Exploring compensation programs and depredation reporting for wolf-livestock conflict across the North American West
Bibliographic record
Abstract
With the continuing reestablishment of wolves (Canis lupus) across the American West, livestock producers will be increasingly exposed to wolf-related conflict such as livestock depredation. The financial implications of wolf conflict can be significant depending on the context of an individual livestock operation. Compensation programs administered by government agencies and occasionally non-government organizations aim to ameliorate some of the financial risks associated with wolves and the loss of livestock; yet the effectiveness of these programs at fostering tolerance and adequately addressing losses is increasingly questioned. Reporting depredation is often required for compensation eligibility, and reports are the primary source of data used by wildlife agencies to address conflict and inform local management. Yet not all producers report depredation or utilize compensation, and we know very little about what factors motivate reporting and compensation use. Additionally, we know very little about producer perspectives on existing compensation programs or whether producers are interested in alternatives. I designed an exploratory survey based on an expanded version of the Theory of Planned Behavior to identify the social-psychological and demographic factors most strongly correlated with compensation use and wolf depredation reporting intentional outcomes. I also utilized a simplified Discrete Choice Question to gauge producer interest in alternatives to traditional compensation programs. My online survey was sent to livestock producers across Arizona, California, Colorado, Idaho, Montana, New Mexico, Washington, Wyoming, and Alberta, Canada (n=165 responses). While 87% of respondents experiencing wolf depredation had reported a depredation in the past, only 69% had utilized compensation. Levels of satisfaction with existing compensation programs were mixed. The most common reasons stated for not applying for compensation included dissatisfaction with the depredation confirmation process (too much validation and/or paperwork), that the amount of compensation available is not enough or not worth the hassle of applying for compensation, and a lack of trust and satisfaction with state government employees and their wolf management decisions. Using Lasso regression, I found that descriptive norms (p<0.01), age (p<0.01), and past experience with depredation (p<0.05) were the strongest predictors of reporting intention. Trust (p<0.001), perceived risk (p<0.05), descriptive and personal norms (p≦0.05), attitudes (p<0.05), and state of residence (varied by state) had the strongest relationship with compensation use intention. The overall predictive power of my models was high, suggesting the expanded Theory of Planned Behavior model was effective at predicting both behavioral intentions. The results of my Choice Question suggest that my surveyed population wants access to diverse and adaptive payment and engagement options for wolf depredation. I also found that although these producers are interested in alternatives like Habitat Leases and Cost-Shares for financial and technical assistance with conflict reduction tools, they still want access to traditional compensation for depredation to address local variation in depredation across neighboring operations. Although limited by my sample size, these findings suggest that 1. building interpersonal trust between wildlife agency personnel and livestock producers, 2. reducing wolf-related financial vulnerability by providing compensation for indirect losses and/or undetected wolf depredations in addition to payments for depredation, and 3. building descriptive norms by providing peer-to-peer knowledge sharing opportunities for producers to share with one another may all increase reporting and compensation use intentions among livestock producers, and by extension, may influence behavior.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".