Predation and parasitism as determinants of animal personalities
Bibliographic record
Abstract
1. Within the same population, proactive (i.e. bolder, more exploratory, active and aggressive) and reactive (i.e. more timid, less exploratory, less active and more passive) individuals could be hypothetically maintained due a trade-off between foraging and anti-predator behaviour, provided that both phenotypes differ in their state (e.g. metabolic rates, body condition or energetic needs). 2. Yet, recent findings indicate that among-individual variation in intrinsic state can explain only a small proportion of variation in behaviour, meaning that other mechanisms, such as the presence of trophically transmitted parasites, might contribute to maintaining inter-individual behavioural differences. Empirical evidence, indeed, suggests strong relationships between certain animal personality traits and parasitic load within host populations. However, the direction of causation between these traits remains unclear: are different behaviours in infected hosts in contrast to uninfected ones the result of manipulation by parasites to increase host predation, or are some personalities inherently more susceptible to infection than others? 3. To better understand the role of parasites in shaping behavioural differences within host populations and examine to what extent parasite manipulation and/or intrinsic differences in parasite susceptibility contribute to maintaining behavioural differences, we used a simulation approach and analyzed the change in the frequencies of proactive and reactive individuals over time under different predation and starvation scenarios, when individual phenotype either affected a host's risk of infection or not. 4. We found that in the absence of parasites, predation pressure strongly affected the expression of host personality, but trade-offs between foraging and anti-predator behaviour alone could not explain the maintenance of inter-individual behavioural differences without temporal variation in predation pressure. By contrast, in the presence of parasites, the two host phenotypes could coexist within populations even when individuals experienced no temporal variations in predation risk, but only when proactive and reactive hosts were equally susceptible to parasitism. 5. Our findings thus indicate that parasites can play an important role in maintaining genetic diversity in their host populations in addition to generating behavioural differences though manipulation.
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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 | 0.004 |
| 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 | 0.000 |
| 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".