moose are a valuable economic and ecological resource in ontario. understanding their spatial distribution throughout the forest is essential for managing populations and preserving habitat. one method of identifying the spatial distribution of species is through the development of habitat suitability indices. suitability models use presence points and environmental variables to predict the likely distribution of a species across a given landscape. this thesis examined the feasibility of using trail camera imagery to create a habitat suitability index for moose alces alces in the english river forest, on. this was accomplished by using recreational trail camera purchased from cabela's canada, and an open-source maximum entropy modeling software called maxent. three runs through the modeling software were completed in order to produce the most accurate model possible. results showed varying performance with the three models. the binary model had the highest auc at 0.808. however, it was determined that suitable habitat was highly correlated to the unclassified layer, which represents roads. the non-binary run rectified the issues with the binary model, but only produced an auc of 0.661. interestingly the pre-sapling – sapling layer was found to include information which was highly correlated to other variables. this resulted in the layer being relatively unimportant to the model, and it was subsequently removed. the non- binary run with omitted layers was determined to be the best spatial distribution fit with an auc value of 0.771 and a standard deviation of 0.161. overall, results concluded that it was possible to use trail camera imagery to develop a habitat suitability index for moose in the english river forest.