Climate influences broadly, landscape influences narrowly: Implications for agricultural beneficial insects
Bibliographic record
Abstract
Insects provide critical ecosystem services, like pollination, in both natural and agricultural ecosystems. Delivery of these services depends on their ability to develop, survive, and move through their environment. Whether they can do this depends on the weather, climate, and landscape; but a changing climate means these systems are potentially vulnerable to disruption. Short-term fluctuations in weather can disrupt development, impede movement, and affect survival, while long-term climate norms influence environmental niches and influence species distribution. Landscape composition also influences beneficial insect distribution and has the potential to reduce the impacts of climate change. Here we use a database of >97,000 bee occurrence records, collected from 320 sampling sites across a 90,000+ km 2 area in the North American Prairies to generate models of species occurrence for 50 species, sampling in and around crop fields. We use a tree-based machine learning method with extreme gradient boosting to create predictive classification models. These models are then used to analyze the relative importance of weather, climate, and landscape variables. The variables with the highest mean absolute importance are cumulative degree days, cumulative precipitation, and percent tree cover. When we analyzed individual species models, bee taxonomic groups responded most strongly to weather, and the direction of response corresponded to trait-grouping. The responses to landscape were weak and species-specific. The results indicate that pollination service supply is largely determined by heat and moisture, and that cavity nesters and ground-nesters have opposite responses to rising temperature, which could impact taxonomic and functional diversity. • Occurrence of ecosystem service-providing insects depends on many abiotic factors • Machine learning can test importance of 42 climate, weather, and landscape factors • Responses are split between eusocial cavity-nesters and solitary ground-nesters • Warming and drying trends threaten the delivery of agricultural ecosystem services • Factors outside human management are more important at broad spatial scales to bees
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".