or tolerance of conditions not experienced at present) and it also does not consider the possibility of increasing species richness in some ecoregions if species are able to exploit new habitat conditions as the climate becomes more suitable. Thus, the model presents a somewhat pessimistic estimate of biodiversity loss from climate change, a common issue with many species distribution model approaches 50–52. However, native species assemblages have greater complementarity than exotic species assemblages due to longer histories of interactions. Thus, increasing species richness by adding species not previously present in the ecosystem may have a relatively small effect on productivity and may even decrease pro- ductivity or decrease the effects of biodiversity on productivity 53–55. Third, it is important to correctly interpret thefindings from our analysis. The BILBI model uses the species-area relationship to assess plant species persistence, meaning that it projects plant species losses expected in the long term due to habitat conditions in a given year (e.g., poor conditions in 2050 might generate losses beyond 2050). Because the BILBI model does not predict exactly how long it will take for species to disappear once environmental conditions have changed, we do not have an exact date for the projected changes in plant persistence. Therefore, our carbon storage loss estimates are also what is expected over the long term, when eco- systems approach their new equilibrium states, based on climate and land-use changes projected for 2050, whereas land-use and perma- frost emissions were estimated from climate and land-use changes from present conditions up to 2100. Although long term is not easily defined, the way that species loss scales with area becomes larger over longer time frames 56. That is, some species will disappear right away when they lose all suitable habitat, whereas others may dis- appear over time as remaining habitats are not able to sustain viable populations. Similarly, the effects of biodiversity grow stronger (and less saturating) over time 23. Thus, estimates produced using smaller species-area ( z-values) and biodiversity-biomass production esti- mates are more likely over shorter timescales, while larger losses become increasingly likely as more time elapses. By using a range of species-area relationship values, we attempted to capture the range of future biodiversity-loss-driven emissions that might be seen over different time scales. Finally, our estimates of total carbon loss are based on projected carbon maps from a single general circulation model from CMIP5 (IPSL-CM5A-MR). Our goal was to compare scenarios with each other and provide a range of reasonable carbon loss estimates rather than absolute losses. Scenarios (including emissions and land-use) are a major source of uncertainty compared to global climate models when modeling persistence probability, and the selection of biodiversity modeling approach is also a major source of uncertainty 57,58.W eu s e d projected carbon maps from the IPSL-CM5A-MR model to be con- sistent with our biodiversity model input parameters, but terrestrial carbon uptake estimates vary across CMIP5 and CMIP6 models 59. Among CMIP5 models assessed, IPSL-CM5A-MR correctly reproduced the global land sink in comparison with historical data, but was not the best performing model for the cVeg variable that we used in this analysis 60. Recent analysis found that IPSL-CM5A-MR produced esti- mates of near-present plant carbon within the range of observation- based estimates in the non-circumpolar region, but overestimated the Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 6 circumpolar regions21. Thus, our carbon loss estimates from biodi- versity loss may also be overestimated in these regions. Biological carbon sequestration and biodiversity are tightly linked (Fig. 5). Biodiversity-mediated carbon loss has the potential to rival emissions from other sources, so achieving Sustainable Development Goal 15 (Life on Land) can contribute to achieving Goal 13 (Climate Action)61. While meeting the Paris Agreement would prevent a large amount of carbon loss compared to a fossil-fueled economic devel- opment strategy, this scenario is still associated with potentially high carbon loss via biodiversity loss. Therefore, additional mitigation measures may be needed to meet Paris Agreement expectations even if current emission reduction targets are met. Improving our under- standing of how biodiversity will adapt to climate change will be key to improving climate impact predictions. Carbon sequestration by natu- rally functioning ecosystems is an important element to offset the residual emissions that would occur even with maximum effort toward carbon neutrality. Addressing climate change and biodiversity loss together will more effectively address these crises. Although policymakers are starting to think about climate change mitigation initiatives that have co-bene fits for biodiversity, the role of biodiversity itself in promoting carbon storage is often overlooked, with much focus simply on biomass or ecosystem extent. On one hand, this may mean that the scienti fic community is underestimating future carbon emissions by not accounting for biodiversity-driven carbon losses, thus increasing the urgency for mitigating climate and land-use impacts. On the other hand, this highlights the impor- tant role that ecosystem restoration, focusing on the composition of these ecosystems, can play in climate change mitigation. In other words, there is potential to link the restoration