Global effects of land use on local terrestrial biodiversity

Journal name:
Nature
Volume:
520,
Pages:
45–50
Date published:
DOI:
doi:10.1038/nature14324
Received
Accepted
Published online

Abstract

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.

At a glance

Figures

  1. Locations of sites and responses of four metrics of local diversity to human pressures.
    Figure 1: Locations of sites and responses of four metrics of local diversity to human pressures.

    a, Sites used in the models. bd, Responses44 of richness (b), total abundance (c) and community-weighted mean (CWM) organism size—plant height (crosses) and animal mass (triangles)—(d) to anthropogenic variables. Error bars show 95% confidence intervals. Primary, primary vegetation; YSV, young secondary vegetation; ISV, intermediate secondary vegetation; MSV, mature secondary vegetation; plantation, plantation forest. Land-use intensity is categorized as minimal (circle), light (triangle), intense (diamond), or combined light and intense (square). HPD, human population density45; PR, proximity to roads46 (as −log(distance to nearest road)); and ACC, accessibility to humans47 (as −log(travel time to nearest major city)), are shown as fitted effects from a model with no interactions between continuous effects and land use, at the lowest (L), median (M) and highest (H) values in the data set. Sample sizes are given in full in the Methods.

  2. Similarity in assemblage composition as a function of land use.
    Figure 2: Similarity in assemblage composition as a function of land use.

    a, Average dissimilarity of species composition (1 − Sørenson Index) between pairs of sites within and among land uses (shown relative to the similarity between pairs of primary-vegetation sites); blue and red colours indicate, respectively, more or less similar composition; numbers indicate numbers of studies within which comparisons could be made. b, Clustering of land-use types based on average compositional dissimilarity; urban sites were excluded owing to the small sample size.

  3. Net change in local richness caused by land use and related pressures by 2000.
    Figure 3: Net change in local richness caused by land use and related pressures by 2000.

    Projections used an IMAGE reference scenario10. The baseline landscape was assumed to be entirely uninhabited, unused primary vegetation. Shown using a Lambert Cylindrical Equal-Area projection at 0.5° × 0.5° resolution.

  4. Projected net change in local richness from 1500 to 2095.
    Figure 4: Projected net change in local richness from 1500 to 2095.

    Future projections were based on the four RCP scenarios (Table 1). Historical (shading) and future (error bars) uncertainty is shown as 95% confidence intervals, rescaled to zero in 2005. The baseline for projections is a world entirely composed of uninhabited, unused primary vegetation; thus, the value at 1500 is not constrained to be zero because by then non-primary land uses were present (and in some regions widespread). The global average projection for MESSAGE 8.5 does not join the historical reconstruction because that scenario's human population projections start in 2010 and because human population and plantation forest extent have not been harmonized among scenarios.

  5. Biodiversity projections at the country level.
    Figure 5: Biodiversity projections at the country level.

    a, b, Country-level projections of average net local richness change between 2005 and 2095 under the worst (a, MESSAGE 8.5) and best (b, MiniCAM 4.5) RCP scenarios for biodiversity, shown in relation to countries’ Human Development Index. Colours indicate biogeographic realms; colour intensity reflects natural vertebrate species richness (more intense colour represents higher richness); point diameter is proportional to (log) country area. c, Correlation between projected richness changes under the MiniCAM 4.5 and MESSAGE 8.5 scenarios, with dashed line showing equality; colours as in a and b; colour intensity is proportional to the Human Development Index (more intense colour represents higher index).

  6. Taxonomic and geographic representativeness of the data set used.
    Extended Data Fig. 1: Taxonomic and geographic representativeness of the data set used.

    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.

  7. Detailed response of local diversity to human pressures.
    Extended Data Fig. 2: Detailed response of local diversity to human pressures.

    ai, 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 (ad), interactions between pairs of these variables (e), and interactions between these variables and land use (fi) on site-level diversity. ac, f, g, Within-sample species richness; e, h, i, total abundance; and d, community-weighted mean vertebrate body mass. Shaded polygons in ad show 95% confidence intervals. For clarity, shaded polygons in fi 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.

  8. Robustness of modelled effects of human pressures.
    Extended Data Fig. 3: Robustness of modelled effects of human pressures.

    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. cd, 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.

  9. Tests of the potential for publication bias to influence the richness models and projections.
    Extended Data Fig. 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).

  10. Tests for spatial autocorrelation in the model residuals.
    Extended Data Fig. 5: Tests for spatial autocorrelation in the model residuals.

    ad, 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.

  11. Current, past and future projections of all metrics of local biodiversity.
    Extended Data Fig. 6: Current, past and future projections of all metrics of local biodiversity.

    ad, 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. eg, 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.

