Species distribution models of an endangered rodent offer conflicting measures of habitat quality at multiple scales. Issue 4 (13th June 2014) - Bean, William T.; Prugh, Laura R.; Stafford, Robert; Butterfield, H. Scott; Westphal, Michael; Brashares, Justin S.; Stephens, Philip
Summary The high cost of directly measuring habitat quality has led ecologists to test alternate methods for estimating and predicting this critically important ecological variable. In particular, it is frequently assumed but rarely tested that models of habitat suitability ('species distribution models', SDMs) may provide useful indices of habitat quality, either from an individual animal or manager's perspective. Critically, SDMs are increasingly used to estimate species' ranges, with an implicit assumption that areas of high suitability will result in higher probability of persistence. This assumption underlies efforts to use SDMs to design protected areas, assess the status of cryptic species or manage responses to climate change. Recent tests of this relationship have provided mixed results, suggesting SDMs may predict abundance but not other measures of high‐quality habitat (e.g. survival, persistence). In this study, we created a suite of SDMs for the endangered giant kangaroo rat Dipodomys ingens at three distinct scales using the machine‐learning method Maxent. We compared these models with three measures of habitat quality: survival, abundance and body condition. Species distribution models were not correlated with survival, while models at all scales were positively correlated with abundance. Finer‐scale models were more closely correlated with abundance than the largest scale. Body condition was not correlated with habitat suitability at any scale. The inability of models to predict survival may be due to a lack of information in environmental covariates; unmeasured community processes or stochastic events; or the inadequacy of using models that predict species presence to also predict demography. Synthesis and applications. Species distribution models (SDMs), especially fine scale ones, may be useful for longer‐term management goals, such as identifying high‐quality habitat for protection. However, short‐term management decisions should be based only on models that use covariates appropriate for the necessary temporal and spatial scales. Assumptions about the relationship between habitat suitability and habitat quality must be made explicit. Even then, care should be taken in inferring multiple types of habitat quality from SDMs.