Use of resource selection functions to identify conservation corridors
Bibliographic record
Abstract
Summary Corridors are commonly used to connect fragments of wildlife habitat, yet the identification of conservation corridors typically neglects processes of habitat selection and movement for target organisms. New technologies and analytical tools make it possible to better integrate landscape patterns with behavioural processes. We illustrate the integration of resource selection functions (RSFs) and least‐cost path (LCP) analyses for the purpose of corridor planning for two large carnivores. We used RSFs developed from Global Positioning System telemetry data to predict the seasonal distribution of two large carnivores: grizzly bears Ursus arctos and cougars Puma concolor. We then applied LCP analyses to identify potential corridors in two fragmented montane landscapes – Canmore and Crowsnest Pass – in Alberta, Canada. Grizzly bear habitat selection in both areas positively correlated with greenness in all seasons and soil wetness and proximity to water in the summer when both variables were associated with bear forage. During spring, grizzly bear occurrence in Canmore inversely correlated with road density. For cougars, habitat selection varied by region: it negatively correlated with road density in Canmore during non‐winter and positively correlated with terrain ruggedness in Crowsnest Pass. Cougar occurrence during the non‐winter season in Canmore positively correlated with greenness. For each species, seasonal RSFs were used to develop a cost surface for LCP analyses to identify potential corridor locations in each study area. Overlaying the paths for the two species highlighted where the landscape could support corridors for both species and potential highway crossing zones. The telemetry data supported some of these modelled crossings. Synthesis and applications. We show how to integrate RSFs and least‐cost modelling to identify corridors for conservation. We focus on two large carnivores in the Canadian Rocky Mountains to identify potential corridors in Canmore and provide a framework for corridor planning in Crowsnest. We suggest that our approach is applicable to many other target species in addition to large carnivores in human‐dominated landscapes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".