Positive association between rat abundance and breeding success of the Critically Endangered Mariana Crow <i>Corvus kubaryi</i>
Bibliographic record
Abstract
Summary Diagnosing the causes of population declines of highly endangered species is vital to ensure that appropriate conservation measures are undertaken. Although experiments can provide critical information on potential causes, these can be time consuming and costly, and so it is important to determine the most plausible hypothesis for such tests. The last wild population of the Critically Endangered Mariana Crow Corvus kubaryi has been declining rapidly on Rota in the Mariana Islands. Because nest predation by introduced rats Rattus spp. has been implicated in the declines of other Pacific island birds, we tested for a correlation between breeding success of Mariana Crows and abundance of rats R. diardii surrounding their nest sites. If rats are a contributing factor in this species’s decline, we predicted that breeding success would be lower in areas where rats were more abundant. In complete contrast to this prediction, we found that breeding success was significantly higher where rats were most abundant, suggesting that rats are unlikely to be a major driving force behind the decline in this species. The positive association between crow breeding success and rat abundance suggests these species may be responding to similar habitat needs or a shared predator. This study illustrates the importance of gathering sufficient knowledge on the ecology of a species prior to undertaking costly, logistically difficult and time consuming experiments.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".