Advancing Drone Methods for Pinniped Ecology and Management

• Main Author: Larsen, Gregory David
• Other Authors: Johnston, David W
• Format: Doctoral or Postdoctoral Thesis
• Language: unknown
• Published: 2022
• Subjects: Wildlife conservation; Conservation biology; Ecology; Conservation; Drone; Pinniped; Remote sensing; Spatial; Northwest Atlantic
• Online Access: https://hdl.handle.net/10161/25847

Dissertation Pinniped species undergo a life history, unique among marine mammals, that includes discrete periods of occupancy on land or ice within a predominantly marine existence. This makes many pinniped species valuable sentinels of marine ecosystem health and models of marine mammal physiology and behavior. Pinniped research has often progressed hand-in-hand with advances at the technological frontiers of wildlife biology, and drones represent a leap forward in the long-established field of aerial photography, heralding opportunities for data collection and integration at new scales of biological importance. The following chapters employ and evaluate recent and emerging methods of wildlife surveillance that are uniquely enabled and facilitated by drone methods, in applied research and management campaigns with near-polar pinniped species. These methods represent advancements in abundance estimation and distribution modeling of pinniped populations that are dynamically shifting amid climate change, fishing pressure, and recovery from historical depletion.Conventional methods of counting animals from aerial imagery—typically visual interpretation by human analysts—can be time-consuming and limits the practical use of this data type. Deep learning methods of computer vision can ease this burden when applied to drone imagery, but are not yet characterized for practical and generalized use. To this end, I used a common implementation of deep learning for object detection in imagery to train and test models on a variety of datasets describing breeding populations of gray seals (Halichoerus grypus) in the northwest Atlantic Ocean (Chapter 2). I compare standardized performance metrics of models trained and tested on different combinations of datasets, demonstrating that model performance varies depending on both training and testing data choices. We find that models require careful validation to estimate error rates, and that they can be effectively deployed to aid, but not replace, conventional human visual ...