Data from: The influence of warming on the biogeographic and phylogenetic dependence of herbivore-plant interactions
Bibliographic record
Abstract
Evolutionary experience and the phylogenetic relationships of plants have both been proposed to influence herbivore-plant interactions and plant invasion success. However, the direction and magnitude of these effects, and how such patterns are altered with increasing temperature, are rarely studied. Through laboratory functional response (FR) experiments, we tested whether the per capita feeding efficiency of an invasive generalist herbivore, the golden apple snail, Pomacea canaliculata, is dependent on the biogeographic origin and phylogenetic relatedness of host plants, and how increasing temperature alters these dependencies. The feeding efficiency of the herbivore was highest on plant species with which it had no shared evolutionary history, i.e. novel plants. Further, amongst evolutionarily familiar plants, snail feeding efficiency was higher on those species more closely related to the novel plants. However, these biogeographic dependencies became less pronounced with increasing temperature, whereas the phylogenetic dependence was unaffected. Collectively, our findings indicate that the susceptibility of plants to this invasive herbivore is mediated by both biogeographic origin and phylogenetic relatedness. We hypothesize that warming erodes the influence of evolutionary exposure, thereby altering herbivore-plant interactions and perhaps the invasion success of plants.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.047 |
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".