Keystone Predation and Plant Species Coexistence: The Role of Carnivore Hunting Mode
Bibliographic record
Abstract
Plant communities are shaped by bottom-up processes such as competition for nutrients and top-down processes such as herbivory. Although much theoretical work has studied how herbivores can mediate plant species coexistence, indirect effects caused by the carnivores that consume herbivores have been largely ignored. These carnivores can have significant indirect effects on plants by altering herbivore density (density-mediated effects) and behavior (trait-mediated effects). Carnivores that differ in traits, particularly in their hunting mode, cause different indirect effects on plants and, ultimately, different plant community compositions. We analyze a food-web model to determine how plant coexistence is affected by herbivore-consuming carnivores, contrasting those causing only density-mediated effects with those causing trait-mediated effects as well. In the latter case, herbivores can adjust their consumption of a refuge plant species. We derive a general graphical model to study the interplay of density- and trait-mediated effects. We show that carnivores eliciting both effects can sustain plant species coexistence, given intermediate intensities of behavioral adjustments. Coexistence is more likely, and more stable, if the refuge plant is competitively dominant. These results extend our understanding of carnivore indirect effects in food webs and show that behavioral effects can have major consequences on plant community structure, stressing the need for theoretical approaches that incorporate dynamical traits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".