Proximate and Ultimate Drivers of Animal Movement

Research in the young and exciting scientific field of “Movement Ecology” has already led to major insights in our understanding of ecological and evolutionary processes, from ecosystem function, to individual and social behaviours, population dynamics, community ecology and conservation. Technological advances in animal biologging, paralleled by data storage and sharing initiatives, have opened the “big data” era for animal movement. In parallel, a myriad of analytical tools and code packages are being developed to process these large datasets to address broad-scale and overarching questions in ecology, evolution, and behaviour. However, to keep on par with these methodological advances, we now need to solidify the theoretical basis of movement to better understand the causes of animal movement, and its consequences at different spatio-temporal scales. What are the proximate and ultimate drivers of movement, how do these drivers function and can we make some general rules that apply across living organisms ? This Gordon conference will address these aspects of movement ecology by focusing on the evolution of movement and dispersal and the processes that shape these traits. It will also explicitly examine the ecological and evolutionary consequences of movement - from individual fitness to community dynamics, from disease epidemiology to conservation - in order to link our fundamental advances to current applied questions related to the dynamics of animal populations in a rapidly changing world and to the challenging future scenarios that lie ahead.

Building off past meetings, the planned sessions for the 2023 conference will fall into three main themes: the evolution of animal movement - including its fundamental basis and underlying variability -, the proximate processes that define movement decisions, and the ecological and evolutionary consequences of movement, with explicit consideration of the general patterns that emerge across systems and how these patterns match on-going and future changes in natural systems.