Aerial insectivores breeding in agricultural landscapes: Highlighting the value of wetlands and heterogeneous habitats
Bibliographic record
Abstract
Abstract Aerial insectivores have experienced steep population declines in North America, with an estimated loss of nearly 160 million individuals since the 1970s. Agricultural intensification, habitat loss, pesticide use, extreme climatic events, and loss of high-quality insect prey are all threats to aerial insectivore populations worldwide. Several species within the guild breed sympatrically or co-occur in agricultural areas, and thus aerial insectivores can act as indicators of ecosystem health within agricultural landscapes. Here, we highlight the valuable contributions of new research published as part of a Special Feature series on “Aerial Insectivores Breeding in Agricultural Landscapes.” In reviewing this research and the broader literature on aerial insectivores, we developed the hypothesis that habitat heterogeneity, and especially wetlands, support aerial insectivores breeding in agricultural landscapes. Importantly, wetlands are a source of emergent aquatic insects, and these high-quality prey items have been found in the diets of several species, including those of conservation concern. We provide evidence that (1) aerial insectivores use wetland habitats for foraging and roosting while breeding in agricultural landscapes; (2) wetlands improve the health, reproductive success, or fitness prospects of aerial insectivores; and, importantly, (3) wetlands may offset the negative effects of agricultural intensification. While evidence for the benefits of wetlands to aerial insectivores is biased toward breeding swallows in the Canadian Prairies, the higher nutritional value of insects emerging from wetlands and other aquatic sources points to the widespread value of wetlands for aerial insectivores in North America. Overall, protecting wetland habitat may be critically important for the conservation of healthy aerial insectivore populations in agricultural landscapes.
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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 | 0.000 |
| 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 teacher head, 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".