Predation of experimental nests is linked to local population dynamics in a fragmented bird population
Bibliographic record
Abstract
Artificial nest experiments (ANEs) are widely used to obtain proxies of natural nest predation for testing a variety of hypotheses, from those dealing with variation in life-history strategies to those assessing the effects of habitat fragmentation on the persistence of bird populations. However, their applicability to real-world scenarios has been criticized owing to the many potential biases in comparing predation rates of artificial and natural nests. Here, we aimed to test the validity of estimates of ANEs using a novel approach. We related predation rates on artificial nests to population viability analyses in a songbird metapopulation as a way of predicting the real impact of predation events on the local populations studied. Predation intensity on artificial nests was negatively related to the species' annual population growth rate in small local populations, whereas the viability of large local populations did not seem to be influenced, even by high nest predation rates. The potential of extrapolation from ANEs to real-world scenarios is discussed, as these results suggest that artificial nest predation estimates may predict demographic processes in small structured populations.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".