Parasites and Host Performance: Incorporating Infection into Our Understanding of Animal Movement
Bibliographic record
Abstract
SYNOPSIS: Studies of animal locomotion and movement largely assume that individuals are healthy and performing to the best of their abilities in ways which are adapted to their survival. However, wild animals face numerous ecological challenges that can compromise their health, reduce their performance capacity, impair their movement abilities and, ultimately, lower their fitness. By diverting resources and increasing host energetic demands, parasites, bacteria, and viruses (hereafter parasites) can dramatically influence the ways in which their hosts allocate energy to movement. Yet, the role of parasites in influencing animal locomotor performance and movement remains relatively unexplored, perhaps because animals often hide outward signs of sickness, and parasites tend to be small and inconspicuous to researchers. Here, we review how parasite infection can alter host locomotor performance via impacts on host morphology and physiology. We also give examples of behavioral strategies that some hosts employ to help overcome the disadvantages imposed by infection. Finally, we discuss how parasites can lead to both increased and decreased host movement patterns, either as an adaptive strategy for the host or due to manipulation by the parasite. The dynamic interplay between host movement (such as migration and dispersal) and infection has profound consequences for population and ecosystem-level processes that are influenced by movement. Acknowledging the important functional role played by parasites in driving the evolution of host locomotor performance and behavior is a critical step toward developing a comprehensive understanding of the causes and consequences of animal movement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".