Proximity to agriculture alters abundance and community composition of wild sunflower mutualists and antagonists
Bibliographic record
Abstract
Anthropogenic modifications of the landscape, such as agriculture, are widespread globally and can reduce native biodiversity and homogenize communities by decreasing variation in species composition across sites. Partitioning anthropogenic impacts among species that have positive versus negative effects on plants may improve our ability to forecast the ecological and evolutionary consequences of these alterations in communities. Here, we manipulated the distance of populations of a wild sunflower species (Helianthus annuus texanus) to fields of its domesticated relative (crop sunflowers, H. annuus) and contrasted subsequent shifts in the abundance and community composition of mutualists (pollinators) and antagonists (seed predators, folivores) of H. a. texanus. With some exceptions, populations of H. a. texanus near crop sunflowers supported higher numbers of pollinators than those far from crop sunflowers. In contrast, in the majority of cases, populations of H. a. texanus supported more seed predators when located far from crop sunflowers. Folivore damage to plants was greater far from crop sunflowers, and was never greater near crop sunflowers. Contrary to the prediction that proximity to agriculture homogenizes community composition, we found β‐diversity of pollinators (species turnover between populations) was greater near crop sunflowers. Our results demonstrate that mutualists and antagonists of a wild plant species respond differently to the proximity of a related crop species, indicating the potential for both altered population dynamics and complex selection pressures on wild species in agricultural landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".