Uncertainty of risk and information limitations shape neophobic antipredator responses in Trinidadian guppies
Bibliographic record
Abstract
The combined effects of changing climates, anthropogenic disturbances, and invasive species lead to short and long-term ecosystem changes. As a result, prey populations may be faced with increased uncertainty of risk (i.e., inability to predict predation events due to limitations on the quantity or quality of information). This uncertainty impacts decision-making and risk-assessment abilities among prey. Therefore, it is critical to understand the ecological factors driving uncertainty, and how prey deal with information limitations. Recently, phenotypically plastic neophobic predator avoidance (NPA, increased vigilance towards novel stimuli) has been suggested as a response of prey to uncertainty, without the costs associated with learning specific predator cues. Trinidadian guppies (Poecilia reticulata) from high-predation populations exhibit NPA, and NPA can be induced in predator-naïve populations after increasing mean predation risk. However, it remains unclear what specific factors drive uncertainty and the resulting NPA. Using Trinidadian guppies as a model system, I conduct a combination of laboratory and field experiments to identify potential drivers of uncertainty of risk within prey populations. I use an information ecology framework to argue that uncertainty of risk may arise from prey experience during risk information detection, from the risk information source (i.e., conspecifics and/or predators), and from the environment through which risk information must travel. I demonstrate that NPA increases when prey experience multiple novel cues, simultaneously or over time. After repeated encounters with a single novel cue, NPA is lost unless the cue is spatially unpredictable. Furthermore, NPA to a novel cue is mediated when paired with a social safety cue. I also assess predator guild composition and demonstrate that increased predator density (i.e., mean risk), and to a weaker extent predator diversity, increases NPA. Lastly, I show that NPA is greater in microhabitats with greater water velocity and habitat complexity, smaller dimensions, and decreased substrate heterogeneity and substrate diversity. Uncertainty of risk may have deleterious impacts on the distribution and abundance of predator and prey species. My thesis identifies factors contributing to uncertainty, enables prediction of prey responses to such conditions, and can contribute to conservation and management efforts of socio-economically important, endangered, or invasive species.
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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) | 0.000 | 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) | 0.001 | 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".