target (T2) of the Convention on Biological Diversity (CBD) Kunming-Montreal Global Biodiversity Framework with that for climate-change mitigation (T8) and enhancing nature’s contributions to people (T11), emphasizing a need to reconsider the functional value of biodiversity rather than focusing only on area-based measures for conservation (e.g., so- called 30 by 30; T3) 62. At a national and local level, this could mean that a focus on maintaining and restoring diverse ecosystems can increase the return-on-investment for carbon storage over the same land area. This may be particularly important for those ecoregions that are projected to have high levels of biodiversity-driven car- bon loss. Our understanding of how biodiversity underpins ecosystem functions and services such as carbon storage has been increasing, but incorporating this knowledge into global projections and conservation policy lags behind 25,28,63–65. Our modeling effort provides an important example of how we can effectively link biodiversity, ecosystem func- tions, and ecosystem services models. As our understanding of bio- diversity and ecosystem function relationships improves, our analysis can be updated to reduce the uncertainty in the estimates. Building on and improving the modeling approach used in this study, including by filling the gaps identified in Table 1,c a nb e n efit ESM development and also help identify areas for conservation and restoration and thereby contribute to ongoing processes such as national biodiversity strategy and action plans under the CBD, nationally determined contributions for emissions reduction under the Paris Agreement, and payment for ecosystem services programs. Fig. 5 | Relationship between plant diversity carbon storage.Conceptual graphic representing the role biodiversity plays in biological carbon sequestration. Increasing plant species diversity increases biomass stock. This is depicted as an increasing ratio between carbon sequestration (green arrows) and carbon emis- sions (yellow arrows). Illustrations from the Integration and Application Network, with no changes made. Images include:“Acer pensylvanicum”, originally published by Joanna Woerner. Integration and Application Network (2010); released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0,Acer pensylvanicum (Striped Maple) | Media Library | Integration and Application Network (umces.edu)). “Eucalyptus spp.”, originally published by Lana Heydon. QLD Department of Environment and Resource Management (2008); released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0; Eucalyptus spp. (Eucalypt) 1 | Media Library | Integration and Application Network (umces.edu)). Acer pensylvanicum (Striped Maple) | Media Library | Integration and Application Network (umces.edu)). “Acacia spp.”, originally published by Kim Kraeer and Lucy Van Essen-Fishman. Integration and Application Network (2008); released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0; Acacia spp. (Acacia) | Media Library | Integration and Application Network (umces.edu)). “Process; primary production”, originally published by Tracey Saxby. Integration and Application Network (2003); released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0; Process; primary production | Media Library | Integration and Application Network (umces.edu)). “Process; organic carbon release”, originally published by Tracey Saxby. Integration and Application Network (2003); released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0;Process; organic carbon release | Media Library | Integration and Application Network (umces.edu)). Table 1 | Knowledge gaps and future research directions Model component Future research directions Biodiversity model  Incorporate species adaptation into future projections of persistence  Project changes in local scale plant species richness that account for future climate and land-use changes to better match the scale of biodiversity-biomass production relationships Biodiversity-biomass production relationship  Collect more data on biodiversity-biomass production relationships in natural ecosystems to: ∘ better understand how the relationship varies across ecosystems to narrow the uncertainty range used in this analysis ∘ understand how processes operating over larger scales, such as dispersal, affect the relationship ∘ understand the effects of changing species composition in addition to changing species richness  Assess the biodiversity-biomass relationship for soil  Assess how plant functional traits, and thus biodiversity-biomass production relationships, will change under future climates Carbon estimates  Improve understanding of how productivity and carbon storage are affected by changing climates Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 7 Methods Step 1—Use BILBI model to estimate proportion of plant species expected to persist in each ecoregion under different climate and land-use scenarios To assess how land-use change and climate change affect biodiversity, the BILBI model uses land-use data and projections to create a map of habitat condition, which is expressed in units of the proportion of native species expected to remain in each grid-cell, given the land-use type of that cell (Table S1 in ref. 31). The model is also able to project climate-driven change in beta-diversity patterns, expressed in terms of the predicted dissimilarity (or conversely similarity) in species com- position between any specified pair of grid-cells over both space and time. These projections are coupled with a modified form of species- area analysis to estimate