Genetically informed captive breeding of hybrids of an extinct species of Galapagos tortoise
Bibliographic record
Abstract
Hybridization poses a major challenge for species conservation because it threatens both genetic integrity and adaptive potential. Yet, hybridization can occasionally offer unprecedented opportunity for species recovery if the genome of an extinct taxon is present among living hybrids such that selective breeding could recapture it. We explored the design elements for establishing a captive-breeding program for Galapagos tortoises (Chelonoidis spp.) built around individuals with admixed ancestry involving an extinct species. The target individuals were hybrids between the extinct species from Floreana Island, C. niger, and an extant species, C. becki, which were recently found in the endemic range of C. becki, from Wolf Volcano on Isabela Island. We combined genotypic data from 35 tortoises with high ancestry from C. niger with forward-in-time simulations to explore captive breeding strategies that maximized overall genetic diversity and ancestry from C. niger while accommodating resource constraints, species biology, and the urgency to return tortoises to Floreana Island for facilitating ecosystem restoration. Overall genetic diversity was maximized when in the simulation tortoises were organized in relatively small breeding groups. Substantial amounts of the C. niger genome were captured despite limited resources available for selectively breeding tortoises in captivity. Genetic diversity was maximized when captive-bred offspring were released to the wild rather than being used as additional breeders. Our results provide genetic-based and practical guidance on the inclusion of hybrids with genomic representation from extinct taxa into species restoration programs and informs the ongoing debate on the value of hybrids in biodiversity conservation.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".