Flowering plant communities mediate the effects of habitat composition and configuration on wild pollinator communities
Bibliographic record
Abstract
Abstract There is strong evidence that landscape‐scale factors such as habitat diversity, composition and configuration are important drivers of declines in pollinators and pollination services. However, context and species‐specific responses make it challenging to draw general conclusions about the most important components of landscapes that support diverse and abundant pollinator communities. In this study, we took a functional‐traits approach to community assembly and tested the hypothesis that landscape properties act most strongly on pollinators indirectly, through their influence on flowering plant communities. Using plant and pollinator data from 96 landscapes in Britain, we tested the associations between plant and pollinator communities and local environmental factors, such as habitat cover and configuration, using path analysis based on Mantel and partial Mantel statistics. When all pollinators were considered, we found that the environmental factors had stronger links to the composition of flowering plant communities than to the composition of pollinator communities. Further, the flowering plant community was strongly linked to the pollinator community suggesting a mediating role between land use and pollinators. When separating the pollinator community into taxonomic groups, we found the same result for hoverflies, but wild bees were linked to both environmental factors and flowering plants. We further explored these links with structural equation models using the response‐effect trait framework as a guiding principle. We found strong evidence that land‐use composition and configuration influence the trait distribution and functional diversity of the pollinator community via plant community composition. These findings suggest that the indirect effect of land use on pollinators via flowering plants should be considered in informing the design of pollinator friendly landscapes and in future research of the effects of land use and management on wild pollinators. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".