Parasite traits shape the association between forest loss and infection: A global meta-analysis
Bibliographic record
Abstract
Forest loss can affect host–parasite dynamics, posing risks to wildlife and human health. Most work has investigated how host traits moderate associations between forest loss and prevalence, but the role that parasite traits play is less understood. We synthesized parasite prevalence and parasite trait data from publicly available databases representing carnivores, ungulates, and primate host species. We combined these data with open-source, remote-sensing forest loss data and conducted multi-level phylogenetic meta-analyses. While we found no overall association between forest loss and prevalence across parasites, trends emerged when considering different parasite taxa. Further, although prevalence did not differ by transmission mode overall, forest-loss prevalence associations varied by transmission mode within parasite taxa. For instance, prevalence decreased with forest loss for closely transmitted helminths but increased for not closely transmitted helminths. These results illustrate that parasite traits must be considered to understand complex associations between environmental change and infection outcomes.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".