10.3 RESOURCE SELECTION BY FEMALE GRIZZLY BEARS WITH CONSIDERATION TO HETEROGENEOUS LANDSCAPE PATTERN AND SCALE
Bibliographic record
Abstract
research suggests that many species perceive multiple scales, few studies have included a range of scale-dependent variables in studies regarding resource selection. We investigate the selection of such features for grizzly bears - a mobile species whose landscape selection could be influenced by landscape pattern. We investigate whether female grizzly bears in the eastern slopes region of the Alberta portion of the Central Rockies Ecosystem select resource characteristics and heterogeneous landscape patterns differently than available within home ranges when landscape patterns are measured at multiple scales simultaneously. Resource characteristics were measured in the 300-m diameter immediate-vicinity of bears, specifically vegetation, slope, aspect, elevation, proximity to edge, proximity to water, and proximity to human activity. Heterogeneous landscape patterns were measured in 300-m, 1.5-km, 3.0-km diameter windows, specifically vegetation diversity, vegetation dominance, terrain ruggedness, density of motorized access, and density of non-motorized access. We used logistic regression to calculate resource selection functions. Female grizzly bears responded to environmental conditions beyond the immediate vicinity of 300 metres, frequently selecting heterogeneous landscape patterns at different scales, and simultaneously at several scales. We describe results for wary individuals during 2 seasons. All female bears selected pockets of low- density non-motorized access by humans at the 1.5-km scale, within larger 3.0-km areas of high-density non- motorized access by humans. For all female bears, relatively high diversity of vegetation types was selected at the 300-m scale in the preberry season, and selection for high diversity at the 1.5-km scale in the berry season. Homogeneous vegetation within the 300-m scale was never selected. Close proximity to edge was consistently selected. Wary females selected high levels of ruggedness at the broadest scales during both seasons. Also commonly selected were general-shrub, graminoid meadows, and avalanche paths, suggesting their general importance to female grizzlies. We recommend that resource selection studies incorporate variables at multiple scales. Management along the eastern slopes should maintain vegetation edge and diversity of vegetation communities through the maintenance of disturbance regimes. Furthermore, management should attempt to minimize human disturbance in areas that have any or all of the following characteristics: are within 60 metres of vegetation edges, have high levels of vegetation diversity within 300-m and 1.5-km windows, consist of rugged terrain within broad 3.0-km areas, contain graminoid meadows and avalanche paths, or are close to riparian areas. To take into account habitat selected by grizzly bears levels of human access should be minimal in contiguous 1.5-km diameter areas that contain these habitat attributes. We recognize that competing land use pressures will often exist. In applying seasonal resource selection functions to the eastern slopes landscape, we identified 4 geographic areas containing a concentration of high probability of adult female occurrence. These areas were: 1) around Lake Louise, 2) from the Red Deer River/Ya Ha Tinda area, south to and including the Burnt Timber drainage, 3) around Banff townsite, and 4) along the Canmore/Bow River corridor as far east as the Kananaskis River drainage and the Old Fort Creek drainage, and extending south to include the Wind Valley and the Evan-Thomas Recreation Area. We also identified numerous smaller pockets of high probability of female grizzly occurrence distributed throughout the study area but especially south of the Trans Canada Highway. Each of the 4 areas with a concentration of high probability of adult female use is a candidate for management that will allow for grizzly bear habitat use with minimal human-caused mortality risk. This will be challenging because of extensive human use in these areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".