The positive influence of wetlands on reproductive success and body mass in an aerial insectivore is more pronounced in intensively cropped agroecosystems
Bibliographic record
Abstract
Abstract Weather and land-use changes can act in complex ways to affect species’ annual demographics and distributions. Biodiversity and agriculture are frequently intertwined where climatic conditions, such as flooding and drought, may be exasperated by intensive agricultural practices. Understanding how climate change and agricultural land use influence avian populations is key to retaining biodiversity in working landscapes. Here we examined a large dataset of breeding Tachycineta bicolor (Tree Swallow) in Canada’s Prairie Pothole Region across 8 years at 8 sites representing a gradient in agricultural intensity and wetland availability, for a total of 42 site-years. We analyzed the influence of agricultural land use, wetland availability, and seasonal temperature on T. bicolor reproductive success and body mass, while testing for potential interactive and curvilinear effects. As predicted, fitness measures were negatively affected by agricultural intensity; specifically, clutch initiation date was later, and nestling body mass was lower at cropped vs. non-cropped sites. Also, as predicted, interactions revealed that the beneficial effects of wetland availability were more pronounced at cropped sites. Associations between temperature and fitness measures were less clear but suggested the potential for detrimental effects of temperature extremes that might also depend on agricultural intensity. Our results contribute to a growing body of literature demonstrating the negative impact of intensive cropping practices and the importance of retaining natural and semi-natural habitats, including wetlands, in working landscapes to conserve biodiversity, and buffer against temperature extremes and future climate changes.
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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".