Species distribution models of an endangered rodent offer conflicting measures of habitat quality at multiple scales

Abstract

Summary1. 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). 2. 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. 3. 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. 4. 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.

Predicting species’ abundances from occurrence data: Effects of sample size and bias

“…Previous studies aiming to estimate abundance by means of modelling procedures comparing the environmental conditions in presence localities against a set of background data randomly distributed in the territory (Gutiérrez et al, 2013;VanDerWal et al, 2009) show that the so obtained suitability values have a triangular relationship with densities (but see Bean et al, 2014). That is, low densities frequently manifest in both low-and high-suitability localities, and, in the best cases, models can account only for the upper limit of abundance.…”

“…Some studies have explored the capacity of some of these models to inform about abundance (or population density) patterns of species (Bean et al, 2014;Estrada and Arroyo, 2012;Gutiérrez et al, 2013;Jiménez-Valverde, 2011;Nielsen et al, 2005;Pearce and Ferrier, 2001;Real et al, 2009;Tôrres et al, 2012;Van Couwenberghe et al, 2013;VanDerWal et al, 2009). Results of these studies are inconclusive, but in general show that the continuous suitability values provided by models based on presence-only or presence-absence, pseudo-absence or background data rarely are able to reflect abundance patterns adequately.…”

Avian SDMs: current state, challenges, and opportunities

“…Results are still inconsistent across studies (Bean et al 2014, Unglaub et al 2015, but there is an increasing number of examples in birds, where environmental suitability derived from SDMs correlates with other population parameters such as breeding density, territory size, or survival (Table 1). More research is needed on whether the observed correlations can be generalized and identified as causative across birds and whether dynamic distribution models (i.e.…”

Using traffic data to estimate wildlife populations

“…There is an old and large body of literature in ecology relating different types of species characteristics to landscape or habitat quality (e.g. Bender et al 1998;Bean et al 2014). It has been common to estimate the effects of landscape patterns on population abundance or survival, but not on population growth in a logistic modelling framework.…”