Collaborative strategies for wildlife health: case studies from the Canadian North
Bibliographic record
Abstract
Abstract Wildlife health and conservation are increasingly recognized as key to improving human, animal, and environmental health (One Health) and detecting and addressing threats such as altered distribution and transmission of zoonotic diseases due to climate change. Wildlife, and therefore wildlife management, are crucial for the livelihood and well‐being of people in the Canadian North, a vast geographic area with low population density and socio‐economic disparities, which can make widespread program implementation challenging. We analyzed and compared 4 case studies on collaborative programs on wildlife health management from 4 distinct jurisdictions in the Canadian North: the Yukon, chronic wasting disease surveillance in ungulates; Northwest Territories, rabies in arctic fox ( Vulpes lagopus ); Nunavut, foodborne diseases in harvested wildlife; and Nunavik, Québec, ringed seal ( Pusa hispida ) health and Trichinella in walrus ( Odobenus rosmarus ). The case studies differed in whether they focused on a specific pathogen, transmission route, or health of a specific wildlife population. Despite these differences, 3 main themes were common to all case studies: collaboration, infrastructure limitations, and adaptation. Collaboration promotes greater community buy‐in and investment as community members help shape the program and is key to long‐term success, sustainability, and local relevance. Laboratory infrastructure and human resource capacity are limited in most regions, highlighting the importance of collaboration among Indigenous‐managed boards and organizations, community members, government agencies at multiple levels, academic institutions, and the national wildlife health non‐governmental organization (Canadian Wildlife Health Cooperative). Examples of adaptations include capitalizing on wildlife harvest for fur and food to obtain samples for surveillance, developing regulations to train and permit veterinary paraprofessionals and community volunteers to facilitate co‐existence of pets and people with wildlife reservoirs of rabies, jointly developing regulations to prevent introduction of non‐endemic diseases, and adapting hunting locations based on the results of wildlife disease monitoring. Combining Indigenous knowledge and a One Health framework is fundamental to co‐managing zoonotic and food‐borne diseases in wildlife while respecting Indigenous ways of life. The case studies provide meaningful examples of collaboration and solutions to address complex problems at the One Health interface and to use a holistic and community‐based approach to facilitate the creation of regionally acceptable and culturally relevant programs, with better outcomes for wildlife, human, and environmental health.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".