Risk-Induced Trait Responses and Non-consumptive Effects in Plants and Animals in Response to Their Invertebrate Herbivore and Parasite Natural Enemies
Bibliographic record
Abstract
Predators kill and consume prey, but also scare living prey. Fitness of prey can be reduced by direct killing and consumption, but also by non-consumptive effects (NCEs) if prey show costly risk-induced trait responses (RITRs) to predators, which are meant to reduce predation risk. Recently, similarities between predators and parasites as natural enemies have been recognized, including their potential to cause victim RITRs and NCEs. However, plant-herbivore and animal host-parasite associations might be more comparable as victim-enemy systems in this context than either is to prey-predator systems. This is because plant herbivores and animal parasites are often invertebrate species that are typically smaller than their victims, generally cause lower lethality, and allow for further defensive responses by victims after consumption begins. Invertebrate herbivores can cause diverse RITRs in plants through various means, and animals also exhibit assorted RITRs to increased parasitism risk. This synthesis aims to broadly compare these two enemy-victim systems by highlighting the ways in which plants and animals perceive threat and respond with a range of induced victim trait responses that can provide pre-emptive defense against invertebrate enemies. We also review evidence that RITRs are costly in terms of reducing victim fitness or abundance, demonstrating how work with one victim-enemy system can inform the other with respect to the frequency and magnitude of RITRs and possible NCEs. We particularly highlight gaps in our knowledge about plant and animal host responses to their invertebrate enemies that may guide directions for future research. Comparing how potential plant and animal victims respond pre-emptively to the threat of consumptionviaRITRs will help to advance our understanding of natural enemy ecology and may have utility for pest and disease control.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".