Biodiversity loss reduces global terrestrial carbon storage

Abstract

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. We find 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) and 10.87-145.95 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.

Full text

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-Plata
4
,
Moreno Di Marco
5
, Mike Harfoot
6
, Justin Johnson
7
,SusannahB.Lerman
8
,
Brian W. Miller
9
, Toni Lyn Morelli
2,10
, Akira S. Mori
11
, Ensheng Weng
12
&
Simon Ferrier
13
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. We find 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) and 10.87-
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 sequestration6–9. 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 use10–12. At the same time, more diverse
assemblages are more likely to contain the most productive species,
which can increase o verall functioning13,14. Indeed, biodiversity loss can
be one of the major drivers of productivity loss within ecosystems, on
par with elevated carbon dioxide or effects of drought15. Thus, while
climate change can affect biodiversity, biodiversity loss can also affect
climate change by altering carbon sequestration and storage4,16.
Received: 4 August 2023
Accepted: 11 April 2024
Check for updates
1
U.S. Geological Survey National Climate Adaptation Science Center, Reston, VA, USA.
2
Department of Environmental Conservation, University of Massa-
chusetts, Amherst, MA, USA.
3
Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN, USA.
4
Département de Sciences
Biologiques, Université de Montréal, Montréal, QC H3T 1J4, Canada.
5
Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.
6
Vizzuality, 123Calle de Fuencarral, 28010 Madrid, Spain.
7
Department of Applied Economics, University of Minnesota, 1994 Buford Ave, Saint Paul, MN 55105,
USA.
8
USDA Forest Service Northern Research Station, Amherst, MA, USA.
9
U.S. Geological Survey North Central Climate Adaptation Science Center,
Boulder, CO, USA.
10
U.S. Geological Survey Northeast Climate Adaptation Science Center, Amherst, MA, USA.
11
Research Center for Advanced Science and
Technology, the University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 153-8904, Japan.
12
Columbia University/NASA Goddard Institute for Space Studies, 2880
Broadway, New York, NY 10025, USA.
13
CSIRO Environment, Canberra, ACT 2601, Australia. e-mail: sweiskopf@usgs.gov
Nature Communications | (2024) 15:4354 1
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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 composition17.
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 feedbacks18,19.
For example, Earth System Models (ESMs) typically model terrestrial
ecosystems using a smallnumber of plant functional types and do not
include biodiversity-productivity mechanisms20,21. 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 significant losses of species diversity22. Incorporating
biodiversity-ecosystem function relationships could improve model
accuracy, especially over long timescales as biodiversity effects
become stronger over time23. For example, an Australian ecosystem
modeling exercise found that including species turnover in marine
ecosystem models led to very different outcomes for marine fisheries
under different climate change scenarios24.
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 ofthe extensive
experimental data on the increase in biomass associated with
increasing plant species richness9,26, 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 method4,22.For
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 stability27–29. 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
richness30, going beyond the species-area22 or endemics-area4rela-
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 change30,31. We link our species-loss estimates with
empirical biodiversity-biomass stock relationships9to 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 specifically by biodiversity loss. Like
previous analyses22, 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
alwaysbemet,thereisconsiderableevidenceforthesecond
assumption28. BILBI produces estimates of plant species loss, based on
the local-scale effectof land-use change on species persistence andthe
regional-scale effect of climate change on species composition.
Therefore, we followed previous anal yses22 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
impactsoncarbonstocks(Fig.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 project32. The BILBI model uses generalized
dissimilarity models fitted with more than 52 million records from over
254,000 plant species to map beta diversity at ~1 km2scale 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.9to 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 ecoregion33.
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 of z-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) modelswere 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 changescenariosas the BILBI model toestimate carbonemissions due
to biodiversity loss (green-dashed line).
