Tracking large carnivore dispersal using isotopic clues in claws: an application to cougars across the Great Plains
Bibliographic record
Abstract
Summary 1. Cougar ( Puma concolor ) populations, like other large carnivores, have increased during recent decades and may be recolonizing their former ranges in Midwestern North America. The dispersal routes taken by these animals from established populations are unknown and insight into these movements would facilitate their conservation and management. 2. We inferred the origin and migration route of four dispersing cougars using stable hydrogen (δD) and carbon (δ 13 C) isotope values along one of their claws. We compared isotopic variations within claws to regional and large‐scale isoscapes of δD and δ 13 C values in prey species. Using a likelihood‐based assignment approach, we predicted the most likely dispersal route of each cougar (among several least‐cost dispersal paths to potential source populations) in a chronological sequence dating back from its final location. 3. Our model predicted the origin of a radio‐collared short‐distance disperser and inferences about the most likely dispersal corridors for two long‐distance dispersers matched reported information from re‐sighting events and genetic investigations. 4. Insights about the most likely migration corridors may help identify critical areas and guide future conservation efforts of cougars and other large carnivores. We encourage managers to extend regional isoscapes based on sedentary prey species as they prove to be valuable tools in isotopic tracking of long‐distance migration. 5. Our isotopic approach may be extended to other metabolically inert tissues that grow continuously, to investigate dispersal paths of species of interest, providing that individuals disperse across known isotopically structured landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".