Animal movement strategies

Movement is a fundamental process in the natural world, and active movement in response to environmental drivers is key to animal ecology. The animal tracking revolution has led to at least two distinct challenges in the field of movement ecology. The first is how to gain ecologically meaningful insights into the proximate, mechanistic drivers of animal movement decisions from vast tracking datasets. The second is how to study their ultimate, evolutionary causes. In this PhD thesis, I attempt to tackle both challenges. In Part 1, I lay out a vision as well as guidelines for the processing of massive, high-throughput animal tracking datasets, which could enable the transition of movement ecology into a true ‘big data’ discipline. I demonstrate these methods and the mechanistic approach I advocate by studying the movement of moulting birds. Combining high-throughput tracking with viewsheds - what individuals can actually see from a location - I show that birds’ movement decisions are strongly influenced by whether potential destinations can be observed by predators. In Part 2, I propose a framework for conceptual insights into the evolution of animals’ movement decisions using individual-based models that include both ecological and evolutionary timescales. With one such model I show how animal movement and foraging competition decisions can evolve in tandem, and how ecological conditions can promote the rapid evolution of correlated suites of behaviours. With another similar model, I show how repeated pathogen spillovers into a population can drive the very rapid evolution of diverse social strategies. Finally, I show how individual-based models, in which all aspects of animals’ movement decision-making are known, can be useful conceptual tools to probe the performance of contemporary statistical methods in animal movement ecology.