Article https://doi.org/10.1038/s41467-024-47872-7
Nature Communications | (2024) 15:4354 2
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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 zand bvalues). 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 of z-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 of zand bvalues). 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 lossvalues 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-
tionswerealsohighinsouthernAustralia,butbecausethisregionhas
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=0.26and z=0.25)
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
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30
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90
120
Plant species loss (%)
−10
−5
0
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10
15
20
25
30
35
40
45
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Fig. 2 | Species loss byecoregion. Plant species loss by ecoregion projectedby the
BiogeographicInfrastructurefor 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. Da rker areas
indicate greaterplant species loss. Species-lossestimates are whatis 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
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PgC of vegetation carbon in the long term under global sustainability
and 10.83–145.32 (42.89 using b=0.26 andz= 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
coverlargerareas(Fig.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)and
fossil-fueled development (SSP5/RCP8.5, B) scenario using the mean biodiversity-
ecosystem functioning slope b= 0.26 and a species-area relationship z=0.25.
Darkerareas indicate greater carbon loss.This carbon lossis 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.
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Nature Communications | (2024) 15:4354 4
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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 richnessand linked it with empirical estimatesof the
plant biodiversity-biomass stock relationship based on experimental
data. We found that biodiversity loss can reduce globalcarbon 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 areas34,35. This engenders a positive feedback wherein
higher levels ofclimate changeleads 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 habitats22. 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 dramatic ally 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 models 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 biomes4. 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.4used estimates of local species loss rather than
ecoregion losses as we did here. Together, these findings 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.2identified
few areas in central Africa that overlapped as being in the top 20% for
both biodiversity and carbon importance. However, we found that
carbonlosses due to biodiversity losswere high in this area (Fig. 3), and
therefore that biodiversity protection and restoration here could be
highly valuable for climate change mitigation40. 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.2looked 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 anddecreasing accumulation in the
tropics41. In our analysis, we found that northern latitudes may also
experience carbon losses due to biodiversity loss. Thiscould 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 additionallosses not captured in current
models. Moreover, when proportional loss is considered, other areas
such as southern Australia and the EuropeanAlps 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 1000Km2
Cumulative carbon loss (PgC)
Plant species loss (%)
(0,10]
(10,20]
(20,30]
(30,Inf]
Scenario
Fossil−fueled development
Global sustainability
Fig. 4 | Carbon loss by ecoregion area. Cumulative carbon loss by cumulative
ecoregion area (added from smallest to largest ecoregion size) grouped by pro-
portionof plant speciesdiversity lostin the ecoregion(depicted as differentcolors)
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-
versityloss. For the global sustainability scenario, carbon loss fromecoregions that
lost morethan 30% of plantspecies is <0.015PgC and thus doesnot 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
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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 withinan area will
affectbiomass. 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 specified 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 estimates41. While the
uncertainty range forbiodiversity-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 ofterrestrial carbon stocks andremaining
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 increasing the extent of these areas, can play
in addressing climatechange. 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 potential3. 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 reforestation17,43. Further, initiatives that
include biodiversity goals do not always provide clear definitions 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-term17.Higherbiodi-
versity, with the right species in the right places45, could even help
reduce the impacts of climate change on biodiversity, and therefore
indirectly help maintain carbon storage potential of ecosystems46.
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 scale9. 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 loss47.Thismakesresultsmoredif-
ficult to generalize to natural ecosystems, and additional work is
needed to do so. See the Pathway A assumptions and challenges sec-
tion in25 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-ecosystem 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 relationships28,48. Addi-
tionally, whole landscapes require more species to maintain ecosystem
functioning than do individuallocations,with more diversity needed at
broader spatial and temporal scales49. 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 approaches50–52. 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 productivity53–55.
Third, it is important to correctly interpret the findings from our
analysis. The BILBI model uses the species-area relationshipto 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 frames56. 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 time23. 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 uncertainty57,58.Weused
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 models59.
