Complex temporal trends in biomass and abundance of Diptera driven by the impact of agricultural intensity on community-level turnover
Bibliographic record
Abstract
Insect declines have been reported widely and are expected to alter ecosystem functions and processes. Land-use change is recognised as a major cause of decline in insect biodiversity and abundance. Variation in local environmental drivers and the scale of available monitoring data have left large knowledge gaps in which taxa are declining and 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 km2 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 abundance over time were greater in areas with higher agricultural intensity, especially with an increase in cereal crops. In contrast, declines in dipteran 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 cover, suggesting the presence of community turnover toward smaller-bodied flies with lower individual biomass. Our results reveal further complexities in insect trends driven by changes in land-use and show the importance of long-term monitoring and the use of multiple indicators for understanding biodiversity change.
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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 | 0.001 | 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 | 0.001 | 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".