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Record W2761994355 · doi:10.1002/ecy.2050

Fear affects parental care, which predicts juvenile survival and exacerbates the total cost of fear on demography

2017· article· en· W2761994355 on OpenAlexafffund
Blair P. Dudeck, Michael Clinchy, Marek C. Allen, Liana Zanette

Bibliographic record

VenueEcology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJuvenileDemographyEcologyPaternal careBiologyPsychologySociology

Abstract

fetched live from OpenAlex

Fear itself (perceived predation risk) can affect wildlife demography, but the cumulative impact of fear on population dynamics is not well understood. Parental care is arguably what most distinguishes birds and mammals from other taxa, yet only one experiment on wildlife has tested fear effects on parental food provisioning and the repercussions this has for the survival of dependent offspring, and only during early-stage care. We tested the effect of fear on late-stage parental care of mobile dependent offspring, by locating radio-tagged Song Sparrow fledglings and broadcasting predator or non-predator playbacks in their vicinity, measuring their parent's behavior and their own, and tracking the offspring's survival to independence. Fear significantly reduced late-stage parental care, and parental fearfulness (as indexed by their reduction in provisioning when hearing predators) significantly predicted their offspring's condition and survival. Combining results from this experiment with that on early-stage care, we project that fear itself is powerful enough to reduce late-stage survival by 24%, and cumulatively reduce the number of young reaching independence by more than half, 53%. Experiments in invertebrate and aquatic systems demonstrate that fear is commonly as important as direct killing in affecting prey demography, and we suggest focusing more on fear effects and on offspring survival will reveal the same for wildlife.

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 score0.061
Threshold uncertainty score0.382

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations55
Published2017
Admission routes2
Has abstractyes

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