Climate, habitat, and species interactions at different scales determine the structure of a Neotropical bat community
Bibliographic record
Abstract
Climate, habitat, and species interactions are factors that control community properties (e.g., species richness, abundance) across various spatial scales. Usually, researchers study how a few properties are affected by one factor in isolation and at one scale. Hence, there are few multi-scale studies testing how multiple controlling factors simultaneously affect community properties at different scales. We ask whether climate, habitat structure, or insect resources at each of three spatial scales explains most of the variation in six community properties and which theory best explains the distribution of selected community properties across a rainfall gradient. We studied a Neotropical insectivorous bat ensemble in the Isthmus of Panama with acoustic monitoring techniques. Using climatological data, habitat surveys, and insect captures in a hierarchical sampling design we determined how much variation of the community properties was explained by the three factors employing two approaches for variance partitioning. Our results revealed that most of the variation in species richness, total abundance, and feeding activity occurred at the smallest spatial scale and was explained by habitat structure. In contrast, climate at large scales explained most of the variation in individual species' abundances. Although each species had an idiosyncratic response to the gradient, species richness peaked at intermediate levels of precipitation, whereas total abundance was very similar across sites, suggesting density compensation. All community properties responded in a different manner to the factor and scale under consideration.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".