Freshwater fisheries monitoring in northern ecosystems using Indigenous ecological knowledge, genomics, and life history: Insights for community decision-making
Bibliographic record
Abstract
Natural resources in northern regions are often data-limited because they are difficult and expensive to access. Indigenous ecological knowledge (IEK) can provide information similar to, different from, or complementary to Western scientific data (WSD). We evaluated the general hypothesis that congruence in outcomes of IEK and WSD for population monitoring parameters is determined by temporal and spatial scale of the knowledge type. Parameters included population structure, degree of philopatry, morphological variation (and conservation status for one species), and genomics was a key Western scientific method. We evaluated this hypothesis in three subsistence and recreational fisheries (walleye, lake trout, and northern pike) in Mistassini Lake, Quebec, Canada. Concordance of outcomes was varied. IEK provided richer information on the biology, distribution, and morphological variation observable with the eyes. However, IEK cannot “see” into the genome, and WSD identified population structure and history more precisely than IEK. Both knowledge types could “see” change in populations, and the nature of what was seen both converged and was complementary. Determining when IEK and WSD are complementary or reach common conclusions may allow Indigenous communities to use both together, or one knowledge type over another when either is more desired, appropriate, or time- or cost-efficient to adopt.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".