Reductions in global biodiversity loss predicted from conservation spending

  • A Corrigendum to this article was published on 13 December 2017

Abstract

Halting global biodiversity loss is central to the Convention on Biological Diversity and United Nations Sustainable Development Goals1,2, but success to date has been very limited3,4,5. A critical determinant of success in achieving these goals is the financing that is committed to maintaining biodiversity6,7,8,9; however, financing decisions are hindered by considerable uncertainty over the likely impact of any conservation investment6,7,8,9. For greater effectiveness, we need an evidence-based model10,11,12 that shows how conservation spending quantitatively reduces the rate of biodiversity loss. Here we demonstrate such a model, and empirically quantify how conservation investment reduced biodiversity loss in 109 countries (signatories to the Convention on Biological Diversity and Sustainable Development Goals), by a median average of 29% per country between 1996 and 2008 We also show that biodiversity changes in signatory countries can be predicted with high accuracy, using a dual model that balances the effects of conservation investment against those of economic, agricultural and population growth (human development pressures)13,14,15,16,17,18. Decision-makers can use this model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, and then compare these forecasts to any chosen policy target. We find that the impact of spending decreases as human development pressures grow, which implies that funding may need to increase over time. The model offers a flexible tool for balancing the Sustainable Development Goals of human development and maintaining biodiversity, by predicting the dynamic changes in conservation finance that will be needed as human development proceeds.

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Figure 1: Global biodiversity declines and the effects of conservation spending.
Figure 2: The country-scale rate of biodiversity decline (BDS) depends on conservation spending levels.
Figure 3: Conditional influence of human pressures on biodiversity.

Change history

  • 13 December 2017

    Please see accompanying Corrigendum (http://doi.org/10.1038/nature25017). The sentence “Here we demonstrate such a model, and empirically quantify how conservation investment between 1996 and 2008 reduced biodiversity loss in 109 countries (signatories to the Convention on Biological Diversity and Sustainable Development Goals), by a median average of 29% per country” has been corrected to “Here we demonstrate such a model, and empirically quantify how conservation investment reduced biodiversity loss in 109 countries (signatories to the Convention on Biological Diversity and Sustainable Development Goals), by a median average of 29% per country between 1996 and 2008”.

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Acknowledgements

This research was supported by UKDWP (A.W.), the USDA National Institute of Food and Agriculture Hatch project 1009327 (D.C.M. and A.W.), the MacArthur Foundation through the Advancing Conservation in a Social Context research initiative (D.C.M. and J.T.R.), Natural Sciences and Engineering Research Council Canada Discovery and Accelerator Grants (A.M.), the UK Natural Environment Research Council grants NE/I028068/1 and NE/K016431/1 (J.A.T.) and the Odum School of Ecology (J.L.G.). We thank J. Drake, P. Holland and P. Stephens for comments on earlier manuscripts.

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A.W. conceived the study and analysed the data, based on ideas from A.M., J.L.G. and D.C.M.; A.W., D.C.M., D.R., N.N. and J.T.R. collected the data; A.W., A.M., T.S.K., D.C.M., J.L.G. and J.A.T. wrote the paper with contributions from all authors.

Corresponding author

Correspondence to Anthony Waldron.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks A. Balmford, H. Possingham and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Figure 1 The mean BDS per species for each country (BDS per total fractional species richness, expressed as a percentage).

Dark red, ≥5%; dark orange–red, 2.5–5%; orange, 1–2.5%; pale yellow, 0–1%; grey, 0%; blue, improving (negative percentage); light grey hatching, cannot be calculated (zeroes in the denominator). Note that in more species-poor countries (for example, much of Europe, North Africa and the Middle East), zeroes are expected at random (see Supplementary methods). See Supplementary Table 1 for precise values per country. Country outlines from esri_dm (https://www.arcgis.com/home/item.html?id=d86e32ea12a64727b9e94d6f820123a2#overview).

Extended Data Figure 2 The context-specific effect of agricultural expansion on decline.

In the binomial part of the model (n = 109 independent countries), the effect of agricultural expansion on decline depends on governance improvement and on the pre-existing percentage of land converted to agricultural use. The effect (coefficient) of agricultural expansion is shown on the y axis and varies with the rate of governance improvement on the x axis. Coefficients >0 (above the dashed line) indicate that agricultural growth increases the probability of a decline occurring; coefficients <0 indicate that agricultural growth decreases the probability of a decline occurring. The coefficient also depends on a second moderator, the percentage of land already converted to agriculture: red, 50th percentile of percentage of land converted; grey, 25th percentile; lines show mean, and coloured bands show conditional 95% confidence intervals. The effects of agricultural expansion are most strongly deleterious on land bases that are less heavily converted to agriculture overall. Rug plot along the bottom shows empirical distribution of x-axis values (but note that countries with higher percentages of agricultural land generally have slower rates of governance improvement). All variables are z standardized.

