Persistence and expansion of cryptic endangered red wolf genomic ancestry along the American Gulf coast
Bibliographic record
Abstract
Admixture and introgression play a critical role in adaptation and genetic rescue that has only recently gained a deeper appreciation. Here, we explored the geographical and genomic landscape of cryptic ancestry of the endangered red wolf that persists within the genome of a ubiquitous sister taxon, the coyote, all while the red wolf has been extinct in the wild since the early 1980s. We assessed admixture across 120,621 single nucleotiode polymorphism (SNP) loci genotyped in 293 canid genomes. We found support for increased red wolf ancestry along a west-to-east gradient across the southern United States associated with historical admixture in the past 100 years. Southwestern Louisiana and southeastern Texas, the geographical zone where the last red wolves were known prior to extinction in the wild, contained the highest and oldest levels of red wolf ancestry. Further, given the paucity of inferences based on chromosome types, we compared patterns of ancestry on the X chromosome and autosomes. We additionally aimed to explore the relationship between admixture timing and recombination rate variation to investigate gene flow events. We found that X-linked regions of low recombination rates were depleted of introgression, relative to the autosomes, consistent with the large X effect and enrichment with loci involved in maintaining reproductive isolation. Recombination rate was positively correlated with red wolf ancestry across coyote genomes, consistent with theoretical predictions. The geographical and genomic extent of cryptic red wolf ancestry can provide novel genomic resources for recovery plans targeting the conservation of the endangered red wolf.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".