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Record W1865610873 · doi:10.1155/2015/894579

Nonconsumptive Effects of Predation and Impaired Chemosensory Risk Assessment on an Aquatic Prey Species

2015· article· en· W1865610873 on OpenAlexaff
Chris K. Elvidge, G. E. Brown

Bibliographic record

VenueInternational Journal of Ecology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton UniversityConcordia University
Fundersnot available
KeywordsPredationForagingBiologySTREAMSSalmoJuvenileEcologyPopulationPredatorDiel vertical migrationZoologyFish <Actinopterygii>FisheryDemography

Abstract

fetched live from OpenAlex

Weak levels of acidity impair chemosensory risk assessment by aquatic species which may result in increased predator mortalities in the absence of compensatory avoidance mechanisms. Using replicate populations of wild juvenile Atlantic salmon ( Salmo salar ) in neutral and acidic streams, we conducted a series of observational studies and experiments to identify differences in behaviours that may compensate for the loss of chemosensory information on predation risk. Comparing the behavioural strategies of fish between neutral and acidic streams may elucidate the influence of environmental degradation on nonconsumptive effects (NCEs) of predation. Salmon in acidic streams are more active during the day than their counterparts in neutral streams, and are more likely to avoid occupying territories offering fewer physical refugia from predators. Captive cross-population transplant experiments indicate that at equal densities, salmon in acidic streams do not demonstrate relative decreases in growth rate as a result of their different behavioural strategies. Instead, altering diel activity patterns to maximize visual information use and occupying relatively safer territories appear sufficient to offset increased predation risk in acidic streams. Additional strategies such as elevated foraging rates during active periods or adopting riskier foraging tactics are necessary to account for the observed similarities in growth rates.

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 score0.005
Threshold uncertainty score0.235

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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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