Large carnivore recolonization of Eastern North America: habitat connectivity and human dimensions
Bibliographic record
Abstract
Cougars (Puma concolor) have been recolonizing Midwestern North America over the past 2 decades with >950 cougar confirmations east of established populations. Management and public interest in habitat suitability and connectivity east of current cougar range have grown as confirmations increase and models predicting habitat connectivity and population viability for the Midwest show potential for breeding populations. However, although long-range dispersal and recolonization continues, no studies have assessed potential habitat associated with cougars throughout their historical range in eastern North America. I used ArcGIS, the Analytical Hierarchy Process, and geospatial data to model cougar habitat and potential dispersal corridors in eastern North America. The total amount of potential habitat was >2,400,000 km2 and mean patch size was 257,500 km2. Patches of habitat ranged in size from 3,868 km2 (Ozark Mountains) to >2,490,850 km2 (central and eastern Canada) with ≤53,643 km of dispersal corridors connecting patches. With cougars potentially recolonizing areas previously devoid of large carnivores, public acceptance of management efforts is pivotal for the success of their recolonization. However, targeted surveys assessing public perceptions and knowledge of cougars and red wolves (Canis rufus), who have faced similar extirpation in the Southeastern United States are limited. I mailed 20,000 questionnaires and 2,000 follow-up postcards to residents near areas of potential red wolf and cougar habitat in the Southeastern United States in 2020. I used cumulative link models to gauge the associations between sociodemographic predictor with respondent’s knowledge and attitude towards large carnivores. Total response rate was 4.6% with the majority of respondents identifying as male (53.6%), having a 4-year degree or above (54.1%), and 57 ± 16 (SE) years of age. Respondents’ knowledge and attitudes towards large carnivores were largely positive (≥63% positive) with higher education, older age, and current livestock ownership being largest predictors for responses. Attitudes towards red wolves were largely driven by knowledge of red wolves while attitudes towards cougars were primarily driven by livestock ownership. Livestock owners (71.5%) were concerned about safety of livestock in large carnivore habitat, and most respondents (61%) did not trust their local agency to effectively manage large carnivore populations. My research provides a foundation for wildlife managers to develop informed plans, educational programs, and policy decisions for potentially recolonizing large carnivore populations.
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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) | 0.000 | 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 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".