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
analysis60. 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
Content courtesy of Springer Nature, terms of use apply. Rights reserved

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-benefits 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 scientific 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 behind25,28,63–65. 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,canbenefit ESM development and
also help identify areas for conservation and restoration and thereby
contribute to ongoing processes such asnational 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 | Relationshipbetween plant diversity carbonstorage. Conceptualgraphic
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 4.0International(CC BY-SA 4.0;
Eucalyptus spp. (Eucalypt) 1 | Media Library | Integration and Application Network
(umces.edu)). Acer pensylvanicum (StripedMaple) | 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
CommonsAttribution-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 uncertaint yran ge 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
Content courtesy of Springer Nature, terms of use apply. Rights reserved

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 assesshow 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 predicttrends in biodiversity31,66–70.GDMsperform
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 (fitandvalidation)aspartofthePREDICTSproject
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 species67,71. 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 overthe 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 richness73), 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 of z=0.1
22,74.Weuseda
range of zvalues between 0.1 and 0.65, similar to Isbell et al.22,to
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 °Cby
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 scenario75,76. Note that the global sus-
tainability scenario still entail s a significant amount of land- use change
due to bioenergy production and increased food demand77.Wechose
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 dataset79. 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 2040–206031.
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 extensiveenvironments have a higher weight, since theseareas 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 inspecies
richness lead to changes in biomass for primary producers as follows9:
Biomass = a*ðrichnessÞbð1Þ
where ais a constant representing the average biomass of a mono-
culture for the ecosystem, and bdescribes 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 asmore species are added. This equation can be converted
to proportion of remaining biomass (pbiomass) based on proportional
change in species richness per ecoregion as follows:
pbiomass =ðpregionÞbð2Þ
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.9found 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 time23. Although bvalues 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 relationship9.Iftheplaces
where habitat destruction is highest are also the places that tend to
have the highest or lowest biodiversity-ecosystem functioning rela-
tionships, thenusing 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 reflectsvariationinbothcom-
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 bvalues 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
Content courtesy of Springer Nature, terms of use apply. Rights reserved

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 availability81.
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 difficult 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 model33. 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
requirements32. 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 278, while the CMIP5 IPSL-CM5A-MR model
used land use data from the Land Use Harmonization dataset version
182. 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-
shipsgrowstrongerovertime
37–39. If soil carbon depends strongly on
plant diversity, then it is important toconsider the possible magnitude
of plant loss on soil carbon, even if the strength of these relationships
is not fully established. We assessed howlarge 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) soils22.Wenote
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-48facd3c9e1483); to the same resolution as
the cSoil raster layer using the nearest neighbor method in the R
software program terra package84, 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 carbon6,85,86. 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 monocultures39,86. 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
experiments23,36,39.
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/m2(ΔC), such that:
ΔC=cVegcVeg*ðpbiomass Þfor vegetation carbon only,or
ΔC=ðcVeg+cSoilÞðcVeg+cSoilÞ*ðpbiomassÞfor vegetation carbon and soil carbon:
ð3Þ
To convert this to total Cstorage in PgC (Ctotal), we used the
cellSize function in the terra 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 Cstorage loss values:
Ctotal =1:0E12 X
n
k=1
ΔCk*Akð4Þ
where k= a given ecoregion and n= total number of ecoregions.
We conducted all analyses in R version 4.1.187, and produced all
graphics using either the tmap or ggplot2 packages88,89 (Supplemen-
tary Software 1–6).
Data availability
BILBI model data are available at https://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 classification 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/P13WUFMU90. Source data are
provided with this paper.
Code availability
R scripts are included as Supplementary files.
References
1. Di Marco, M. et al. Synergies and trade-offs in achieving global
biodiversity targets. Conserv. Biol. 30,189–195 (2016).
2. Soto-Navarro, C. et al. Mapping co-benefits for carbon storage and
biodiversity to inform conservation policy and action. Philos. Trans.
R. Soc. B Biol. Sci. 375,20190128(2020).
3. Strassburg,B.B.N.etal.Globalpriorityareasforecosystem
restoration. Nature 586,724–729 (2020).
Article https://doi.org/10.1038/s41467-024-47872-7
Nature Communications | (2024) 15:4354 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved

4. Mori,A.S.etal.Biodiversity–productivity relationships are key to
nature-based climate solutions. Nat. Clim. Change 11,1–8 (2021).
5. 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).
6. Cardinale, B. J. et al. The functional role of producer diversity in
ecosystems. Am. J. Bot. 98,572–592 (2011).
7. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity.
Nature 489,326–326 (2012).
8. Duffy,E.J.,Godwin,C.M.&Cardinale,B.J.Biodiversityeffectsin
the wild are common and as strong as key drivers of productivity.
Nature 549,261–264 (2017).
9. O’Connor, M. I. et al. A general biodiversity–function relationship is
mediated by trophic level. Oikos 126,18–31 (2017).
10. Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning:
a consensus of current knowledge. Ecol. Monogr. 75,3–35 (2005).
11. Loreau, M. & Hector, A. Partitioning selection and complementarity
in biodiversity experiments. Nature 412,72–76 (2001).
12. Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and
ecosystem productivity: theoretical considerations. Proc. Natl
Acad.Sci.USA94,1857–1861 (1997).
13. Aarssen, L. W. High productivity in grassland ecosystems: effected
by species diversity or productive species? Oikos 80, 183 (1997).
14. Hooper, D. U. The role of complementarity and competition in
ecosystem responses to variation in plant diversity. Ecology 79,
704–719 (1998).
15. Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a
major driver of ecosystem change. Nature 486,105–108 (2012).
16. 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).
17. Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J.
Grounding nature-based climate solutions in sound biodiversity
science. Nat. Clim. Change 9,84
–87 (2019).
18. Ferrier,S.,Ninan,K.N.,Leadley,P.&Alkemade,R.The Methodo-
logical Assessment Report on Scenarios and Models of Biodiversity
and Ecosystem Services 32 (IPBES, 2016).
19. 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. Proc.R.Soc.BBiol.Sci.288,
20210783 (2021).
20. Zhou, J. et al. A traceability analysis system for model evaluation on
land carbon dynamics: design and applications. Ecol. Process. 10,
12 (2021).
21. Wei, N. et al. Evolution of uncertainty in terrestrial carbon storage in
EarthsystemmodelsfromCMIP5toCMIP6.J. Clim. 35,
5483–5499 (2022).
22. Isbell, F., Tilman, D., Polasky, S. & Loreau, M. The biodiversity-
dependent ecosystem service debt. Ecol. Lett. 18, 119–134 (2015).
23. Reich, P. B. et al. Impacts of biodiversity loss escalate through time
as redundancy fades. Science 336,589–592 (2012).
24. Fulton, E. A. & Gorton, R. Adaptive Futures for SE Australian Fisheries
& Aquaculture: Climate Adaptation Simulations (CSIRO, 2014).
25. Weiskopf, S. R. et al. A conceptual framework to integrate biodi-
versity, ecosystem function, and ecosystem service models.
BioScience. 72,1–12 (2022).
26. O’Connor,M.I.,Bernhardt,J.R.,Stark,K.,Usinowicz,J.&Whalen,M.
A. In The Ecological and Societal Consequences of Biodiversity Loss
(eds Loreau, M., Hector, A. & Isbell, F.) 97–118 (Wiley, 2022).
27. Wang, S. & Loreau, M. Biodiversity and ecosystem stability across
scales in metacommunities. Ecol. Lett. 19,510–518 (2016).
28. Isbell, F. et al. Linking the influence and dependence of people on
biodiversity across scales. Nature 546,65–72 (2017).
29. Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly,
and ecosystem functioning. Trends Ecol. Evol. 33,549–564 (2018).
30. Hoskins, A. J. et al. BILBI: supporting global biodiversity assessment
through high-resolution macroecological modelling. Environ.
Model. Softw. 132,104806(2020).
31. Di Marco, M. et al. Projecting impacts of global climate and land-use
scenarios on plant biodiversity using compositional-turnover
modelling. Glob. Change Biol. 25,2763–2778 (2019).
32. 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. 11,4537–4562 (2018).
33. Dufresne, J. L. et al. Climate change projections using the IPSL-CM5
Earthsystemmodel:fromCMIP3toCMIP5.Clim. Dyn. 40,
2123–2165 (2013).
34. Asamoah, E. F. et al. Land-use and climate risk assessment for
Earth’s remaining wilderness. Curr. Biol. 32,4890–4899.e4 (2022).
