Persistent Genomic Erosion in Whooping Cranes Despite Demographic Recovery
Bibliographic record
Abstract
Integrating in-situ (wild) and ex-situ (captive) conservation efforts can mitigate genetic diversity loss and help prevent extinction of endangered wild populations. The whooping crane (Grus americana) experienced severe population declines in the 18th century, culminating in a collapse to ~20 individuals by 1944. Legal protections and conservation actions have since increased the census population from a stock of 16 individuals to approximately 840 individuals, yet the impact on genomic diversity remains unclear. We analysed the temporal dynamics of genomic erosion by sequencing a high-quality reference genome, and re-sequencing 16 historical (years 1867-1893) and 37 modern (2007-2020) genomes, including wild individuals and four generations of captive-bred individuals. Genomic demographic reconstructions reveal a steady decline, accelerating over the past 300 years with the European settlement of North America. Temporal genomic analyses show that despite demographic recovery, the species has lost 70% of its historical genetic diversity and has increased its inbreeding. Although the modern population bottleneck reduced the ancestral genetic load, modern populations possess more realised load than masked load, possibly resulting in a chronic loss of fitness. Integrating pedigree and genomic data, we underscore the role of breeding management in reducing recent inbreeding. Yet ongoing heterozygosity loss, load accumulation, and persistent effects of historical inbreeding (i.e., background inbreeding) argue against the species' downlisting from its current Endangered status on the IUCN Red List and the Endangered Species Act. The presence of private genetic variation in wild and captive populations suggests that wild-captive crosses could enhance genetic diversity and reduce the realised load. Our findings emphasise the role of genomics in informing conservation management and policy.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".