Ungrazed seminatural habitats around farms benefit bird conservation without enhancing foodborne pathogen risks
Bibliographic record
Abstract
Abstract Context Conservation in working landscapes is critical for halting biodiversity declines and ensuring farming system sustainability. However, concerns that wildlife may carry foodborne pathogens has created pressure on farmers to remove habitat and reduce biodiversity, undermining farmland conservation. Nonetheless, simplified farming landscapes may host bird communities that carry higher foodborne disease risks. Objectives We analyzed the effects of local farming practices and surrounding landscapes on bird communities and food-safety risks across 30 California lettuce farms. Specifically, we sought to determine how farmland diversification affects bird diversity, fecal contamination, and foodborne pathogen incidences, thereby identifying potential tradeoffs between managing farms for bird conservation versus food safety. Methods We surveyed birds at 227 point-count locations, quantified fecal contamination along 120 transects, and assayed 601 bird feces for pathogenic E. coli , Campylobacter spp . , and Salmonella spp. We then used hierarchical models to quantify effects of farm management and landscape context on bird communities and food-safety risks. Results Surrounding ungrazed seminatural areas were associated with higher bird diversity, more species of conservation concern, and fewer flocks that may increase risks from foodborne pathogens. In contrast, on-farm diversification practices and surrounding grazing lands offered weaker bird conservation benefits. Surrounding grazed lands were associated with more potentially pathogenic bird feces in crop fields. Conclusions Our results suggest that habitat conservation around produce farms could support bird conservation without increasing foodborne pathogens, especially on farms further from grazing lands. Thus, interventions that diversify farming systems offer potential to simultaneously conserve biodiversity and provide safe food for human consumption.
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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 | 0.001 | 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) | 0.000 | 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".