the proportion of species expected to persist (i.e., avoid extinction) under a given scenario of land-use and climate change, within any given region. The use of a species-area relationship through generalized dissimilarity modeling (GDM) approach has been shown to be able to predict trends in biodiversity 31,66–70.G D M sp e r f o r m well as predictors of species composition68,70. The BILBI model relies on how species richness responds to land-use change locally, and this was tested ( fita n dv a l i d a t i o n )a sp a r to ft h eP R E D I C T Sp r o j e c t 66. Although more work is needed to understand the speed of extinctions following land-use change, species-area-based approaches have been found to be fairly good predictors of the number of threatened or extinct species 67,71. See Supplementary Methods and refs.30,31 for full model and model validation details, but briefly, this is achieved by: (1) Calculating the total area of similar ecological environments relative to a given cell, by summing the predicted compositional similarity with all other cells under the present climate, and hypothetically assuming the habitat of all cells is in perfect condition. (2) Calculating the potential area of similar ecological environments under a given future scenario, accounting for both the projected change in climate and the expected condition of habitat under that scenario. (3) Expressing the effective area of habitat, across similar ecological environments, expected under a given scenario (from step 2 above), as a proportion of the total area of similar environments prior to climate and land-use change (from step 1 above, data available at 72), and then using the species-area relationship to translate this proportion into the predicted proportion of species expected to persist over the long term. A species-area exponent of z = 0.25 was used in these calculations, as widely employed in other studies predicting the proportion of species expected to persist in fragmented habitats. However, intact habitats also experience species relaxation (i.e., long-term loss of species as the community approaches equilibrium species richness 73), com- monly estimated at z = 0.15. To estimate the additional loss of species due to climate and land-use change, we subtracted these two estimates of z to obtain a lower bound ofz =0 . 122,74.W eu s e da range of z values between 0.1 and 0.65, similar to Isbell et al.22,t o capture some of the uncertainty around the magnitude of species extinction debts. Our scenario analysis followed the protocols laid out in Kim et al. 32. We used two scenarios: SSP1/RCP 2.6 (“global sustainability”), a low land-use change and low climate change scenario which is com- pliant with the Paris target of keeping global warming to below 2 °C by the end of the century compared to pre-industrial times, and SSP5/ RCP8.5 ( “fossil-fueled development”), a high climate change and intermediate land-use change scenario 75,76. Note that the global sus- tainability scenario still entails a significant amount of land- use change due to bioenergy production and increased food demand77.W ec h o s e these scenarios to represent the extreme low- and high-end outcomes to provide a full range of uncertainty estimates. We used land use data from the 0.25° Land Use Harmonization dataset version 2 (LUH2) 78 and climate data from the 1 km WorldClim dataset 79. For current condi- tions, we used the LUH2 data for 2015 and the WorldClim data for 1960–1990, and for the future conditions, we used LUH2 data for 2050 and WorldClim data for 2040–2060 31. To obtain estimates of the proportion of species expected to persist at the ecoregion level (pregion), we used a weighted geometric mean of all cells in the ecoregion. The weight applied to each cell is inversely proportional to the total effective area covered by cells with a similar environment to the cell of interest. This means that cells within less extensive environments have a higher weight, since these areas are likely to support more unique species and thus are expected to con- tribute more to regional species persistence. Step 2: Use empirical relationships to link changes in species richness to changes in biomass We use empirical biodiversity-biomass relationships from a recent meta-analysis based on 374 experiments (>500 entries from primary producers, dominated by terrestrial plant studies). They found general support for using a power function to describe how changes in species richness lead to changes in biomass for primary producers as follows 9: Biomass =aðrichnessÞb ð1Þ where a is a constant representing t he average biomass of a mono- culture for the ecosystem, and b describes the power relationship between a change in richness and biomass. As species richness increases, the biomass of the system will increase compared to the monoculture baseline, but the amount of increase per species decelerates as more species are added. This equation can be converted to proportion of remaining biomass ( p biomass) based on proportional change in species richness per ecoregion as follows: pbiomass = ðpregionÞb ð2Þ We apply this transformation to the BILBI model output to assess the proportion of remaining biomass from the proportion of remain- ing plant species richness, using the mean b = 0.26, as well as the 95% CI to provide uncertainty estimates around our results. O ’Connor et al. 9 found that for primary producers, b = 0.26 (with a 95% CI of 0.16–0.37) was valid for most assemblages and was robust to differ- ences in experimental design and the range of species richness