Managing hybridization of a recovering endangered species: The red wolf Canis rufus as a case study
Bibliographic record
Abstract
Abstract Hybridization presents a unique challenge for conservation biologists and managers. While hybridization is an important evolutionary process, hybridization is also a threat formany native species. The endangered species recovery effort for the red wolf Canis rufus is a classic system for understanding and addressing the challenges of hybridization. From 1987?1993, 63 red wolves were released from captivity in eastern North Carolina, USA, to establish a free-ranging, non-essential experimental population. By 1999, managers recognized hybridization with invasive coyotes Canis latrans was the single greatest threat to successful recovery, and an adaptive management plan was adopted with innovative approaches for managing the threat of hybridization. Here we review the application and results of the adaptive management efforts from 1993 to 2013 by comparing: (1) the numbers of wolves, coyotes, and hybrids captured, (2) the numbers of territorial social groups with presumed breeding capabilities, (3) the number of red wolf and hybrid litters documented each year and (4) the degree of coyote introgression into the wild red wolf gene pool. We documented substantial increases in the number of known red wolves and red wolf social groups from 1987–2004 followed by a plateau and slight decline by 2013.The number of red wolf litters exceeded hybrid litters each year and the proportion of hybrid litters per year averaged 21%. The genetic composition of the wild red wolf population is estimated to include < 4% coyote ancestry from recent introgression since reintroduction. We conclude that the adaptive management plan was effective at reducing the introgression of coyote genes into the red wolf population, but population recovery of red wolves will require continuation of the current management plan, or alternative approaches, for the foreseeable future. More broadly, we discuss the lessons learned from red wolf adaptive management that could assist other endangered species recovery efforts facing the challenge of minimizing hybridization.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".