Limited cougar recolonization of eastern North America predicted by an individual-based model
Bibliographic record
Abstract
As carnivores recolonize parts of their historical range, and such recolonization is increasingly recognized for its ability to reconfigure and revitalize degraded ecosystems, understanding where and when range expansion may continue is useful for facilitating scenario planning and identifying roles for conservation measures. We developed an individual-based model to predict carnivore range expansion and applied it to cougars ( Puma concolor ) in North America between 2023 and 2100. We parameterized our model with empirical movement, demographic, and survival data, and validated its performance by hindcasting nine observed recolonization events from the 1990s – 2023. Our model accurately recreated historical recolonization events and forecasted cougars reclaiming 2.1 % of unoccupied range by 2100, mostly in boreal Canada. Of currently unoccupied states/provinces (“jurisdictions”), only Manitoba received universal support across model runs for hosting a breeding population by 2100. Oklahoma, Minnesota, Kansas, and Iowa, requiring dispersal across nonhabitat, had 30 %, 30 %, 11 %, and 2 % probability of recolonization, respectively. No other jurisdictions were forecast to be recolonized. Mortality from harvest in Midwestern source populations and vehicle collisions dominated outcomes for eastward-moving females. Simulated management scenarios eliminating such hunting and adding nine wildlife crossing structures, however, did not significantly change recolonization probability. In areas where current cougar range abuts unoccupied breeding habitat, we estimated slower rates of expansion, varying from 2 to 3 km·yr −1 , yielding roughly 150–230 km of linear expansion in those regions by 2100. Our mechanistic model and underlying empirical data provide a credible and transferable approach to forecast carnivore range expansion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".