Nested population structure of threatened boreal caribou revealed by network analysis
Bibliographic record
Abstract
Delineating relevant local populations of widely distributed species is a common challenge in conservation ecology. Caribou and reindeer (Rangifer tarandus) are in general decline throughout their global range, despite ongoing conservation efforts. In Canada, recovery actions for the threatened boreal population of woodland caribou (Rangifer tarandus caribou) are stratified by ‘local population units’ (LPUs) on ranges distributed across 2.4 × 10 km2 of the species’ geographic range. To estimate local population dynamics, LPUs are assumed to be geographically closed, though supporting evidence varies widely. We assembled an exceptionally large database of GPS telemetry locations (891,306 telemetry days, 1998–2020) from 1586 adult female caribou across the 19 northwesternmost LPUs. We generated a many-to-many Gaussian Bayesian Network to identify candidate local populations at range-level extents, as well as subpopulations, termed ‘communities’ in network analysis. We detected local population boundaries that in some cases were consistent with accepted LPUs and consistent with the assumption of geographic closure. In other cases, local population boundaries did not map well to currently delineated LPUs. Several communities at smaller spatial extents were consistent with expert and local knowledge of caribou movements and support recovery planning and actions “stepped down” from entire ranges. Evidence consistent with population fragmentation was confirmed along the southern and southwestern boundaries of the species’ geographic range within the study area, as were more continuous distributions confirmed to the north. We suggest that network analysis can help to inform conservation planning for boreal caribou and other wide-ranging species that would benefit from data-driven characterizations of multiscale population spatial structure.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".