Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042–2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34–50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats—such as climate change—must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
npj Biodiversity Open Access 01 June 2023
Scientific Reports Open Access 05 May 2023
Spaceborne LiDAR reveals the effectiveness of European Protected Areas in conserving forest height and vertical structure
Communications Earth & Environment Open Access 29 March 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
The code and data used to generate the BDM outputs are publicly available from a data repository under a CC-BY-NC license (http://dare.iiasa.ac.at/57/)33 for all BDMs. The code and data used to analyse IAM and BDM outputs and generate figures are publicly available from a data repository under a CC-BY-NC license (http://dare.iiasa.ac.at/57/)33.
IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2019).
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).
Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).
Van Vuuren, D. P. et al. Pathways to achieve a set of ambitious global sustainability objectives by 2050: explorations using the IMAGE integrated assessment model. Technol. Forecast. Soc. Change 98 303–323 (2015).
Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (Liveright, 2016).
Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A 365, 2053–2075 (2007).
IPBES. Summary for Policymakers of the Methodological Assessment of Scenarios and Models of Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2016).
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).
Efron, B. & Tibshirani, R. Statistical data analysis in the computer age. Science 253, 390–395 (1991).
Briscoe, N. J. et al. Forecasting species range dynamics with process-explicit models: matching methods to applications. Ecol. Lett. 22, 1940–1956 (2019).
McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156 (2017).
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
Newbold, T., Sanchez-Ortiz, K., De Palma, A., Hill, S. L. L. & Purvis, A. Reply to ‘The biodiversity intactness index may underestimate losses’. Nat. Ecol. Evol. 3, 864–865 (2019).
Martin, P. A., Green, R. E. & Balmford, A. The biodiversity intactness index may underestimate losses. Nat. Ecol. Evol. 3, 862–863 (2019).
Phalan, B. et al. How can higher-yield farming help to spare nature? Science 351, 450–451 (2016).
Lambin, E. F. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl Acad. Sci. USA 108, 3465–3472 (2011).
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).
Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, eaat2616 (2018).
Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
Eker, S., Rovenskaya, E., Obersteiner, M. & Langan, S. Practice and perspectives in the validation of resource management models. Nat. Commun. 9, 5359 (2018).
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change 42, 251–267 (2017).
Leclère, D. et al. Supporting material for the article entitled “Bending the curve of terrestrial biodiversity needs an integrated strategy” [Data Collection]. http://dare.iiasa.ac.at/57/ (2020).
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).
IUCN & UNEP-WCMC. The World Database on Protected Areas (WDPA). https://www.protectedplanet.net/ (UNEP-WCMC, accessed October 2017).
Key Biodiversity Area Partnership World Database of Key Biodiversity Areas. http://www.keybiodiversityareas.org/site/requestgis (BirdLife International, accessed 5 October 2017).
Allan, J. R., Venter, O. & Watson, J. E. M. Temporally inter-comparable maps of terrestrial wilderness and the last of the wild. Sci. Data 4, 170187 (2017).
Scholes, R. J. & Biggs, R. A biodiversity intactness index. Nature 434, 45–49 (2005).
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
Hurtt, G. et al. Harmonization of global land-use change and management for the period 850–2100. Preprint at https://doi.org/10.5194/gmd-2019-360 (2020).
IUCN. Red List of Threatened Species. version 2017.3 http://www.iucnredlist.org (2017).
BirdLife International & Handbook of the Birds of the World. Bird Species Distribution Maps of the World. version 7.0. http://datazone.birdlife.org/species/requestdis (2017).
Harfoot, M. et al. Integrated assessment models for ecologists: the present and the future. Glob. Ecol. Biogeogr. 23, 124–143 (2014).
Fujimori, S., Masui, T. & Matsuoka, Y. AIM/CGE [basic] Manual. Discussion Paper Series No. 2012-01 (Center for Social and Environmental Systems Research, NIES, 2012).