  12. Reconstructed and projected total global land-use areas under the RCP scenarios.
    Extended Data Fig. 7: Reconstructed and projected total global land-use areas under the RCP scenarios.

    a, Estimated total area of the major land-use types. bf, estimated total area of secondary vegetation in different stages of recovery.

  13. Biodiversity projections at the country level.
    Extended Data Fig. 8: Biodiversity projections at the country level.

    ad, 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.

Tables

  1. Land use and land-use intensity classification definitions (from ref. 16)
    Extended Data Table 1: Land use and land-use intensity classification definitions (from ref. 16)
  2. Conversion between Global Land Systems data set and our intensity classification for each major land-use type.
    Extended Data Table 2: Conversion between Global Land Systems data set and our intensity classification for each major land-use type.

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Author information

  1. These authors contributed equally to this work.

    • Tim Newbold &
    • Lawrence N. Hudson
  2. Present addresses: Department of Animal and Plant Sciences, University of Sheffield, Alfred Denny Building, Western Bank, Sheffield S10 2TN, UK (R.A.S.); Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK and Institute of Zoology, Zoological Society of London, London NW1 4RY, UK (D.B.); College of Life and Environmental Sciences, Hatherly Laboratories, University of Exeter, Prince of Wales Road, Exeter EX4 4PS, UK (J.D.); School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK (D.J.I.); School of Biological and Ecological Sciences, University of Stirling, Stirling FK9 4LA, UK (L.K.); School of Biological Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK (H.J.W.).

    • Rebecca A. Senior,
    • Dominic J. Bennett,
    • Julie Day,
    • Daniel J. Ingram,
    • Lucinda Kirkpatrick &
    • Hannah J. White

Affiliations

  1. United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 0DL, UK.

    • Tim Newbold,
    • Samantha L. L. Hill,
    • Rebecca A. Senior &
    • Jörn P. W. Scharlemann
  2. Computational Science Laboratory, Microsoft Research Cambridge, 21 Station Road, Cambridge CB1 2FB, UK.

    • Tim Newbold &
    • Drew W. Purves
  3. Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK.

    • Lawrence N. Hudson,
    • Samantha L. L. Hill,
    • Sara Contu,
    • Argyrios Choimes,
    • Adriana De Palma,
    • Susy Echeverria-Londoño,
    • Melanie J. Edgar,
    • David Laginha Pinto Correia,
    • Helen R. P. Phillips &
    • Andy Purvis
  4. Department of Life Sciences, Imperial College London, Silwood Park, London SL5 7PY, UK.

    • Igor Lysenko,
    • Dominic J. Bennett,
    • Argyrios Choimes,
    • Julie Day,
    • Adriana De Palma,
    • Morgan Garon,
    • Michelle L. K. Harrison,
    • Tamera Alhusseini,
    • Daniel J. Ingram,
    • Victoria Kemp,
    • Lucinda Kirkpatrick,
    • Callum D. Martin,
    • Yuan Pan,
    • Helen R. P. Phillips,
    • Alexandra Robinson,
    • Jake Simpson,
    • Hannah J. White,
    • Robert M. Ewers &
    • Andy Purvis
  5. Department of Biosciences, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK.

    • Luca Börger
  6. Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, University College London, Gower Street, London WC1E 6BT, UK.

    • Ben Collen &
    • Georgina M. Mace
  7. Instituto Multidisciplinario de Biología Vegetal (CONICET-UNC) and FCEFyN, Universidad Nacional de Córdoba, Casilla de Correo 495, 5000 Córdoba, Argentina.

    • Sandra Díaz
  8. Deptartment of Zoology, Faculty of Life Sciences, Tel-Aviv University, 6997801 Tel Aviv, Israel.

    • Anat Feldman,
    • Yuval Itescu,
    • Shai Meiri &
    • Maria Novosolov
  9. Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, 07743 Jena, Germany.

    • Jens Kattge
  10. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.

    • Jens Kattge
  11. Landscape Ecology Group, Institute of Biology and Environmental Sciences, University of Oldenburg, D-26111 Oldenburg, Germany.

    • Michael Kleyer
  12. Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK.

    • Sean L. Tuck
  13. Biology Department, University of Wisconsin–Eau Claire, Eau Claire, Wisconsin 54701, USA.

    • Evan Weiher
  14. School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK.

