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Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change

Nature Ecology & Evolution volume 4, pages 1630–1638 (2020)Cite this article

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Abstract

Global biodiversity is undergoing rapid declines, driven in large part by changes to land use and climate. Global models help us to understand the consequences of environmental changes for biodiversity, but tend to neglect important geographical variation in the sensitivity of biodiversity to these changes. Here we test whether biodiversity responses to climate change and land-use change differ among biomes (geographical units that have marked differences in environment and species composition). We find the strongest negative responses to both pressures in tropical biomes and in the Mediterranean. A further analysis points towards similar underlying drivers for the sensitivity to each pressure: we find both greater reductions in species richness in the types of land use most disturbed by humans and more negative predicted responses to climate change in areas of lower climatic seasonality, and in areas where a greater proportion of species are near their upper temperature limit. Within the land most modified by humans, reductions in biodiversity were particularly large in regions where humans have come to dominate the land more recently. Our results will help to improve predictions of how biodiversity is likely to change with ongoing climatic and land-use changes, pointing toward particularly large declines in the tropics where much future agricultural expansion is expected to occur. This finding could help to inform the development of the post-2020 biodiversity framework, by highlighting the under-studied regions where biodiversity losses are likely to be greatest.

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Fig. 1: Differences in species richness among land-use types, across different biomes.
Fig. 2: Predicted sensitivity of biodiversity to climate change across biomes.
Fig. 3: Relationship across biomes between climate and land-use sensitivity.
Fig. 4: Patterns of species richness among land-use types moderated by putative explanatory variables.
Fig. 5: Relationships of predicted biodiversity sensitivity to climate change with putative explanatory variables.

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Data availability

All data required to run the analyses are published on FigShare: https://doi.org/10.6084/m9.figshare.12674372.

Code availability

All code used in the analyses is publicly available at: https://github.com/timnewbold/BiomeSpecificResponsesPublic.

References

  1. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    CAS  PubMed  Google Scholar 

  2. Grooten, M. & Almond, R. E. A. (eds) Living Planet Report ‒ 2018: Aiming Higher (WWF, 2018).

  3. Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).

    PubMed  PubMed Central  Google Scholar 

  4. Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).

    CAS  PubMed  Google Scholar 

  5. Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).

    CAS  PubMed  Google Scholar 

  6. Newbold, T. et al. Climate and land-use change homogenise terrestrial biodiversity, with consequences for ecosystem functioning and human well-being. Emerg. Top. Life Sci. 3, 207–219 (2019).

    Google Scholar 

  7. Nicholson, E. et al. Scenarios and models to support global conservation targets. Trends Ecol. Evol. 34, 57–68 (2019).

    PubMed  Google Scholar 

  8. Ferrier, S. et al. (eds) The Methodological Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2016).

  9. Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).

    Google Scholar 

  10. Marshall, L. et al. The interplay of climate and land use change affects the distribution of EU bumblebees. Glob. Change Biol. 24, 101–116 (2018).

    Google Scholar 

  11. Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).

    PubMed  PubMed Central  Google Scholar 

  12. Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).

    Google Scholar 

  13. Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).

    Google Scholar 

  14. Araújo, M. B., Alagador, D., Cabeza, M., Nogués-Bravo, D. & Thuiller, W. Climate change threatens European conservation areas. Ecol. Lett. 14, 484–492 (2011).

    PubMed  PubMed Central  Google Scholar 

  15. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).

    CAS  PubMed  Google Scholar 

  16. Alkemade, R. et al. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390 (2009).

    Google Scholar 

  17. Martins, I. S. & Pereira, H. M. Improving extinction projections across scales and habitats using the countryside species-area relationship. Sci. Rep. 7, 12899 (2017).

    PubMed  PubMed Central  Google Scholar 

  18. Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).

    CAS  PubMed  Google Scholar 

  20. 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).

    Google Scholar 

  21. Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).

    CAS  PubMed  Google Scholar 

  22. Stevens, G. C. The latitudinal gradient in geographic range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).

    Google Scholar 

  23. Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).

    Google Scholar 

  24. Forister, M. L. et al. The global distribution of diet breadth in insect herbivores. Proc. Natl Acad. Sci. USA 112, 442–447 (2015).

    CAS  PubMed  Google Scholar 

  25. Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. Lond. B Biol. Sci. 280, 20122131 (2013).

    Google Scholar 

  26. Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).

    Google Scholar 

  27. Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).

    Google Scholar 

  28. Wiersma, P., Munoz-Garcia, A., Walker, A. & Williams, J. B. Tropical birds have a slow pace of life. Proc. Natl Acad. Sci. USA 104, 9340–9345 (2007).

    CAS  PubMed  Google Scholar 

  29. Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).

    CAS  PubMed  Google Scholar 

  30. Orme, C. D. L. et al. Distance to range edge determines sensitivity to deforestation. Nat. Ecol. Evol. 3, 886–891 (2019).

