Tracking long-term landscape change : habitat connectivity for moose and fisher in Tsilhqot’in territory
Bibliographic record
Abstract
Traditional ecological knowledge and values of Indigenous peoples are increasingly incorporated into resource management. However, science still lacks sufficient examples of collaborative research with Indigenous communities. To explore the potential of geospatial approaches in this regard, I examined changes in habitat connectivity from 1984 to 2019 for two species of significance to the Tŝilhqot’in First Nation: fisher (Pekania pennanti) and moose (Alces alces). Within the Tŝilhqot’in traditional territory, I used existing geospatial datasets in conjunction with ecological literature to map fisher and moose habitat suitability over time. Then, these suitability ratings were used as proxies for animal movement to create resistance surfaces. These resistance surfaces were further used in a long-term connectivity analysis for each species (via Linkage Mapper software) throughout the study area and within a highly disturbed sub-region. Connectivity was quantified using measures of patch size, corridors (i.e. cost-weighted distance), as well as importance values for patches and corridors (i.e. centrality) and areas of high resistance (i.e. barriers). Based on the accounts of Tŝilhqot’in communities and regionally reported population declines linked to landscape-level disturbances, I expected that connectivity would decline for both species. However, between 1984 and 2019, quantitative connectivity metrics for fisher habitat showed conflicting results, including decreased patch size in the disturbed sub-region but not across the total study area. Habitat connectivity indicators for moose generally indicated improved connectivity, including increased patch size and reduced intensity of barriers. Centrality (indicating the most important pathways) shifted greatly for fisher, yet not for moose habitat. Possible associations between decreased fisher population and habitat connectivity are discussed (such as habitat loss), as are the factors potentially explaining moose population declines despite increasing habitat connectivity (such as greater predation risk while accessing abundant browse). This research demonstrates a collaborative application of geospatial techniques that can enhance our understanding of landscape connectivity of Indigenous wildlife resources and offers suggestions for managing connectivity in the face of landscape alteration and climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".