Seasonality of native and non‐native flowers does not influence butterfly nectar foraging decisions in a semi‐urban meadow habitat
Bibliographic record
Abstract
Abstract The negative impacts of non‐native species on ecological communities have been well documented, but there is increasing evidence that non‐native species can play a positive role in some ecosystems. Non‐native plants can positively impact native butterflies by provisioning nectar, yet little is known about when and how often this happens. Here, we investigate whether native butterflies utilize non‐native plants as a source of nectar in a semi‐urban ecosystem around Ottawa, Canada. We explore the influence of seasonality on estimated nectar availability from native and non‐native plants, and nectar foraging by native butterflies. We test the null hypothesis that native butterflies select nectar sources in proportion to their availability. Additionally, we compare the usage of non‐native nectar by eastern monarchs, an important flagship species in insect conservation, to the rest of the butterfly community. We found that non‐native nectar was relatively more available earlier in the season and was well integrated into the diet of the butterfly community. Butterflies did not increase their visitation to non‐native flowers as this source of nectar increased in availability, suggesting other factors affect butterfly foraging decisions. Monarchs preferred foraging for nectar on common milkweed flowers when they were available. Monarchs also readily visited non‐native flowers later in the season, a critical time for energy acquisition in preparation for the monarch's fall migration. Our findings reinforce the idea that restoration strategies in disturbed ecosystems need to consider how seasonality influences the availability and relative importance of non‐native plants as key resources for native insect communities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".