Modeling the potential distribution of forest birds at risk in the Grand River watershed using a maximum entropy approach
The protection of species at risk is an issue of increasing concern with human growth and development. To identify habitats of potential conservation value, species distribution models (SDMs) have emerged as popular statistical tools for predicting and mapping the distribution of species. This thesis presents a SDM to predict and map the potential distribution of forests birds at risk in the Grand River watershed, Ontario, Canada. The SDM was developed using a maximum entropy approach (Maxent) and optimized by i) minimizing the potential effects of geographic sampling bias using weight-bias files and ii) developing species-specific set of environmental data using a backward stepwise variable selection approach. Results provide further insights to the methodology of species distribution modelling and model predictions may also be used to inform local conservation management in the Grand River watershed.