levels considered. While they did not find an effect of study duration on b values, previous studies have found that biodiversity-productivity relationships grow stronger over time 23. Although b values can vary spatially4,80, there is still uncertainty in how biodiversity-ecosystem functioning relationships differ across space and in how factors like climate, environmental conditions, and species trait compositions might systematically affect the observed relationship 9.I ft h ep l a c e s where habitat destruction is highest are also the places that tend to have the highest or lowest biodiversity-ecosystem functioning rela- tionships, then using a narrower range of spatially explicit values could systematically over or underestimate the carbon storage loss asso- ciated with this biodiversity loss. To address this concern, we esti- mated productivity losses associated with the full confidence interval range from O ’Connor et al. 9. Thus, rather than considering a single slope for the biodiversity-ecosystem functioning relationship, we consider a range of relationships that re fle c t sv a r i a t i o ni nb o t hc o m - position and site-to-site differences found among previous biodi- versity experiments. For example, the range of relationship values is wider than spatially explicit values estimated from in-situ forest re- measurement data globally (range = 0.198 –0.299, mean = 0.26) 80. Therefore, our range of b values provides a conservative range of estimates of productivity loss associated with biodiversity loss. In addition, although estimates come from historical data, we do not Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 8 currently have estimates of how relationships may change in the future. Experimental evidence suggests, however, that positive biodiversity-productivity relationships are robust to droughts and changes in nutrient availability 81. Step 3: Estimate total changes in carbon storage and compare to other global change drivers The previous step provided spatially explicit estimates of proportional change in biomass associated with loss of biodiversity for each sce- nario. To convert biomass change to carbon storage change, we mul- tiplied the gridded estimates of proportional change in biomass from the BILBI model by a global map of terrestrial carbon stock from CMIP5. The CMIP model estimates changes in carbon storage from climate change and land use change, but does not account for changes in species richness. By using these model projections as our baseline carbon estimate, we can assess the effects of biodiversity loss on car- bon storage that are expected on top of the direct changes from cli- mate change and land use change that have already been incorporated in initial carbon storage projections. We used terrestrial carbon storage maps that considered only vegetation carbon, as well as maps considering vegetation and soil carbon. We calculated the average cVeg and cSoil value over a 12- month period in 2050 (the end year for the BILBI model output). Model inputs were not exactly the same, making it dif ficult to be consistent with the climate and land use input data used to estimate biodiversity loss and carbon storage. We downloaded the total carbon in vegetation (cVeg) and total carbon in soil (cSoil) layer from the CMIP5 IPSL-CM5A-MR model 33. The biodiversity and ecosystem ser- vices models using harmonized scenarios (BES-SIM) used climate data from either the lower resolution IPSL-CM5A-LR or 1 km WorldClim data downscaled from the IPSL-CM5A-LR depending on biodiversity model requirements 32. We chose to use the mid-resolution 1.25° × 2.5° CMIP5 IPSL-CM5A-MR model to obtain higher resolution carbon maps. We obtained cVeg and cSoil for both of our scenarios—global sustainability (SSP1/RCP 2.6) and fossil-fueled development (SSP5/RCP8.5) from the Earth System Grid Federation (ESGF;https://esgf-node.llnl.gov/search/ cmip5/). The BILBI model used land use data from the Land Use Har- monization dataset version 2 78, while the CMIP5 IPSL-CM5A-MR model used land use data from the Land Use Harmonization dataset version 1 82. Version 2 provides higher resolution data over a longer time frame with more detailed land-use categories78. Although the input data are slightly different, we do not believe this invalidates the approach, as we are not comparing carbon storage changes between the models but instead using the CMIP5 IPSL-CM5A-MR model to estimate potential carbon storage under the global sustainability and fossil-fueled development scenarios, which we then use to estimate the possible magnitude of carbon storage loss driven by biodiversity loss under the same scenarios. Soil type, climate, and land use are important drivers of soil car- bon. Plant diversity can also increase soil carbon, and these relation- s h i p sg r o ws t r o n g e ro v e rt i m e 37–39. If soil carbon depends strongly on plant diversity, then it is important to consider the possible magnitude of plant loss on soil carbon, even if the strength of these relationships is not fully established. We assessed how large soil carbon losses could be if plant biodiversity-soil carbon relationships are on a similar mag- nitude to aboveground biomass. We thus consider the additional impact that plant loss may have on soil carbon on top of the local environmental conditions considered in the CMIP models. We exclu- ded soil types that are more likely to be impacted by drying and warming than by changes in plant diversity, including wetland (Gley- sols), peatland (Histosols), and permafrost (Cryosols) soils 22.W en o t e that