Hasegawa, T., Fujimori, S., Ito, A., Takahashi, K. & Masui, T. Global land-use allocation model linked to an integrated assessment model. Sci. Total Environ. 580, 787–796 (2017).
Havlík, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).
Stehfest, E. et al. Integrated Assessment of Global Environmental Change with IMAGE 3.0: Model Description and Policy Applications. https://www.pbl.nl/en/publications/integrated-assessment-of-global-environmental-change-with-IMAGE-3.0 (Netherlands Environmental Assessment Agency (PBL), 2014).
Woltjer, G. et al. The MAGNET Model: Module Description. https://edepot.wur.nl/310764 (LEI, part of Wageningen University and Research Centre, The Hague, 2014).
Popp, A. et al. Land-use protection for climate change mitigation. Nat. Clim. Change 4, 1095–1098 (2014).
Brooks, T. M. et al. Analysing biodiversity and conservation knowledge products to support regional environmental assessments. Sci. Data 3, 160007 (2016).
Klein Goldewijk, K., Beusen, A., van Drecht, G. & de Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).
Ohashi, H. et al. Biodiversity can benefit from climate stabilization despite adverse side effects of land-based mitigation. Nat. Commun. 10, 5240 (2019).
Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).
Rondinini, C. & Visconti, P. Scenarios of large mammal loss in Europe for the 21st century. Conserv. Biol. 29, 1028–1036 (2015).
Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).
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).
Hoskins, A. J. et al. BILBI: supporting global biodiversity assessment through high-resolution macroecological modelling. Environ. Model. Softw. 104806 (2020).
Chaudhary, A. & Brooks, T. M. National Consumption and Global Trade Impacts on Biodiversity. World Dev. 121, 178–187 (2017).
UNEP & SETAC. Global Guidance for Life Cycle Impact Assessment Indicators, vol. 1 (United Nations Environment Programme, 2016).
Chaudhary, A., Verones, F., de Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).
Alkemade, R. et al. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390 (2009).
De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Preprint at https://doi.org/10.1101/311688 (2018).
Hill, S. L. L. et al. Worldwide impacts of past and projected future land-use change on local species richness and the Biodiversity Intactness Index. Preprint at https://doi.org/10.1101/311787 (2018).
Purvis, A. et al. Modelling and projecting the response of local terrestrial biodiversity worldwide to land use and related pressures. Adv. Ecol. Res. 58, 201–241 (2018).
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).
D.L., S.L.L.H. and N.J. acknowledge funding from WWF-NL and WWF-UK. N.D.B., T.N., P.H., T. Krisztin, H.V. and D.L. acknowledge funding from the UK Research and Innovation’s Global Challenges Research Fund under the Trade, Development and the Environment Hub project (ES/S008160/1). S.E.C. acknowledges partial support from the European Research Council under the EU Horizon 2020 research and innovation programme (743080 – ERA). A.C. acknowledges funding from the Initiation Grant of IIT Kanpur, India (2018386). G.M.M. acknowledges the Sustainable and Healthy Food Systems (SHEFS) programme supported by the Welcome Trust’s ‘Our Planet, Our Health’ programme (205200/Z/16/Z). H.O. and T.M. acknowledge partial support from the Environment Research and Technology Development Fund (JPMEERF15S11407 and JPMEERF20202002) of the Environmental Restoration and Conservation Agency of Japan, and used the supercomputer of AFFRIT, MAFF, Japan. S. Fujimori, T. Hasegawa and W.W. were supported by the Environment Research and Technology Development Fund (JPMEERF20202002) of the Environmental Restoration and Conservation Agency of Japan and JSPS KAKENHI (JP20K20031, 19K24387) of the Japan Society for the Promotion of Science. S. Fujimori and T. Hasegawa were supported by the Sumitomo Foundation. M.D.M. acknowledges support from the MIUR Rita Levi Montalcini programme. F.H. and D.L. received funding from the ENGAGE project of the European Union’s Horizon 2020 research and innovation programme under grant agreement 821471. J.C.D. acknowledges support from the SIM4NEXUS project of the European Union’s Horizon 2020 research and innovation programme under grant agreement 689150. A.D.P., S.L.L.H. and A. Purvis were funded by the UK Natural Environment Research Council (grant number NE/M014533/1 to A. Purvis), the