Recent analyses have reported catastrophic global declines in vertebrate populations1,2. However, the distillation of many trends into a global mean index obscures the variation that can inform conservation measures and can be sensitive to analytical decisions. For example, previous analyses have estimated a mean vertebrate decline of more than 50% since 1970 (Living Planet Index2). Here we show, however, that this estimate is driven by less than 3% of vertebrate populations; if these extremely declining populations are excluded, the global trend switches to an increase. The sensitivity of global mean trends to outliers suggests that more informative indices are needed. We propose an alternative approach, which identifies clusters of extreme decline (or increase) that differ statistically from the majority of population trends. We show that, of taxonomic–geographic systems in the Living Planet Index, 16 systems contain clusters of extreme decline (comprising around 1% of populations; these extreme declines occur disproportionately in larger animals) and 7 contain extreme increases (around 0.4% of populations). The remaining 98.6% of populations across all systems showed no mean global trend. However, when analysed separately, three systems were declining strongly with high certainty (all in the Indo-Pacific region) and seven were declining strongly but with less certainty (mostly reptile and amphibian groups). Accounting for extreme clusters fundamentally alters the interpretation of global vertebrate trends and should be used to help to prioritize conservation efforts.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
npj Biodiversity Open Access 01 June 2023
Communications Biology Open Access 04 April 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Data can be obtained from the LPI database (www.livingplanetindex.org), AmphiBio30 (https://figshare.com/articles/Oliveira_et_al_AmphiBIO_v1/4644424), FishBase (www.fishbase.org)28 and life-history traits can be obtained from the amniote life-history database27 (https://doi.org/10.6084/m9.figshare.c.3308127.v1).
Code for the BHM model is available at: https://doi.org/10.5281/zenodo.3901586.
IUCN. The IUCN Red List of Threatened Species. version 2019-3 http://www.iucnredlist.org (2019).
WWF. Living Planet Report 2018: Aiming Higher (eds. Grooten, N. & Almond, R. E. A.) (WWF, 2018).
Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: a review of its drivers. Biol. Conserv. 232, 8–27 (2019).
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).
Willig, M. R. et al. Populations are not declining and food webs are not collapsing at the Luquillo Experimental Forest. Proc. Natl Acad. Sci. USA 116, 12143–12144 (2019).
Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).
Dornelas, M. et al. A balance of winners and losers in the Anthropocene. Ecol. Lett. 22, 847–854 (2019).
Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).
Leung, B., Greenberg, D. A. & Green, D. M. Trends in mean growth and stability in temperate vertebrate populations. Divers. Distrib. 23, 1372–1380 (2017).
McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropocene. Trends Ecol. Evol. 30, 104–113 (2015).
Anderson, S. C., Branch, T. A., Cooper, A. B. & Dulvy, N. K. Black-swan events in animal populations. Proc. Natl Acad. Sci. USA 114, 3252–3257 (2017).
LPI. Living Planet Index. www.livingplanetindex.org/ (2016).
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).
Connors, B. M., Cooper, A. B., Peterman, R. M. & Dulvy, N. K. The false classification of extinction risk in noisy environments. Proc. R. Soc. Lond. B 281, 20132935 (2014).
Hanks, E. M., Hooten, M. B. & Baker, F. A. Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence. Ecol. Appl. 21, 1173–1188 (2011).
Youngflesh, C. & Lynch, H. J. Black-swan events: population crashes or temporary emigration? Proc. Natl Acad. Sci. USA 114, E8953–E8954 (2017).
Fournier, A. M. V., White, E. R. & Heard, S. B. Site-selection bias and apparent population declines in long-term studies. Conserv. Biol. 33, 1370–1379 (2019).
Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. Lond. B 280, 20122131 (2013).
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
Allan, J. R. et al. Hotspots of human impact on threatened terrestrial vertebrates. PLoS Biol. 17, e3000158 (2019).
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
O’Neill, S. & Nicholson-Cole, S. “Fear won’t do it”: promoting positive engagement with climate change through visual and iconic representations Sci. Commun. 30, 355–379 (2009).
Brennan, L. & Binney, W. Fear, guilt, and shame appeals in social marketing. J. Bus. Res. 63, 140–146 (2010).
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).
Froese, R. & Pauly, D. FishBase version 12/2019 www.fishbase.org (2019).
Boettiger, C., Lang, D. T. & Wainwright, P. C. rfishbase: exploring, manipulating and visualizing FishBase data from R. J. Fish Biol. 81, 2030–2039 (2012).
Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci. Data 4, 170123 (2017).
Collen, B. et al. Monitoring change in vertebrate abundance: the living planet index. Conserv. Biol. 23, 317–327 (2009).
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, 2016).
McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156 (2017).
We thank E. Hudgins, D. Nguyen, S. Varadarajan and A. Jones for discussions, T. Coulson for comments and S. Varadarajan and F. Moyes for help creating the figures. This work was supported by a Natural Sciences and Engineering Research Council (NSERC) Discovery grant to B.L.
The authors declare no competing interests.
Peer review information Nature thanks Tim Coulson 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
The p–p plots show that the posterior distributions for each estimated parameter are unbiased and largely follow a 1:1 line for each hyper parameter (σ, τ) as well as the fraction in each cluster (f1, f2 = 1 − f1). The 1:1 line is the theoretic expectation, indicating that the true parameter value falls below the 0.01 quantile 1% of the time, the 0.02 quantile 2% of the time, and so on.
The trends of the primary clusters (θ1), for the main analysis (x axis) versus the sensitivity analysis (y axis) for the threshold for extreme clusters (top) and the offset when n = 0 was observed (bottom).
Each point represents a trend estimate for the primary cluster of a system, with the full dataset (x axis) versus data excluding time series with less than 10 data points (y axis). The red dot indicates the freshwater Indo-Pacific mammals, which was reduced from 22 populations (full) to 2 populations (only data with at least 10 data points).
Top, all species (14,700 populations). Middle, only large species (9,596 populations). Bottom, only small species (5,103 populations). The small species appear to be declining more than large species, although this finding needs to be interpreted with caution, as most primary distributions did not significantly deviate from zero for small species.
Extended Data Fig. 5 Histograms of observed growth rates and output of the BHM model for systems 1–16.
Blue line, primary cluster; red line, extreme cluster(s) from the model. Grey vertical lines show the range of observed values. In comparing the model output to the data we show the following. (1) The variation of the BHM primary cluster (blue line) is much lower than the raw data, because the BHM separates variation in among-population trends from variation due to within-population fluctuations. (2) The BHM model identifies evidence for extreme clusters in both directions (for example, terrestrial Indo-Pacific birds) or only one direction (for example, terrestrial Neotropical mammals), but not for other apparent clusters (for example, terrestrial Indo-Pacific herps). The BHM integrates the magnitude of within-population fluctuations, time-series sizes, number of populations, among-population variance, and the magnitude and frequency of the extreme populations in determining whether additional (extreme) clusters are needed to account for the observations.
About this article
Cite this article
Leung, B., Hargreaves, A.L., Greenberg, D.A. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020). https://doi.org/10.1038/s41586-020-2920-6
This article is cited by
npj Biodiversity (2023)
Communications Biology (2023)