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Record W4388567829 · doi:10.1007/s00227-023-04318-w

Infaunal invertebrate community relationships to water column and sediment abiotic conditions

2023· article· en· W4388567829 on OpenAlexafffundabout
Samantha A. McGarrigle, Heather L. Hunt

Bibliographic record

VenueMarine Biology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbiotic componentWater columnBaySedimentEcologyIntertidal zoneOceanographyInvertebrateAbundance (ecology)Community structureSalinityBiologyEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract Infaunal invertebrates are affected by the overlying water and the sediment in which they live. Therefore, understanding how these environmental conditions impact infauna is critical for evaluating how they may respond to future changes in these conditions due to climate change. Here, we considered which abiotic variables, for example, salinity, sediment characteristics (i.e. mean grain size, sorting), and water column and sediment carbonate chemistry, influence infaunal invertebrate communities and juvenile bivalve abundance at intertidal sites. We used data from sites in two regions in New Brunswick, Canada with contrasting tidal regimes and oceanographic conditions, the Bay of Fundy and the Southern Gulf of St. Lawrence. We were particularly interested in bivalve recruitment due to the importance of bivalves in ecosystem services and predicted sensitivity to climate change impacts. Using data collected in 2020 and 2021, statistical modeling was done to determine which abiotic variables were potential drivers of multivariate community composition as well as species richness, total abundance, and juvenile bivalve abundance. We found that carbonate chemistry variables, both sediment and water, explained a large amount of variation (~ 7–44%) in infaunal invertebrate communities in the two regions in both our multivariate and univariate analyses. Sediment pH explained the most variation (16.9%) in the multivariate analyses for the Bay of Fundy sites. However, in the Southern Gulf of St. Lawrence, salinity explained the most variation (9.8%) in the multivariate community composition. In the univariate modeling, alkalinity, either water column or sediment, was included in all top models for all four dependent variables, suggesting the importance of this carbonate chemistry variable for bivalves and infaunal communities. Climate change is expected to have large impacts on carbonate chemistry conditions in the oceans, specifically pH, carbonate availability, and alkalinity. The influence of carbonate chemistry parameters on infaunal invertebrate communities in these regions shows the potential sensitivity these animals have to future oceanic conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.053
GPT teacher head0.278
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2023
Admission routes3
Has abstractyes

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