Wilderness areas halve the extinction risk of terrestrial biodiversity

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Abstract

Reducing the rate of global biodiversity loss is a major challenge facing humanity1, as the consequences of biological annihilation would be irreversible for humankind2,3,4. Although the ongoing degradation of ecosystems5,6 and the extinction of species that comprise them7,8 are now well-documented, little is known about the role that remaining wilderness areas have in mitigating the global biodiversity crisis. Here we model the persistence probability of biodiversity, combining habitat condition with spatial variation in species composition, to show that retaining these remaining wilderness areas is essential for the international conservation agenda. Wilderness areas act as a buffer against species loss, as the extinction risk for species within wilderness communities is—on average—less than half that of species in non-wilderness communities. Although all wilderness areas have an intrinsic conservation value9,10, we identify the areas on every continent that make the highest relative contribution to the persistence of biodiversity. Alarmingly, these areas—in which habitat loss would have a more-marked effect on biodiversity—are poorly protected. Given globally high rates of wilderness loss10, these areas urgently require targeted protection to ensure the long-term persistence of biodiversity, alongside efforts to protect and restore more-degraded environments.

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Fig. 1: Global probabilities of species extinction for communities of invertebrates and vascular plants associated with 1-km2 grid cells.
Fig. 2: Relative contribution of wilderness areas to the persistence of plant and invertebrate communities.
Fig. 3: Relative contribution of each wilderness grid cell to the estimated probability of persistence of species within invertebrate and vascular plant communities.

Data availability

All input data used in these analyses derive from published sources cited in the Methods. Extended Data Table 1, 2 and Supplementary Table 1 report the results for each realm and each wilderness block. Any other datasets generated in the current study are available from the corresponding author upon reasonable request.

Code availability

R code for deriving estimates of compositional dissimilarity and the proportion of persisting species is available from ref. 17.

References

  1. 1.

    Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).

  2. 2.

    Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).

  3. 3.

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

  4. 4.

    Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl Acad. Sci. USA 114, E6089–E6096 (2017).

  5. 5.

    Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).

  6. 6.

    Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).

  7. 7.

    Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).

  8. 8.

    Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).

  9. 9.

    Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl Acad. Sci. USA 100, 10309–10313 (2003).

  10. 10.

    Watson, J. E. M. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).

  11. 11.

    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).

  12. 12.

    Watson, J. E. M. et al. Protect the last of the wild. Nature 563, 27–30 (2018).

  13. 13.

    Clark, J. A. & May, R. M. Taxonomic bias in conservation research. Science 297, 191–192 (2002).

  14. 14.

    Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).

  15. 15.

    Chapman, A. D. Numbers of Living Species in Australia and the World, http://www.environment.gov.au/biodiversity/abrs/publications/other/species-numbers/2009/06-references.html (Report for the Australian Biological Resources Study, Canberra, 2009).

  16. 16.

    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).

  17. 17.

    Hoskins, A. J. et al. Supporting global biodiversity assessment through high-resolution macroecological modelling: methodological underpinnings of the BILBI framework. Preprint at https://www.biorxiv.org/content/10.1101/309377v3 (2019).

  18. 18.

    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).

  19. 19.

    Ferrier, S. et al. Mapping more of terrestrial biodiversity for global conservation assessment. Bioscience 54, 1101–1109 (2004).

  20. 20.

    Allnutt, T. F. et al. A method for quantifying biodiversity loss and its application to a 50-year record of deforestation across Madagascar. Conserv. Lett. 1, 173–181 (2008).

  21. 21.

    Di Marco, M. et al. Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling. Glob. Chang. Biol. 25, 2763–2778 (2019).

  22. 22.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933–938 (2001).

  23. 23.

    Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. L. Southeast Asian biodiversity: an impending disaster. Trends Ecol. Evol. 19, 654–660 (2004).

  24. 24.

    IUCN & UNEP–WCMC. The World Database on Protected Areas (WDPA) version July/2018, www.protectedplanet.net (UNEP–WCMC, Cambridge, 2018).

  25. 25.

    Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).

  26. 26.

    Gorenflo, L. J., Romaine, S., Mittermeier, R. A. & Walker-Painemilla, K. Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas. Proc. Natl Acad. Sci. USA 109, 8032–8037 (2012).

  27. 27.

    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).

  28. 28.

    CBD. Strategic Plan for Biodiversity 2011–2020 (CBD, 2010).

  29. 29.

    Mappin, B. et al. Restoration priorities to achieve the global protected area target. Conserv. Lett. 12,e12646 (2019).

  30. 30.

    United Nations General Assembly. Transforming our World: the 2030 Agenda for Sustainable Development, A/RES/70/1 (United Nations General Assembly, 2015).

  31. 31.

    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).

  32. 32.

    Leclère, D. et al. Towards Pathways Bending the Curve of Terrestrial Biodiversity Trends Within the 21st Century (IIASA, 2018).

  33. 33.

    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).

  34. 34.

    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).

  35. 35.

    Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).

  36. 36.

    Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).

  37. 37.

