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Record W3046058440 · doi:10.1111/gcb.15295

Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore

2020· article· en· W3046058440 on OpenAlexafffundabout
Gregory J. M. Rickbeil, Nicholas C. Coops, Ethan E. Berman, Cameron J. R. McClelland, Douglas K. Bolton, Gordon Stenhouse

Bibliographic record

VenueGlobal Change Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia
FundersTeck ResourcesSeven Generations EnergyForest Resource Improvement Association of AlbertaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAlberta Newsprint CompanyRepsolfRI ResearchShell Canada
KeywordsForageUrsusSnowEnvironmental scienceSpring (device)WildlifeCarnivoreHabitatEcologyPhysical geographyGeographyPredationBiologyPopulationDemographyMeteorology

Abstract

fetched live from OpenAlex

Changing climates are altering wildlife habitats and wildlife behavior in complex ways. Here, we examine how changing spring snow cover dynamics and early season forage availability are altering grizzly bear (Ursus arctos) behavior postden emergence. Telemetry data were used to identify spring activity dates for 48 individuals in the Yellowhead region of Alberta, Canada. Spring activity date was related to snow cover dynamics using a daily percent snow cover dataset. Snow melt end date, melt rate, and melt consistency explained 45% of the variation in spring activity date. We applied this activity date model across the entire Yellowhead region from 2000 to 2016 using simulated grizzly bear home ranges. Predicted spring activity date was then compared with a daily spring forage availability date dataset, resulting in "wait time" estimates for four key early season forage species. Temporal changes in both spring activity date and early season forage "wait times" were assessed using non-parametric regression. Grizzly bear activity date was found to have either remained constant (95%) or become earlier (5%) across the study area; virtually no areas with significantly later spring activity dates were detected. Similarly, the majority of "wait times" did not change (85%); however, the majority of significant changes in "wait times" for the four early season forage species indicated that "wait times" were lessening where changes were detected. Our results show that spring activity date is largely dictated by snow melt characteristics and that changing snow melt conditions may result in earlier spring activity. However, early season food stress conditions are likely to remain unchanged or improve as vegetation phenology also becomes earlier. Our findings extend the recent work examining animal movement in response to changing phenology from migratory birds and ungulates to an apex predator, further demonstrating the potential effects of changing climates on wildlife species.

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)0.000
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 score0.218
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.249
Teacher spread0.229 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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