Spotlight

Global species richness estimates have not converged M. Julian Caley1, Rebecca Fisher1, and Kerrie Mengersen2 1 2

Australian Institute of Marine Science, PMB 3, Townsville MC, Queensland, Australia School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia

We demonstrate that after more than six decades, estimates of global species richness have failed to converge, remain highly uncertain, and in many cases, are logically inconsistent. Convergence in these estimates could be accelerated by adaptive learning methods where the estimation of uncertainty is prioritised and used to guide future research.

The need for convergence in global species richness estimates Our ability to estimate the total number of species that live on our planet, or in any of its major habitats, realms, or ecosystems, has immense practical and symbolic importance [1–3]. Global species richness, whether estimated by taxon, habitat, ecosystem, or the entire planet, is a key metric of biodiversity. In the absence of agreed, and relatively certain, estimates of global species richness, we are unable to adequately understand the magnitude of what is at risk from global change during the Anthropocene, or our successes and failures in mitigating those risks and remediating impacts. During the past six decades, awareness of the importance of knowing how many species live on Earth has steadily increased, resulting in the development and application of a range of estimation methods (see sources listed in the supplementary materials). Consequently, we can now evaluate our ability to estimate global species richness and whether it is improving. Although it might be difficult to assess accuracy in absolute terms, because the truth in this case cannot yet be known, we can evaluate the precision of these estimates. In particular, if our ability to estimate species richness has been improving, our estimates should be converging, and the uncertainty around them progressively decreasing. Indeed, such convergence in global species richness estimates has recently been claimed [4]. Convergence has not been reached yet Here we review published estimates of global species richness, but argue instead that these estimates have failed to converge over more than six decades of research. Since the 1950s, estimates of global species richness, and the methods that underpin them, have steadily increased Corresponding author: Caley, M.J. ([email protected]). Keywords: adaptive learning; Bayesian analysis; biodiversity; global species richness. 0169-5347/$ – see front matter ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tree.2014.02.002

in sophistication and diversity. During this period, these estimates have been dominated by point values with no associated estimates of uncertainty, or estimated ranges with no central estimate (Figure 1). Only very recently have central estimates with associated uncertainty appeared in the literature (Figure 1). With the exception of a few substantial outliers, which, in at least one case [5] has subsequently been shown to be highly improbable [6], estimates of global species richness have persisted for decades in the same broad band, more than an order of magnitude wide, between approximately 0.5 million to 10 million species. Moreover, estimates among studies are often logically inconsistent. For example, estimates of species richness for coral reefs have exceeded estimates for all marine species, and estimates for all marine species have exceeded global estimates for all realms combined (Figure 1). Anomalies of this sort, and orders of magnitude differences in species richness estimates, persist even in the past few years of this time series (Figure 1). In addition to such anomalies, we found no evidence of convergence through time in the slopes of upper, lower, or central estimates of species richness for any realm (simple least-squares regression, in all cases: R2 < 0.24, P > 0.31 for trend coefficients, see the supplementary material online). Throughout this time frame, upper and lower bounds have been estimated using a range of metrics (for details see sources in the supplementary material). The different methods employed may have contributed to some trends that might have been present being more difficult to detect. Nevertheless, it is unlikely that the consistent use of a single metric would have corrected the logical inconsistencies that have emerged where estimates for subcomponents of realms, or estimates for realms exceed the estimates for realms or the entire planet, respectively. It is also unlikely that using other metrics for estimating upper and lower bounds would have made a substantive difference in the many situations where the estimated ranges for the same components of global species richness do not overlap. Although the upper and lower bounds that have been reported to date are useful in the context they are used here, in the future, uncertainty surrounding central estimates of global species richness should always be reported. This uncertainty should be expressed probabilistically, with details about assumptions, distributions and computation provided. This information will facilitate the use of a variety of metrics to estimate upper and lower bounds in a consistent manner across studies. A path to convergence The lack of convergence evident so far in estimates of global species richness reduces our confidence in them, and Trends in Ecology & Evolution, April 2014, Vol. 29, No. 4

