Ecological factors underlying the spatiotemporal dynamics in a key forest beetle pollinator
Bibliographic record
Abstract
Abstract Beetles are the oldest taxon of pollinating insects and can account for most beetles found in forest habitats where Apidae are uncommon. Nevertheless, pollinating beetles are little studied compared to other pollinators, even though they play a key role in forest plant reproduction. In this study, we model the population dynamics of Eusphalerum , a predominant pollinating beetle genus in northeastern North American forests, to improve our understanding of this abundant taxon. The models were derived from beetle samples collected using flight‐intercept traps from 2015 to 2022 across various forest habitats in Eastern Canada. We also examined whether this genus was exhibiting congeneric aggregation and intergeneric aggregation patterns. In this study, 113,563 beetles from 606 species were collected. Congeneric aggregation was excessively high in Eusphalerum , while intergeneric aggregation with all other pollinating and non‐pollinating beetles was low. Eusphalerum populations peaked in early July and were mainly influenced by elevation as well as years, and little by habitat variables such as forest composition and management. Eusphalerum was more abundant in high‐elevation northern regions, a territory not coveted by dominant pollinator species because of the harsher climate and shorter growing seasons. This study represents a first step towards a better understanding of this abundant forest pollinator and how it varies at the landscape scale.
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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) | 0.000 | 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 teacher head, 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".