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Record W4387423887 · doi:10.1016/j.hal.2023.102496

Emerging phylogeographic perspective on the toxigenic diatom genus Pseudo-nitzschia in coastal northern European waters and gateways to eastern Arctic seas: Causes, ecological consequences and socio-economic impacts

2023· article· en· W4387423887 on OpenAlexfundno aff
Allan Cembella, Kerstin Klemm, Uwe John, Bengt Karlson, Lars Arneborg, Dave Clarke, Tsuyuko Yamanaka, Caroline Cusack, Lars‐Johan Naustvoll, Eileen Bresnan, Luka Šupraha, Nina Lundholm

Bibliographic record

VenueHarmful Algae · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiSvenska Forskningsrådet FormasAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und MeeresforschungEuropean CommissionRéseau de cancérologie RossyAgence Nationale de la RechercheU.S. Environmental Protection Agency
KeywordsNitzschiaEcologyDiatomBiologyArcticAlgal bloomClimate changeDomoic acidGeographyOceanographyPhytoplankton

Abstract

fetched live from OpenAlex

The diatom Pseudo-nitzschia H. Peragallo is perhaps the most intensively researched genus of marine pennate diatoms, with respect to species diversity, life history strategies, toxigenicity, and biogeographical distribution. The global magnitude and consequences of harmful algal blooms (HABs) of Pseudo-nitzschia are particularly significant because of the high socioeconomic impacts and environmental and human health risks associated with the production of the neurotoxin domoic acid (DA) among populations of many (although not all) species. This has led to enhanced monitoring and mitigation strategies for toxigenic Pseudo-nitzschia blooms and their toxins in recent years. Nevertheless, human adaptive actions based on future scenarios of bloom dynamics and proposed shifts in biogeographical distribution under climate-change regimes have not been implemented on a regional scale. In the CoCliME (Co-development of climate services for adaptation to changing marine ecosystems) program these issues were addressed with respect to past, current and anticipated future status of key HAB genera such as Pseudo-nitzschia and expected benefits of enhanced monitoring. Data on the distribution and frequency of Pseudo-nitzschia blooms in relation to DA occurrence and associated amnesic shellfish toxin (AST) events were evaluated in a contemporary and historical context over the past several decades from key northern CoCliME Case Study areas. The regional studies comprised the greater North Sea and adjacent Kattegat-Skagerrak and Norwegian Sea, eastern North Atlantic marginal seas and Arctic gateways, and the Baltic Sea. The first evidence of possible biogeographical expansion of Pseudo-nitzschia taxa into frontier eastern Arctic gateways was provided from DNA barcoding signatures. Key climate change indicators, such as salinity, temperature, and water-column stratification were identified as drivers of upwelling and advection related to the distribution of regional Pseudo-nitzschia blooms. The possible influence of changing variables on bloom dynamics, magnitude, frequency and spatial and temporal distribution were interpreted in the context of regional ocean climate models. These climate change indicators may play key roles in selecting for the occurrence and diversity of Pseudo-nitzschia species within the broader microeukaryote communities. Shifts to higher temperature and lower salinity regimes predicted for the southern North Sea indicate the potential for high-magnitude Pseudo-nitzschia blooms, currently absent from this area. Ecological and socioeconomic impacts of Pseudo-nitzschia blooms are evaluated with reference to effects on fisheries and mariculture resources and coastal ecosystem function. Where feasible, effective adaptation strategies are proposed herein as emerging climate services for the northern CoCLiME region.

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 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 score0.627

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 studies0.0000.001
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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