Search for: All records

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 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.