Wildlife agency responses to chronic wasting disease in free‐ranging cervids
Bibliographic record
Abstract
Abstract Complex ecological and human‐influenced factors that are characteristic of chronic wasting disease (CWD) have created substantial and unique challenges for effective management in free‐ranging cervids. We sought to summarize and characterize management experiences and actions from 30 U.S. states, 4 Canadian provinces, and 3 European countries that have direct experience with CWD. We surveyed wildlife agencies that had detected CWD in their free‐ranging cervid population and collected information from journal articles, published reports, and agency webpages. We report management approaches and their apparent impacts by state, provincial, or national jurisdiction during 3 stages of response to CWD: 1) pre‐detection, 2) initial response, and 3) altered response. Agencies took a proactive approach to CWD during the pre‐detection phase; 12 of the 24 responding agencies had a weighted‐surveillance program in place and 17 had regulations aimed at disease prevention. There was no apparent difference in initial apparent prevalence of CWD among agencies with weighted surveillance in place and those without, but complicating factors, such as differing sampling methods and sample size, were present. Agencies reported 5 common surveillance strategies, and first detections were primarily from sampling hunter‐harvested deer. Bans or restrictions on interstate movement of carcasses or live animals and increased bag limits were common responses to the detection of CWD and were used by 83% and 78% of the 24 responding agencies, respectively. Similarly, adapted surveillance was a common response to CWD detection; 14 of the 24 agencies either initiated or adjusted their weighted surveillance program following detection. However, of the 20 U.S. states and 6 Canadian provinces that have not yet detected CWD, only 3 are currently applying weighted surveillance approaches to their CWD sampling efforts. As demonstrated by New York and Minnesota, localized eradication of CWD may be possible if it is detected in its emergent stage when there are few infected deer in an area. We found that 4 of the 20 U.S. states and 2 of the 6 Canadian provinces that have yet to detect CWD had a response plan available online. Further analyses to assess the impacts of various management approaches on the spatial and temporal trajectory of CWD prevalence requires more data collection and reporting, such as consistent and fine‐scale surveillance in management and control areas. We recommend that agencies be proactive in public messaging of CWD response plans well before initial detection and that cervid managers dealing with CWD collaborate regularly with one another to share their variable and collective experiences. We also recommend that agencies provide detailed public reports on CWD responses, disease progression, and management outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".