Habitat filtering influences plant–pollinator interactions in prairie ecosystems
Bibliographic record
Abstract
The xeric hypothesis is that bees are more abundant pollinators than anthophilous flies in dry, temperate biomes, and the habitat filtering hypothesis is that differences in the proportions will impact plant community composition because different pollinators favour different floral traits. However, few studies have examined the predictive value of these hypotheses. In particular, differences in plant–pollinator compositions within biomes, such as the Prairie Ecozone, have not been compared. We documented plant–pollinator interactions and plant abundance in three Canadian prairie types. Flower visits in moist tall grass prairie were mainly by flies in the Syrphidae, whereas visits in the drier fescue and mixed grass prairie were mainly by long-tongued bees in the Apidae. Short-tongued bee visits were not significantly different between the prairie types. Insect visits to tubular, zygomorphic, violet/blue-, and white-flowered plants were higher in drier fescue and mixed grass prairie than in moister tall grass prairie. Further, proportions of plants with these features were lower in the tall grass prairie. Thus differences in the proportion of flies and long-tongued bees, likely affected by habitat conditions including moisture levels, appear to be influencing the types of plants that dominate each prairie type, providing some support for these hypotheses.
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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 | 0.001 |
| 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".