Natural History Museum Departmental Investment Fund and the Prince Albert II of Monaco Foundation (Plants Under Pressure II). T.N. is funded by a University Research Fellowship from the Royal Society and by a grant from the UK Natural Research Council (NE/R010811/1). D.P.v.V. acknowledges partial support from the European Research Council under the grant agreement 819566 (PICASSO). M.O. acknowledges support from the European Research Council under the grant agreement 610028 (IMBALANCE-P). H.v.M. and A.T. acknowledge partial support from the Dutch Knowledge Base programme Circular and Climate neutral society of the Dutch ministry of LNV. R. Alkemade, J.P.H. and A.M.S. acknowledge support from the GLOBIO project of the PBL Netherlands Environmental Assessment Agency. G.S.T., M.J., M.O. and P.V. acknowledge support from the Norwegian Ministry of Climate and Environment through Norway’s International Climate and Forest Initiative (NICFI) under grant agreement 18/4135. B.B.N.S. acknowledges funding by the Serrapilheira Institute (grant number Serra-1709-19329).
WWF supported the research in kind and funding for editorial and research support.
Peer review information Nature thanks Brett Bryan, Carlo Rondinini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Datasets used to provide spatially explicit input for modelling increased conservation efforts into the land-use models.
a–c, The proportion of land under the assumed expanded protected areas at 30-arcmin resolution (a; based on all areas from the World Database on Protected areas35, areas from Key Biodiversity Areas36 and wilderness areas37) and the value of the assumed spatial priority score for restoration at 30-arcmin resolution (b; relative range rarity-weighted species richness score RRRWSR, based on species range maps from the ICUN Red List41 and the Handbook of the Birds of the World42), as well as the impact of various land uses on the BII38 of various land-use classes (c; estimated from assemblage data for 21,702 distinct sites worldwide from the PREDICTS database20, 11,534 from naturally forested biomes and 10,168 from naturally non-forest biomes). Datasets from a and c were used to implement spatially explicit restrictions to land-use change within land-use models (from 2020 onwards), and datasets from b and c were used to implement spatially explicit priorities for restoration and landscape-level conservation planning (from 2020 onwards) in scenarios for which increased conservation efforts were assumed (Methods).
Extended Data Fig. 2 Spatial patterns in projected changes in the value of biodiversity indicators for BASE and IAP scenarios (and the difference between the IAP and BASE scenarios) for the 17 IPBES subregions by 2050 and 2100 (compared to 2010 value).
a–e, The projected changes (mean across IAMs) for each of the eight combinations of BDIs and BDMs (Table 2) for which values at the scale of the IPBES subregions were available, grouped according to the five aspects of biodiversity. a, Extent of suitable habitat. b, Wildlife population density. c, Local composition intactness. d, Regional extinctions. e, Global extinctions. The FGRS indicator was estimated by the cSAR_US16 model only at the global scale.
a, b, For each scenario (colours, mean across all four IAMs), the relative change from 2010 to 2050 (a) and 2100 (b) in nine variables are shown. The symbols indicate the IAM-specific values. The variables displayed from the top left to bottom right are: agricultural demand for livestock products (Agr. Demand|Liv.), agricultural demand for short-rotation bioenergy crops (Agr. Demand|Crops|Ene.), agricultural demand for crops other than short-rotation bioenergy crops (Agr. Demand|Crops|Non-E.), agricultural supply of livestock products (Agr. Supply|Liv.), agricultural supply of all crop products (Agr. Supply|Crops|Tot.), average yield of crops other than short-rotation bioenergy crops (in metric tonnes dry matter per hectare, Productivity|Crops|Non-E.), and the land dedicated to cropland (LC|Cropland) and pasture (LC|Pasture). Values displayed for each variable are change relative to the value of the same variable simulated for 2010, except for two variables (Agr. Demand|Crops|Ene. and Agr. Demand|Crops|Non-E.), for which the change in each of the variables is normalized to the sum of values simulated in 2010 for the two variables (that is, normalization to the total demand for crops).