these soil types represent major global carbon stores. If biodi- versity does in fact drive carbon loss in these ecosystems, we could be missing additional losses. Specifically, we resampled the 250 m pre- dicted World Reference Base 2006 subgroup soil classification (ISRIC, https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/ 5c301e97-9662-4f77-aa2d-48facd3c9e14 83); to the same resolution as the cSoil raster layer using the nearest neighbor method in the R software program terra package 84, and then masked out these soil types from the cSoil raster. We did not account for potential changes in litter carbon. Increasing biodiversity increases the rate of litter decomposition (i.e., less litter mass storage), which could add to increasing decomposition from warming, and thus we would expect biodi- versity loss to increase litter carbon storage. While the strength of the biodiversity-carbon relationships for soil and litter are not fully established, the effects on litter carbon are likely weaker than those on plant biomass or soil carbon 6,85,86. For example, decomposition was 34.7% higher in mixed species forests compared to mono- cultures, while soil carbon storage was 178% higher in mixed grass- lands than in monocultures 39,86. Moreover, the estimated effects of diversity on plant biomass and soil carbon were driven by short-term studies, and these relationships grow stronger over time in long-term experiments 23,36,39. To obtain cVeg and cSoil values on the same scale as the biodi- versity data, we resampled by ecoregion using bilinear interpolation. Then, we multiplied our raster layers (proportion of remaining plant biomass and 2050 carbon maps), to obtain changes in carbon storage in 2050 in kg/m 2 (ΔC), such that: ΔC =c V e g/C0 cVegðpbiomassÞ for vegetation carbon only,or ΔC = ðc V e g+c S o i lÞ/C0ð c V e g+c S o i lÞ*ðpbiomassÞ for vegetation carbon and soil carbon: ð3Þ To convert this to total C storage in PgC ( Ctotal), we used the cellSize function in theterra package84 to calculate the total area in m2 of each ecoregion (A). We then multiplied this by the carbon storage layer to obtain total carbon storage lost per ecoregion, which we summed to obtain global C storage loss values: Ctotal =1 :0E/C0 12 Xn k =1 ΔCk *Ak ð4Þ where k = a given ecoregion and n = total number of ecoregions. We conducted all analyses in R version 4.1.1 87, and produced all graphics using either the tmap or ggplot2 packages88,89 (Supplemen- tary Software 1–6). Data availability BILBI model data are available athttps://doi.org/10.6084/m9.figshare. 25188650. CMIP data are available from the Earth System Grid Fed- eration (ESGF; https://esgf-node.llnl.gov/search/cmip5/). World Reference Base 2006 subgroup soil classi fication data are available from ISRIC, https://data.isric.org/geonetwork/srv/eng/catalog. search#/metadata/5c301e97-9662-4f77-aa2d-48facd3c9e1483. Raster data for the maps generated in this study have been deposited in ScienceBase at https://doi.org/10.5066/P13WUFMU 90. Source data are provided with this paper. Code availability R scripts are included as Supplementary files. References

  1. Di Marco, M. et al. Synergies an d trade-offs in achieving global biodiversity targets.Conserv. Biol. 30,1 8 9– 195 (2016).
  2. Soto-Navarro, C. et al. Mapping co-bene fits for carbon storage and biodiversity to inform conservation policy and action.Philos. Trans. R. Soc. B Biol. Sci. 375,2 0 1 9 0 1 2 8( 2 0 2 0 ) . 3 . S t r a s s b u r g ,B .B .N .e ta l .G l o b a lp r i o r i t ya r e a sf o re c o s y s t e m restoration.Nature 586,7 2 4– 729 (2020). Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 9 4 . M o r i ,A .S .e ta l .B i o d i v e r s i t y– productivity relationships are key to nature-based climate solutions.Nat. Clim. Change 11,1 – 8 (2021).
  3. Pörtner, H. O. et al. IPBES-IPCC Co-Sponsored Workshop Report on Biodiversity and Climate Change. www.ipbes.net; https://doi.org/ 10.5281/zenodo.4782538(2021).
  4. Cardinale, B. J. et al. The functional role of producer diversity in ecosystems.Am. J. Bot. 98,5 7 2– 592 (2011).
  5. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489,3 2 6– 326 (2012). 8 . D u f f y ,E .J . ,G o d w i n ,C .M .&C a r d i n a l e ,B .J .B i o d i v e r s i t ye f f e c t si n the wild are common and as strong as key drivers of productivity. Nature 549,2 6 1– 264 (2017).
  6. O ’Connor, M. I. et al. A general biodiversity– function relationship is mediated by trophic level. Oikos 126,1 8– 31 (2017).
  7. Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge.Ecol. Monogr. 75,3 – 35 (2005).
  8. Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments.Nature 412,7 2– 76 (2001).
  9. Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations.Proc. Natl A c a d .S c i .U S A94,1 8 5 7– 1861 (1997).
  10. Aarssen, L. W. High productivity in grassland ecosystems: effected by species diversity or productive species?Oikos 80, 183 (1997).
  11. Hooper, D. U. The role of complementarity and competition in ecosystem responses to variation in plant diversity. Ecology 79, 704– 719 (1998).
  12. Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change.Nature 486,1 0 5– 108 (2012).
  13. Weiskopf, S. R. et al. Climate change effects on biodiversity, eco- systems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782 (2020).
  14. Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9,8 4 – 87 (2019). 1 8 . F e r r i e r ,S . ,N i n a n ,K .N . ,L e a d l e y ,P .&A l k e m a d e ,R .The Methodo- logical Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services32 (IPBES, 2016).