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Figure 1. Estimates of global species richness over six decades have failed to converge, remain highly uncertain, and in many cases, are logically inconsistent among realms. Single points indicate estimates with no estimate of uncertainty. Points joined by lines indicate an estimated range with no central estimate. Points intersected by lines ending in cross bars indicate a central estimate and an associated estimate of uncertainty. See supplementary material for details of data sources and types of estimates.

hence, limits their utility. To achieve convergence, new analytical approaches that better estimate uncertainty, including uncertainty around estimates for individual taxa and asymmetrical uncertainties are required, as are adaptive learning methods that build on knowledge gained through previous attempts to estimate these values. Options include methods that facilitate adaptive learning by incorporating previous estimates of species richness as prior information in contemporary analyses (e.g.,[7]), meta-analysis to synthesize estimates across studies and methods (e.g.,[8]), and the formal elicitation of expert knowledge from taxonomists (e.g.,[9]). Wherever possible, estimates should be underpinned by models that identify key variables from which global species richness and associated uncertainties can be estimated. Ideally, as such models are developed a small group will emerge as the agreed upon best. However, until a large proportion of some taxa are described and named, validation of the estimates from these will not be possible. Meanwhile, as the inventories of some taxa become more complete, it should become evident if any of these methods are inferior to the others if the number of species named progressively diverges from their estimates. To the extent that they also illuminate variable degrees of uncertainty around species richness estimates for particular taxa, such methods can also support more robust meta-analyses, and inform future research regarding where additional research is required to reduce current uncertainty to the greatest extent and with the greatest efficiency. In doing so, it will be necessary to appropriately weight estimates in such meta-analyses to account for

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differences in their quality. For example, point estimates that constitute simple best guesses and lack explicit estimates of uncertainty might be down weighted, possibly to zero, compared to estimates based on say some model of spatial turnover of species that are reported along with the uncertainty of these estimates. The success of the approaches used will be evident in the degree to which estimates of global species richness begin to converge and their uncertainties decrease in ways not previously achieved. While evidence of convergence has not yet emerged, some authors who have contributed estimates to this time series have already engaged in a form of informal adaptive learning whereby they have updated their thinking based on previous estimates (see sources in the supplementary material). As convergence begins to emerge through the application of more formal approaches to adaptive learning, caution will be required to avoid convergence on incorrect values resulting from biases arising from the use of specific methods or singular focus on alternative explanations or theories. Such biases are most likely to be avoided in the future if multiple lines of evidence derived from different and well-justified methods are employed and the uncertainty around these estimates become better understood. As species inventories becomes more complete, these estimates can continue to be updated further supporting their progressive convergence on the true number of species on Earth. Acknowledgements This work was funded by BHP Billiton through CReefs Australia, a field project of the International Census of Marine Life. We thank Nigel Stork and two anonymous reviewers for insisting on greater clarity.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.tree.2014.02.002. References 1 May, R.M. (1992) How many species inhabit the earth? Sci. Am. 267, 18–24 2 Stork, N. (1993) How many species are there? Biodivers. Conserv. 2, 215–232 3 May, R.M. (2002) The future of biological diversity in a crowded world. Curr. Sci. 82, 1325–1331 4 Costello, M.J. et al. (2013) Can we name Earth’s species before they go extinct? Science 339, 413–416 5 Erwin, T.L. (1991) How many species are there?: Revisited. Conserv. Biol. 5, 330–333 6 Hamilton, A. et al. (2013) Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis. Oecologia 171, 357–365 7 McCarthy, M.A. (2007) Bayesian Methods for Ecology, Cambridge University Press 8 Koricheva, J. et al., eds (2013) Handbook of Meta-analysis in Ecology and Evolution, Princeton University Press 9 Fisher, R. et al. (2012) A software tool for elicitation of expert knowledge about species richness or similar counts. Environ. Model. Softw. 30, 1–14

Global species richness estimates have not converged.

We demonstrate that after more than six decades, estimates of global species richness have failed to converge, remain highly uncertain, and in many ca...
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