Current state of phytoplankton in the littoral area of Lake Baikal, spring 2017

Бондаренко, Н. А.; Vorobyova, Svetlana S.; Жученко, Н.А.; Golobokova, L. P.

doi:10.1016/j.jglr.2019.10.001

Cited by 20 publications

(10 citation statements)

References 40 publications

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“…There peaks of Synedra are confined to warm isotopic stages (Khursevich et al, 2001). Recently it was established that due to global warming there has been an increase of Synedra and a decrease in endemic species of diatoms in the bottom sediments and within the water column of Southern Baikal (Roberts et al, 2018;Vologina et al, 2019;Bondarenko et al, 2020). Our new data confirm this conclusion.…”

Section: Resultssupporting

confidence: 82%

Late Holocene sediments in the profound abyss of Southern Lake Baikal

Вологина

Sturm

Vorob'eva

et al. 2020

LFWB

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Here we present new data of bottom sediments, which were collected in March 2018 in the deep water of Southern Baikal. The deposits consist of pelagic mud, intercalated by three turbidites. The uppermost 2 cm of the core are formed by the light layer of a diatomite, consisting mainly of valves of Synedra acus (up to 219 million cells / g). We attribute the increased content of this diatom species in the upper part of the core to warmer climatic conditions.

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Section: Resultssupporting

confidence: 82%

Late Holocene sediments in the profound abyss of Southern Lake Baikal

Вологина

Sturm

Vorob'eva

et al. 2020

LFWB

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“…Chrysophyta were formed by Chrysochromulina parva (up to 6439 thousand cells L-1) and D.cylindricum (up to 192 thousand cells L-1). Chlorophyta was represented by Monoraphidium contortum, M. arcuatum, K. longiseta, and Chlamydomonas sp (Vorobyeva, 2018;Bondarenko et al, 2020). Hence, the constructed three networks can help to identify the main taxon of the Baikal phytoplankton with high accuracy.…”

Section: Resultsmentioning

confidence: 99%

Identification of Baikal phytoplankton inferred from computer vision methods and machine learning

Lysenko¹,

Oznobikhin²,

Kireev³

et al. 2021

LFWB

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Abstract. This study discusses the problem of phytoplankton classification using computer vision methods and convolutional neural networks. We created a system for automatic object recognition consisting of two parts: analysis and primary processing of phytoplankton images and development of the neural network based on the obtained information about the images. We developed software that can detect particular objects in images from a light microscope. We trained a convolutional neural network in transfer learning and determined optimal parameters of this neural network and the optimal size of using dataset. To increase accuracy for these groups of classes, we created three neural networks with the same structure. The obtained accuracy in the classification of Baikal phytoplankton by these neural networks was up to 80%.

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“…Notably, for the past period (~30 years), the structure and functioning of shallow spring phytoplankton have changed. The periodicity of years with high productivity (B ≥ 1000 mg m −3 ) and low productivity (B ≤ 100 mg m −3 ) of large-volume A. baicalensis has disappeared [62,63]. In the last ten years, the small-volume diatom S. acus subsp.…”

Section: Fa Composition and Fa Peroxidation Product Analysismentioning

confidence: 99%

Fatty Acid Changes in Nearshore Phytoplankton under Anthropogenic Impact as a Biodiversity Risk Factor for the World’s Deepest Lake Baikal

et al. 2022

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In this study, we present results on fatty acid analysis of phytoplankton of Lake Baikal, the world’s deepest lake, which differs from other lakes by its oceanic features. Since we used a large-mesh net, the net sample phytoplankton were primarily represented by the large elongated diatom Synedra acus. subsp. radians (Kützing) Skabichevskij. The similar algae composition of net samples of spring season phytoplankton collected at different sites of the lake allows us to compare results of the fatty acid analysis of these samples. The phytoplankton diversity of the sedimentation samples was contrary represented by 32 algae species. There are clear changes in the fatty acid composition of net phytoplankton exposed to anthropogenic impacts of varying intensity. The content of polyunsaturated fatty acids in phytoplankton collected from central stations (pelagic stations at a distance of ~10–30 km from the shoreline) without anthropogenic impact was higher by up to 15% than phytoplankton collected from nearshore stations (littoral stations at a distance of ~0.01–0.05 km from the shoreline) and offshore stations (pelagic stations at a distance of ~3 km from the shoreline). The interlaboratory precision of fatty acid determination of phytoplankton is estimated as ≤10%. We found high content of the lipid peroxidation marker (80–340 μg g−1 of dry weight) in phytoplankton from nearshore and offshore stations with intensive anthropogenic impact. In phytoplankton from central stations, we did not find any lipid peroxidation. Determination of unsaturated fatty acids, coupled with analysis of fatty acid peroxidation products, can be used to evaluate the level of anthropogenic impact in terms of ecological health and biodiversity conservation.

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Current state of phytoplankton in the littoral area of Lake Baikal, spring 2017

Cited by 20 publications

References 40 publications

Late Holocene sediments in the profound abyss of Southern Lake Baikal

Late Holocene sediments in the profound abyss of Southern Lake Baikal

Identification of Baikal phytoplankton inferred from computer vision methods and machine learning

Fatty Acid Changes in Nearshore Phytoplankton under Anthropogenic Impact as a Biodiversity Risk Factor for the World’s Deepest Lake Baikal

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