35. Mendez Angarita, V. Y., Maiorano, L., Dragonetti, C. & Di Marco, M.
Implications of exceeding the Paris Agreement for mammalian
biodiversity. Conserv. Sci. Pract. 5,e12889(2023).
36. Yang, Y. et al. Restoring abandoned farmland to mitigate climate
change on a full Earth. One Earth 3,176–186 (2020).
37. Chen, X. et al. Effects of plant diversity on soil carbon in diverse
ecosystems: a global meta-analysis. Biol. Rev. 95,167–183 (2020).
38. Chen, X. et al. Tree diversity increases decadal forest soil carbon
and nitrogen accrual. Nature 618,94–101 (2023).
39. Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon seques-
tration accelerated by restoration of grassland biodiversity. Nat.
Commun. 10,1–7 (2019).
40. Cimatti,M.,Chaplin-Kramer,R.&Marco,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).
41. Canadell, J. G. et al. Global carbon and other biogeochemical cycles
and feedbacks. In ClimateChange2021:ThePhysicalScienceBasis.
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).
42. Pereira,H.M.etal.Globaltrendsinbiodiversityandecosystem
services from 1900 to 2050. bioRxiv 1,1–5 (2020).
43. Mori, A. S. Advancing nature-based approaches to address the
biodiversity and climate emergency. Ecol. Lett. 23,
1729–1732 (2020).
44. Andres, S. E. et al. Defining biodiverse reforestation: why it matters
for climate change mitigation and biodiversity. Plants People Planet
1–12 https://doi.org/10.1002/ppp3.10329 (2022).
45. 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).
46. 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. 93,439–456 (2018).
47. 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).
48. Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning
research. Ecol. Lett. 23,757–776 (2020).
49. Thompson, P. L. et al. Scaling up biodiversity-ecosystem function-
ing relationships: the role of environmental heterogeneity in space
and time. Proc.R.Soc.BBiol.Sci.288, 20202779 (2021).
50. Benito Garzón, M., Robson, T. M. & Hampe, A. ΔTraitSDMs: species
distribution models that account for local adaptation and pheno-
typic plasticity. N. Phytologist 222,1757–1765 (2019).
51. 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(2010).
Article https://doi.org/10.1038/s41467-024-47872-7
Nature Communications | (2024) 15:4354 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved

52. Bush, A. et al. Incorporating evolutionary adaptation in species
distribution modelling reduces projected vulnerability to climate
change. Ecol. Lett. 19,1468–1478 (2016).
53. Wilsey,B.J.,Teaschner,T.B.,Daneshgar,P.P.,Isbell,F.I.&Polley,H.
W. Biodiversity maintenance mechanisms differ between native and
novel exotic-dominated communities. Ecol. Lett. 12,432–442 (2009).
54. 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. 14,2607(2023).
55. Isbell, F. I. & Wilsey, B. J. Increasing native, but not exotic, biodi-
versity increases aboveground productivity in ungrazed and
intensely grazed grasslands. Oecologia 165, 771–781 (2011).
56. Rosenzweig, M. L. Heeding the warning in biodiversity’sbasiclaw.
Science 284,276–277 (1999).
57. Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann,
N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat.
Commun. 10, 1446 (2019).
58. Leclère,D.etal.Bendingthecurve of terrestrial biodiversity needs
an integrated strategy. Nature 585,551–556 (2020).
59. Arora, V. K. et al. Carbon-concentration and carbon-climate feed-
backs in CMIP6 models and their comparison to CMIP5 models.
Biogeosciences 17,4173–4222 (2020).
60. 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).
61. United Nations. Transforming Our World: The 2030 Agenda for
Sustainable Development (2015).
62. United Nations Convention on Biological Diversity. Kunming-
Montreal Global Biodiversity Framework. (2022).
63. Rosa, I. M. D. et al. Multiscale scenarios for nature futures. Nat. Ecol.
Evol. 1,1416–1419 (2017).
64. Rosa, I. M. D. et al. Challenges in producing policy-relevant global
scenarios of biodiversity and ecosystem services. Glob. Ecol. Con-
serv. 22,e00886(2020).
