Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 308

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

Pelagic harmful algal blooms and climate change: Lessons from nature’s experiments with extremes

Open access
Trainer, Vera L., Moore, Stephanie K., Hallegraeff, Gustaaf, Kudela, Raphael M., Clement, Alejandro, Mardones, Jorge I., Cochlan, William P.·2020·Harmful Algae

Abstract

Time series now have sufficient duration to determine harmful algal bloom (HAB) responses to changing climate conditions, including warming, stratification intensity, freshwater inputs and natural patterns of climate variability, such as the El Niño Southern Oscillation and Pacific Decadal Oscillation. Against the context of time series, such as those available from phytoplankton monitoring, dinoflagellate cyst records, the Continuous Plankton Recorder surveys, and shellfish toxin records, it is possible to identify extreme events that are significant departures from long-term means. Extreme weather events can mimic future climate conditions and provide a "dress rehearsal" for understanding future frequency, intensity and geographic extent of HABs. Three case studies of extreme HAB events are described in detail to explore the drivers and impacts of these oceanic outliers that may become more common in the future. One example is the chain-forming diatom of the genus Pseudo-nitzschia in the U.S. Pacific Northwest and its response to the 2014-16 northeast Pacific marine heat wave. The other two case studies are pelagic flagellates. Highly potent Alexandrium catenella group 1 dinoflagellate blooms (up to 150 mg/kg PST in mussels; 4 human poisonings) during 2012-17 created havoc for the seafood industry in Tasmania, south-eastern Australia, in a poorly monitored area where such problems were previously unknown. Early evidence suggests that changes in water column stratification during the cold winter-spring season are driving new blooms caused by a previously cryptic species. An expansion of Pseudochattonella cf. verruculosa to the south and A. catenella to the north over the past several years resulted in the convergence of both species to cause the most catastrophic event in the history of the Chilean aquaculture in the austral summer of 2016. Together, these two massive blooms were colloquially known as the "Godzilla-Red tide event", resulting in the largest fish farm mortality ever recorded worldwide, equivalent to an export loss of USD$800 million which when combined with shellfish toxicity, resulted in major social unrest and rioting. Both blooms were linked to the strong El Niño event and the positive phase of the Southern Annular Mode, the latter an indicator of anthropogenic climate change in the southeastern Pacific region. For each of these three examples, representing recent catastrophic events in geographically distinct regions, additional targeted monitoring was employed to improve the understanding of the climate drivers and mechanisms that gave rise to the event and to document the societal response. Scientists must be poised to study future extreme HAB events as these natural experiments provide unique opportunities to define and test multifactorial drivers of blooms.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.hal.2019.03.009

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 69 references

Cited by

See all 308

Metadata

Authors
Trainer, V. L., Moore, S. K., Hallegraeff, G., Kudela, R. M., Clement, A., Mardones, J. I., & Cochlan, W. P.
Venue
Harmful Algae
Publisher
Elsevier BV
Published
2020-01
Volume
91
Pages
101591
ISSN
1568-9883 (ISSN-L)
OpenAlex
W2943633724
Cited by
308
References
69
FWCI
13.98
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Pelagic harmful algal blooms and climate change: Lessons from nature’s experiments with extremes. https://app.cochat.ai/papers/10.1016/j.hal.2019.03.009
The paper
Trainer, V. L., Moore, S. K., Hallegraeff, G., Kudela, R. M., Clement, A., Mardones, J. I., & Cochlan, W. P. (2020). Pelagic harmful algal blooms and climate change: Lessons from nature’s experiments with extremes. Harmful Algae. https://doi.org/10.1016/j.hal.2019.03.009

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.