    Ferrier, S., Harwood, T., Williams, K. J. & Dunlop, M. Using Generalised Dissimilarity Modelling to Assess Potential Impacts of Climate Change on Biodiversity Composition in Australia, and on the Representativeness of the National Reserve System (CSIRO Climate Adaption Flagship Working Paper Series 13E) (CSIRO, Canberra, 2012).

  38. 38.

    Hoskins, A. J. et al. Downscaling land-use data to provide global 30′′ estimates of five land-use classes. Ecol. Evol. 6, 3040–3055 (2016).

  39. 39.

    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).

  40. 40.

    European Commission Joint Research Centre & Columbia University Center for International Earth Science Information Network. GHS Population Grid, derived from GPW4, Multitemporal (1975, 1990, 2000, 2015), http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a (2015).

  41. 41.

    Pesaresi, M. et al. GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014) https://ec.europa.eu/jrc/en/publication/ghs-built-grid-derived-landsat-multitemporal-1975-1990-2000-2014-ir2017-v10 (2015).

  42. 42.

    DiMiceli, C. M. et al. Annual global automated MODIS vegetation continuous fields (MOD44B) at 250 m spatial resolution for data years beginning day 65, 2000–2010 (2011).

  43. 43.

    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).

  44. 44.

    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://www.biorxiv.org/content/10.1101/311787v1 (2018).

  45. 45.

    Drielsma, M., Ferrier, S. & Manion, G. A raster-based technique for analysing habitat configuration: the cost-benefit approach. Ecol. Modell. 202, 324–332 (2007).

  46. 46.

    Chaudhary, A. & Mooers, A. Terrestrial vertebrate biodiversity loss under future global land use change scenarios. Sustainability 10, 2764 (2018).

  47. 47.

    Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).

  48. 48.

    Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).

  49. 49.

    Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (Academic, 1988).

  50. 50.

    GRASS Development Team. Geographic resources analysis support system (GRASS GIS) software, version 7.2, http://grass.osgeo.org (2017).

  51. 51.

    QGIS Development Team. QGIS geographic information system, http://qgis.osgeo.org (2017).

  52. 52.

    R Core Team. R: A Language and Environment for Statistical Computing, https://www.r-project.org/ (2018).

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Acknowledgements

This work was funded by Research Agreement no. 2017113325 between CSIRO and the University of Queensland. M.D.M. acknowledges support from the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement no. 793212).

Author information

M.D.M., S.F. and J.E.M.W. framed the study. M.D.M., T.D.H. and A.J.H. carried out the analyses. M.D.M., S.F., T.D.H., A.J.H. and J.E.M.W. discussed and interpreted the results. M.D.M., S.F. and J.E.M.W. wrote the manuscript with support from T.D.H. and A.J.H.

Correspondence to Moreno Di Marco.

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Competing interests

The authors declare no competing interests.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks Elizabeth Boakes, Samantha Hill and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Global-scale probabilities of species extinction for communities of vascular plants associated with each grid cell.

The underlying map reports the estimated proportion of native species—originally associated with a particular grid cell—that are expected to disappear from their distribution, owing to the current condition of the habitats in which they occur.

Extended Data Fig. 2 Global-scale probabilities of species extinction for communities of invertebrates associated with each grid cell.

The underlying map reports the estimated proportion of native species—originally associated with a particular grid cell—that are expected to disappear from their distribution, owing to the current condition of the habitats in which they occur.

Extended Data Fig. 3 Global-scale probabilities of species extinction for communities of invertebrates and vascular plants associated with each grid cell, accounting for habitat connectivity.

The underlying map reports the estimated proportion of native species—originally associated with a particular grid cell—that are expected to disappear from their distribution (owing to the current condition of the habitats in which they occur, as well as the level of connectivity between habitats).

Extended Data Fig. 4 Distribution of the top-five blocks of wilderness identified for each realm.

Numbers in the map report the identifier codes for the block (corresponding to Supplementary Table 1).

Extended Data Fig. 5 Frequency distribution of the contributions that individual wilderness grid cells make to the probability of persistence of invertebrate and vascular plant communities (δp).

The histogram bars represent the relative frequency distribution of the δp values for wilderness pixels inside (blue bars) and outside (grey bars) protected areas, in each biogeographical realm.

Extended Data Fig. 6 Analytical framework used to estimate the probability of persistence of biological communities.

The framework combines estimates of spatial turnover in species composition (from which ecologically scaled environments are derived) with estimates of habitat condition. The framework produces a spatially explicit (1 km2) estimate of biodiversity persistence, from which a number of metrics are derived: the proportion of species committed to extinction, the contribution of wilderness areas to global species persistence, and the potential reduction in persistence in case of wilderness degradation.

Extended Data Table 1 Mean extinction risk (with s.d. in parentheses) observed across communities of invertebrates and vascular plants in each biogeographical realm, inside and outside wilderness areas
Extended Data Table 2 Difference in the estimated reduction of global species persistence (δp) associated with the loss of a protected or non-protected wilderness pixel

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