Breeding Microhabitat Patterns among Sympatric Tropical Larids
Bibliographic record
Abstract
The selection of breeding microhabitat (nesting sites) affects successful reproduction in seabirds, yet the process of nesting-site selection in sympatric tropical species remains poorly known. During the 2021 breeding season, we assessed nesting-site selection among five larid species at three cays of Cuba and quantified explanatory variables. Random forest classification models were used to assess which landscape features among eight variables best explained site selection by each species. Patterns were clear for most species, especially the highly gregarious Roseate Tern Sterna dougallii, Royal Tern Thalasseus maximus, and Sandwich Tern T. sandvicensis. Patterns were consistent among the study cays. Dominant plant species, minimal distance to cay edge, vegetation cover, and substratum at nesting sites were among the more important explanatory variables. Interspecific differences in nesting-site selection may be important for the assemblage of multispecific colonies by reducing aggressive interactions, competition, and breeding failures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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; both teacher heads agree on what is shown here.
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".