How to study parasites and host migration: a roadmap for empiricists
Bibliographic record
Abstract
Animal migration (round-trip, predictable movements) takes individuals across space and time, bringing them into contact with new communities of organisms. In particular, migratory movements shape (and are shaped by) the costs and risk of parasite transmission. Unfortunately, our understanding of how migration and parasite infection interact has not proceeded evenly. Although numerous conceptual frameworks (e.g. mathematical models) have been developed, most empirical evidence of migration-parasite interactions are drawn from pre-existing empirical studies that were conducted using other conceptual frameworks, which limits our understanding. Here, we synthesise and analyse existing work, and then provide a roadmap for future (especially empirical) studies. First, we synthesise the conceptual frameworks that have been developed to understand interactions between migration and parasites (e.g. migratory exposure, escape, allopatry, recovery, culling, separation, stalling and relapse). Second, we highlight current challenges to studying migration and parasites empirically, and to integrating empirical and theoretical perspectives, particularly emphasizing the challenge of feedback loops. Finally, we provide a guide to overcoming these challenges in empirical studies, using comparative, observational and experimental approaches. Beyond guiding future empirical work, this review aims to inspire stronger collaboration between empiricists and theorists studying the intersection of migration and parasite infection. Such collaboration will help overcome current limits to our understanding of how migration and parasites interact, and allow us to predict how these critical ecological processes will change in the future.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.010 | 0.035 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".