An Investigation of Five Decades of Canid Management Research in the United States and Canada
Bibliographic record
Abstract
Predator removal has been the dominant method of mitigating predator damage to livestock for centuries in the United States and Canada. The 1970s saw legislative and cultural shifts from predator eradication to selective and non-lethal mitigation strategies. Research concurrently increased and focused on which strategies were effective at reducing livestock depredations. I collected research findings published between 1970 and 2018 on mitigating livestock depredation by coyotes and wolves. I investigated potential issues in this literature with implications for current canid management, such as whether traditional management strategies have been properly evaluated or whether the research endorsed a particular strategy. I also investigated the characteristics of the research over time and whether the research showed evidence of publication bias. Lastly, I evaluated whether the confounding effect of context has been accounted for in the research. I found there were nearly three times as many non-lethal than lethal research findings and twice as many types of non-lethal strategies than lethal strategies. My results also justify the use of producer assessments in future research on mitigating livestock depredations. I found differences in research characteristics, such as the canid species evaluated and how research findings are disseminated, across the five decades between 1970 and 2018. I also report that research quality improved across the five decades as there were fewer lower quality research findings after the 1980s. There was no evidence of traditional success oriented publication bias. I did find evidence that non-success related research characteristics were associated with publication in journals and I termed these relationships ‘non-traditional publication bias’. Research findings that evaluated wolves, had academic Principal Investigators, or used statistical analyses were more likely to be published in journals. My final analysis focused on five contextual factors: historical/concurrent lethal control, wild prey, landscape, season, and anthropogenic characteristics. Research findings did not consistently report contextual information. Similarly, there were only a few instances of authors reporting an effect of contextual factors on their results. Based on the CONSORT checklist used in medical research, I developed guidelines for the reporting of future research to ensure replicability and usability in meta-analyses.
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 | 0.000 | 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".