Shifts in species interactions and farming contexts mediate net effects of birds in agroecosystems
Bibliographic record
Abstract
Some birds are viewed as pests and vectors of foodborne pathogens in farmlands, yet birds also benefit growers by consuming pests. While many growers seek to prevent birds from accessing their farms, few studies have attempted to quantify the net effects of bird services and disservices, let alone how net effects shift across farm management strategies. We quantified the net effect of birds on crop production across 20 California strawberry (Fragaria × ananassa) farms that varied in local management practices and landscape context. We surveyed farms for berry damage and bird droppings (as potential sources of pathogens) and implemented a large-scale exclusion experiment to quantify the impact of birds on production. We found that birds had only a slightly negative overall impact on strawberry production, reducing economic value by 3.6%. Direct bird damage and intraguild predation contributed equally to this net effect, underscoring the importance of indirect trophic interactions that may be less apparent to growers. In simple landscapes (e.g., low proportions of surrounding seminatural habitat), birds provided pest control in the interiors of farm fields, and costs from bird damage to crops peaked at field edges. In complex landscapes (e.g., high proportions of seminatural habitat), birds were more likely to disrupt pest control by feeding as intraguild predators. Nonetheless, seminatural habitat dampened bird services and disservices, and our models predicted that removing habitat around farm fields would increase costs from bird damage to crops by up to 76%. Fecal contamination of crops was extremely rare (0.01%). However, both fecal contamination and bird damage did increase on farms with higher densities of fencing and wires, where birds often perch. Our results demonstrate that maintaining seminatural habitat around farms may enhance bird diversity and mitigate bird damage without increasing food safety risks. We also show that the net effects of birds depend on farming context and vary in complex ways in relation to locations within a farm, local farm attributes, and the surrounding landscape. This context-specific variation must be considered in order to optimize the management of wild birds in agroecosystems.
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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) | 0.000 | 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".