Climate change aggravates non‐target effects of pesticides on dragonflies at macroecological scales
Bibliographic record
Abstract
Critical gaps in understanding how species respond to environmental change limit our capacity to address conservation risks in a timely way. Here, we examine the direct and interactive effects of key global change drivers, including climate change, land use change, and pesticide use, on persistence of 104 odonate species between two time periods (1980-2002 and 2008-2018) within 100 × 100 km quadrats across the USA using phylogenetic mixed models. Non-target effects of pesticides interacted with higher maximum temperatures to contribute to odonate declines. Closely related species responded similarly to global change drivers, indicating a potential role of inherited traits in species' persistence or decline. Species shifting their range to higher latitudes were more robust to negative impacts of global change drivers generally. Inherited traits related to dispersal abilities and establishment in new places may govern both species' acclimation to global change and their abilities to expand their range limits, respectively. This work is among the first to assess effects of climate change, land use change, and land use intensification together on Odonata, a significant step that improves understanding of multispecies effects of global change on invertebrates, and further identifies conditions contributing to global insect loss.
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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) | 0.044 | 0.004 |
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".