Phytoplankton assemblages of the Southern Baikal in 1990-1995 and 2016-2018

Vorobyeva, S. S.

doi:10.31951/2658-3518-2018-a-2-141

Cited by 4 publications

(3 citation statements)

References 7 publications

(10 reference statements)

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“…Diatom abundance increased from 5 % in March-April to 44-75 % in the first decade of June. This event has been noticed earlier and is typical for Baikal (Vorobyeva, 2018). High contents of monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) characterize the planktonic assemblage with the Synedra acus subsp.…”

Section: Discussionmentioning

confidence: 56%

Nonspecific response of Lake Baikal phytoplankton to anthropogenic impact

Nikonova

Vorobyeva

2022

Vestn. VOGiS

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In this study, we present the first results on oxidation stress in Lake Baikal phytoplankton and its adaptation to environmental changes under anthropogenic impact. As was shown, the changing of the dominant species of phytoplankton collected from the surface water layer (~0.3 m) took place from February to June 2021. Phytoplankton were collected at a nearshore station (a littoral station at a distance of ~0.01 km from the shoreline, depth to bottom is ~5 m) and an offshore station (a pelagic station at a distance of ~1 km from the shoreline, depth to bottom is ~543 m). In February, dinoflagellates were dominant (~40 %) as well as diatoms (≤33 %) and green algae (≤12 %). Their biomass was 100 mg·m–3. In March, chrysophytes were dominant (up to 50 %) as well as cryptophytes (≤43 %) and dinoflagellates (≤30 %). Their biomass was 160–270 mg·m–3. In April, biomass increased up to 700–3100 mg·m–3 with the dominance of large cell dinoflagellates (up to 99 %), chrysophytes (up to 50 %), and cryptophytes (up to 35 %). By the end of the first decade of May, the percentage of dinoflagellates decreased and that of cryptophytes increased. In the second decade of May, the percentage of diatoms increased up to ~26–38 % but phytoplankton biomass was minimal (13–30 mg·m–3). By June, the percentage of diatoms in the samples reached 44–75 % at 60–550 mg·m–3. The oxidation stress of phytoplankton as a nonspecific adaptive response to a prolonged, intensive, or recurrent effect of a stress factor was estimated from the content of thiobarbituric acid reactive substances (TBARS). The mean content of these substances (markers of the lipid peroxidation) was determined spectrophotometrically. The oxidation stress of phytoplankton was revealed only when diatom algae dominated. It can be explained by adaptation of algae of other classes to the stress factor. The content of the lipid peroxidation markers in the coastal phytoplankton collected close to the settlement of Listvyanka known as a large touristic center was estimated from 100 to 500 μg·g–1 of dry weight of sample. During the period of diatom blooming in 2016 and 2018, oxidation stress of phytoplankton collected near large settlements was found. In phytoplankton from deep-water pelagic stations most remote from settlements, stress was not revealed. Using the method of gas chromatography, we showed a lower (up to 15 %) content of polyunsaturated fatty acids in phytoplankton characterized by stress occurrence. This confirms cell membrane damages. In Lake Baikal surface water, we found a higher content of synthetic anionic surfactants (sodium alkylbenzene sulfonates), which are components of detergents and cause oxidation stress of hydrobionts (up to 30 ± 4 μg·L–1). The presence of these substances in a water ecosystem can result in exhausting of phytoplankton cell resources, homeostasis imbalance, stress, pathological changes, and rearrangements in phytoplankton assemblage.

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Section: Discussionmentioning

confidence: 56%

Nonspecific response of Lake Baikal phytoplankton to anthropogenic impact

Nikonova

Vorobyeva

2022

Vestn. VOGiS

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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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Phytoplankton assemblages of the Southern Baikal in 1990-1995 and 2016-2018

Cited by 4 publications

References 7 publications

Nonspecific response of Lake Baikal phytoplankton to anthropogenic impact

Nonspecific response of Lake Baikal phytoplankton to anthropogenic impact

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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