Delineating demographic units of woodland caribou (Rangifer tarandus caribou) in Ontario: cautions and insights
Bibliographic record
Abstract
Delineating demographic structure across an organism’s range can reveal the extent to which population dynamics in different geographic areas are driven by local or external factors and can be crucial for effective conservation and management. Obtaining optimal data for such analyses can be time and resource-intensive and impending development and resource extraction pressures may necessitate the examination of existing data, even when they are less than ideal. We analyzed a historic telemetry dataset containing satellite radio-collar locations of 73 forest-dwelling woodland caribou in northern Ontario to determine demographic structure. We applied several clustering methods (i.e., agglomerative, divisive and fuzzy k-means) to median seasonal locations. Results were used to distinguish demographic units and minimum convex polygons and fixed-kernel density estimates were used to delineate unit boundaries and core areas. For areas where sampling was considered representative of the distribution of caribou on the landscape, we assessed demographic distinctness by evaluating intra-individual variation in cluster membership, membership strength and distance between boundaries and core areas of adjacent units. The number and composition of clusters identified was similar among methods and caribou were grouped into 6 general clusters. The distinctions between the three clusters identified in the central portion of the province (i.e., Lac Seul, Wabakimi, Geraldton) and the two clusters identified in the eastern portion of the province (i.e., Cochrane and Cochrane-Quebec) were determined to represent demographic structuring. Additional distinctions in other areas (i.e., between The Red Lake and Lac Seul clusters in the west and between the central and eastern clusters) may just be artifacts of the original sampling effort. Amongst demographic units, there was no evidence of individual flexibility in cluster membership and average membership strength was very high. There was little to no overlap between boundaries and core areas of adjacent units, but distances between adjacent unit boundaries were relatively low. Additional sampling effort is needed to further delineate demographic structure in Ontario caribou.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".