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Abstract The unsustainable harvest of species for the global wildlife trade is a major cause of vertebrate extinction. Through the anthropogenic Allee effect (AAE), overexploitation to extinction can occur when a species' rarity drives up its market price, enabling profitable harvest of all remaining individuals. Even in the absence of rarity value, however, the harvest of other species can subsidize the overexploitation of a rare species to the point of extinction, a phenomenon termed opportunistic exploitation. These two pathways to extinction have been considered independently, but many traded species experience them simultaneously.In this study, we develop a simple model that incorporates these mechanisms simultaneously and demonstrate that including multiple harvest strategies with market‐based feedbacks fundamentally alters rare species extinction risk and the rate at which overexploitation occurs. As a pertinent case study, we consider the harvest of ground pangolinsSmutsia temminckii.Our results show that pangolin extinction was generally associated with high rarity value, the use of multiple harvest strategies and the simultaneous harvest of a common species that has a fast life history. Pangolin population depletion and short‐term extinction risk were greatest when harvesters used a combination of pursuit and opportunistic (i.e. multi‐species) harvest strategies.Policy implications.Our results suggest that feedbacks between multiple financial incentives to overharvest can exacerbate the risk of extinction of rare species. As a result, continuing to address AAE and opportunistic exploitation as separate extinction pathways may insufficiently capture extinction risk for many exploited species. Criteria for assessing extinction risk or harvest sustainability of exploited species should incorporate multiple drivers of harvest pressure, with an expanded focus on including species with high rarity value that are exploited in multi‐species harvest regimes. 

Abstract Habitat loss is rarely truly random and often occurs selectively with respect to the plant species comprising the habitat. Such selective habitat removal that decreases plant species diversity, that is, habitat simplification or homogenization, may have two negative effects on other species. First, the reduction in plant community size (number of individuals) represents habitat loss for species at higher trophic levels who use plants as habitat. Second, when plants are removed selectively, the resulting habitat simplification decreases the diversity of resources available to species at higher trophic levels. It follows that habitat loss combined with simplification will reduce biodiversity more than habitat loss without simplification. To test this, we experimentally implemented two types of habitat loss at the plant community level and compared biodiversity of resident arthropods between habitat loss types. In the first type of habitat loss, we reduced habitats by 50% nonselectively, maintaining original relative abundance and diversity of plant species and therefore habitat and resource diversity for arthropods. In the second type of habitat loss, we reduced habitats by 50% selectively, removing all but one common plant species, dramatically simplifying habitat and resources for arthropods. We replicated this experiment across three common plant species:Asclepias tuberosa,Solidago altissima, andBaptisia alba. While habitat loss with simplification reduced arthropod species richness compared with habitat loss without simplification, neither type of habitat loss affected diversity, measured as effective number of species (ENS), or species evenness as compared with controls. Instead, differences in ENS and evenness were explained by the identity of the common plant species. Our results indicate that the quality of remaining habitat, in our case plant species identity, may be more important for multi‐trophic diversity than habitat diversity per se. 

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. 

Abstract 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.