Dissertation Proposal Seminar - Rostam Mirzadi

COASTAL AND MARINE SYSTEM SCIENCE PROGRAM
DEPARTMENT OF PHYSICAL AND ENVIRONMENTAL SCIENCES
TEXAS A&M UNIVERSITY-CORPUS CHRISTI

SUBJECT: The Use of Drones in Estimating Colonial Waterbird Productivity

GRADUATE ADVISOR: Dr. Dale Gawlik

COMMITTEE MEMBERS: Dr. Michael Starek, Dr. Shawn McCracken, Dr. Bart Ballard

ABSTRACT

Globally, 55% of waterbirds are declining, with 51% of waterbird species declining in the western hemisphere, 53% of waterbird species declining in Europe, 56% of waterbird species declining in Africa, and 61% of waterbird species declining in Asia.  The loss of breeding habitat is of particular concern, especially for colonial waterbird species that depend on islands for colony-sites.  However, managers are limited in their ability to implement restoration or other conservation plans because of a lack of understanding of how island characteristics affect key demographic parameters such as nest survival or brood size. Therefore, this study proposes to measure nest survival and productivity of five species of colonial waterbirds that nest on the Texas coast (Reddish Egret (Egretta rufescens), Tricolored Heron (Egretta tricolor), Great Egret (Ardea alba), Black Skimmer (Rynchops niger), and Caspian Tern (Hydroprogne caspia)), using imagery obtained with a uncrewed aircraft system (hereafter drone) and to identify breeding habitat and environmental characteristics that positively influence productivity. Additionally, because the use of drones for waterbird research has increased dramatically in the last two decades, it is imperative that researchers understand how the inclusion of drone methodology affects breeding measure estimates compared to traditional survey methods, and the effect of Ground Sampling Distance (GSD; i.e. image resolution) and image processing techniques on breeding and demographic parameters. Therefore, I will also test and evaluate how drone-derived demographic estimates of waterbirds compare to traditional observational surveys, and the effects of resolution and image-processing techniques on drone-derived breeding and demographic parameters.