Occupancy Rates of Eastern Wild Turkeys: Estimating effects of experimental harvest regulations and automating image analysis

Abstract

Each chapter in this thesis contributes to the available knowledge about turkey populations in Alabama, the effects of management on those populations, and the ability to conduct cost-effective surveys for monitoring their dynamics. In Chapter 2, I present the results of using Machine Learning (ML) to automate image classification as a means of reducing the cost of camera trap surveys. I also compare occupancy estimates from ML classified images to those that were manually interpreted and suggest ways to improve estimates of occupancy based on ML classified images. In Chapter 3, I present an analysis of the effects of experimental changes in hunting regulations at several wildlife management areas across Alabama on turkey occupancy dynamics using camera trap surveys conducted during late summer. Together these chapters represent significant advances in methods to monitor the effects of management on turkey populations across their range.