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Record W3092172295 · doi:10.5194/bg-17-4831-2020

Factors controlling plankton community production, export flux, and particulate matter stoichiometry in the coastal upwelling system off Peru

2020· article· en· W3092172295 on OpenAlexaff
Lennart T. Bach, A. J. Paul, Tim Boxhammer, Elisabeth von der Esch, Michelle Graco, Kai G. Schulz, Eric P. Achterberg, Paulina Aguayo, Javier Arı́stegui, Patrizia Ayón, Isabel Baños, Avy Bernales, Anne Sophie Boegeholz, Francisco P. Chávez, Gabriela M. Chavez, Shao-Min Chen, Kristin Doering, Alba Filella, Martin A. Fischer, Patricia Grasse, Mathias Haunost, Jan Hennke, Nauzet Hernández‐Hernández, Mark J. Hopwood, Maricarmen Igarza, Verena Kalter, Leila Kittu, Peter Kohnert, Jesús Ledesma, Christian Lieberum, Silke Lischka, Carolin R. Löscher, Andrea Ludwig, Úrsula Mendoza, Jana Meyer, Judith Meyer, Fabrizio Minutolo, Joaquín Ortiz, Jonna Piiparinen, Claudia Sforna, Kristian Spilling, Sonia Sánchez, Carsten Spisla, Michael Sswat, Mabel Zavala Moreira, Ulf Riebesell

Bibliographic record

VenueBiogeosciences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsMemorial University of NewfoundlandDalhousie University
FundersLeibniz-GemeinschaftStrongConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaBundesministerium für Bildung und ForschungEuropean CommissionDeutsche Forschungsgemeinschaft
KeywordsUpwellingParticulatesOceanographyPlanktonEnvironmental scienceFlux (metallurgy)StoichiometryProduction (economics)GeologyEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract. Eastern boundary upwelling systems (EBUS) are among the most productive marine ecosystems on Earth. The production of organic material is fueled by upwelling of nutrient-rich deep waters and high incident light at the sea surface. However, biotic and abiotic factors can modify surface production and related biogeochemical processes. Determining these factors is important because EBUS are considered hotspots of climate change, and reliable predictions of their future functioning requires understanding of the mechanisms driving the biogeochemical cycles therein. In this field experiment, we used in situ mesocosms as tools to improve our mechanistic understanding of processes controlling organic matter cycling in the coastal Peruvian upwelling system. Eight mesocosms, each with a volume of ∼55 m3, were deployed for 50 d ∼6 km off Callao (12∘ S) during austral summer 2017, coinciding with a coastal El Niño phase. After mesocosm deployment, we collected subsurface waters at two different locations in the regional oxygen minimum zone (OMZ) and injected these into four mesocosms (mixing ratio ≈1.5 : 1 mesocosm: OMZ water). The focus of this paper is on temporal developments of organic matter production, export, and stoichiometry in the individual mesocosms. The mesocosm phytoplankton communities were initially dominated by diatoms but shifted towards a pronounced dominance of the mixotrophic dinoflagellate (Akashiwo sanguinea) when inorganic nitrogen was exhausted in surface layers. The community shift coincided with a short-term increase in production during the A. sanguinea bloom, which left a pronounced imprint on organic matter C : N : P stoichiometry. However, C, N, and P export fluxes did not increase because A. sanguinea persisted in the water column and did not sink out during the experiment. Accordingly, export fluxes during the study were decoupled from surface production and sustained by the remaining plankton community. Overall, biogeochemical pools and fluxes were surprisingly constant for most of the experiment. We explain this constancy by light limitation through self-shading by phytoplankton and by inorganic nitrogen limitation which constrained phytoplankton growth. Thus, gain and loss processes remained balanced and there were few opportunities for blooms, which represents an event where the system becomes unbalanced. Overall, our mesocosm study revealed some key links between ecological and biogeochemical processes for one of the most economically important regions in the oceans.

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)0.001
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.228
Teacher spread0.191 · 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

Citations55
Published2020
Admission routes1
Has abstractyes

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