Seasonal patterns in Tree Swallow prey (Diptera) abundance are affected by agricultural intensification
Bibliographic record
Abstract
In many parts of the world, farmland bird species are declining at faster rates than other birds. For aerial insectivores, this decline has been related to a parallel reduction in the abundance of their invertebrate prey in agricultural landscapes. While the effects of agricultural intensification (AI) on arthropod communities at the landscape level have been substantially studied in recent years, seasonal variation in these impacts has not been investigated. To assess the contention that intensive cultures negatively impact food resources for aerial insectivorous birds, we analyzed the spatiotemporal distribution patterns of Diptera, the main food resource for breeding tree swallows Tachycineta bicolor), across a gradient of AI in southeastern Quebec, Canada. Linear mixed models computed from a data set of 5000 samples comprising >150,000 dipterans collected over three years (2006-2008) suggest that both Diptera abundance and biomass varied greatly during swallow breeding season, following a quadratic curve. Globally, AI had a negative effect on Diptera abundance (but not biomass), but year-by-year analyses showed that in one of three years (2008), dipterans were more abundant in agro-intensive landscapes. Analyses also revealed a significant interaction between the moment in the season and AI: In early June, Diptera abundances were similar regardless of the landscape, but differences increased as the season progressed, with highly intensive landscapes harboring fewer prey, possibly creating an "ecological trap" for aerial insectivores. While global trends in our results are in agreement with expectations (negative impact of Al on insect abundance), strong discrepancies in 2008 highlight the difficulty of predicting the abundance of insect communities. Our study indicates that predicting the effects of AI may prove more challenging than generally assumed, even when large data sets are collected, and that temporal variation within a season is important to take into consideration. While further work is required to assess the direct impacts of these seasonal trends in Diptera abundance on bird breeding success and post-fledging survival, management strategies in agricultural landscapes may need to consider the phenology of breeding birds and their dipteran prey in order to mitigate the potentially negative effects of AI late in the breeding season.
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 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".