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Record W2111750351

Deer mice mediate red-backed vole behaviour and abundance along a gradient of habitat alteration

2010· article· en· W2111750351 on OpenAlexaffabout
Jérôme Lemaître, Daniel Fortin, Douglas W. Morris, Marcel Darveau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPredationHabitatEcologyPeromyscusBiologyCompetition (biology)Disturbance (geology)TaigaVoleForagingBorealPopulation
DOInot available

Abstract

fetched live from OpenAlex

Hypotheses: (1) Intra- and inter-specific competition should increase with anthropogenic disturbances that reduce habitat quality. (2) In forested ecosystems, predation risk for small consumers should increase with the intensity of disturbance. (3) For the same level of disturbance, foragers living in higher-quality habitats should protect their assets by investing more in anti-predatory behaviours than those living in low-quality habitats. Organisms: Red-backed voles (Myodes gapperi) living in sympatry with deer mice (Peromyscus maniculatus). Place and time: Twenty-nine pairs of natural and logged habitats sampled during 2006 in managed boreal forest, Province of Québec, Canada. Methods: We identified a gradient of habitat disturbance along principal components summarizing 12 habitat variables. We estimated competition by measuring the giving-up density of rodents along the gradient of habitat disturbance. We assessed predation risk by measuring the giving-up density of rodents foraging in safe and risky patches. We tested for differences with multi-level statistical modelling. Conclusions: Competition and predation risk increased with habitat disturbance in the boreal forest studied. Foragers living in higher-quality habitats experienced higher predation costs than foragers living in low-quality habitats. Intra- and inter-specific competition, rather than predation, was the main mechanism responsible for the decline of red-backed vole populations associated with forest harvesting.

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 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 score0.076
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 communication0.0000.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 teacher head, 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

Citations24
Published2010
Admission routes2
Has abstractyes

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