Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local ecological assemblages are responding is less clear—a concern given their importance for many ecosystem functions and services. We analysed a terrestrial assemblage database of unprecedented geographic and taxonomic coverage to quantify local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these pressures reduce within-sample species richness by an average of 76.5%, total abundance by 39.5% and rarefaction-based richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample richness is projected to fall by a further 3.4% globally by 2100, with losses concentrated in biodiverse but economically poor countries. Strong mitigation can deliver much more positive biodiversity changes (up to a 1.9% average increase) that are less strongly related to countries' socioeconomic status.
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We thank all the many researchers who have made their data available to us; S. Butchart and Birdlife International for sharing bird body-size data; F. Gilbert for hoverfly body-size data; the IMAGE, HYDE, MESSAGE and MiniCAM teams, especially R. Alkemade, M. Bakkenes and A. Thomson for sharing additional data from their integrated assessment models; D. Tittensor for statistical advice; C. Sleep and S. Patlola at the Natural History Museum in London for IT support with the database; members of the GARD initiative (http://www.gardinitiative.org/index.html) for help with estimating the reptile species richness map; K. Jones, J. Tylianakis, M. Crawley and E. J. Milner-Gulland for discussion, N. Burgess for comments on a draft of the paper. We also thank C. D. Thomas and two anonymous reviewers for very helpful comments on the manuscript. This study is part of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project, which is supported by the UK Natural Environment Research Council (NERC, grant number: NE/J011193/1), the Biotechnology and Biological Sciences Research Council (grant number: BB/F017324/1) a Hans Rausing PhD scholarship. The study was also supported by the TRY initiative on plant traits, whose database is maintained at Max-Planck-Institute for Biogeochemistry, Jena, Germany, and which is supported by DIVERSITAS, IGBP, the Global Land Project, NERC, the French Foundation for Biodiversity Research, and GIS ‘Climat, Environnement et Société’ France. This is a contribution from the Imperial College Grand Challenges in Ecosystem and the Environment Initiative.
The authors declare no competing financial interests.
Extended data figures and tables
a, The relationship between the number of species represented in our data with the number estimated to have been described17 for 47 major taxonomic groups. Lines show (from bottom to top) 0.1%, 1% and 10% representation of described species in our data set; magenta, invertebrates; red, vertebrates; green, plants; blue, fungi; and grey, all other taxonomic groups. b, The relationship across biomes400 between the percentage of global net primary production and the number of sites in our data set; A, tundra; B, boreal forests and taiga; C, temperate conifer forests; D, temperate broadleaf and mixed forests; E, montane grasslands and shrublands; F, temperate grasslands, savannahs and shrublands; G, Mediterranean forests, woodlands and scrub; H, deserts and xeric shrublands; J, tropical and subtropical grasslands, savannahs and shrublands; K, tropical and subtropical coniferous forests; M, tropical and subtropical dry broadleaf forests; N, tropical and subtropical moist broadleaf forests; P, mangroves; note that the flooded grasslands and savannah biome is not represented in the data set; grey line shows a 1:1 relationship.
a–i, Modelled effects (controlling for land use) of human population density (HPD), distance to nearest road, time since 30% conversion of a landscape to human uses (TSC) and time to nearest population centre with greater than 50,000 inhabitants (a–d), interactions between pairs of these variables (e), and interactions between these variables and land use (f–i) on site-level diversity. a–c, f, g, Within-sample species richness; e, h, i, total abundance; and d, community-weighted mean vertebrate body mass. Shaded polygons in a–d show 95% confidence intervals. For clarity, shaded polygons in f–i are shown as ±0.5 × s.e.m. Confidence intervals in e are omitted. Rugs along the x axes in the line graphs show the values of the explanatory variables represented in the data set used for modelling. Only significant effects are shown. Note that distance to nearest road and travel time to major population centre measures are the raw (log-transformed) values fitted in the models rather than the proximity to roads and accessibility values (obtained as 1 minus the former values) presented in Fig. 1. Sample sizes are given in full in the Methods.
a, Effects of land use and land-use intensity on rarefaction-based species richness. b, To test that any differences between these results and the results for within-sample species richness presented in the main manuscript were not because rarefied species richness could only be calculated with a smaller data set, we also show modelled effects on within-sample species richness with the same reduced data set. c–d, Cross-validated robustness of coefficient estimates for land use and land-use intensity. Crosses show 95% confidence intervals around the coefficient estimates under tenfold cross-validation, excluding data from approximately 10% of studies at a time (c), and under geographical cross-validation, excluding data from one biome at a time (d); colours, points, error bars and land-use labels are as in Fig. 1 in the main text. Primary, primary vegetation; YSV, young secondary vegetation; ISV, intermediate secondary vegetation; MSV, mature secondary vegetation; plantation, plantation forest. Sample sizes are given in full in the Methods.
