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Record W2178227715 · doi:10.1139/cjfas-2015-0029

The interplay between resource supply and demand determines the influence of predation on prey body size

2015· article· en· W2178227715 on OpenAlexvenueno aff
John P. DeLong, Matthew Walsh

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPredationIntraspecific competitionInterspecific competitionEcologyBiologyGrowth ratePredatorResource (disambiguation)Variable (mathematics)Mathematics

Abstract

fetched live from OpenAlex

Predation has been shown to either increase or decrease the body mass of fish, as well as cause variable changes in growth rate. The mechanisms underlying these contrasting responses are not well understood. Here we compared intraspecific body size and growth responses to predation against a backdrop of 2006 estimates of asymptotic mass and growth constants (i.e., von Bertalanffy parameters) across species. We show that intraspecific responses can be quite large relative to interspecific variation and confirm that the magnitude and direction of body size responses is variable. We then employed the supply–demand (SD) model of body mass evolution to explore how predator-induced changes in resource demand or supply could alter body mass. The SD model predicts that any combination of increasing or decreasing body mass and increasing or decreasing growth rate is possible when predation risk is increased, which is consistent with the literature. Finally, we use three case studies to illustrate how the interplay of resource supply and resource demand determines the actual body mass and growth rate response to predation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→