Seasonal modulation of landscape effects on predatory beetle assemblage.
Bibliographic record
Abstract
Arthropod assemblages are influenced by various factors including landscape structure. In temperate areas, agricultural landscapes undergo drastic seasonal changes, which can directly affect arthropod communities and their responses to landscape structure. In this study, we aimed to test whether the effects of landscape structure vary throughout the summer season, using predatory beetles as a model system (Coleoptera Carabidae: Carabinae and Cicindelinae). Our main hypothesis was that predatory beetle assemblages were more influenced by landscape structure at mid-season (July-August) when the vegetation is fully grown. Then, we hypothesized that differences between species would be related to their biological and ecological characteristics. Ground and tiger beetles were sampled with pitfall traps in 20 ditch borders adjacent to cornfields, from early June to the end of September in 2006 and 2007, in the Vacher creek watershed (Quebec, Canada). Landscape cartography was measured at 200 m and 500 m radius around each site. As predicted, landscape structure had a strong seasonal component in structuring these communities, with the greatest influence observed at mid-season. Regarding species abundances, landscape structure mainly had the highest influence at mid-season, but variations were observed between species. Landscape effect on Harpalus pensylvanicus (DeGeer) appeared the most variable throughout the season (0-53.8%) whereas landscape effect on Poecilus lucublandus lucublandus (Say) appeared the most consistent (13.9.1-41.0%). Overall, our results demonstrate the importance of considering seasonality when assessing the effects of landscape structure on arthropod assemblage in temperate areas, but further studies are needed to determine species ecological characteristics that explain their differential responses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".