A parametric model for estimating the timing and intensity of animal migration
Bibliographic record
Abstract
Abstract Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by changes in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal’s movement changes when it begins migrating. We tested our model on three datasets: migratory ferruginous hawks ( Buteo regalis ) in the Great Plains and barren-ground caribou ( Rangifer tarandus groenlandicus ) in northern Canada, and non-migratory brown bears ( Ursus arctos ) from the Canadian Arctic. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bear population. The flexibility of our modelling framework allowed us to assess intricacies associated with each dataset: long-term stopover behaviour in ferruginous hawks and a priori knowledge of caribou calving areas and behaviour. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how the two populations migrated; ferruginous hawks achieved efficient migrations by increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. Our approach is broadly applicable to many animal movement studies. We anticipate that rigorous assessment of migration metrics will aid understanding of both how and why animals move.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".