Conserving wetlands in agroecosystems can sustain aerial insectivore productivity and survival
Bibliographic record
Abstract
Agricultural intensification simplifies natural landscapes and frequently results in the loss of biodiversity. Wetlands are highly productive and may offset these losses, but the amount of wetland area needed to support declining avian species on farmland is unknown. Using an avian aerial insectivore, the Tree Swallow ( Tachycineta bicolor (Vieillot, 1808)), we tested whether a gradient of pond area (visible surface water in wetland basins within 500 m of nest boxes; range 0.2%–30% pond area) at cropland and grassland sites was related to aquatic insect biomass, reproductive success, and adult female or nestling body condition. Aquatic insect biomass was ∼2–8 times higher at the cropland sites with intermediate (5.2%) pond areas than at sites with the highest (15.6%) and lowest (0.2%) pond areas. Swallow clutch initiation date was ∼3–4 days earlier, and nestling body condition and model-predicted first-year survival were ∼10% higher among cropland sites with more pond area and were comparable to birds hatched at grassland sites. Loss of ponds due to agricultural drainage can reduce aquatic insect prey during the breeding season with apparent individual and demographic consequences for insectivorous birds. Overall, the results suggest that where wetlands are conserved, intensive croplands can sustain Tree Swallow populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".