Human impact modulates chytrid fungus occurrence in amphibians in the Brazilian Atlantic Forest
Bibliographic record
Abstract
Here, we investigate the influence of scale on different drivers influencing the occurrence of the chytrid fungus, Batrachochytrium dendrobatidis (Bd), in the Atlantic Forest, Brazil. We used gridded values of proxies of the abiotic, biotic and anthropogenic components of landscapes where Bd infects amphibians. Building upon disease prevalence data obtained from a previous work, we fitted GLS multiple regression models using extracted values of the three predictors for each prevalence centroid in space, explicitly controlling for spatial autocorrelation among predictors. To test for the effect of scale on driving the macroecology of Bd infection, we performed tests at different spatial scales. We then used model selection procedures to evaluate the relative contribution of the different predictors on the occurrence of the fungus. The Human Footprint Index better explained a pathogenic species occurrence than largely studied biotic and abiotic factors (i.e., host species distribution and minimum monthly potential evapotranspiration). That effect was, however, not observed at landscape scale, where we found no difference among the relative influence of predictors. Our results indicate that human-mediated impacts on environments can be strong drivers of spread of infectious diseases on native faunas worldwide, thus, suggesting that anthropogenic landscapes may create favourable conditions for the occurrence of this and other infectious diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".