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Assessing Nutritional Parameters of Brown Bear Diets among Ecosystems Gives Insight into Differences among Populations

2015· article· en· W653907360 on OpenAlexafffundabout
Claudia López-Alfaro, Sean C. P. Coogan, Charles T. Robbins, Jennifer K. Fortin, Scott E. Nielsen

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates Bio SolutionsWashington State University
KeywordsGrizzly BearsUrsusPopulationEcosystemEcologyBiologyWildlifeTroutGeographyFisheryDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Food habit studies are among the first steps used to understand wildlife-habitat relationships. However, these studies are in themselves insufficient to understand differences in population productivity and life histories, because they do not provide a direct measure of the energetic value or nutritional composition of the complete diet. Here, we developed a dynamic model integrating food habits and nutritional information to assess nutritional parameters of brown bear (Ursus arctos) diets among three interior ecosystems of North America. Specifically, we estimate the average amount of digestible energy and protein (per kilogram fresh diet) content in the diet and across the active season by bears living in western Alberta, the Flathead River (FR) drainage of southeast British Columbia, and the Greater Yellowstone Ecosystem (GYE). As well, we estimate the proportion of energy and protein in the diet contributed by different food items, thereby highlighting important food resources in each ecosystem. Bear diets in Alberta had the lowest levels of digestible protein and energy through all seasons, which might help explain the low reproductive rates of this population. The FR diet had protein levels similar to the recent male diet in the GYE during spring, but energy levels were lower during late summer and fall. Historic and recent diets in GYE had the most energy and protein, which is consistent with their larger body sizes and higher population productivity. However, a recent decrease in consumption of trout (Oncorhynchus clarki), whitebark pine nuts (Pinus albicaulis), and ungulates, particularly elk (Cervus elaphus), in GYE bears has decreased the energy and protein content of their diet. The patterns observed suggest that bear body size and population densities are influenced by seasonal availability of protein an energy, likely due in part to nutritional influences on mass gain and reproductive success.

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 score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.100
GPT teacher head0.250
Teacher spread0.150 · 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

Citations76
Published2015
Admission routes3
Has abstractyes

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