Agriculture erases climate‐driven β‐diversity in Neotropical bird communities
Bibliographic record
Abstract
Earth is experiencing multiple global changes that will, together, determine the fate of many species. Yet, how biological communities respond to concurrent stressors at local-to-regional scales remains largely unknown. In particular, understanding how local habitat conversion interacts with regional climate change to shape patterns in β-diversity-differences among sites in their species compositions-is critical to forecast communities in the Anthropocene. Here, we study patterns in bird β-diversity across land-use and precipitation gradients in Costa Rica. We mapped forest cover, modeled regional precipitation, and collected data on bird community composition, vegetation structure, and tree diversity across 120 sites on 20 farms to answer three questions. First, do bird communities respond more strongly to changes in land use or climate in northwest Costa Rica? Second, does habitat conversion eliminate β-diversity across climate gradients? Third, does regional climate control how communities respond to habitat conversion and, if so, how? After correcting for imperfect detection, we found that local land-use determined community shifts along the climate gradient. In forests, bird communities were distinct between sites that differed in vegetation structure or precipitation. In agriculture, however, vegetation structure was more uniform, contributing to 7%-11% less bird turnover than in forests. In addition, bird responses to agriculture and climate were linked: agricultural communities across the precipitation gradient shared more species with dry than wet forest communities. These findings suggest that habitat conversion and anticipated climate drying will act together to exacerbate biotic homogenization.
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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 | 0.001 |
| 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".