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Title: When are extinctions simply bad luck? Rarefaction as a framework for disentangling selective and stochastic extinctions

A key challenge in conservation biology is that not all species are equally likely to go extinct when faced with a disturbance, but there are multiple overlapping reasons for such differences in extinction probability. Differences in species extinction risk may represent extinction selectivity, a non‐random process by which species’ risks of extinction are caused by differences in fitness based on traits. Additionally, rare species with low abundances and/or occupancies are more likely to go extinct than common species for reasons of random chance alone, that is, bad luck. Unless ecologists and conservation biologists can disentangle random and selective extinction processes, then the prediction and prevention of future extinctions will continue to be an elusive challenge.

We suggest that a modified version of a common null model procedure, rarefaction, can be used to disentangle the influence of stochastic species loss from selective non‐random processes. To this end we applied a rarefaction‐based null model to three published data sets to characterize the influence of species rarity in driving biodiversity loss following three biodiversity loss events: (a) disease‐associated bat declines; (b) disease‐associated amphibian declines; and (c) habitat loss and invasive species‐associated gastropod declines. For each case study, we used rarefaction to generate null expectations of biodiversity loss and species‐specific extinction probabilities.

In each of our case studies, we find evidence for both random and non‐random (selective) extinctions. Our findings highlight the importance of explicitly considering that some species extinctions are the result of stochastic processes. In other words, we find significant evidence for bad luck in the extinction process.

Policy implications. Our results suggest that rarefaction can be used to disentangle random and non‐random extinctions and guide management decisions. For example, rarefaction can be used retrospectively to identify when declines of at‐risk species are likely to result from selectivity, versus random chance. Rarefaction can also be used prospectively to formulate minimum predictions of species loss in response to hypothetical disturbances. Given its minimal data requirements and familiarity among ecologists, rarefaction may be an efficient and versatile tool for identifying and protecting species that are most vulnerable to global extinction.

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Journal of Applied Ecology
Page Range / eLocation ID:
p. 101-110
Medium: X
Sponsoring Org:
National Science Foundation
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