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Record W2807903676 · doi:10.1002/ecy.2381

Quantifying fear effects on prey demography in nature

2018· article· en· W2807903676 on OpenAlexafffund
Michael J. L. Peers, Yasmine N. Majchrzak, Eric W. Neilson, Clayton T. Lamb, Anni Hämäläinen, Jessica A. Haines, Laura Garland, Darcy Doran‐Myers, Kate Broadley, Rudy Boonstra, Stan Boutin

Bibliographic record

VenueEcology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationPredatorEcologyConsumption (sociology)Sensory cueTrophic cascadePsychologyBiologyCognitive psychology

Abstract

fetched live from OpenAlex

In recent years, it has been argued that the effect of predator fear exacts a greater demographic toll on prey populations than the direct killing of prey. However, efforts to quantify the effects of fear have primarily relied on experiments that replace predators with predator cues. Interpretation of these experiments must consider two important caveats: (1) the magnitude of experimenter-induced predator cues may not be realistically comparable to those of the prey's natural sensory environment, and (2) given functional predators are removed from the treatments, the fear effect is measured in the absence of any consumptive effects, a situation which never occurs in nature. We contend that demographic consequences of fear in natural populations may have been overestimated because the intensity of predator cues applied by experimenters in the majority of studies has been unnaturally high, in some instances rarely occurring in nature without consumption. Furthermore, the removal of consumption from the treatments creates the potential situation that individual prey in poor condition (those most likely to contribute strongly to the observed fear effects via starvation or reduced reproductive output) may have been consumed by predators in nature prior to the expression of fear effects, thus confounding consumptive and fear effects. Here, we describe an alternative treatment design that does not utilize predator cues, and in so doing, better quantifies the demographic effect of fear on wild populations. This treatment substitutes the traditional cue experiment where consumptive effects are eliminated and fear is simulated with a design where fear is removed and consumptive effects are simulated through the experimental removal of prey. Comparison to a natural population would give a more robust estimate of the effect of fear in the presence of consumption on the demographic variable of interest. This approach represents a critical advance in quantifying the mechanistic pathways through which predation structures ecological communities. Discussing the merits of both treatments will motivate researchers to go beyond simply describing the existence of fear effects and focus on testing their true magnitude in wild populations and natural communities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

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)0.0010.002

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 head0.245
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations62
Published2018
Admission routes2
Has abstractyes

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