    PubMed  Google Scholar 

  31. Frishkoff, L. O., Hadly, E. A. & Daily, G. C. Thermal niche predicts tolerance to habitat conversion in tropical amphibians and reptiles. Glob. Change Biol. 21, 3901–3916 (2015).

    Google Scholar 

  32. Frishkoff, L. O. et al. Climate change and habitat conversion favour the same species. Ecol. Lett. 19, 1081–1090 (2016).

    PubMed  Google Scholar 

  33. Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).

    Google Scholar 

  34. De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).

    PubMed  Google Scholar 

  35. Williams, J. J., Bates, A. E. & Newbold, T. Human‐dominated land uses favour species affiliated with more extreme climates, especially in the tropics. Ecography 43, 391–405 (2020).

    Google Scholar 

  36. Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).

    Google Scholar 

  37. Srinivasan, U., Elsen, P. R. & Wilcove, D. S. Annual temperature variation influences the vulnerability of montane bird communities to land‐use change. Ecography 42, 2084–2094 (2019).

    Google Scholar 

  38. Newbold, T. et al. Global patterns of terrestrial assemblage turnover within and among land uses. Ecography 39, 1151–1163 (2016).

    Google Scholar 

  39. Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).

    Google Scholar 

  40. 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).

    PubMed  Google Scholar 

  41. Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    CAS  Google Scholar 

  42. Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A. Pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).

    Google Scholar 

  44. Fu, B., Wang, J., Chen, L. & Qiu, Y. The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau, China. Catena 54, 197–213 (2003).

    Google Scholar 

  45. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).

    Google Scholar 

  46. Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).

    CAS  PubMed  Google Scholar 

  47. García-Vega, D. & Newbold, T. Assessing the effects of land use on biodiversity in the world’s drylands and Mediterranean environments. Biodivers. Conserv. 29, 393–408 (2020).

    Google Scholar 

  48. Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).

    CAS  PubMed  Google Scholar 

  49. Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    CAS  PubMed  Google Scholar 

  50. Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).

    Google Scholar 

  52. 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).

    PubMed  Google Scholar 

  53. Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744 (2017).

    CAS  PubMed  Google Scholar 

  54. The IUCN Red List of Threatened Species Version 2013.7 (IUCN, 2013); http://www.iucnredlist.org/

  55. Bird Species Distribution Maps of the World Version 2.0 (BirdLife International & NatureServe, 2012); http://www.birdlife.org/datazone/info/spcdownload

  56. Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).

    PubMed  PubMed Central  Google Scholar 

  57. Zero Draft of the Post-2020 Global Biodiversity Framework Resolution CBD/WG2020/2/3 (Convention on Biological Diversity, 2020).

  58. Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).

    CAS  PubMed  Google Scholar 

  59. Kissling, W. D., Sekercioglu, C. H. & Jetz, W. Bird dietary guild richness across latitudes, environments and biogeographic regions. Glob. Ecol. Biogeogr. 21, 328–340 (2012).

    Google Scholar 

  60. Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).

    PubMed  Google Scholar 

  61. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).

    PubMed  PubMed Central  Google Scholar 

  62. Terrestrial Ecoregions of the World (The Nature Conservancy, 2009); http://maps.tnc.org/gis_data.html

  63. Hudson, L. N. et al. Dataset: The 2016 Release of the PREDICTS Database (Natural History Museum Data Portal, 2016); https://doi.org/10.5519/0066354

  64. Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).

    Google Scholar 

  65. Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2008).

    Google Scholar 

  66. Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).

    Google Scholar 

  67. Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: a call for caution. Glob. Ecol. Biogeogr. 26, 930–941 (2017).

    Google Scholar 

  68. Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Google Scholar 

  69. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Google Scholar 

  70. Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).

    Google Scholar 

  71. Bivand, R. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).

    Google Scholar 

Download references

Acknowledgements

This work was supported by a Royal Society University Research Fellowship to T.N. J.J.W. and A.E. were supported by research grants from the Royal Society, awarded to T.N.

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Authors and Affiliations

  1. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

    Tim Newbold, Philippa Oppenheimer, Adrienne Etard & Jessica J. Williams

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Contributions

T.N. and P.O. conceived and designed the study and carried out the main analyses. A.E. and J.J.W. input analytical tools and important insight on aspects of the work. T.N. wrote the final manuscript, with substantial inputs from all authors.

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Correspondence to Tim Newbold.

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Extended data

Extended Data Fig. 1 Map of sites with data in the PREDICTS database used for analysing land-use responses.

Points are coloured by one of the classifications of biomes, which we used in our analyses.

Extended Data Fig. 2 Modelled differences in biodiversity among land-use types.

