Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
Bibliographic record
Abstract
Monitoring carnivores is critical for conservation, yet challenging because they are rare and elusive. Few methods exist for monitoring wide-ranging species over large spatial and sufficiently long temporal scales to detect trends. Remote cameras are an emerging technology for monitoring large carnivores around the world because of their low cost, non-invasive methodology, and their ability to capture pictures of species of concern that are difficult to monitor. For species without uniquely identifiable spots, stripes, or other markings, cameras collect detection/non-detection data that are well suited for monitoring trends in occupancy as its own independent useful metric of species distribution, as well as an index for abundance. As with any new monitoring method, prospective power analysis is essential to ensure meaningful trends can be detected. Here we test camera-based occupancy models as a method to monitor changes in occupancy of a threatened species, grizzly bears (Ursus arctos), at large landscape scales, across 5 Canadian national parks (~21,000 km2). With n = 183 cameras, the top occupancy model estimated regional occupancy to be 0.79 across all 5 parks. We evaluate the statistical power to detect simulated 5–40% declines in occupancy between two sampling years and test applied questions of how power is affected by the spatial scale of interest (park level vs. regional level), the number of cameras deployed, and duration of camera deployment. We also explore several ecological mechanisms (i.e., spatial patterns) of decline in occupancy, and examine how power changes when focusing only on grizzly bears family groups. As hypothesized, statistical power increased with the number of cameras and with the number of days deployed. Power was unaffected, however, by the ecological mechanisms of decline, indicating that our systematic sampling design can detect a decline regardless of whether occupancy declined due to range edge attrition, ecological trap or other mechanisms. Despite their lower occupancy, power was similarly high for grizzly bear family groups compared to grizzly bears in general. We highlight which study design attributes contributed to high power and we provide advice for establishing cost-effective camera-based programs for monitoring large carnivore occupancy at large spatial scales.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".