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Record W2087605139 · doi:10.5194/bgd-7-3605-2010

Spatial and temporal variability of the dimethylsulfide to chlorophyll ratio in the surface ocean: an assessment in the light of phytoplankton composition determined from space

2010· preprint· en· W2087605139 on OpenAlexaff
Ítalo Masotti, Sauveur Belviso, S. Alvain, J. E. Johnson, T. Bates, Philippe D. Tortell, Nobue Kasamatsu, Mathieu Mongin, Christa Marandino, E. S. Saltzman, C. Moulin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
FundersGoddard Space Flight CenterCentre National de la Recherche ScientifiqueNational Aeronautics and Space Administration
KeywordsDimethylsulfoniopropionateOceanographyPhytoplanktonChlorophyll aDominance (genetics)EquatorProchlorococcusEnvironmental scienceSynechococcusNutrientChemistryBiologyLatitudeGeologyEcologyBotanyCyanobacteria

Abstract

fetched live from OpenAlex

Abstract. Dimethylsulfide (DMS) is biologically produced in the surface ocean and is the dominant natural source of sulfur to the atmosphere. Although DMS is an algal by-product, the ratio of DMS to chlorophyll (DMS:Chl) varies widely in the surface ocean. This is presumably because dimethylsulfoniopropionate (DMSP), the major precursor of DMS, DMSP-lyases, which catalyze the conversion of DMSP to DMS, and Chl vary as well with taxonomic composition than with the physiological state of the algal assemblage. Here we use remote sensing of Chl and phytoplankton dominance from PHYSAT with in-situ measured DMS concentrations to assess on an unprecedented spatial scale the affect of species composition on the DMS:Chl ratio in the surface ocean. Meridional distributions at 22° W in the Atlantic, and 95° W and 110° W in the Pacific, showed the same marked drop in DMS:Chl ratios near the equator, down to few mmol g−1, yet the basins exhibited different species dominance signatures. Hence, our results suggest that species composition was of secondary importance in controlling DMS and DMS:Chl variations in equatorial upwellings as well as physiological shifts in algal DMS production since mixed layer growth conditions (i.e., nutrient stress, temperature and light) were relatively homogeneous over the eastern equatorial Pacific. In the Indian sector of the Southern Ocean, warm core eddies with contrasting PHYSAT signatures displayed similar DMS levels. However, DMS:Chl ratios in eddies dominated by Synechococcus (SYN) were about 50% lower than that found in eddies showing nanoeucayotes or Phaeocystis-like signatures. DMS:Chl ratios varied with latitude in SYN dominated regions with ratios at low latitudes (away from equatorial upwellings) about twice that found at high northern and southern latitudes. This is the sole piece of coherent observations which indicates that species composition and growth conditions affect the large-scale dynamics of the DMS:Chl ratio. Overall, it appears that the DMS:Chl ratio is not consistent within specific phytoplankton groups determined from space. So DMS concentrations can not be derived from water-leaving radiance spectra obtained simultaneously from ocean color sensor measurements of Chl concentrations and dominant phytoplankton functional types. To proceed with the global investigation and better discriminate between factors affecting DMS:Chl ratios in the surface ocean, we recommend the use of PHYSAT records with higher spatial resolution in conjunction with other satellite products (e.g. particulate backscattering coefficients and indices of phytoplankton physiology and bloom status).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

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 communication0.0000.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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