Complex temporal trends in biomass and abundance of Diptera communities driven by the impact of agricultural intensity
Bibliographic record
Abstract
Abstract Insect biodiversity and abundance declines have been reported widely and are expected to alter ecosystem functions and processes. Land use change has been recognised as a major cause of such declines. However, variation in local environmental drivers and the scale of available monitoring data have left large knowledge gaps in which taxa are declining, where declines are the greatest, and how these declines will impact ecosystems. We used 11 years (2006–2016) of monitoring data on 40 farms distributed over ~10,000 km 2 in southern Québec, Canada, to quantify the impact of agricultural intensity on temporal trends in abundance and biomass of Diptera (true flies). There was a large difference in temporal trends between farms, which we found to be driven by agricultural landcover. Contrary to expectation, increases in Diptera abundance over time were greater in areas with higher agricultural intensity, especially with an increase in cereal crops. In contrast, declines in biomass were steeper in areas of higher agricultural intensity, although only with greater maize and soy production rather than cereals such as wheat. Variation in forest cover around farms had the least effect on trends. We found steeper declines in biomass per total number of Diptera with increasing agricultural intensive cover, suggesting the presence of community turnover towards smaller bodied flies with lower individual biomass. Our results imply that land use may not only alter abundance and species composition of insect species assemblages but also the distribution of key functional traits such as body size.
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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 | 0.001 | 0.001 |
| 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".