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Record W2152314412 · doi:10.1111/evo.12811

Intraguild predation leads to genetically based character shifts in the threespine stickleback

2015· article· en· W2152314412 on OpenAlexafffund
Sara Miller, Daniel Metcalf, Dolph Schluter

Bibliographic record

VenueEvolution · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSticklebackBiologySculpinGasterosteusPredationEcologyIntraguild predationSympatric speciationZoologyPredatorFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Intraguild predation is a common ecological interaction that occurs when a species preys upon another species with which it competes. The interaction is potentially a mechanism of divergence between intraguild prey (IG-prey) populations, but it is unknown if cases of character shifts in IG-prey are an environmental or evolutionary response. We investigated the genetic basis and inducibility of character shifts in threespine stickleback from lakes with and without prickly sculpin, a benthic intraguild predator (IG-predator). Wild populations of stickleback sympatric with sculpin repeatedly show greater defensive armor and water column height preference. We laboratory-raised stickleback from lakes with and without sculpin, as well as marine stickleback, and found that differences between populations in armor, body shape, and behavior persisted in a common garden. Within the common garden, we raised stickleback half-families from multiple populations in the presence and absence of sculpin. Although the presence of sculpin induced trait changes in the marine stickleback, we did not observe an induced response in the freshwater stickleback. Behavioral and morphological trait differences between freshwater populations thus have a genetic basis and suggest an evolutionary response to intraguild 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.164

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 head0.040
GPT teacher head0.251
Teacher spread0.211 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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