Apparent survival of tropical birds in a wet, premontane forest in Costa Rica
Bibliographic record
Abstract
Despite the importance of tropical birds in the development of life history theory, we lack information about demographic rates and drivers of population dynamics for most species. We used a 7-year (2007–2013) capture-mark-recapture dataset from an exceptionally wet premontane forest at mid-elevation in Costa Rica to estimate apparent survival for seven species of tropical passerines. For four of these species, we provide the first published demographic parameters. Recapture probabilities ranged from 0.21 to 0.53, and annual estimates of apparent survival varied from 0.23 to 1.00. We also assessed the consequences of inter-annual variation in rainfall on demographic rates. Our results are consistent with inter-annual rainfall increasing estimates of apparent survival for two species and decreasing estimates for three species. For the three species where we could compare our estimates of apparent survival to estimates from drier regions, our estimates were not consistently higher or lower than those published previously. The temporal and spatial variability in demographic rates we document within and among species highlights the difficulties of generalizing life history characteristics across broad biogeographic gradients. Most importantly, this work emphasizes the context-specific role of precipitation in shaping tropical avian demographic rates and underscores the need for mechanistic studies of environmental drivers of tropical life histories.
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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 | 0.000 |
| 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) | 0.000 | 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".