Predicting spatial variation in grizzly bear abundance to inform conservation
Bibliographic record
Abstract
ABSTRACT Understanding the spatial structure of populations is fundamental to effective assessment, planning, and management for species conservation. Because of their sensitivity and public interest, grizzly bears (Ursus arctos) are focused upon in some localized development issues and proactive conservation initiatives. Knowledge regarding the spatial context of regional grizzly bear populations is important, but often a defensible representation of probable population distribution, core areas, and connectivity is lacking. We describe the development and assessment of a regional landscape model of grizzly bear density and distribution. Our study region comprised 180,000 km2 and 20 management units of southeast British Columbia and southwest Alberta. Our meta‐analysis was based on data from 20 independent grizzly bear population surveys across the region. While accounting for differing design parameters among surveys, we contrasted grizzly bear detections against sampling representation relative to scale‐dependent landscape factors. Our predictors pertained to the influence of climate, terrain, land cover, vegetation indices, and human activity. Associations within survey areas were consistent with ecological influences on grizzly bear foods and human influences on grizzly bear mortality risk and landscape avoidance. A multiple logistic regression model based on independent components of ecological variation fit well the data pooled across the region and within individual survey areas. Average values of detection probability among survey areas predicted population density (R2 = 0.64 or 0.79 depending on one outlier). Our results support the application of our model across southeast British Columbia and southwest Alberta for assessment and planning that requires regional and local context of grizzly bear population abundance and distribution, and inference of core areas and population connections among them. For any geographic area, a population estimate can be obtained that is reflective of surveys used in the model. Spatial predictions for any defined population are likely to be more reliable than those extrapolated from tracking data of individual animals given limitations typical of such sampling. Ultimately, model output provides regional population context for environmental assessment, management, and conservation planning, nested within which should be finer‐scale data and prediction where available. © 2016 The Wildlife Society.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".