    • Jörn P. W. Scharlemann

Contributions

T.N., L.N.H., S.L.L.H., S.C., I.L., B.C., D.W.P., R.M.E., G.M.M., J.P.W.S. and A.P. designed the project and this study; T.N., L.N.H., I.L., R.A.S., L.B., J.P.W.S. and A.P. performed the analyses; T.N., L.N.H., S.L.L.H., S.C., D.J.B., A.C., B.C., J.D., A.D.P., S.E.-L., M.G., M.L.K.H., T.A., D.J.I., V.K., L.K., D.L.P.C., C.D.M., Y.P., H.R.P.P., A.R., J.S., H.J.W. and A.P. collated the assemblage composition data; T.N., L.N.H., S.L.L.H., S.C., A.D.P., I.L., H.R.P.P., J.P.W.S. and A.P. designed the data-collection protocols and database; R.A.S., S.D., M.J.E., A.F., Y.I., J.K., M.K., S.M. and E.W. made substantial contributions to the trait data used in the analyses and S.L.T. to the site-level environmental data; R.A.S., A.F., Y.I., S.M., and M.N. generated the maps of species richness used in the model projections; T.N., L.N.H. and A.P. wrote the manuscript with contributions from G.M.M., L.B., D.W.P., R.M.E., A.D.P., H.R.P.P., S.L.L.H., R.A.S., B.C., S.D., A.F., Y.I., J.K., M.K., S.M., J.P.W.S and S.L.T.; T.N. and L.N.H. contributed equally to the study.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Taxonomic and geographic representativeness of the data set used. (142 KB)

    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.

  2. Extended Data Figure 2: Detailed response of local diversity to human pressures. (384 KB)

    ai, 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 (ad), interactions between pairs of these variables (e), and interactions between these variables and land use (fi) on site-level diversity. ac, f, g, Within-sample species richness; e, h, i, total abundance; and d, community-weighted mean vertebrate body mass. Shaded polygons in ad show 95% confidence intervals. For clarity, shaded polygons in fi 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.

  3. Extended Data Figure 3: Robustness of modelled effects of human pressures. (199 KB)

    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. cd, 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.

  4. Extended Data Figure 4: Tests of the potential for publication bias to influence the richness models and projections. (401 KB)

    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).

  5. Extended Data Figure 5: Tests for spatial autocorrelation in the model residuals. (126 KB)

    ad, 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.

  6. Extended Data Figure 6: Current, past and future projections of all metrics of local biodiversity. (420 KB)

    ad, 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. eg, 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.

  7. Extended Data Figure 7: Reconstructed and projected total global land-use areas under the RCP scenarios. (206 KB)

    a, Estimated total area of the major land-use types. bf, estimated total area of secondary vegetation in different stages of recovery.

  8. Extended Data Figure 8: Biodiversity projections at the country level. (558 KB)

    ad, 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.

Extended Data Tables

  1. Extended Data Table 1: Land use and land-use intensity classification definitions (from ref. 16) (299 KB)
  2. Extended Data Table 2: Conversion between Global Land Systems data set and our intensity classification for each major land-use type. (271 KB)

Supplementary information

PDF files

  1. Supplementary Information (213 KB)

    This file contains Supplementary Text and Data and Supplementary Table 1.

Comments

  1. Report this comment #65903

    Dr. PRABHAT KUMAR RAI said:

    Species decline due to Land use Change: Interrelationship with plant invasion and climate change
    Prabhat Kumar Rai

    In his recent paper Newbold1 (Nature 520, 45-50; 2015) demonstrated that land use changes decline species richness in multifaceted aspects. Land use changes also exacerbate ecosystem functioning through plant invasion in an intricate and complex manner 2. Land use change is one of the factors, instead of acting as drivers, merely act as passengers along for the invasion as well as climate change ride and hence species richness decline. Climate change may be also inextricably linked with species richness decline along with land use changes2. Several studies demonstrated that invasion may act in concert with climate or land use change. In an integrated study3 on the islands of Kauai and Hawaii showed that anthropogenic climate change is likely to combine with past land-use changes and biological invasions to drive several of the remaining species to extinction. Climate and land use change also transmogrify the invasion process2, 4. In nutshell, land use policies must evolve in tandem with the changes induced by invasion5 and climate change.

    References
    1. Newbold et al. Nature 520 45-50 (2015).
    2. Rai, P.K. Nova Science Publisher, New York, pp. 196 (2013).
    3. Benning, T.L., Lapointe, D., Atkinson, C.T., Vitousek, P.M. 2002. Proc. Nati. Acad Sci. 99 (22), 14246?14249 (2002).
    4. Sala, O.E et al.Science 287 1770?1774 (2000).
    5. Tassin, J., Kull, C.A. Land Use Policy 42 165-169 (2015).

    Prabhat Kumar Rai
    Department of Environmental Science
    School of Earth Science and Natural Resource Management
    Mizoram University, Aizawl, India
    Email: prabhatrai24@gmail.com
    pkrai@mzu.edu.in


    Response of Author
    Dear Dr. Rai,

    You are correct that the effects of biodiversity loss on ecosystem functioning are of course complex. We were only trying to suggest in our paper that land-use-driven biodiversity loss might influence ecosystem functioning. There is a lot more work required to properly assess whether this is the case and to work out how biodiversity loss causes loss of ecosystem functioning.

    Kind regards,
    Tim

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