Extended Data Figure 4 Tests of the potential for publication bias to influence the richness models and projections.
Left-hand panels (a, d, g, j, m) show funnel plots of the relationship between the standard error around coefficient estimates (inversely related to the size of studies) and the coefficient estimates themselves for each coarse land-use type; there is evidence for publication bias with respect to some of the land-use types, as indicated by an absence of points on one or other side of zero for studies with large standard errors (but note that small studies are down-weighted in the model). Red points show studies with more than five sites in the land use in question (ten for secondary vegetation and plantation forest because there were more sites for these land uses and some studies with between five and ten sites showed variable responses); horizontal dashed lines show the modelled coefficients for each land use. Central panels (b, e, h, k, n) show the relationship between study size (log-transformed total number of sites) and the random slope of the land use in question with respect to study identity, from a random-slopes-and-intercepts model. Where a significant relationship was detected using a linear model, fitted values and 95% confidence intervals are shown as a red dashed line and red dotted lines, respectively. Conversely to what would be expected if publication bias was present, where significant relationships between study size and random slopes were detected, these were negative (that is, larger studies detected more negative effects). Right-hand panels (c, f, i, l, o) show the robustness of modelled coefficients to removal of studies with few sites in a given land use (black points in the left-hand panels). Left-hand error bars show coefficient estimates for all studies and right-hand error bars show coefficient estimates for studies with more than five sites in that land use (ten for secondary vegetation and plantation forest).
a–d, For the four main modelled metrics of site-level diversity—within-sample species richness (a), total abundance (b), community-weighted mean plant-height (c) and community-weighted mean animal mass (d)—histograms of P values from sets of Moran's tests for spatial autocorrelation in the residuals of the best models for individual studies are shown. The percentage of studies with significant spatial autocorrelation (P < 0.05; indicated by a vertical red line) is shown.
a–d, Net change in local diversity caused by land use and related pressures by 2000 under an IMAGE reference scenario10. Changes in richness (a), rarefied richness (b), total abundance (c) and community-weighted mean plant height (d) are shown. Note that the values used to divide the colours are the same in all panels, but that the maximum and minimum values are different, as indicated in the legends. e–g, Historical and future estimates of net change in local diversity from 1500–2095, based on estimates of land-use, land-use intensity and human population density from the four RCP scenarios (Table 1). Net changes in richness (e), total abundance (f) and community-weighted mean plant height (g) are shown. Historical (shading) and future (error bars) uncertainty shown as 95% confidence intervals, with uncertainty rescaled to be zero in 2005 to show uncertainty in past and future change separately. The global average projection for the MESSAGE scenario does not directly join the historical reconstruction because projections start in 2010 (human population estimates are available at 15-year intervals) and because human population (and thus land-use intensity) and plantation forest extent have not been harmonized among scenarios. In panel e, the dashed line shows projected diversity change under land-use change only (that is, without land-use intensity and human population density, the projections of which involved simplifying assumptions), and the dotted line shows projections of rarefaction-based species richness.
Extended Data Figure 7 Reconstructed and projected total global land-use areas under the RCP scenarios.
a, Estimated total area of the major land-use types. b–f, estimated total area of secondary vegetation in different stages of recovery.
a–d, Country-level projections of net change in local richness between 2005 and 2095 under the four RCP scenarios (IMAGE 2.6 (a), MiniCAM 4.5 (b), AIM 6.0 (c) and MESSAGE 8.5 (d)), shown in relation to the Human Development Index (an indicator of education, life expectancy, wealth and standard of living) in the most recent year for which data are available. e, f, Country-level projections of net change in local richness between 2005 and 2095 under the best- and worst-performing RCP scenarios in terms of biodiversity (MiniCAM 4.5 (e) and MESSAGE 8.5 (f), respectively), shown in relation to past change in biodiversity from a baseline with no human land-use effects to 2005 according to the HYDE land-use reconstruction. Colours indicate biogeographic realms (key in b); colour intensity reflects native vertebrate species richness (more intense colour represents higher species richness); point size is proportional to (log) country area.
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Newbold, T., Hudson, L., Hill, S. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015). https://doi.org/10.1038/nature14324
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