From Diffusion to Cognition: Analytical, Statistical and Mechanistic Approaches to the Study of Animal Movement
Bibliographic record
Abstract
Ecology is the scientific study of processes that determine the distribution and abundance of organisms in space and time. Animal movement plays a crucial role in determining the fates of individuals, populations, communities, and ecosystems. Hence, understanding how and why animals change their spatial location through time is fundamental to ecological research. Animal movement patterns reflect behavioral, physiological and physical interactions between individuals and their environment. Coupling movement and environmental data may thus provide a rich source of information regarding many aspects of animal ecology. In my PhD thesis, I develop and demonstrate different approaches to understanding and predicting animal movement patterns in relation to their environment. In the first chapter, I merge two fundamental ecological models, the functional response and random walk, to formally derive diffusion rates of consumers as function of their handling time and the abundance, distribution and mobility of their resources. This mechanistic null model provides a simple behavior-free explanation to commonly observed negative associations between movement rates and resource abundance, often attributed to area-restricted search behavior. In the second chapter, I use positional data of woodland caribou in Ontario to calculate random walk-based movement expectations for each individual during each month. I then statistically link these expectations to ecologically significant environmental conditions. I show that landscape correlates of forage abundance and habitat permeability explain much of the observed variation in caribou movement characteristics and that residual variability may be attributed to spatial population structure. In the third chapter, I develop a novel state-space approach, enabling simultaneous consideration of resource preference, cognitive capacities and movement limitations, within a simulation model of animal movement across heterogeneous landscapes. The model is designed to enable direct parameterization based on empirical movement and landscape data. This approach allows one to both theoretically explore the consequences of different cognitive abilities and to predict animal space-use patterns across novel or altered landscapes. Overall, my thesis contributes to the rapidly developing field of movement ecology by formulating mechanistically defendable linkages between animal movement and landscape characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".