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Foraging strategies by omnivores: are black bears actively searching for ungulate neonates or are they simply opportunistic predators?

2010· article· en· W2116954734 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEcography · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère des TransportsRussian Science FoundationFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsUngulatePredationForagingOmnivoreVegetation (pathology)EcologyGeographyHabitatForageCarnivoreOptimal foraging theorySelection (genetic algorithm)Abundance (ecology)Resource (disambiguation)BiologyComputer scienceArtificial intelligence

Abstract

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Omnivores feed on animals with dynamic distributions and on plants with static distributions. The search tactics they adopt will not only define the risk for the targeted prey, but also for other prey that may be consumed when encountered. The potential impact of omnivores on the dynamics of multi‐prey systems thus depends on resource selection and on the tactics used to find their prey. We present an approach that can clarify the foraging decisions of omnivores by combining analyses of habitat selection, local residency time, and interpatch movements. We use this framework to evaluate whether predation by omnivorous black bears on ungulate neonates resulted from an active search or from incidental encounters. We monitored 12 bears, 22 forest‐dwelling caribou, and 36 moose during calving seasons. We estimated the spatial patterns in relative occurrence probability of ungulate neonates using Resource Selection Functions (RSFs). We also mapped plant abundance from vegetation surveys. RSF were then built to assess the link between bear distribution and the distribution of these three food types (vegetation, moose calves, caribou fawns). We further evaluated the search tactic used by bears that led to this spatial dependency by exploring patterns of residency times and interpatch movements. Bears did not select areas with a high probability of encounter with neonates, but selected areas with abundant vegetation. Surprisingly, bears displayed shorter residency times in vegetation‐rich areas. The selection for vegetation‐rich areas was therefore achieved by moving preferentially, but frequently, between areas offering abundant vegetation. Such frequent interpatch movements could result in high rates of fortuitous encounters with neonates, even if bears are not actively searching for them. To mitigate the impacts of forest harvesting on threatened caribou populations, vegetation‐rich areas selected by bears (e.g. roadsides) should be segregated from large patches of mature conifer forest suitable for caribou.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.650

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)0.0000.000

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 head0.016
GPT teacher head0.252
Teacher spread0.237 · 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