How plastic is migratory behavior? Quantifying elevational movement in a partially migratory alpine ungulate, the Sierra Nevada bighorn sheep (<i>Ovis canadensis sierrae</i>)
Bibliographic record
Abstract
Migratory species face well-documented global declines, but the causes of these declines remain unclear. One obstacle to better understanding these declines is uncertainty surrounding how migratory behavior is maintained. Most migratory populations are partially migratory, displaying both migrant and resident behaviors. Theory only provides two possible explanations for this coexistence of migration and residency: either these behaviors are fixed at the individual level or both behaviors are part of a single conditional strategy in which an individual’s migratory status (adoption of migrant or resident behavior) is plastic. Here we test for plasticity in migratory status and tactics (timing, distance, and duration of migration) in a federally endangered mountain caprid, the Sierra Nevada bighorn sheep (Ovis canadensis sierrae Grinnell, 1912). We used nonlinear modeling to quantitatively describe migratory behavior, analyzing 262 animal-years of GPS location data collected between 2005 and 2016 from 161 females across 14 subpopulations. Migratory tactics and prevalence varied by subpopulation. On average, individuals from partially migratory subpopulations switched migratory status every 4 years. Our results support the hypothesis that partial migration is maintained through a single conditional strategy. Understanding plasticity in migratory behavior will improve monitoring efforts and provide a rigorous basis for evaluating threats, particularly those associated with changing climate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".