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Record W2803472713 · doi:10.1002/ecy.2393

Oysters and eelgrass: potential partners in a high <scp>pCO</scp><sub>2</sub> ocean

2018· article· en· W2803472713 on OpenAlexafffund
Maya L. Groner, Colleen A. Burge, Ruth Cox, Natalie Rivlin, Mo Turner, Kathryn L. Van Alstyne, Sandy Wyllie‐Echeverria, John P. Bucci, Philip Staudigel, Carolyn S. Friedman

Bibliographic record

VenueEcology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of Prince Edward Island
FundersSchool of Aquatic and Fishery SciencesWashington Sea Grant, University of WashingtonUniversity of Prince Edward IslandCanada Research ChairsUniversity of MarylandUniversity of WashingtonUniversity of Maryland, Baltimore CountyNational Science Foundation
KeywordsZostera marinaOysterOcean acidificationBiologySeagrassCrassostreaTotal inorganic carbonFisherySeawaterEcologyCarbon dioxideEcosystem

Abstract

fetched live from OpenAlex

Abstract Climate change is affecting the health and physiology of marine organisms and altering species interactions. Ocean acidification (OA) threatens calcifying organisms such as the Pacific oyster, Crassostrea gigas. In contrast, seagrasses, such as the eelgrass Zostera marina, can benefit from the increase in available carbon for photosynthesis found at a lower seawater pH. Seagrasses can remove dissolved inorganic carbon from OA environments, creating local daytime pH refugia. Pacific oysters may improve the health of eelgrass by filtering out pathogens such as Labyrinthula zosterae (LZ), which causes eelgrass wasting disease (EWD). We examined how co‐culture of eelgrass ramets and juvenile oysters affected the health and growth of eelgrass and the mass of oysters under different pCO2 exposures. In Phase I, each species was cultured alone or in co‐culture at 12°C across ambient, medium, and high pCO2 conditions, (656, 1,158 and 1,606 μatm pCO2, respectively). Under high pCO2, eelgrass grew faster and had less severe EWD (contracted in the field prior to the experiment). Co‐culture with oysters also reduced the severity of EWD. While the presence of eelgrass decreased daytime pCO2, this reduction was not substantial enough to ameliorate the negative impact of high pCO2 on oyster mass. In Phase II, eelgrass alone or oysters and eelgrass in co‐culture were held at 15°C under ambient and high pCO2 conditions, (488 and 2,013 μatm pCO2, respectively). Half of the replicates were challenged with cultured LZ. Concentrations of defensive compounds in eelgrass (total phenolics and tannins), were altered by LZ exposure and pCO2 treatments. Greater pathogen loads and increased EWD severity were detected in LZ exposed eelgrass ramets; EWD severity was reduced at high relative to low pCO2. Oyster presence did not influence pathogen load or EWD severity; high LZ concentrations in experimental treatments may have masked the effect of this treatment. Collectively, these results indicate that, when exposed to natural concentrations of LZ under high pCO2 conditions, eelgrass can benefit from co‐culture with oysters. Further experimentation is necessary to quantify how oysters may benefit from co‐culture with eelgrass, examine these interactions in the field and quantify context‐dependency.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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 designBench or experimental
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

Citations60
Published2018
Admission routes2
Has abstractyes

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