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Record W4254571761 · doi:10.24124/2005/bpgub341

Plasticity in selection strategies of woodland caribou ( Rangifer tarandus caribou) during winter and calving.

2005· dissertation· en· W4254571761 on OpenAlexaff
David D. Gustine

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsLibrary and Archives Canada
Fundersnot available
KeywordsWoodland caribouUrsusIce calvingPredationForageGeographyEcologyHabitatUngulateBiologyLactationPopulationDemography

Abstract

fetched live from OpenAlex

Woodland caribou may be an important indicator' or focal species for management agencies because they require large areas to persist and are sensitive to both direct and indirect forms of disturbance. Prior to industrial development in northern regions, it is important to acquire baseline information on areas that are important to local 'herds' as well as to identify physiological and ecological mechanisms of resource selection. I used global positioning system (GPS) data from caribou {Rangifer tarandus caribou), wolves {Canis lupus), and grizzly bears {Ursus arctos), and satellite imagery, resource selection functions, and cause-specific mortality data from 50 caribou neonates to define calving and wintering areas of woodland caribou in northern British Columbia. I identified scale-dependant mechanisms of selection relative to predation risk (calving, summer, winter, and late winter) and forage availability (calving and summer), and energetic costs of movement (winter and late winter) at 2 spatial scales, and quantified the variation in responses to these mechanisms among individual caribou. In all seasons, caribou selected habitats in a hierarchical fashion, and exhibited high variation among individuals. Three unique calving areas, or calving strategies, were defined for the Greater Besa Prophet area; each calving area had different attributes of risk and forage. During calving, spatial separation from areas of high wolf risk was important to parturient females as was access to areas of high vegetative change (i.e., forage quality); animals made trade-off decisions between minimizing the risk of predation and securing forage to address the high nutritional demands of lactation. Calf survival through the first 2 months of life ranged from 54% in 2002 to 79% in 2003. A total of 19 of 50 neonates died during the summers, of which 17 were by predation: wolverines (age of calves <14 d) and wolves (age of calves >18 d) each killed 5 calves. Movements away from calving sites (>1 km) peaked during the third week of life and increased the odds of a neonate surviving by 196%. These movements coincided with a change in vegetative phenology and the high energetic demands of lactation. During winter and late winter, minimizing the energetic costs of movement was the most important parameter in the selection of resources at a smaller spatial scale defined by seasonal movement, whereas Individual caribou showed increased sensitivity to the components of risk at a larger scale of the home range. Variation in the selection of resources by individuals was high, but some similarities facilitated using pooled use/availability data to model resource selection. These pooled models, however, collapsed important biological variation in the selection of resources, limiting biological interpretation of selection models. Variation in the selection of resources among individuals (i.e., plasticity) during all times of the year may be an important life-history strategy for woodland caribou to decrease their predictability on the landscape to major predators. Identifying and maintaining this variation within selection strategies is an important step towards determining the ability of caribou populations to persist in the presence of environmental and anthropogenic disturbance.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.214
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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