The evolution and ecology of multiple antipredator defences
Bibliographic record
Abstract
Prey seldom rely on a single type of antipredator defence, often using multiple defences to avoid predation. In many cases, selection in different contexts may favour the evolution of multiple defences in a prey. However, a prey may use multiple defences to protect itself during a single predator encounter. Such "defence portfolios" that defend prey against a single instance of predation are distributed across and within successive stages of the predation sequence (encounter, detection, identification, approach (attack), subjugation and consumption). We contend that at present, our understanding of defence portfolio evolution is incomplete, and seen from the fragmentary perspective of specific sensory systems (e.g., visual) or specific types of defences (especially aposematism). In this review, we aim to build a comprehensive framework for conceptualizing the evolution of multiple prey defences, beginning with hypotheses for the evolution of multiple defences in general, and defence portfolios in particular. We then examine idealized models of resource trade-offs and functional interactions between traits, along with evidence supporting them. We find that defence portfolios are constrained by resource allocation to other aspects of life history, as well as functional incompatibilities between different defences. We also find that selection is likely to favour combinations of defences that have synergistic effects on predator behaviour and prey survival. Next, we examine specific aspects of prey ecology, genetics and development, and predator cognition that modify the predictions of current hypotheses or introduce competing hypotheses. We outline schema for gathering data on the distribution of prey defences across species and geography, determining how multiple defences are produced, and testing the proximate mechanisms by which multiple prey defences impact predator behaviour. Adopting these approaches will strengthen our understanding of multiple defensive strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".