Grizzly bear population ecology and large carnivore conflicts in southwestern Alberta
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human-wildlife conflicts are a global conservation challenge. Reserves and protected areas usually do not adequately provide for the space needs of large carnivores, resulting in overlap between carnivore home ranges and private lands. Private lands often can provide valuable habitats, but wherever large carnivores and people share the landscape there is potential for conflict. I reviewed 16 years of records of complaints about grizzly bears, wolves, black bears, and cougars in southwestern Alberta and evaluated temporal and distribution patterns of these complaints. Conflicts were most frequently associated with bears reflecting a diversity of conflict types attributable to their omnivorous diets. In contrast, wolf and cougar incidents were almost exclusively related to killing or injury of livestock. Complaints for both bear species have increased over the past 16 years while cougar and wolf complaints have remained relatively constant. Increasing grizzly bear conflicts could be due to an increasing grizzly bear population. I used non-invasive genetic sampling and spatially explicit capture-recapture methods to estimate grizzly bear density and abundance in southwestern Alberta – a small part of a much larger international population of grizzly bears. I established 899 bear rub objects for bear hair sample collection across the study area by surveying trail networks, using GIS layers, and working with over 70 landowners to identify priority sampling areas. Though yearly variation occurred, I estimated an abundance of approximately 67.4 (95% CI 50.0 – 91.1) resident grizzly bears. However, the number of grizzly bears using the study area was much higher [2013: females = 68.9 (95% CI 58.4 – 97.2), males = 102.6 (95% CI 81.2 – 154.2); 2014: females = 63.0 (95% CI 48.9 – 102.6), males = 108.6 (95% CI 80.8 – 177.0)]. In contrast with my resident bear estimate, these numbers represent the number of bears that southwestern Alberta residents could have encountered, i.e., the total population of bears that had potential to have been involved in conflict. Access to supplemental food sources might have contributed to the population increase. The provincial government fed grizzly bears road-killed ungulates each spring during 1998-2013 attempting to reduce spring predation of livestock by grizzly bears. I evaluated the efficacy of this intercept-feeding program by monitoring 12 feeding locations, and using DNA, I identified 22 grizzly bears (19 males, 3 females) at the intercept-feeding sites – a small portion of the number of bears using the study area. Despite intercept feeding, conflicts between grizzly bears and agriculture have increased at a rate that exceeds the estimated rate of increase in the grizzly bear population. The propensity for a grizzly bear to be develop conflict behaviour might be a result of social learning between mothers and cubs, genetic inheritance, or both learning and inheritance. In addition to hair samples collected from rub objects, I targeted private agricultural lands for additional hair samples at grizzly bear incident sites. I completed a parentage analysis to evaluate evidence for social learning versus genetic inheritance of conflict behavior. My results support the social-learning hypothesis but not the genetic-inheritance hypothesis. Offspring from non-problem mothers are not likely to be involved in incidents or human-bear conflicts themselves, whereas offspring are more likely to show conflict behaviour when their mothers are problem bears. Proactive mitigation measures that prevent female bears from becoming problem individuals will likely help to prevent the perpetuation of conflicts through social learning, and will help to reduce grizzly bear-agricultural conflicts in southwestern Alberta.
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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 | 0.000 | 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 | 0.001 |
| 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 it