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.
Land‐use change is a significant cause of anthropogenic extinctions, which are likely to continue and accelerate as habitat conversion proceeds in most biomes. One way to understand the effects of habitat loss on biodiversity is through improved tools for predicting the number and identity of species losses in response to habitat loss. There are relatively few methods for predicting extinctions and even fewer opportunities for rigorously assessing the quality of these predictions. In this paper, we address these issues by applying a new method based on rarefaction to predict species losses after random, but aggregated, habitat loss. We compare predictions from three rarefaction models, individual‐based, sample‐based, and spatially clustered, to those derived from a commonly used extinction estimation method, the species–area relationship (SAR). We apply each method to a mesocosm experiment, in which we aim to predict species richness and extinctions of arthropods immediately following 50% habitat loss. While each model produced strikingly accurate predictions of species richness immediately after the habitat loss disturbance, each model significantly underestimated the number of extinctions occurring at both the local (within‐mesocosm) and regional (treatment‐wide) scales. Despite the stochastic nature of our small‐scale, short‐term, and randomly applied habitat loss experiment, we found surprisingly clear evidence for extinction selectivity, for example, when abundant species with low extinction probabilities were extirpated following habitat loss. The important role played by selective extinction even in this contrived experimental system suggests that ecologically driven, trait‐based extinctions play an equally important role to stochastic extinction, even when the disturbance itself has no clear selectivity. As a result, neutrally stochastic null models such as the SAR and rarefaction are likely to underestimate extinctions caused by habitat loss. Nevertheless, given the difficulty of predicting extinctions, null models provide useful benchmarks for conservation planning by providing minimum estimates and probabilities of species extinctions.
more » « less- Award ID(s):
- 1650554
- NSF-PAR ID:
- 10363512
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 27
- Issue:
- 4
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 793-803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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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Abstract Habitat loss disrupts species interactions through local extinctions, potentially orphaning species that depend on interacting partners, via mutualisms or commensalisms, and increasing secondary extinction risk. Orphaned species may become functionally or secondarily extinct, increasing the severity of the current biodiversity crisis. While habitat destruction is a major cause of biodiversity loss, the number of secondary extinctions is largely unknown. We investigate the relationship between habitat loss, orphaned species, and bipartite network properties. Using a real seed dispersal network, we simulate habitat loss to estimate the rate at which species are orphaned. To be able to draw general conclusions, we also simulate habitat loss in synthetic networks to quantify how changes in network properties affect orphan rates across broader parameter space. Both real and synthetic network simulations show that even small amounts of habitat loss can cause up to 10% of species to be orphaned. More area loss, less connected networks, and a greater disparity in the species richness of the network's trophic levels generally result in more orphaned species. As habitat is lost to land‐use conversion and climate change, more orphaned species increase the loss of community‐level and ecosystem functions. However, the potential severity of repercussions ranges from minimal (no species orphaned) to catastrophic (up to 60% of species within a network orphaned). Severity of repercussions also depends on how much the interaction richness and intactness of the community affects the degree of redundancy within networks. Orphaned species could add substantially to the loss of ecosystem function and secondary extinction worldwide.
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Abstract Aim We tested the effects of season and migratory status (residents‐versus‐seasonal migrants) on island biogeography of bird assemblages through partitioning beta diversity into richness and turnover components and community nestedness metrics. We predicted that total beta diversity, the richness component of beta diversity and community nestedness will be lower for bird assemblages in winter than in summer, and lowest of all for winter visitors. These predictions were derived from published ideas about resource availability, movement and habitat choice in birds in different seasons.
Location Thousand Island Lake, China.
Methods Bird species were sampled using line transects on 36 islands during five breeding and winter seasons (2009−2014). Birds were grouped into assemblages of winter residents, winter visitors and summer residents. Associations between beta diversity partitioning, island area, isolation and habitat richness were tested using partial Mantel correlations. We complemented these tests with measures of nestedness and null model approaches.
Results Contrary to expectation, beta diversity, nestedness and difference of beta diversity or its components from null models were higher for winter residents than either summer resident or winter visitor assemblages. As predicted, winter visitors showed little association with habitat richness, and beta diversity was rarely different from null communities. Summer residents had the highest correlations of beta diversity components with habitat richness, but showed the lowest level of total beta diversity, a low richness component and were anti‐nested (less nested than random).
Main conclusions Substantial differences were found in the biogeography of winter‐versus‐summer residents, and seasonal visitor (migratory)‐versus‐resident bird assemblages, which match expectations derived from bird biology and population ecology. Summer residents highlighted the role of habitat‐related niche differences, whereas winter residents showed area‐related selective extinction. By contrast, winter visitors appeared to be more randomly distributed.
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