Flowers for habitat enhancement primarily benefit common insect pollinators across temperate grasslands
Bibliographic record
Abstract
Abstract Pollinator habitat enhancement typically relies on flowering species that are possible to cultivate and produce in large quantities, instead of species that fulfil valuable functional roles for plant–pollinator interaction diversity. Using plant–pollinator interaction data from 673 plant–pollinator networks within 17 different studies in temperate grasslands of Europe, we evaluated if native plant genera that are readily available from commercial seed suppliers frequently occur across plant–pollinator networks, are attractive to pollinators and support pollinator assemblages that occupy complementary functional roles in interaction space. Readily available flowers frequently occurred across plant–pollinator networks and were attractive to pollinators. On average, only 8.29 (SE = 0.90) readily available plant genera were required to support most of the pollinator species across all the studies analysed here, compared to 11.53 (SE = 1.64) plant genera with limited availability. However, they fulfilled redundant functional roles within communities by supporting overlapping assemblages of abundant pollinators. Simulated flower mixes of native plant genera that frequently occurred across pollinator networks supported fewer rare and less‐selective pollinators than a random selection of plant genera, indicating that revegetation may result in the functional homogenization of pollinator communities. Synthesis and applications : Flowers that are attractive and occupy a complementary position in interaction space could be prioritized in flower mixes to recover rare and specialized pollinators. By defining the ecological roles of readily available plants in plant–pollinator networks, particularly those that receive high visitation rates from complementary pollinator communities, this study provides a practical guide for conserving pollinators with ecologically informed restoration practices.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".