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Ecosystem decay exacerbates biodiversity loss with habitat loss

Nature volume 584, pages 238–243 (2020)Cite this article

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Abstract

Although habitat loss is the predominant factor leading to biodiversity loss in the Anthropocene1,2, exactly how this loss manifests—and at which scales—remains a central debate3,4,5,6. The ‘passive sampling’ hypothesis suggests that species are lost in proportion to their abundance and distribution in the natural habitat7,8, whereas the ‘ecosystem decay’ hypothesis suggests that ecological processes change in smaller and more-isolated habitats such that more species are lost than would have been expected simply through loss of habitat alone9,10. Generalizable tests of these hypotheses have been limited by heterogeneous sampling designs and a narrow focus on estimates of species richness that are strongly dependent on scale. Here we analyse 123 studies of assemblage-level abundances of focal taxa taken from multiple habitat fragments of varying size to evaluate the influence of passive sampling and ecosystem decay on biodiversity loss. We found overall support for the ecosystem decay hypothesis. Across all studies, ecosystems and taxa, biodiversity estimates from smaller habitat fragments—when controlled for sampling effort—contain fewer individuals, fewer species and less-even communities than expected from a sample of larger fragments. However, the diversity loss due to ecosystem decay in some studies (for example, those in which habitat loss took place more than 100 years ago) was less than expected from the overall pattern, as a result of compositional turnover by species that were not originally present in the intact habitats. We conclude that the incorporation of non-passive effects of habitat loss on biodiversity change will improve biodiversity scenarios under future land use, and planning for habitat protection and restoration.

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Fig. 1: Conceptual illustration of the hypotheses and data structure.
Fig. 2: Ecosystem decay drives patterns of biodiversity loss in habitat fragments.
Fig. 3: Study-level variation in the response of species richness to habitat loss.
Fig. 4: Relationship between the species-richness response and the species compositional turnover and nestedness.

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Data availability

All of the data used in this analysis are open access and available in ref. 22 (117 of the datasets) and ref. 143 (5 of the datasets). Raw data (before standardization) are available from GitHub (https://github.com/FelixMay/FragFrame_1), and are mirrored on Zenodo (https://doi.org/10.5281/zenodo.3862409).

Code availability

The R code used for standardizing the data and doing the analyses presented here are available from GitHub (https://github.com/FelixMay/FragFrame_1), and are mirrored on Zenodo (https://doi.org/10.5281/zenodo.3862409).

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Acknowledgements

All authors were supported by the German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (funded by the German Research Foundation; FZT 118). The contribution of T.M.K. was also supported by the Helmholtz Association and by the Alexander von Humboldt Foundation. We thank the many authors who supplied the data that went into the core analyses of this paper, and A. Sagouis and M. Liebergesell for help with data acquisition, collation and harmonization. A. Sagouis helped with the preparation of the simulations in Extended Data Fig. 1, and Fig. 1 was created by F. Arndt (Formenorm.de) for the express use in this paper. Finally, we thank R. Colwell and J. Hortal for important comments and criticisms that helped us to improve the manuscript.

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Authors and Affiliations

  1. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Jonathan M. Chase, Shane A. Blowes, Tiffany M. Knight, Katharina Gerstner & Felix May

  2. Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Jonathan M. Chase & Shane A. Blowes

  3. Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Tiffany M. Knight

  4. Department of Community Ecology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany

    Tiffany M. Knight

  5. Leuphana University, Lüneburg, Germany

    Felix May

  6. Theoretical Ecology, Institute of Biology, Freie Universität Berlin, Berlin, Germany

    Felix May

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Contributions

J.M.C. and T.M.K. conceived the project; J.M.C., K.G. and F.M. developed the initial protocol for data collation and hypothesis tests; J.M.C., F.M. and S.A.B. organized and cleaned the data; F.M. and S.A.B. performed the analyses; J.M.C. wrote the first draft, and all authors contributed to revisions.

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Peer review information Nature thanks Robert Colwell, Joaquin Hortal, James O’Dwyer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Simulations of the null expectation under the random sampling hypothesis for varying degrees of within-species aggregation.

To evaluate the robustness of the null expectation of a zero slope of biodiversity patterns in standardized samples with fragment size, we took different-sized samples from a simulated landscape and estimated biodiversity patterns. a, Examples of landscapes with different levels of intraspecific aggregation (left to right is from completely random to most-aggregated). From within each of these landscapes, we illustrate four different fragment sizes (shaded squares), and then take standardized (constant-sized) samples from each fragment. b, Density plots showing slope estimates of linear models fit to numbers of individuals (top), species richness (centre) and evenness (bottom) as a function of fragment areas for 2,000 simulated landscapes of each level of aggregation. Both the response and fragment area were log-transformed before model fitting. Densities are shaded by quantiles and the black diamond shows the median for each combination of aggregation and metric; vertical dashed line shows the zero expectation and the median result is given. In some cases, the median result lies very slightly above or below zero (though this does not seem to be associated with levels of aggregation). This is an outcome of the stochastic simulation we performed, and is sensitive to parameters and numbers of iterations, and thus we do not perform statistical tests.

Extended Data Fig. 2 Different measures of species richness related to the size of habitat fragments.

