Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great bustards

Abstract

Summary: Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land‐use change over large areas and new methods are needed for regional‐scale mapping. We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse‐grained, we used a 12‐month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species’ very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling.