Improved Wildlife Health and Disease Surveillance through the Combined Use of Local Knowledge and Scientific Knowledge
Bibliographic record
Abstract
Effective health and disease surveillance of wildlife populations is necessary for evidence-based wildlife management and conservation, as well as for the protection of human and animal health. Wildlife surveillance, however, is often challenging to undertake due to numerous limitations associated with gathering and interpreting field data from free-ranging populations. This thesis illustrates a novel approach to wildlife health surveillance which overcomes these limitations by capitalizing on the experiential-based knowledge of resource users documented with participatory methods and applied in combination with conventional surveillance methods. This participatory approach was developed and applied in – and with the active participation of – the community of Cambridge Bay in the Canadian Arctic to improve veterinary surveillance of muskoxen (Ovibos moschatus). In the North, harvesting muskoxen improves food security, the local economy and is connected to local indigenous culture and traditions. In Cambridge Bay, an accurate understanding of muskoxen health was urgently needed due to local concerns of possible declines and disease emergence. A participatory surveillance program composed of different activities which drew on both local knowledge and scientific knowledge was developed. Semi-structured interviews of key informants applied participatory epidemiology techniques to document local knowledge on muskox health, while scientific knowledge was generated by testing samples collected through collaboration with hunters, field investigations, and available archives. Local knowledge of key informants proved critical for filling historic and contemporary knowledge gaps on muskox health, including data on demography, morbidity, mortality and body condition, highlighting its potential to serve as an early warning system for detecting changes in wildlife health. Local knowledge informed the design of targeted scientific studies, and when combined the two knowledge systems reduced the overall uncertainty of the surveillance output. Participation of local resource users throughout the study enabled development of a surveillance adapted to the local context and needs, including customization of surveillance interventions. In addition to producing important information for Cambridge Bay and the local muskox population, this thesis develops the field of participatory wildlife surveillance by illustrating the broader applicability of this approach for enhancing the capacity for health surveillance of other wildlife species, both harvested and not, and in other settings.
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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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".