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Record W3014008634 · doi:10.5334/oq.80

Salt-Marsh Foraminiferal Distributions from Mainland Northern Georgia, USA: An Assessment of Their Viability for Sea-Level Studies

2020· article· en· W3014008634 on OpenAlexaff
Huixian Chen, Timothy A. Shaw, Jianhua Wang, Simon E. Engelhart, Daria Nikitina, Jessica E. Pilarczyk, Jennifer Walker, Ane García‐Artola, Benjamin P. Horton

Bibliographic record

VenueOpen Quaternary · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsSimon Fraser University
FundersEarth Observatory of SingaporeMinistry of Education, IndiaEarthwatch InstituteNational Research Foundation SingaporeNational Research FoundationChina Scholarship Council
KeywordsMarshSalt marshTransectOceanographyDetrended correspondence analysisRange (aeronautics)SedimentSea levelEcologySalinitySubfossilHoloceneGeologyEnvironmental sciencePhysical geographyGeographyBiologyOrdinationWetlandPaleontology

Abstract

fetched live from OpenAlex

We investigated foraminiferal distributions from two salt-marsh sites at Thunderbolt and Georgetown, in mainland northern Georgia, U.S. Atlantic coast. We analyzed modern epifaunal foraminiferal assemblages across multiple transects consisting of 54 surface samples. Multivariate statistical analysis (Partitioning Around Medoids and Detrended Correspondence Analysis) revealed that dead foraminiferal assemblages are divided into three faunal zones, which are elevation-dependent and site-specific. At Thunderbolt, an intermediate salinity marsh (17‰), high marsh assemblages are dominated by Haplophragmoides spp. with an elevational range of 1.19 to 1.68 m mean tide level (MTL) between Mean Higher High Water (MHHW) to Highest Astronomical Tide (HAT). Low marsh assemblages are dominated by Miliammina fusca and Ammobaculites spp. with an elevational range of – 0.05 to 1.14 m MTL (between MTL and MHHW). At Georgetown, a low salinity marsh (6‰), the assemblages are dominated by Ammoastuta inepta with an elevational range of 0.43 to 1.16 m MTL (between MTL and MHHW). We also enumerated living infaunal foraminiferal populations from six 50-cm sediment cores from the two salt marshes to assess implications for interpretations of sea-level change. Peak concentrations of living foraminiferal populations occur in the upper 1-cm surface sediment in five of the six cores. An exception was observed in high marsh settings of Thunderbolt, where Haplophragmoides spp. and Arenoparrella mexicana were observed living down to 40 cm depth and both the live and dead abundance peaked (32 and 520 specimens per 10 cc respectively) between depths of 15–35 cm in the core. The dominant infaunal species were similar to those observed in modern surface samples, and the total number of infaunal foraminifera was typically less than 15% compared to the total number of dead specimens in the surface samples. Finally, we com­pared the down-core patterns of living and dead foraminiferal abundance that suggest that 90% of the tests were removed within the upper 10 cm of sediment in most cores. This may be due to taphonomic alteration from bioturbation and/or microbial processes. Selective preservation between resistant species such as A. mexicana and fragile species like M. fusca and Ammobaculites spp. can change the subsurface foraminiferal assemblage. This has the potential to cause errors in sea-level reconstructions using foraminiferal assemblage from low marsh sediments. This study highlights the modern vertical distribution of salt-marsh foraminifera in mainland northern Georgia and their potential as modern analogues for fos­sil counterparts in reconstructing sea-level changes. Taphonomic processes may cause the absence of foraminiferal tests or differences between modern and fossil assemblages, which could be problematic when performing RSL reconstructions in low marsh environment. Publisher's note: a correction article relating to this paper has been published and can be found at http://doi.org/10.5334/oq.91

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.998

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 science0.0010.001
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 head0.065
GPT teacher head0.336
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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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