Scale-dependent proximity of wildlife habitat in a spatially-neutral Bayesian model

Abstract

Organisms may be constrained by the energetic costs incurred while obtaining resources in fragmented landscapes. We used a spatially neutral model of deer wintering habitat to evaluate the effects of landscape fragmentation on the aggregation of deer habitat. The spatially neutral model used Bayesian probabilities to predict where deer wintering areas occurred. The probabilities were conditional on 12 landscape variables measured at 22,750 contiguous 0.4 ha locations. The model predicted deer habitat at each location independently, thereby enabling a comparison of habitat aggregation in observed, simulated, and random distributions of deer habitat. The predictions of the neutral model exhibited greater fragmentation than observed in nature, suggesting that suitable, yet isolated, locations were not visited by deer. The most suitable sites for deer were clumped in the neutral model, regardless of scale. The inclusion of less suitable sites preserved significant aggregation at fine scales but not at broad scales. Species operate at different scales within a landscape, so ecologists, nature reserve designers and natural resource planners may benefit from models that focus on the proximity of habitat sites as a function of both spatial scale and habitat quality.