During my fellowship I contributed towards this goal by (1) conceptualizing the links between environmental drivers and animal movement, (2) developing a universal individual-based model capable of generating any of the movement pattern types observed in nature, (3) revealing unknown processes underlying space use fidelity, segregations, migrations, consumer-resource and metapopulation dynamics, and (4) demonstrating the appropriateness of a recent statistical method for fitting individual-based models of animal movement on data.
(1) My early investigations revealed the central role that environmental predictability plays in the determination of animal movement. Crucially, I showed that the concept of environmental predictability was still lacking a proper definition and methodologies to quantify it. I therefore developed a comprehensive framework for environmental predictability and animal movement [1]. I synthesised the literature into a general definition, a typology, and a set of methodologies for quantifying environmental predictability. I then reviewed the movement-related adaptations to environmental predictability and the methods designed to detect and characterise them, I outlined recent ideas on the feedbacks between animal movement and environmental predictability, and I discussed how human activities can alter patterns of wildlife movement by affecting environmental predictability.
(2) I developed a universal Individual-Based Model that integrates individual perception, memory, social information use, and environmental dynamics. Through extensive simulations, I showed that this parsimonious model can lead to the emergence of all existing types of individual- and population-level space use patterns (nomadism, home ranging, migration, spatial segregation, and aggregation), depending on its parametrization. I showed using this model that the interaction of individual cognition with environmental conditions determine emerging space use patterns.
(3) With collaborators, I participated in showing that:
- collective spatial segregation between neighbouring colonies can emerge from the simple use of memory by colony members [2],
- short-term temporal memory can be sufficient for foragers to efficiently exploit seasonal resources [3],
- ungulates’ site faithfulness increases with environmental predictability [4],
- overlooking dispersers’ habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount in a landscape [5],
- the expected covariation of animal movement attributes can drive consumer–resource patterns across space and time and could underlie the role of consumers in driving spatial heterogeneity in resource abundance [6]
- I conducted a theoretical work that showed that migration can emerge from foraging behaviour alone when resources renew slowly compared to the foraging depletion rate. I will draft a manuscript on these results in the coming year.
(4) Approximate Bayesian Computation (ABC) is a recent, computationally efficient methodology for fitting complex models that has still very rarely been used to fit Individual-Based Models (IBMs). It has never been used to fit an IBM of animal movement to field data. During my project, I showed that the output variables of my universal IBM are sensitive to the hidden parameters that need to be inferred from data (for example, memory capacity). I made the demonstration that ABC is an appropriate methodology to infer these hidden parameters from movement data. This work will lead to a methodological paper that will be drafted in the coming year. With the collaborative group I created at the University of Glasgow, I have initiated several studies linking my universal model of animal movement to ungulate and seabird movement data, using ABC. I will apply to funding to continue this line of research in the future.
