Evidence of reduced abundance, density, and survival of coyotes under federal management for red wolf recovery
Bibliographic record
Abstract
Abstract To mitigate coyote ( Canis latrans ) introgression in the wild red wolf ( Canis rufus ) genome, the United States Fish and Wildlife Service (USFWS) Red Wolf Recovery Program used a combination of reproductive sterilization and lethal removal of coyotes to minimize hybridization and increase the endangered red wolf population. Although sterilization assisted in limiting coyote introgression to ≤4% in the wild red wolf genome, its potential negative effect on coyote and hybrid abundance and density is unknown. Using long‐term capture–mark–recapture and radio‐telemetry data collected on red wolves, coyotes, and hybrids under the USFWS Red Wolf Adaptive Management Plan implemented during 2000–2013, we explored three areas of research: (1) spatial modeling to correlate land cover characteristics with the relative probabilities of capture for red wolves, coyotes, and hybrids; (2) survival analysis of radio‐marked canids; and (3) annual population estimates for the three Canis taxa. We detected no differences in the relative probability of capture among Canis taxa. Red wolves, coyotes, and hybrids were most frequently captured in areas proximate to road networks with low canopy cover (i.e., cropland) and away from coastal bottomland forests. Annual apparent survival for red wolves and hybrids was greater than survival for coyotes; however, wolves and hybrids exhibited similar annual survival. Mortality of coyotes and hybrids was predominately attributed to deliberate take through lethal control by the USFWS biologists and harvest by hunters and trappers. We observed annual densities of coyotes ranging between 2.5 and 21.5 coyotes/1000 km 2 , with densities annually increasing during 2005–2013 when the red wolf population plateaued before declining after 2013. Despite the increase in coyote density, our density estimates are less than most estimates reported throughout the coyote's geographic range, and similar to those reported in areas where coyote populations are limited by extreme environments such as their northern range limits in Alaska, United States, and Canada. Our findings indicate that red wolf presence and federal management of coyotes using fertility control may have limited coyote densities in northeastern North Carolina.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 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".