Taking cues from ecological and evolutionary theories to expand the landscape of disgust
Bibliographic record
Abstract
1. Individual animals can attempt to prevent or mitigate parasite risks by altering their behaviour or space use. Behavioural change in response to the presence of parasites in the environment generates what is known as the “landscape of disgust” (analogous to the predator-induced “landscape of fear”). Using a spatial description of cues that indicate parasite risk, and characterizing individual responses to those cues, can allow researchers to quantify and interpret how hosts navigate the landscape of disgust. The landscape of disgust framework could facilitate much needed research on the ecological impacts of parasitism, advancing the fields of disease, spatial, and behavioural ecology. 2. Despite the potential for improving our inference of host-parasite dynamics, three key limitations of the landscape of disgust restrict the potential insight that can be gained from current research. First, many host-parasite systems will not be appropriate for invoking the landscape of disgust framework. Second, existing research has primarily focused on immediate choices made by hosts on small scales, limiting predictive power, generalizability, and the value of the insight obtained from the landscape of disgust framework. Finally, relevant ecological and evolutionary theory has not been integrated into the framework, challenging our ability to interpret and understand the application of the landscape of disgust within the context of most host-parasite systems. 3. In this review, we explore the specific requirements for implementing a landscape of disgust framework in empirical systems. We propose an expansion to the landscape of disgust framework that integrates principles from habitat selection and evolutionary theories, aiming to generate novel insight. To discuss the integration of classic ecological and evolutionary theory, we explore how the landscape of disgust varies both within and across generations, presenting opportunities for future research. 4. Despite recent interest in understanding the impact of parasitism on animal behavioural, spatial, and movement ecology, many unanswered questions remain. We build on the landscape of disgust framework by identifying weaknesses and possible applications in different ecological and evolutionary contexts. We encourage researchers to implement this framework empirically to further our understanding of host-parasite systems.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".