Grizzly bears without borders: Spatially explicit capture–recapture in southwestern Alberta
Bibliographic record
Abstract
ABSTRACT Local perceptions of grizzly bear ( Ursus arctos ) numbers in southwestern Alberta, Canada are incongruent with their threatened status. We used non‐invasive genetic sampling to estimate grizzly bear density and abundance in southwestern Alberta. We established 899 bear rub objects (e.g., tree, power pole, fence post) for hair sample collection across the study area by surveying trail networks, using geographic information system layers, and working with >70 landowners to identify priority sampling areas. The study area included 2 management zones: the Recovery Zone where the objective was to recover the grizzly bear population, and a Support Zone intended to maintain those bears not exclusively within the Recovery Zone. We visited rub objects every 3 weeks from late May through early November for 8 visits (7 sampling occasions) per field season. We also allowed for opportunistically collected hair samples (e.g., trapped bears, hair at agricultural bear‐conflict sites). We identified species, individual identity, and sex based on nuclear DNA extracted from hair follicles. From 2013 through 2014, we identified 164 individual grizzly bears. Using spatially explicit capture–recapture models (SECR), we estimated density in 2 ways. First, we estimated density for each sex and year separately (2013: M = 9.2/1,000 km 2 in the Recovery Zone and 8.1/1,000 km 2 in the Support Zone, F = 14.9/1,000 km 2 in the Recovery Zone and 13.6/1,000 km 2 in the Support Zone; 2014: M = 7.2/1,000 km 2 in the Recovery Zone and 5.7/1,000 km 2 in the Support Zone, F = 9.0/1,000 km 2 in the Recovery Zone and 8.5/1,000 km 2 in the Support Zone). Second, we did not allow density to vary across years and instead estimated a single density for the study area (M = 8.0/1,000 km 2 in the Recovery Zone and 7.1/1,000 km 2 in the Support Zone, F = 12.4/1,000 km 2 in the Recovery Zone and 10.0/1,000 km 2 in the Support Zone). Though yearly variation occurred, we derived from our density estimates an expected abundance of approximately 67.4 resident grizzly bears, indicating a 4% per year increase since a 2007 estimate of 51 bears. These SECR density estimates pertain only to bears with home ranges that were centered within the study area. Using traditional capture‐mark‐recapture (CMR) models with the same data yielded a higher estimate of bears because it included all bears that were using the study area (2013: F = 68.9, M = 102.6; 2014: F = 63.0, M = 108.6), including >50% of bears previously genotyped in Montana or British Columbia. In contrast with the SECR estimates, the CMR estimates represent the number of bears that southwestern Alberta residents could have encountered (i.e., the population of bears that had potential to have been involved in conflict). Shifts in grizzly bear distribution resulted in large changes in our SECR density estimates between years, whereas our estimate of the number of bears using the area remained constant. We recommend increased inter‐jurisdictional monitoring and management of this international grizzly bear population. © 2016 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".