Bending the curve of terrestrial biodiversity needs an integrated strategy

Abstract

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.

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Fig. 1: Estimated recent and future global biodiversity trends resulting from land-use change, with and without coordinated efforts to reverse trends.
Fig. 2: Contributions of various efforts to reverse land-use change-induced biodiversity trends.

Data availability

The World Database of Protected Areas35 can be accessed at https://www.protectedplanet.net/, IUCN species range maps41 are available at https://www.iucnredlist.org/resources/spatial-data-download, access to the World Database of Key Biodiversity Areas36 can be requested at http://www.keybiodiversityareas.org/site/requestgis, wilderness areas are available from a previous study37, LUH2 datasets can be accessed at https://luh.umd.edu/data.shtml, the HYDE 3.1 database51 can be accessed at https://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html. The 30-arcmin resolution raster layers (extent of expanded protected areas, land-use change rules in expanded protected areas, coefficients allowing the estimation of the pixel-specific and land-use change transition-specific biodiversity impact of land-use change) used by the IAMs to model increased conservation efforts cannot be made freely available due to the terms of use of their source, but will be made available upon reasonable request to the corresponding authors. The 30-arcmin resolution raster layers, which provide the proportion of grid cell area occupied by each of the twelve land-use classes, four IAMs, seven scenarios and ten time horizons, are publicly available from a data repository under a CC-BY-NC license (http://dare.iiasa.ac.at/57/)33, together with the IAM outputs that underpin the global scale results of Extended Data Figs. 3, 8 (for all time horizons), the global and IPBES subregion-specific results of Extended Data Figs. 4, 5, and the BDM outputs that underpin the global and IPBES subregion-specific results shown in Figs. 1, 2, Extended Data Figs. 2, 6, 7 and Extended Data Tables 1, 2 (for all available time horizons, BDIs, IAMs and scenarios).

Code availability

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.

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Acknowledgements

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).

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Authors

Contributions

D.L. and M.O. led the study. D.L. coordinated the modelling, performed the analysis, coordinated the writing of the preliminary draft and performed the writing of later drafts. D.L., M.B., S.H.M.B., A.C., A.D.P., F.A.J.D., M.D.M., J.C.D., M.D., R.F., M. Harfoot, T. Hasegawa, S.H., J.P.H., S.L.L.H., F.H., N.J., T. Krisztin, G.M.M., H.O., A. Popp, A. Purvis, A.M.S., A.T., H.V., H.v.M., W.J.v.Z. and P.V. were involved in the core modelling and writing. R. Alkemade, R. Almond, G.B., N.D.B., S.E.C., F.D.F., S. Ferrier, S. Fritz, S. Fujimori, M.G., T. Harwood, P.H., M. Herrero, A.J.H., M.J., T. Kram, H.L.-C., T.M., C.M., D.N., T.N., G.S.-T., E.S., B.B.N.S., D.P.v.V., C.W., J.E.M.W., W.W. and L.Y. contributed through several iterations to the study design, result analysis and article writing.

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Correspondence to David Leclère or Michael Obersteiner.

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WWF supported the research in kind and funding for editorial and research support.

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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.

ac, 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).

ae, 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.

Extended Data Fig. 3 Projected future global trends in drivers of habitat loss and degradation.

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).

Extended Data Fig. 4 Projected global trends in land-use change across all scenarios.

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).

Extended Data Fig. 5 Spatial patterns of projected habitat loss and restoration by 2100.

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.

ae, 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.

Extended Data Table 1 Prolongation of historical biodiversity trends in the BASE scenario
Extended Data Table 2 Key statistics for the date of peak loss, share of avoided loss and relative recovery speed

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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

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