A species-centered approach for uncovering generalities in organism responses to habitat loss and fragmentation


M. G. Betts, Dept of Forest Ecosystems and Society, Oregon State Univ., Corvallis, OR 97331, USA. E-mail: matthew.betts@oregonstate.edu


Theoretical models predict strong influences of habitat loss and fragmentation on species distributions and demography, but empirical studies have shown relatively inconsistent support across species and systems. We argue that species’ responses to landscape-scale habitat loss and fragmentation are likely to appear less idiosyncratic if it is recognized that species perceive the same landscapes in different ways. We present a new quantitative approach that uses species distribution models (SDMs) to measure landscapes (e.g. patch size, isolation, matrix amount) from the perspective of individual species. First, we briefly summarize the few efforts to date demonstrating that once differences in habitat distributions are controlled, consistencies in species’ responses to landscape structure emerge. Second, we present a detailed example providing step-by-step methods for application of a species-centered approach using freely available land-cover data and recent statistical modeling approaches. Third, we discuss pitfalls in current applications of the approach and recommend avenues for future developments. We conclude that the species-centered approach offers considerable promise as a means to test whether sensitivity to habitat loss and fragmentation is mediated by phylogenetic, ecological, and life-history traits. Cross-species generalities in responses to habitat loss and fragmentation will be challenging to uncover unless landscape mosaics are defined using models that reflect differing species-specific distributions, functional connectivity, and domains of scale. The emergence of such generalities would not only enhance scientific understanding of biotic processes driving fragmentation effects, but would allow managers to estimate species sensitivities in new regions.