A win–win between farmers and an apex predator: investigating the relationship between bald eagles and dairy farms
Bibliographic record
Abstract
Abstract Human–wildlife conflicts on farms and ranches are common and well‐documented, particularly with apex predators. Predation of livestock, for example, can result in serious economic burdens for farmers and can become threats to wildlife populations as farmers take action to eliminate or displace populations. Among apex predators, bald eagles ( Haliaeetus leucocephalus ) have received increased media attention in recent years due to conflicts with farmers across the United States. This raises challenges for both farmers and wildlife managers as eagle abundance continues to increase and natural prey resources decline. Interestingly, a recent study in northwestern Washington State reported high eagle activity on dairy farms in response to declines in salmon carcass availability, an important resource for wintering eagles across western North America. Despite the potential for human–wildlife conflict in these areas, little is known of the relationship between eagles and dairy farms. In this study, we investigated the extent of eagle activity on dairy farms and the relationship between eagles and dairy farmers using semistructured interviews with dairy farmers. We found that (1) eagles were attracted to dairy farms to feed primarily on cow afterbirth and calf carcasses, (2) responding farmers had no issue with the presence of eagles on their farms, and (3) many dairy farmers felt that eagles provided services to their farms. Of these services, the most recognized were scavenging of dairy farm byproducts and removal or deterrence of unwanted pest species. Increased eagle abundance on dairy farms and the subsidy of anthropogenic resources may also influence the ecological role of eagles as top predators in agroecosystems. Ultimately, farmers' decisions to provide anthropogenic resources have apparently mitigated human–eagle conflict while potentially reducing top–down pressures on other wild prey species. Farmers and wildlife managers may each benefit through cooperation in continuing to understand the intricacies of dairy farm–eagle relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".