Testing for facultative switching between migratory strategies in a partially migratory, long-lived herbivore population
Bibliographic record
Abstract
Migration is the seasonal movement of animals from one region to another, a behavioral strategy adapted to spatial variation in resource abundance. In addition to the access of resources, migration can also allow animals to escape from predators at local scales by moving to areas of lower predator density. While we often think of migration in a binary sense (either a population migrates or it doesn’t), many species of animal exhibit partial migration where a portion of the population exhibits migratory behavior and a portion remains as resident year round. As migratory paths and migratory behavior disappear globally, it is important to understand why individuals within a population migrate while some remain as residents and how this behavioral decision influences reproduction and survival. To answer these questions, I am studying the migratory behavior and resulting population dynamics of the Ya Ha Tinda elk herd in Alberta, Canada. The population has declined over the past 20 years from ~2,200 individuals to ~450 individuals and has seen dramatic shifts in the ratios of migrant and resident individuals in the population. What is interesting about this behavior is that recent research on this population determined that an individual elk switches between resident and migrant strategies throughout its life. We will use over 16 years of data on survival, reproduction, and movement to understand the effect that switching has on the population dynamics of this herd and why this switching behavior occurs. The management and persistence of this elk herd is important to both Provincial managers and Parks Canada biologists because this elk herd summers both in Banff National Park and on Provincial lands and winters on Provincial lands outside of the park providing both viewing opportunities within the park and hunting opportunities outside of the park.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".