Results are shown for three community-level measures of biodiversity: total sampled species richness, total sampled community abundance, and community-average range size. The last is a measure of the inverse of the endemicity of species within communities, and is the average of the range sizes of all sampled species in the community, weighted by sampled abundance. All values are expressed as a percentage change relative to primary vegetation as the baseline. Numbers in parentheses are the lower and upper bounds of the 95% confidence limits.

Extended Data Fig. 3 Statistics for mixed-effects models of species richness with different land-use groupings.

Species richness was modelled as a function of both land use (using the different combinations as shown here) and biome (using the finest division into 11 different biomes). Shown are the model degrees of freedom (DF), difference in AIC compared with the best-fitting model (ΔAIC), and the conditional and marginal R2 values72. The best-fitting model is shown in italics, while the land-use combination used in the final models is shown in bold. PV = Primary Vegetation; SV = Secondary Vegetation; PF = Plantation Forest; CR = Cropland; PA = Pasture; Agric. = Cleared Agriculture (Cropland + Pasture); Harv. = Harvested agriculture (Plantation Forest + Cropland); Human = Human-dominated Land use (Plantation Forest + Cropland + Pasture).

Extended Data Fig. 4 Statistics for species richness models with different biome groupings.

Species richness was modelled as a function of both land use (using the finest division into five different land-use categories) and biome (using the different combinations as shown here). Shown are the model degrees of freedom (DF), difference in AIC compared with the best fitting model (ΔAIC), and the conditional and marginal R2 values72. The best-fitting model is shown in italics, while the biome combination used in the final models is shown in bold. BF = Boreal Forests/Taiga; TeCF = Temperate Conifer Forests; TeBF = Temperate Broadleaf and Mixed Forests; TrCF = Tropical and Subtropical Coniferous Forests; TrDBF = Tropical and Subtropical Dry Broadleaf Forests; TrMBF = Tropical and Subtropical Moist Broadleaf Forests; TeG = Temperate Grasslands, Savannas and Shrublands; TrG = Tropical and Subtropical Grasslands, Savannas and Shrublands; MoG = Montane Grasslands and Shrublands; MED = Mediterranean Forests, Woodlands and Scrub; DRY = Deserts and Xeric Shrublands; TeF = Temperate Forests (Coniferous and Broadleaf); TrF = Tropical Forests (Coniferous, Dry Broadleaf and Moist Broadleaf); TeMoG = Temperate and Montane Grasslands; Temp. = Temperate (Forests and Grasslands, including Montane Grasslands); Trop. = Tropical (Forests and Grasslands); NonTrop. = Non-Tropical (Boreal and Temperate Forest and Grasslands, including Montane Grasslands); For. = Forest (Boreal, Temperate and Tropical); Grass. = Grasslands (Temperate, Montane and Tropical).

Extended Data Fig. 5 Differences in total abundance among land-use types, across different biomes.

a) Tropical forest; b) Tropical grasslands; c) Drylands; d) Mediterranean; e) Temperate forest; and f) Temperate grasslands. Plots show the percentage change in species richness compared to primary vegetation (PV), in secondary vegetation (SV), pasture (PAS) and areas of harvested agriculture (woody plantations and herbaceous croplands; HARV). Error bars show 95% confidence intervals. Sample sizes at the bottom of each panel refer to the number of sites in each combination of land use and biome. The final model plotted here had an R2conditional of 0.89 and an R2marginal of 0.031.

Extended Data Fig. 6 Differences in community-average range size (RCAR) among land-use types, across different biomes.

a) Tropical forest; b) Tropical grasslands; c) Drylands; d) Mediterranean; e) Temperate forest; and f) Temperate grasslands. Plots show the percentage change in species richness compared to primary vegetation (PV), in secondary vegetation (SV), pasture (PAS) and areas of harvested agriculture (woody plantations and herbaceous croplands; HARV). Error bars show 95% confidence intervals. Sample sizes at the bottom of each panel refer to the number of sites in each combination of land use and biome. The final model plotted here had an R2conditional of 0.87 and an R2marginal of 0.10.

Extended Data Fig. 7 Sensitivity of biodiversity to climate change across biomes.

Shown is the predicted percentage change in species richness for each °C of climate warming expected under the RCP 2.6 scenario. Results for the RCP 8.5 scenario are shown in Fig. 2. Biomes considered were tropical forests (TrF), tropical grasslands (TrG), drylands (Dry), Mediterranean (Med), temperate forest (TeF), temperate grasslands (TeG) and boreal forest (BoF). Thick horizontal black lines show median values across all grid cells within the biome, boxes extend to the first and third quartiles, and whiskers to 1.5 × the inter-quartile range.

Extended Data Fig. 8 Overview of input datasets used.

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Newbold, T., Oppenheimer, P., Etard, A. et al. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat Ecol Evol 4, 1630–1638 (2020). https://doi.org/10.1038/s41559-020-01303-0

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