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Record W2794748347 · doi:10.1139/facets-2016-0062

Trade-offs between sight lines and escape habitat determine spatial strategies of risk management by a keystone herbivore

2018· article· en· W2794748347 on OpenAlexaffvenue
Douglas W. Morris, Sundararaj Vijayan

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsLakehead University
Fundersnot available
KeywordsForagingVigilance (psychology)PredationHabitatEcologyHerbivoreTrade-offBiology

Abstract

fetched live from OpenAlex

Prey individuals possess four basic strategies to manage predation risk while foraging: time allocation, space use, apprehension, and foraging tenacity. But there are no direct tests of theory detailing how spatial strategies change and covary from fine to coarse scales of environmental variability. We address this shortcoming with experiments that estimated space use and vigilance of snowshoe hares while we measured foraging tenacity in artificial resource patches placed in risky open versus safe alder habitat. Hares employed only two of eight a priori options to manage risk. Hares increased vigilance and reduced foraging in open areas as the distance from cover increased. Hares did not differentiate between open and alder habitats, increase vigilance at the coarse-grained scale, or reduce vigilance and foraging tenacity under supplemental cover. Hares were more vigilant in the putatively safe alder than in the purportedly risky open habitat. These apparently paradoxical results appear to reflect a trade-off between the benefit of alder as escape habitat and the cost of obscured sight lines that reduce predator detection. The trade-off also appears to equalize safety between habitats at small scales and suggests that common-sense predictions detailing how prey reduce risk may make no sense at all.

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 score0.017
Threshold uncertainty score0.416

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)0.0000.000

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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