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Record W2797618920 · doi:10.7939/r3wd3q88b

Grizzly bear population ecology and large carnivore conflicts in southwestern Alberta

2016· article· en· W2797618920 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsCarnivoreGeographyEcologyGrizzly BearsPopulationHuman–wildlife conflictUrsusPredationBiologyWildlifeDemographySociology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.162
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it