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Abstract 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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NetworkExtinction : An R package to simulate extinction propagation and rewiring potential in ecological networks
Abstract Earth's biosphere is undergoing drastic reorganization due to the sixth mass extinction brought on by the Anthropocene. Impacts of local and regional extirpation of species have been demonstrated to propagate through the complex interaction networks they are part of, leading to secondary extinctions and exacerbating biodiversity loss. Contemporary ecological theory has developed several measures to analyse the structure and robustness of ecological networks under biodiversity loss. However, a toolbox for directly simulating and quantifying extinction cascades and creating novel interactions (i.e. rewiring) remains absent.Here, we presentNetworkExtinction—a novel R package which we have developed to explore the propagation of species extinction sequences through ecological networks and quantify the effects of rewiring potential in response to primary species extinctions. WithNetworkExtinction, we integrate ecological theory and computational simulations to develop functionality with which users may analyse and visualize the structure and robustness of ecological networks. The core functions introduced withNetworkExtinctionfocus on simulations of sequential primary extinctions and associated secondary extinctions, allowing user‐specified secondary extinction thresholds and realization of rewiring potential.With the packageNetworkExtinction, users can estimate the robustness of ecological networks after performing species extinction routines based on several algorithms. Moreover, users can compare the number of simulated secondary extinctions against a null model of random extinctions. In‐built visualizations enable graphing topological indices calculated by the deletion sequence functions after each simulation step. Finally, the user can estimate the network's degree distribution by fitting different common distributions. Here, we illustrate the use of the package and its outputs by analysing a Chilean coastal marine food web.NetworkExtinctionis a compact and easy‐to‐use R package with which users can quantify changes in ecological network structure in response to different patterns of species loss, thresholds and rewiring potential. Therefore, this package is particularly useful for evaluating ecosystem responses to anthropogenic and environmental perturbations that produce nonrandom and sometimes targeted, species extinctions.
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- PAR ID:
- 10418656
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 8
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 1952-1966
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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