a, Global trends in the sum of restored land, unmanaged forest and other natural land classes compared to 2010 (with and without excluding the land abandoned and not yet in restoration—different only for scenarios without increased conservation efforts; Methods). Thick lines show the average values across all four IAMs; shading shows the range across IAMs. b, c, Global changes projected in the area of each of the 12 land-use classes (compared to 2010) for the seven scenarios averaged across the four IAMs by 2050 and 2100 (b), and for each individual IAM by 2100 (c).
Data are shown for the BASE and IAP scenarios and the difference (IAP − BASE), and are shown as the mean across IAMs (top) and separately for each of the four IAMs (AIM, GLOBIOM, IMAGE, MAgPIE).
Extended Data Fig. 6 Estimated recent and future global biodiversity trends that resulted from land-use change for all seven scenarios.
a–e, The trends—for the five different biodiversity aspects—that result from changes in seven biodiversity indicators (see Table 2 for definitions). Indicator values are shown as differences from the 2010 value (which was set to 1); a value of −0.01 means a loss of 1% in: the extent of suitable habitat (a), the wildlife population density (b), the local compositional intactness (c), the regional number of species not already extinct or committed to extinction (d) or the global number of species not already extinct or committed to extinction (e). Indicator values are projected in response to land-use change derived from one source over the historical period (1970–2010, black line; 2010 is indicated with a vertical dashed line) and from four different IIAMs (AIM, GLOBIOM, IMAGE and MAgPIE; thick lines show the mean across models and shading shows the range across models) for each of the seven future scenarios (Table 1).
Extended Data Fig. 7 Spatial patterns of the date of peak loss in the twenty-first century and the share of avoided future peak loss.
a, b, Across the 17 IPBES subregions, individual maps show, for each region and for each of the seven scenarios, the mean value of the date of peak loss in the twenty-first century (a) and the share of avoided future peak loss (b). Means were estimated from 10,000 bootstrapped samples of the simulated IAM and BDI combinations (a, n = 24; b, n = 18–24, as regions and combinations for which the baseline peak loss was less than 0.1% were excluded). Colour codes are based on the mean (m.) and standard deviation (sd) estimates (across the 10,000 samples for each region and scenario) of the sample mean value.
Extended Data Fig. 8 Global relative changes in the price index of non-energy crops, total greenhouse gas emissions from agriculture, forestry and other land uses, total irrigation water withdrawal and nitrogen fertilizer use between 2010 and 2050.
Top left, global changes in the price index of non-energy crops. Top right, global changes in total greenhouse gas emissions from agriculture, forestry and other land uses. AFOLU, agriculture, forestry and other land uses. Bottom left, global changes in total irrigation water withdrawal. Irrigation water withdrawal was reported by only two IAMs (MAgPIE and GLOBIOM); values were not reported for the other two IAMs. Bottom right, global changes in nitrogen fertilizer use. Nitrogen fertilizer use was reported by only three IAMs (MAgPIE, GLOBIOM and IMAGE); values were not reported for AIM. Data are shown for the seven scenarios and four IAMs. Averages across IAMs are shown as bars, individual IAMs are shown as symbols.
About this article
Cite this article
Leclère, D., Obersteiner, M., Barrett, M. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020). https://doi.org/10.1038/s41586-020-2705-y
This article is cited by
Nature Climate Change (2023)
Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series
Nature Communications (2023)
Nature Human Behaviour (2023)
Spaceborne LiDAR reveals the effectiveness of European Protected Areas in conserving forest height and vertical structure
Communications Earth & Environment (2023)
Nature Reviews Earth & Environment (2023)