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Record W2069426054 · doi:10.2983/035.028.0211

Shell Traits of a Marine Mussel Mediate Predation Selectivity by Crabs and Sea Stars

2009· article· en· W2069426054 on OpenAlexaffabout
BriAnne Addison

Bibliographic record

VenueJournal of Shellfish Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiologyPredationMusselIntertidal zoneFisheryMytilusEcologyDecapodaZoologyCrustacean

Abstract

fetched live from OpenAlex

Crabs and sea stars are known to preferentially select mussels with morphological traits that diminish the predators' searching or handling times. I compared two distinct morphotype of mussels (Mytilus trossulus; Gould, 1850) from Howe Sound, British Columbia, Canada, by dissection and measurement. Then, I experimentally offered mussels of the two morphotypes on the same patch to crabs (Cancer productus; Randall, 1839) and sea stars (Pisaster ochraceus; Brandt, 1835) to quantify the extent to which these predators select prey based on morphological features. Sea stars preferentially consumed mussels with gaps in shell closures, although these mussels also had larger adductor muscles compared with mussels rejected. Gaps at the shell margin presumably allowed sea stars easier access between shell valves to insert their stomachs and begin digestion. Small crabs preferentially consumed mussels with thin shells, which are easier to crush, whereas, large crabs consumed more thick-shelled mussels, possibly because these mussels were larger and offered greater energetic return. However, overall, crabs and sea stars did not exhibit strong preferences for smaller or larger mussel prey. These findings indicate that morphological features of mussels are important in prey selection by crab and sea star predators. Predator selectivity could cause a trade-off in defense strategies in mussels, and ultimately mediate indirect effects between these predators in the rocky intertidal community.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 integrity0.0000.001
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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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.

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

Citations5
Published2009
Admission routes2
Has abstractyes

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