  15. O ’Connor, M. I. et al. Grand challenges in biodiversity– ecosystem functioning research in the era of science– policy platforms require explicit consideration of feedbacks.P r o c .R .S o c .BB i o l .S c i .288, 20210783 (2021).
  16. Zhou, J. et al. A traceability analysis system for model evaluation on land carbon dynamics: design and applications.Ecol. Process. 10, 12 (2021).
  17. Wei, N. et al. Evolution of uncertainty in terrestrial carbon storage in E a r t hs y s t e mm o d e l sf r o mC M I P 5t oC M I P 6 .J. Clim. 35, 5483– 5499 (2022).
  18. Isbell, F., Tilman, D., Polasky, S. & Loreau, M. The biodiversity- dependent ecosystem service debt.Ecol. Lett. 18, 119– 134 (2015).
  19. Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336,5 8 9– 592 (2012).
  20. Fulton, E. A. & Gorton, R. Adaptive Futures for SE Australian Fisheries & Aquaculture: Climate Adaptation Simulations(CSIRO, 2014).
  21. Weiskopf, S. R. et al. A conceptual framework to integrate biodi- versity, ecosystem function, and ecosystem service models. BioScience.72,1 – 12 (2022).
  22. O ’C o n n o r ,M .I . ,B e r n h a r d t ,J .R . ,S t a r k ,K . ,U s i n o w i c z ,J .&W h a l e n ,M . A. In The Ecological and Societal Consequences of Biodiversity Loss (eds Loreau, M., Hector, A. & Isbell, F.) 97– 118 (Wiley, 2022).
  23. Wang, S. & Loreau, M. Biodiversi ty and ecosystem stability across scales in metacommunities.Ecol. Lett. 19,5 1 0– 518 (2016).
  24. Isbell, F. et al. Linking the in fluence and dependence of people on biodiversity across scales.Nature 546,6 5– 72 (2017).
  25. Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning.Trends Ecol. Evol.33,5 4 9– 564 (2018).
  26. Hoskins, A. J. et al. BILBI: supporting global biodiversity assessment through high-resolution macroecological modelling.Environ. Model. Softw. 132,1 0 4 8 0 6( 2 0 2 0 ) .
  27. Di Marco, M. et al. Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling.Glob. Change Biol. 25,2 7 6 3– 2778 (2019).
  28. Kim, H. et al. A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios.Geosci. Model Dev. 11,4 5 3 7– 4562 (2018).
  29. Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 E a r t hs y s t e mm o d e l :f r o mC M I P 3t oC M I P 5 .Clim. Dyn. 40, 2123– 2165 (2013).
  30. Asamoah, E. F. et al. Land-use and climate risk assessment for Earth’s remaining wilderness.Curr. Biol. 32,4 8 9 0– 4899.e4 (2022).
  31. Mendez Angarita, V. Y., Maiorano, L., Dragonetti, C. & Di Marco, M. Implications of exceeding the Paris Agreement for mammalian biodiversity.Conserv. Sci. Pract. 5,e 1 2 8 8 9( 2 0 2 3 ) .
  32. Yang, Y. et al. Restoring abandoned farmland to mitigate climate change on a full Earth. One Earth 3,1 7 6– 186 (2020).
  33. Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis.Biol. Rev. 95,1 6 7– 183 (2020).
  34. Chen, X. et al. Tree diversity increases decadal forest soil carbon and nitrogen accrual.Nature 618,9 4– 101 (2023).
  35. Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon seques- tration accelerated by restoration of grassland biodiversity.Nat. Commun. 10,1 – 7 (2019). 4 0 . C i m a t t i ,M . ,C h a p l i n - K r a m e r ,R .&M a r c o ,M .D .Regions of High Biodiversity Value Preserve Nature’s Contributions to People under Climate Change. https://www.researchsquare.com/article/rs- 2013582/v1; https://doi.org/10.21203/rs.3.rs-2013582/v1(2022).
  36. Canadell, J. G. et al. Global carbon and other biogeochemical cycles and feedbacks. InC l i m a t eC h a n g e2 0 2 1 :T h eP h y s i c a lS c i e n c eB a s i s. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change(eds Masson-Delmotte, V. et al.) 673– 816 (Cambridge University Press, 2021). 4 2 . P e r e i r a ,H .M .e ta l .G l o b a lt r e n d si nb i o d i v e r s i t ya n de c o s y s t e m services from 1900 to 2050. bioRxiv 1,1 – 5 (2020).
  37. Mori, A. S. Advancing nature-based approaches to address the biodiversity and climate emergency.Ecol. Lett. 23, 1729– 1732 (2020).
  38. Andres, S. E. et al. De fining biodiverse reforestation: why it matters for climate change mitigation and biodiversity.Plants People Planet 1– 12 https://doi.org/10.1002/ppp3.10329(2022).
