Validating Habitat Conservation Models: UAS-Based Density Estimation of Breeding Ducks and Sensitivity Analysis of the Wisconsin Waterfowl Habitat Conservation Strategy

Abstract

In the early 20th century, waterfowl populations in North America faced sharp declines primarily due to overharvesting and habitat loss. In response, the North American Waterfowl Management Plan (NAWMP) was established in 1986, emphasizing habitat management as a key strategy for waterfowl recovery. Through NAWMP, Joint Venture committees were formed to implement conservation strategies across ecologically distinct regions. In the Midwestern United States, the Upper Mississippi River and Great Lakes Region Joint Venture developed a regional waterfowl conservation strategy, which the Wisconsin Department of Natural Resources later adapted into the Wisconsin Waterfowl Habitat Conservation Strategy (WWHCS) in 2020. This strategy outlines a decision support tool that prioritizes Wisconsin’s HUC-12 watersheds for habitat restoration and retention based on several biological and social conservation objectives. The model assigns priority levels to watersheds ranging from level 1 (low priority) to level 9 (high priority), guiding conservation efforts to areas with the highest priority. Priority levels are determined by a committee of experts and stakeholders, who assign weights to each objective based on its relative importance to conservation. To ensure the WWHCS effectively guides conservation, we validated the model’s breeding distribution objective, as the reproductive success and survival of breeding ducks are key drivers of population growth. Using uncrewed aerial systems (UAS) with thermal cameras, we surveyed breeding pairs and broods in Wisconsin during the 2022 and 2023 breeding seasons. We applied line-transect distance sampling to estimate waterfowl densities and assess UAS as a tool for monitoring breeding waterfowl (Chapter II). The data we collected during UAS surveys was then used to validate the WWHCS’s breeding distribution objective. We used pair and brood presence and densities to verify breeding distributions and assess habitat quality across different priority watersheds (Chapter III). Lastly, we conducted a sensitivity analysis to examine how varying weights for model objectives impact priority level designations (Chapter IV). The findings from each chapter in this study can guide more targeted management decisions to optimize habitat restoration and retention across Wisconsin.