Improving Avian Species Distribution Models by Incorporating Biotic Interactions

Abstract

Maps of actual or potential species distributions are crucial for many aspects of natural resource management, land use development, and conservation planning. Species distribution models (SDMs) attempt to predict or statistically associate geographic record of a species with abiotic and biospatial variables of interest over large spatial extents and are utilized in wildlife management as aerial imagery and our understanding of distributional patterns advances. Most distributional models use variables such as soil type, climatic patterns, topography, hydrology, vegetative communities, and other abiotic conditions to identify the predicted geographic range of a species. However, species interactions have yet to be successfully quantified and included in distributional models. It is imperative we include interactions in niche models as certain species relationships (i.e. predation, competition, habitat facilitation) have documented influence on species distribution. I demonstrated techniques to improve traditional SDMs by incorporating intra- and inter-specific biotic interactions using birds as an example. Models that incorporate this biotic influence introduce new code in existing statistical languages that can also be applied to other environments. The methods I developed present a fusion of techniques from multiple fields including ecological modeling, remote sensing, and statistical analyses, the synthesis of which result in a novel and elevated approach to modeling and predicting species distributions.