Using a Cost-Distance Time-Geographic Approach to Identify Red Deer Habitat Use in Banff National Park, Alberta, Canada
Bibliographic record
Abstract
Animal movements are realizations of complex spatiotemporal processes. Central to these processes are the varied environmental contexts in which animals move, which fundamentally impact the movement trajectories of individuals at fine spatial and temporal scales. An emerging perspective in time geography is the direct examination of the influence that varying contexts may have on observed movements. An approach that considers environmental context can yield actionable information for wildlife management, planning, and conservation; for instance, identifying areas of probable occupancy by an animal may improve the efficiency of fieldwork. This research develops the first known practical application of a new cost-distance-based, probabilistic voxel space–time prism (CDBPSTP) in efforts to more realistically characterize the unobserved habitat occupancies of animals occurring between known positions provided by location-aware technologies. The CDBPSTP method is applied to trajectory data collected for a group of red deer (Cervus elaphus) tracked near Banff National Park, Alberta, Canada. As a demonstration of the added value from examining how context influences movement, CDBPSTP habitat occupancy results are compared to the earlier PSTP method in context with empirical and theoretical understandings of red deer habitat preference and space-use behaviors. This comparison reveals that with CDBPSTP, variation present in the mover’s environment is explicitly considered as an influence on the mover’s probable path and occupancies between observations of its location. With the increasing availability of high-resolution geolocational and associated environmental data, this study highlights the potential for CDBPSTP to be leveraged as a broadly applicable tool in animal movement analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".