Not all matrix habitat is created equal for rare bee species in forest habitat
Bibliographic record
Abstract
1. Anthropogenic land‐use change alters the ecological communities in a landscape's remaining natural habitat. These matrix effects are understudied in rare species, which comprise a majority of biodiversity, contribute to ecosystem services, and are particularly vulnerable to population declines. 2. This study used bee species survey data from New Jersey forest fragments, in conjunction with an independent museum data set, to identify rare bee species and developed regularised regularised regression models of matrix effects on their richness and abundance. 3. By using a regularised modelling approach, this study explains 44.43% of variation in rare bee species richness between forest fragments and 27.27% of variation in their abundance (average adjusted R 2 ). 4. The present study's results identified specific subcategories of urban land use in the surrounding 1000 m of forest fragments that explained patterns in rare bee species richness and abundance, providing insights into how to select forest fragments to conserve rare bees. Low priority should be given to fragments in close proximity to high‐ and medium‐density housing, which likely harms rare bees by increasing impervious surface cover. Low priority should also be given to fragments near golf courses and athletic fields; this study speculates that these land uses harm rare bees through mowing and pesticide application. Finally, high priority should be given to fragments composed of a larger proportion of mature forest, which likely provides a greater diversity of native floral resources. Altogether, this study's model results reveal the importance of matrix habitats for the biodiversity of rare species in forest fragments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".