AGRICULTURAL EFFECTS ON AMPHIBIAN PARASITISM: IMPORTANCE OF GENERAL HABITAT PERTURBATIONS AND PARASITE LIFE CYCLES
Bibliographic record
Abstract
Agricultural activity can alter host-parasite interactions through associated contaminants and habitat perturbations. It is critical to determine whether agricultural effects are widespread or limited to specific types of agriculture. We examined influences of soybean agriculture on trematode parasitism of larval amphibians (grey tree frogs; Hyla versicolor) to assess the potential effects of a commonly applied pesticide (glyphosate) and landscape factors relative to previous field studies focusing on the herbicide atrazine. Overall, trematode parasite infection did not differ between soybean-adjacent and nonagricultural ponds (87.7% and 72.6% mean infection, respectively). However, host-generalist echinostome species were more common in tadpoles from soybean-associated ponds (86.3% mean infection versus 36.2% in nonagricultural ponds) as well as sites with large or short average distances to forest cover and roads, respectively. In contrast, the occurrence of a host-specialist (Alaria sp.) group was greater in nonagricultural ponds (50.3% mean infection versus 9.8% in soybean-associated ponds) and increased with shorter distances to the closest forest patch and smaller average forest distance. Because glyphosate was not detected at any site and landscape influences were parasite-specific, we suggest that agriculture may have broad effects on wildlife diseases through habitat alterations that affect pathogen transmission via host habitat suitability. Notably, nonagricultural ponds had a lower mean distance to the nearest forest patch and lower mean forest distance compared with soybean-adjacent ponds. As a result, we emphasize the need for wider investigations of habitat perturbations generally associated with agriculture for host-pathogen interactions, and consequently, wildlife conservation and management strategies.
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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 | 0.000 | 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 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".