Habitat and climate influence beetle and spider communities in boreal forests
Bibliographic record
Abstract
Separating the influence of climate and habitat characteristics on forest communities could help better understand their potential sensitivity to environmental change. In this study, we sampled spiders and beetles in similar forest types, located along a ca. 4°C mean annual temperature spatial gradient in the boreal forest zone in Quebec, Canada. Specifically, we aimed to separate the effect on arthropod communities of two habitat-related factors that can be influenced by forest management (stand composition and stand age), and another one that cannot (climate). Overall, spider assemblages tended to be more abundant and species-rich in younger forest stands, while beetle assemblages were more abundant and species-rich in deciduous forest stands. Eight beetle and six spider species were significantly influenced by climate, independently from forest type, whereas 11 beetle and seven spider species were significantly influenced by both forest type and climate. While most of the beetle species affected by climate were associated with warmer locations, several spider species were more abundant in colder locations. By helping to ensure the retention of key forest types along potential dispersal pathways at the landscape level, forest management activities could help the conservation of species belonging to relatively cryptic taxa such as arthropods in a climate change context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".