Flowering phenology influences butterfly nectar foraging on non‐native plants in an oak savanna
Bibliographic record
Abstract
The negative impacts of non-native species have been well documented, but some non-natives can play a positive role in native ecosystems. One way that non-native plants can positively interact with native butterflies is by provisioning nectar. Relatively little is known about the role of phenology in determining native butterfly visitation to non-native plants for nectar, yet flowering time directly controls nectar availability. Here we investigate the phenological patterns of flowering by native and non-native plants and nectar foraging by native butterflies in an oak savanna on Vancouver Island, British Columbia, Canada. We also test whether native butterflies select nectar sources in proportion to their availability. We found that non-native plants were well integrated into butterfly nectar diets (83% of foraging observations) and that visitation to non-natives increased later in the season when native plants were no longer flowering. We also found that butterflies selected non-native flowers more often than expected based on their availability, suggesting that these plants represent a potentially valuable resource. Our study shows that non-native species have the potential to drive key species interactions in seasonal ecosystems. Management regimes focused on eradicating non-native species may need to reconsider their aims and evaluate resources that non-natives provide.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".