  39. van der Plas, F., Hennecke, J., Chase, J. M., van Ruijven, J. & Barry, K. E. Universal beta-diversity– functioning relationships are neither observed nor expected. Trends Ecol. Evol. S0169534723000125 https://doi.org/10.1016/j.tree.2023.01.008(2023).
  40. Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest eco- systems. Biol. Rev. 93,4 3 9– 456 (2018).
  41. Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species,not species richness, drives delivery of a real-world ecosystem service.Ecol. Lett. 18, 626– 635 (2015).
  42. Gonzalez, A. et al. Scaling-up biod iversity-ecosystem functioning research. Ecol. Lett. 23,7 5 7– 776 (2020).
  43. Thompson, P. L. et al. Scaling up biodiversity-ecosystem function- ing relationships: the role of environmental heterogeneity in space and time. P r o c .R .S o c .BB i o l .S c i .288, 20202779 (2021).
  44. Benito Garzón, M., Robson, T. M. & Hampe, A. ΔTraitSDMs: species distribution models that account for local adaptation and pheno- typic plasticity.N. Phytologist 222,1 7 5 7– 1765 (2019).
  45. Sinclair, S. J., White, M. D. & Newell, G. R. How useful are species distribution models for managing biodiversity under future cli- mates? Ecol. Soc. 15,8( 2 0 1 0 ) . Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 10
  46. Bush, A. et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19,1 4 6 8– 1478 (2016). 5 3 . W i l s e y ,B .J . ,T e a s c h n e r ,T .B . ,D a n e s h g a r ,P .P . ,I s b e l l ,F .I .&P o l l e y ,H . W. Biodiversity maintenance mechanisms differ between native and novel exotic-dominated communities.Ecol. Lett.12,4 3 2– 442 (2009).
  47. Dee, L. E. et al. Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference.Nat. Commun. 14,2 6 0 7( 2 0 2 3 ) .
  48. Isbell, F. I. & Wilsey, B. J. Increasing native, but not exotic, biodi- versity increases abovegroundproductivity in ungrazed and intensely grazed grasslands.Oecologia 165, 771– 781 (2011).
  49. Rosenzweig, M. L. Heeding the warning in biodiversity ’sb a s i cl a w . Science 284,2 7 6– 277 (1999).
  50. Thuiller, W., Guéguen, M., Rena ud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios.Nat. Commun. 10, 1446 (2019). 5 8 . L e c l è r e ,D .e ta l .B e n d i n gt h ec u r ve of terrestrial biodiversity needs an integrated strategy.Nature 585,5 5 1– 556 (2020).
  51. Arora, V. K. et al. Carbon-concentration and carbon-climate feed- backs in CMIP6 models and their comparison to CMIP5 models. Biogeosciences17,4 1 7 3– 4222 (2020).
  52. Anav, A. et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth system models.J. Clim. 26, 6801– 6843 (2013).
  53. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development(2015).
  54. United Nations Convention on Biological Diversity. Kunming- Montreal Global Biodiversity Framework. (2022).
  55. Rosa, I. M. D. et al. Multiscale scenarios for nature futures.Nat. Ecol. Evol. 1,1 4 1 6– 1419 (2017).
  56. Rosa, I. M. D. et al. Challenges in producing policy-relevant global scenarios of biodiversity and ecosystem services. Glob. Ecol. Con- serv. 22,e 0 0 8 8 6( 2 0 2 0 ) .
  57. van der Plas, F. Biodiversity and e cosystem functioning in naturally assembled communities.Biol. Rev. https://doi.org/10.1111/brv. 12499 (2019).
  58. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity.Nature 520,4 5 – 50 (2015).
  59. Brooks, T. M. et al. Habitat loss and extinction in the hotspots of biodiversity.Conserv. Biol. 16,9 0 9– 923 (2002).
  60. Elith, J. et al. Novel methods i mprove prediction of species’ dis- tributions from occurrence data.Ecography 29,1 2 9– 151 (2006). 6 9 . B l o i s ,J .L . ,W i l l i a m s ,J .W . ,F i t z p a t r i c k ,M .C . ,J a c k s o n ,S .T .&F e r r i e r , S. Space can substitute for time in predicting climate-change effects on biodiversity.Proc. Natl Acad. Sci.110,9 3 7 4– 9379 (2013).
