Multiple life-stage inbreeding depression impacts demography and extinction risk in an extinct-in-the-wild species
Bibliographic record
Abstract
Inbreeding can depress individuals' fitness traits and reduce population viability. However, studies that directly translate inbreeding depression on fitness traits into consequences for population viability, and further, into consequences for management choices, are lacking. Here, we estimated impacts of inbreeding depression (B, lethal equivalents) across life-history stages for an extinct-in-the-wild species, the sihek (Guam kingfisher, Todiramphus cinnamominus). We then projected population growth under different management alternatives with our B estimates incorporated, as well as without inbreeding depression (B = 0) or with a conventional default B. We found that inbreeding depression severely impacted multiple life-history stages, and directly translated into an effect on population viability under management alternatives. Simulations including our B estimates indicated rapid population decline, whereas projections without inbreeding depression or with default B suggested very gradual population decline. Further, our results demonstrate that incorporation of B across life-history stages can influence management decisions, as projections with our B estimates suggested a need to switch to increased breeding management to avoid species extinction and support wild releases. Our results demonstrate that magnitude of B across life-history stages can translate into demographic consequences, such that incorporation of multiple life-stage B into population models can be important for informed conservation management decision-making.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".