Pollination and nectar larceny by birds and bees in novel forests of the Hawaiian Islands
Bibliographic record
Abstract
The extinction of native species and introduction of non-native species may lead to the disruption of biotic interactions. Pollination is a critical ecosystem process that often requires mutualisms between animals and plants. Non-native animals may interact with native flowering plants, with the potential to pollinate or steal nectar (larceny) from flowers without pollination. In the Hawaiian Islands, many native plants have lost their original pollinators. Birds and insects are known to visit native plant flowers, but it is unclear whether they pollinate or steal nectar, whether native and non-native species differ in their interactions with flowers, and what influences visitation to flowers. On Oʻahu, we deployed camera traps and conducted in-person observations on four at-risk species of Hawaiian lobelioids (Campanulaceae). We observed birds, mammals, and insects visiting flowers, with a native bird and native bee visiting most frequently. Regardless of native versus non-native status, bees made contact with reproductive structures during most visits (90.5% of visits), while birds stole nectar during most visits (99.3% of visits). Bee and bird visitation increased with the number of flowers on focal plants. Bird visitation also increased with canopy cover and the number of nearby conspecific flowers and decreased with the number of nearby heterospecific flowers. Our results indicate that bees may pollinate plants that were historically bird-pollinated, while native and non-native birds have neutral or negative impacts on these plants. Broadly, we contribute to an understanding of how native plant pollination can be altered in changing ecosystems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".