Extended Data Figure 3 The effect of conservation spending on decline depends on threatened species richness and on GDP.

a, Spending effect size and threatened species richness, in the continuous part of the model (n = 50 independent countries). b, Spending effect size and GDP, in the binomial part of the model (n = 109 independent countries). The effect size (coefficient) for spending is shown on the y axis and varies with the value of species richness on the x axis. As coefficients on the y axis become increasingly negative, spending produces more marked reductions in biodiversity decline (continuous) or the probability of such a decline occurring (binomial). Conditional 95% confidence bands are shown; rug plots along the bottom show empirical distribution of x-axis values. All variables are z standardized.

Extended Data Figure 4 Observed declines versus model-predicted declines.

A, BDS versus predicted BDS in the continuous part of the model (n = 50 independent countries). Both axes are log-transformed for clarity. b, As a, but focusing on countries with lower BDSs (note difference between values for the axes in a and b). c, Observed decline events (BDSb) versus the predicted probabilities of a decline event, from the binomial part of the model (n = 109 independent countries). Observed decline events on the x axis (0 = no decline occurred, 1 = decline occurred) have been jittered for visibility. d, Change in model prediction when top three BDS values are excluded. Black line, full dataset prediction; dashed grey line, prediction with exclusions.

Extended Data Figure 5 Distributions of BDS and species range fractions across countries.

a, Index plot of BDSs. For clarity, BDS has been log(x + 10)-transformed; consequently, the straight line at 2.3 shows the long tail of zeroes. b, Distribution of all range fractions in all countries, showing the large number of small, range-edge fractions (in which <10% of a species range is found in a country). c, Distribution of the maximum range fraction for all species; note that a large number of species have >90% of their range in a single country. b, Distribution of the minimum range fraction for all species; note that many species have a small range edge (<10% of their range) in a second country.

Extended Data Figure 6 Differences in absolute Pearson’s correlations between conservation spending and each of its covariates before and after carrying out covariate balancing propensity score weighting.

a, Continuous analysis. b, Binomial analysis; absolute Pearson’s correlations before (upper bars) and after (lower bars) covariate balancing propensity score weighting. Box shows interquartile range; central line, median. Whiskers show the most extreme data point, no more than 1.5× the interquartile range. n = 50 independent countries.

Extended Data Table 1 List of regression terms tested, and the best-fitting four models from continuous analysis with their AICc values, Akaike weights and variables (see Supplementary Table 2 for full results from the continuous part of the model)
Extended Data Table 2 Standardized coefficients for best-fitting models under alternative assumptions
Extended Data Table 3 Cross correlations between variables
Extended Data Table 4 Variance inflation factors for the continuous and binomial parts of the model

Supplementary information

Supplementary Information

This file contains the Supplementary Results and a Supplementary Discussion. It shows results for the sensitivity tests, makes recommendations for the application of the model, gives detailed hypotheses behind interaction terms, discusses assumptions, and further clarifies model results. (PDF 128 kb)

Life Sciences Reporting Summary (PDF 72 kb)

Supplementary Table 1

BDS and mean annual conservation spending 1992-2003 by country. BDS = Biodiversity Decline Score. i$ = millions of international dollars (rounded to nearest i$0.1m). NA = historical strict biodiversity funding cannot be robustly estimated. 0.0 = mean investment in strict biodiversity conservation for 1992-2003 genuinely seems to have been extremely small or zero. (XLS 62 kb)

Supplementary Table 2

Candidate models to predict BDS. The table also shows ordered AICc values for the continuous model part (main text) and related coefficients, rounded to two decimal places; note that the binomial model part does not generate AICc values. "x" indicates interactions; ^2=quadratic term, population = human population density; "declines (t-1)" = declines 1994-2000, (t-2) = declines 1988-1994. NA = not included in model. (XLS 68 kb)

Supplementary Table 3

Predicted impact (reduction in decline) if biodiversity budgets had been i$1 million or i$5 million higher in each country. Reductions are predicted for BDS for the continuous model part, and for prob(BDS>0) for the binomial model part. "Recovery" indicates that BDS is predicted to become negative. (XLS 37 kb)

Supplementary Table 4

Individual adjustments to BDS values. Exceptions where species-specific IUCN reports suggested a different division of political responsibilities (Rij) to those estimated from range fractions (pij). Birds and mammals are reported as separate taxa, and species names are listed in alphabetical order within each of those subdivisions. (PDF 153 kb)

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Waldron, A., Miller, D., Redding, D. et al. Reductions in global biodiversity loss predicted from conservation spending. Nature 551, 364–367 (2017). https://doi.org/10.1038/nature24295

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