High‐resolution imaging particle analysis of freshwater cyanobacterial blooms

Graham, Mark D.; Cook, Jenna; Graydon, Jennifer A.; Kinniburgh, D.G.; Nelson, Harry W.; Pilieci, Stephanie N.; Vinebrooke, Rolf D.

doi:10.1002/lom3.10274

Cited by 24 publications

(37 citation statements)

References 50 publications

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“…This combination maximises the taxonomic resolution for the size range of interest without compromising the running time. Sample preservation with Lugol negates the ability to discriminate cells from detritus through the detection of chlorophyll ( Graham et al 2018 ). Therefore, samples are processed using the AutoImage working mode imaging particles in a user-defined number of frames per second (FPS) (here, 20 FPS) and a flow rate of 1.7 ml min -1 .…”

Section: Sampling Methodsmentioning

confidence: 99%

LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea

Martínez¹,

Mortelmans²,

Dillen³

et al. 2020

BDJ

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This paper describes a phytoplankton data series generated through systematic observations in the Belgian Part of the North Sea (BPNS). Phytoplankton samples were collected during multidisciplinary sampling campaigns, visiting nine nearshore stations with monthly frequency and an additional eight offshore stations on a seasonal basis. The data series contain taxon-specific phytoplankton densities determined by analysis with the Flow Cytometer And Microscope (FlowCAM®) and associated image-based classification. The classification is performed by two separate semi-automated classification systems, followed by manual validation by taxonomic experts. To date, 637,819 biological particles have been collected and identified, yielding a large dataset of validated phytoplankton images. The collection and processing of the 2017–2018 dataset are described, along with its data curation, quality control and data storage. In addition, the classification of images using image classification algorithms, based on convolutional neural networks (CNN) from 2019 onwards, is also described. Data are published in a standardised format together with environmental parameters, accompanied by extensive metadata descriptions and finally labelled with digital identifiers for traceability. The data are published under a CC‐BY 4.0 licence, allowing the use of the data under the condition of providing the reference to the source.

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Section: Sampling Methodsmentioning

confidence: 99%

LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea

Martínez¹,

Mortelmans²,

Dillen³

et al. 2020

BDJ

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show abstract

“…(2) Data acquisition, while Lugol's acid preservative can improve cellular image capture efficiency (Graham et al 2018), some heterotrophic and mixotrophic cells counted using FDA can have very faint green fluorescence, producing false negatives (Steinberg et al 2011). Model-averaged importance of variables retained for each model selection for each voyage.…”

Section: Discussionmentioning

confidence: 99%

Improving estimation of phytoplankton abundance and distribution in ballast water discharges

2020

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With the International Maritime Organization's (IMO) International Convention for the Control and Management of Ships' Ballast Water and Sediments now in force, determining abundance and distribution of phytoplankton inside ballast tanks is critical for successful ballast water management, particularly when assessing compliance. The relationship between the abundance and distribution of cells was examined to obtain the best representative sample of the entire phytoplankton community in ballast tanks, comparing three ballast water sampling techniques including in-line, in-tank, and Van Dorn bottle methods. Lloyd's index, D y , and Gini index were applied to compare methods of sample collection and determine representativeness of samples and performance of sampling methods. Phytoplankton abundance trends from live microscopy counts using fluorescein diacetate (FDA) were also compared to those using a FlowCAM on preserved samples. The phytoplankton community showed a patchy distribution inside the ballast tank and this trend was observed across all voyages. The estimated marginal mean analysis showed that in hypothetical conditions (e.g., 702 m 3 of water in ballast tank and phytoplankton whole-tank abundance of 19,522 cells), the difference among the three methods was small. Conversely, statistical analysis performed on empiric abundances using a negative binomial regression model determined that the volume discharged during sampling of ballast water has an effect on the number of cells collected on a given voyage. Results of this study also confirmed that the in-line method may be a better method at collecting phytoplankton samples from ballast tanks than the in-tank or Van Dorn method, regardless of the time at which samples are collected. Finally, the number of living cells and the number of preserved cells showed similar trends for most of the voyages, despite fewer samples analyzed using FDA.

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“…With proper optimization, FlowCam can automatically classify zooplankton or phytoplankton, making the job of species identification easier and more efficient (Camoying and Yñiguez 2016). FlowCam has been widely used to assess changes in plankton community structure (Graham and Camp 2017), for identification of metazooplankton (Le Bourg et al 2015), and to identify dinoflagellate cells (Buskey and Hyatt 2006) or cyanobacteria associated with harmful algal blooms (Graham et al 2018). Fitted with a crosspolarizing filter (XPL attachment), FlowCam can be used for veliger detection (Spaulding 2009).…”

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confidence: 99%

Detecting a spreading non-indigenous species using multiple methodologies

Johansson

Lavigne

Ramcharan

et al. 2020

Lake and Reservoir Management

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Non-indigenous species (NIS) are often introduced to novel environments at very low population abundance. Detecting the presence of such an NIS can be very challenging, particularly as it spreads from the initial establishment site. This provides an opportunity to test detection limits using different approaches. This study tested the detection capability of 3 methods as zebra mussels (Dreissena polymorpha) spread from south to north through Lake Winnipeg, Manitoba, Canada. Zebra mussel veliger larvae were detected using cross-polarized light microscopy (CPLM), flow cytometry and microscopy (FlowCam), and conventional polymerase chain reaction (cPCR) analysis of environmental DNA (eDNA) on the same samples. Abundance generally declined from south to north in the lake but was lowest at Calder's Dock (central). Although abundances could be quite low (i.e., <1 veliger/m 3 , Calder's Dock) CPLM prevalence-the percentage of samples with at least one veliger-was high throughout the lake (99-100% of samples). Prevalence was lower for cPCR and FlowCam but was statistically associated with veliger abundance. Using standardized 3 mL subsamples (0.06-0.18 m 3 of lake water sampled), all 3 methods had a high probability of veliger detection if large numbers of samples were processed. FlowCam was the most expensive method to process these 3 mL subsamples, while cPCR was least expensive and fastest. eDNA combined with intensive sampling is the most practical method for wide-scale monitoring programs for early detection. However, all 3 methods are complementary and could be deployed sequentially, with rapid initial sample processing using PCR, confirmation and density estimation with FlowCam, and detailed veliger counts using CPLM.

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High‐resolution imaging particle analysis of freshwater cyanobacterial blooms

Cited by 24 publications

References 50 publications

LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea

LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea

Improving estimation of phytoplankton abundance and distribution in ballast water discharges

Detecting a spreading non-indigenous species using multiple methodologies

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