65. van der Plas, F. Biodiversity and ecosystem functioning in naturally
assembled communities. Biol. Rev. https://doi.org/10.1111/brv.
12499 (2019).
66. Newbold, T. et al. Global effects of land use on local terrestrial
biodiversity. Nature 520,45
–50 (2015).
67. Brooks, T. M. et al. Habitat loss and extinction in the hotspots of
biodiversity. Conserv. Biol. 16,909–923 (2002).
68. Elith, J. et al. Novel methods improve prediction of species’dis-
tributions from occurrence data. Ecography 29,129–151 (2006).
69. Blois,J.L.,Williams,J.W.,Fitzpatrick,M.C.,Jackson,S.T.&Ferrier,
S. Space can substitute for time in predicting climate-change
effects on biodiversity. Proc. Natl Acad. Sci. 110,9374–9379 (2013).
70. 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).
71. Pimm,S.L.,Jenkins,C.N.&Li,B.V.Howtoprotecthalfofearthto
ensure it protects sufficient biodiversity. Sci. Adv. 4,1–9(2018).
72. DiMarco,M.,Hoskins,A.J.,Harwood,T.D.,Ware,C.&Ferrier,S.
BILBI model data for SSP1/RCP2.6 and SSP5/RCP8.5. figshare
https://doi.org/10.6084/m9.figshare.25188650 (2024).
73. Diamond, J. M. Biogeographic kinetics: estimation of relaxation
times for Avifaunas of Southwest PacificIslands.Proc.NatlAcad.
Sci. 69,3199–3203 (1972).
74. Gonzalez, A. In Encyclopedia of Life Sciences https://doi.org/10.
1002/9780470015902.a0021230 (Wiley, 2009).
75. Kriegler, E. et al. Fossil-fueled development (SSP5): an energy and
resource intensive scenario for the 21st century. Glob. Environ.
Change 42,297–315 (2017).
76. van Vuuren, D. P. et al. Energy, land-use and greenhouse gas
emissions trajectories under a green growth paradigm. Glob.
Environ. Change 42,237–250 (2017).
77. 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).
78. Hurtt, G. C. et al. Harmonization of global land use change and
management for the period 850-2100 (LUH2) for CMIP6. Geosci.
Model Dev. 13,5425–5464 (2020).
79. Hijmans,R.J.,Cameron,S.E.,Parra,J.L.,Jones,P.G.&Jarvis,A.
WorldClim Global Climate Data Version 1.http://worldclim.org/
version1 (2017).
80. Liang, J. et al. Positive biodiversity-productivity relationship pre-
dominant in global forests. Science 354, aaf8957 (2016).
81. Craven, D. et al. Plant diversity effects on grassland productivity are
robust to both nutrient enrichment and drought. Philos.Trans.R.
Soc. B 371,20150277(2016).
82. 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).
83. Hengl, T. et al. SoilGrids250m: global gridded soil information
based on machine learning. PLoS ONE 12, e0169748 (2017).
84. Hijmans, R. J. terra: Spatial Data Analysis. R package version 1.7-
39. (2023).
85. Balvanera, P. et al. Quantifying the evidence for biodiversity effects
on ecosystem functioning and services. Ecol. Lett. 9,
1146–1156 (2006).
86. Mori,A.S.,Cornelissen,J.H.C.,Fujii,S.,Okada,K.&Isbell,F.
A meta-analysis on decomposition quantifies afterlife effects of
plant diversity as a global change driver. Nat. Commun. 11,
1–9 (2020).
87. R Core Team. R: A Language and Environment for Statistical Com-
puting (R Foundation for Statistical Computing, 2021).
88. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-
Verlag, 2016).
89. Tennekes, M. tmap: Thematic Maps in R. J. Stat. Softw. 84,
1–39 (2018).
90. 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.
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Additional information
Supplementary information The online version contains
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Sarah R. Weiskopf.
Peer review information Nature Communications thanks Rob Alkemade,
Eduardo Gomes and Aafke Schipper for their contribution to the peer
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