Wind energy facilitates have expanded significantly in the United States over the last few decades due to technological advancements, regulatory incentives, and policies aimed at increasing renewable energy production, but poorly sited turbines may have adverse effects on local and migratory birds, bats, and other wildlife and their habitats. In the northeastern United States, Maine has become the leader in wind energy but also has the greatest density of Bald Eagles in the region. As wind energy production continues to be developed across the state and in coastal waters, research is needed to analyze and assess potential risks, including displacement, to this eagle population.
One increasingly powerful and effective tool in the assessment of ecological effects is individual-based modeling. This approach uses unique and autonomous agents that interact with each other and their environment to simulate dynamic systems. IBMs offer a practical and flexible approach to modeling animal movement because they can accommodate landscape patterns, territoriality, and behavioral adaptations.
The objective of this project was to generate an individual-based, spatially-explicit model of breeding Bald Eagle ranging behavior in current and potential wind energy production areas. Bald Eagle movement data were collected through GPS telemetry data and used to parameterize the movement models. These models were based on underlying mechanistic and phenomenological functions and real and derived landscape variables. This model allowed eagle movement patterns to be simulated across actual landscapes and under varying development scenarios. Ultimately, this tool can help provide management decisions for landscape planning and minimize the effects of wind energy on Bald Eagles.