Parasitism and the tradeoffs of social grouping: The role of parasite transmission mode
Bibliographic record
Abstract
Animals use social grouping for numerous fitness-enhancing processes, such as foraging, social learning, defense, and energy expenditure. One broadly referenced negative consequence of social grouping is the increased risk of exposure to parasites, which are defined broadly here as organisms with obligate, persistent, and harmful consumer associations with a host. However, there is growing evidence that group living can also act as a defensive mechanism against parasites. Here, we present a conceptual framework that explores host sociability in the context of parasite life history, arguing that the positive or negative impact of a social lifestyle on infection risk is strongly linked to the parasite’s transmission mode. We discuss the link between host sociability and infection risk with respect to common, non-mutually exclusive differences in transmission: direct vs. indirect, density- vs. frequency-dependent, and simple vs. complex life cycles. We then use our framework to discuss the mechanisms for active parasite avoidance, passive effects of infection-induced phenotypes, and their impacts on host social networks. Further, we highlight additional important factors that can modulate these dynamics (e.g., parasite virulence, infection intensity, co-infection by multiple parasites, and environmental factors). The goal of this broad, comparative approach is to provide researchers from multiple disciplines with a unified framework to better understand the relationship between social grouping and host-parasite interactions across diverse systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".