For each case, richness metrics were positively associated with the size of habitat fragments, supporting ecosystem decay as the predominant driver. a, Richness standardized to a common number of individuals. b, Richness standardized to a common sample completeness. c, Asymptotic richness. Solid black lines and shading show overall relationships and 95% credible intervals for each metric. The slope (β) coefficient and its 95% credible interval are shown at the top. Coloured lines show study-level relationships for taxon groups.

Extended Data Fig. 3 Incorporation of uncertainty by calculating z-scores of observed versus null-expected outcomes.

Because there is always uncertainty surrounding the expected outcomes based on passive sampling, we repeated analyses of all metrics recalculated as z-scores. After standardization, z-scores ((observed − expected)/s.d.(expected)) were calculated for the following. a, Standardized species richness. b, Standardized evenness (SPIE). c, Richness standardized to a common number of individuals. d, Richness standardized to a common sample completeness. e, Asymptotic richness. As with the direct measurements, analyses of z-scores show—in total—greater biodiversity loss than expected from passive sampling in smaller habitat fragments, and thus support the ecosystem decay hypothesis. Solid black lines and shading show overall relationships and 95% credible intervals for each metric. Inset shows β-slope coefficient and its 95% credible interval. Coloured lines show study-level relationships for taxon groups. The β-coefficients are not directly comparable to the results from Fig. 2 and Extended Data Fig. 2, owing to differences in the response scale of the z-scores.

Extended Data Fig. 4 Testing of robustness of results to alternative methods.

a, Testing the sensitivity of our results to the exclusion of 47 studies in which data were pooled, and thus sample extent could not be controlled (Methods). Analyses on this subset of data (n = 79) were consistent with those from the full dataset (that is, the 95% credible intervals did not overlap zero). b–d, Testing the sensitivity of the results to decisions we made when imputing missing sizes of habitats labelled as continuous and the treatment of non-integer species abundances (Methods). In each case, the reference scenario imputed the size of continuous habitats to have 10× the area of the next largest fragment and calculated the biodiversity metrics using the non-integer abundance values. Alternate combinations were: (1) imputed area for continuous habitats assumed to be 2× that of next biggest fragment, and non-integer values unchanged; (2) imputed area for continuous habitats assumed to be 100× that of the next biggest fragment, and non-integer values unchanged; (3) imputed area for continuous habitats assumed to be 10× that of the next biggest fragment, and non-integer values rounded up; (4) imputed area for continuous habitats assumed to be 10× that of the next biggest fragment and all abundance values divided by the lowest value within each study, resulting in the lowest abundance equalling one, but retaining the same relative abundances. b, Slope estimates for the relationship between standardized numbers of individuals and fragment size. c, Slope estimates for the relationship between standardized species richness and fragment size. d, Slope estimates for the relationship between standardized evenness and fragment size. Colours depict different auxiliary decisions and imputation required in the analysis (Methods). Small points represent study-level estimates, and large points and error bars are the overall estimates and their 95% credible intervals.

Extended Data Fig. 5 Study-level variation in the number of individuals and evenness.

a–h, Density plots of posterior distributions of study-level slope estimates for total abundance (a–d) and for evenness (SPIE) (e–h). Groupings are by taxon group (a, e), continent (b, f), time since fragmentation (c, g) and matrix filter (d, h). Each density plot is based on 1,000 samples from the posterior distribution of each study-level slope estimate, and is accompanied by the number of studies for each group. Densities are shaded by quantiles and the black diamond shows the median for each group. Solid black line and surrounding shading show the overall slope estimate and its 95% credible interval.

Extended Data Fig. 6 Study-level slope estimates with latitude.

We found that there was a weak negative signal between study-level slope and the absolute value of latitude, which suggests that the influence of ecosystem decay becomes stronger towards the tropics. Each point shows the study-level slope estimate from the standardized species richness as a function of fragment size. Vertical bars are the 95% credible interval associated with each study-level slope estimate. Solid black line and shading shows the relationship and 95% credible interval between the slope estimates and absolute latitude.

Extended Data Fig. 7 Relationship between size of habitat fragment and species composition.

Overall, we found that turnover contributes more than nestedness to pairwise dissimilarity between fragments within a study, but shows contrasting patterns with increasing fragment size differences. a, Turnover component of Jaccard dissimilarity. b, Turnover or balanced abundance component of Ruzicka dissimilarity. c, Nestedness component of Jaccard dissimilarity. d, Nestedness or abundance gradient component of Ruzicka dissimilarity. Solid black lines and shading show overall relationship and 95% credible interval between each dissimilarity component and the ratio of fragment sizes. Coloured lines show study-level relationships for taxon groups.

Extended Data Fig. 8 Endemics–area relationships.

Here, we illustrate the number of species expected to be lost as a function of area of habitat lost under the typically assumed passive sampling hypothesis (purple), and the number of species expected to be lost with habitat lost by inputting our observed parameters from the effect of ecosystem decay (orange).

Extended Data Table 1 Models of standardized species richness (Sstd)
Full size table
Extended Data Table 2 Models of the standardized number of individuals (Nstd)
Full size table
Extended Data Table 3 Models of standardized evenness (SPIE)
Full size table

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Chase, J.M., Blowes, S.A., Knight, T.M. et al. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020). https://doi.org/10.1038/s41586-020-2531-2

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