Relationships between coyote ecology and sheep management in the Lower Fraser Valley, B.C.
Bibliographic record
Abstract
Domestic sheep farmers in the lower Fraser Valley (L.F.V.) had reported increasing losses of sheep to coyote (Canis latrans) and dog (C. familiaris) predation. The objectives of this study were: (1) to determine if management and geographic factors predispose sheep farms to coyote and dog predation; (2) to assess the relative impact of coyote and dog predation on the L.F.V. sheep population; (3) to record basic attributes of coyote biology (taxonomy, reproduction, food habits, home range, movements, activity patterns, and predatory behaviour); (4) to provide practical and economical recommendations to reduce or prevent coyote and dog predation on sheep in the L.F.V. One hundred and twelve sheep farmers were interviewed over three years, 1979 to 1981. Farms which lost sheep to coyotes characteristically had relatively large flocks (>50 ewes) on large fields (4+ ha), did not confine sheep at night, and either buried or left sheep carcasses exposed. There were no common factors among farms which lost sheep to dogs. Predation accounted for 28.2% of all mortality and 2.4% of the total population sampled. Coyotes killed 69.7% and 74.7% of all ewes and lambs lost to predators. An average of 24.3% of the farms lost sheep to coyotes and dogs each year. However, 55.2% of the farms which lost sheep to coyotes did so in two or three consecutive years compared to 17.4% of farms which lost sheep to dogs. Coyotes in the L.F.V. were similar in most biological aspects studied to other coyote populations in North America. The only exception was that small rodents, primarily Microtus townsendi composed over 70% (scat volume) of their diet, a proportion higher than in other areas. Domestic livestock (mostly poultry carrion) comprised only 4.3% of the diet, sheep only 0.2%. I concluded that in the rural-urban L.F.V. interface, prevention of coyote predation (and secondarily dog predation) on hobby farms is largely a matter of management. The most effective and economical solution is to provide predator-proof enclosures for night confinement of sheep because coyotes were most active at night. This method could be further enhanced by removing livestock carcasses off the farm or by burying and liming them to avoid attracting coyotes to the farm vicinity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".