Wild bee distribution near forested landscapes is dependent on successional state
Bibliographic record
Abstract
Abstract Background Forested landscapes are valuable sources of ecosystem services especially in areas dedicated to intense agricultural activities. Distance from forest margin is known to influence the wild bee community in the landscape surrounding forested patches. Yet little is known regarding how bee communities distribute themselves in landscapes that exhibit different forest successional states. Methods We examined how land use type and distance from the forest edge affect the abundance and richness of the wild bee community across four forest successional states. Bees were collected in sites representing four stages of forest succession and analyzed using generalized linear mixed models with negative binomial distributions. Results Wild bee diversity is reduced in forested environments that maintain dense stands of trees and high canopy cover. Additionally, distance from the forest edge was an important factor determining wild bee distribution in successional stages adjacent to forest edges. Furthermore, we found that bees maintain high specificity for distinct successional states and distances. Conclusions Our results demonstrate how different successional stages can harbor different bee species and highlight the ability of microhabitats to act as reservoirs of bee diversity in and around forest margins. Furthermore, we found that maximizing successional patchiness across forested landscapes increases the amount of available habitat that can support a diverse suite of bees with different nesting biologies and behaviors.
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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 | 0.000 |
| 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) | 0.002 | 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".