Linking Plant Trait Variation to Arthropod Community Ecology From an Ecological Perspective
Bibliographic record
Abstract
In this study, we utilized a suite of plant trait-based approaches towards understanding plant-arthropod interactions from an ecological perspective. Through laboratory and field-based assays on 14 species of woody shrubs native to the Coastal Sage Scrub ecosystem, we compared variance in plant resistance to herbivory and in non-defensive plant traits to variance in components of associated arthropod communities. Our analyses revealed the following: (1) plant resistance to herbivory assessed in a lab bioassay was overall a poor predictor of herbivore density in the field though it did provide insight into arthropod herbivore dynamics for several plant species and over time; (2) plant species varied strongly in predator abundance but this did not correlate with herbivore densities, suggesting a limited role for predators as a from of indirect defense for plants ; and (3) variance in non-defensive plant traits strongly correlated with variance in associated arthropod density and community composition. Taken together, our findings indicate that plant-arthropod interactions on ecological time scales in realistic ecological are driven more by traits presumably evolved for other purposes than by aspects of direct and indirect defense that are tyically the focus of studies on plant-herbivore evolutionary ecology. While these other traits may be the dominant drivers of herbivore abundance, we speculate that aspects of plant defense may still have weaker, hard to detect effects that act to mediate these interactions over evolutionary time scales. While these other traits may be the dominant drivers of herbivore abundance, we speculate that aspects of plant defense may still have weaker, hard to detect effects that act to mediate these interactions over evolutionary time scales.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".