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Record W2754176931 · doi:10.1139/cjz-2016-0268

Scavenging and the ecology of fear: do animal carcasses create islands of risk on the landscape?

2017· article· en· W2754176931 on OpenAlexvenueno aff
Cathleen M. Steinbeiser, C.A. Wawrzynowski, X. Ramos, Zachary H. Olson

Bibliographic record

VenueCanadian Journal of Zoology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsForagingBiologyPredationEcologyOdocoileusPredatorMammalOptimal foraging theoryTrophic levelTrophic cascadeEcosystemInvertebrateZoology

Abstract

fetched live from OpenAlex

Many vertebrate scavengers function as predators in ecosystems, suggesting that the presence of scavengers and occurrence of predator effects may be intertwined near carcasses. We tested for risk effects near a series of experimentally placed carcasses by measuring small-mammal foraging in a before–after control–impact design. Validation efforts revealed low levels of food loss from stations due to human error and invertebrate foraging, and habituation to stations occurred after 2 weeks. Increased perceived predation risk by small mammals relative to controls occurred in three of seven trials. The effect was observed across tested carcass types (beaver, Castor canadensis Kuhl, 1820; white-tailed deer, Odocoileus virginianus (Zimmermann, 1780)) and seasons (summer and fall). However, small mammals also increased foraging relative to controls in two of seven trials, and foraging reached a ceiling in two other trials that prevented inference on a response. Taken together, our results suggest that scavenger recruitment to carcasses can in some instances create islands of risk for prey on the landscape, but the effect is not likely to be universal. Where small-mammal foraging does decrease, further work will be necessary to determine if risk effects cascade to adjacent trophic levels through enhanced seed and seedling survival.

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)0.001
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 score0.115
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.010
GPT teacher head0.194
Teacher spread0.185 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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