  61. Ware, C. et al. Improving biodiversity surrogates for conservation assessment: a test of methods and the value of targeted biological surveys. Divers. Distrib.24, 1333– 1346 (2018). 7 1 . P i m m ,S .L . ,J e n k i n s ,C .N .&L i ,B .V .H o wt op r o t e c th a l fo fe a r t ht o ensure it protects sufficient biodiversity.Sci. Adv. 4,1 – 9( 2 0 1 8 ) . 7 2 . D iM a r c o ,M . ,H o s k i n s ,A .J . ,H a r w o o d ,T .D . ,W a r e ,C .&F e r r i e r ,S . BILBI model data for SSP1/RCP2.6 and SSP5/RCP8.5.figshare https://doi.org/10.6084/m9.figshare.25188650(2024).
  62. Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for Avifaunas of Southwest PacificI s l a n d s .P r o c .N a t lA c a d . Sci. 69,3 1 9 9– 3203 (1972).
  63. Gonzalez, A. In Encyclopedia of Life Scienceshttps://doi.org/10. 1002/9780470015902.a0021230(Wiley, 2009).
  64. Kriegler, E. et al. Fossil-fueled development (SSP5): an energy and resource intensive scenario for the 21st century.Glob. Environ. Change 42,2 9 7– 315 (2017).
  65. van Vuuren, D. P. et al. Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm.Glob. Environ. Change 42,2 3 7– 250 (2017).
  66. Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the iItergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) 465– 570 (Cam- bridge University Press, 2013).
  67. Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6.Geosci. Model Dev. 13,5 4 2 5– 5464 (2020). 7 9 . H i j m a n s ,R .J . ,C a m e r o n ,S .E . ,P a r r a ,J .L . ,J o n e s ,P .G .&J a r v i s ,A . WorldClim Global Climate Data Version 1. http://worldclim.org/ version1 (2017).
  68. Liang, J. et al. Positive biodiversity-productivity relationship pre- dominant in global forests.Science 354, aaf8957 (2016).
  69. Craven, D. et al. Plant diversity e ffects on grassland productivity are robust to both nutrient enrichment and drought.P h i l o s .T r a n s .R . Soc. B 371,2 0 1 5 0 2 7 7( 2 0 1 6 ) .
  70. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500– 2100: 600 years of global gridded annual land-use transi- tions, wood harvest, and resulting secondary lands.Clim. Change 109, 117 (2011).
  71. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning.PLoS ONE 12, e0169748 (2017).
  72. Hijmans, R. J. terra: Spatial Data Analysis. R package version1.7-
  73. (2023).
  74. Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services.Ecol. Lett. 9, 1146– 1156 (2006). 8 6 . M o r i ,A .S . ,C o r n e l i s s e n ,J .H .C . ,F u j i i ,S . ,O k a d a ,K .&I s b e l l ,F . A meta-analysis on decomposition quantifies afterlife effects of plant diversity as a global change driver. Nat. Commun. 11, 1– 9 (2020).
  75. R Core Team. R: A Language and Environment for Statistical Com- puting (R Foundation for Statistical Computing, 2021).
  76. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis(Springer- Verlag, 2016).
  77. Tennekes, M. tmap: Thematic Maps in R. J. Stat. Softw. 84, 1– 39 (2018).
  78. Weiskopf, S. R. et al. Biodiversity Loss Reduces Global Terrestrial Carbon Storage— Data. U.S. Geological Survey data release (2024). Acknowledgements This work was supported by the National Socio-Environmental Synthesis Center under funding received from the National Science Foundation (grant no. DBI-1639145). A portion of this research was supported by the US Geological Survey National and North Central Climate Adaptation Science Centers. MDM received support from the European Union– NextGenerationEU as part of the National Biodiversity Future Center, Italian National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment 1.4 (CUP: B83C22002950007). We thank the Beth Fulton, the Diversity and Eco-Function working group, and the Morelli lab group for their feedback. We thank Alexey Shiklomanov for R coding assistance. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. Author contributions S.R.W., F.I., and S.F. conceived the project. S.R.W., F.I., S.F., M.I.A.P., M.D.M., M.H., J.J., A.S.M., and E.W. contributed to the design of the analysis. S.R.W. led the analyses and wrote the initial paper draft. All authors, including B.W.M., S.B.L., and T.L.M., contributed substantively to revisions. Competing interests The authors declare no competing interests. Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 11 Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-47872-7. Correspondence and requests for materials should be addressed to Sarah R. Weiskopf. Peer review information Nature Communications thanks Rob Alkemade, Eduardo Gomes and Aafke Schipper for their contribution to the peer review of this work. A peer review file is available. Reprints and permissions information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jur- isdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © Isbell, Arce-Plata, Di Marco, Harfoot, Johnson, Mori, Weng, Ferrier. Parts of this work were authored by US Federal Government authors and are not under copyright protection in the US; foreign copyright pro- tection may apply 2024 Article https://doi.org/10.1038/s41467-024-47872-7 Nature Communications| (2024) 15:4354 12