scholarly journals Algoflora of lakes in the Kurgal’sky Nature reserve (Leningrad region)

2021 ◽  
pp. 335-347
Author(s):  
E. V. Stanislavskaya ◽  
A. L. Afanas’eva ◽  
O. A. Pavlova
Keyword(s):  
Chlorophyll A ◽  
Green Algae ◽  
Brackish Water ◽  
Phytoplankton Biomass ◽  
Nature Reserve ◽  
Ecological Status ◽  
Similarity Index ◽  
Leningrad Region ◽  
Algal Flora ◽  
Trophic Conditions

Various algocenosises in the brackish Lake Lipovskoe and ultra-oligotrophic Lake Beloe located in the Kurgal’sky Nature reserve were studied in May and July of 2019. In the algal flora of the lakes studied, we found 291 taxa belonging to 9 orders. The both lakes were characterized by high species richness, namely: 179 and 181 algae taxa were identified in the Lake Lipovskoe and Lake Beloe, respectively. In the Lake Lipovskoe, its phytoplankton was dominated by Cyanophyta, Cryptophyta and Dinophyta, among which brackish-water and marine species were presented. In the periphyton of this Lake, brackish-water diatoms and green algae were the most abundant. In the Lake Beloe, its phytoplankton was dominated by Dinophyta, Bacillariophyta and Chlorophyta; the blue-greens, diatoms and green algae dominated in the periphyton of Lake Beloe. Despite that the lakes are located close to each other, their algal flora is quite different: the Sorensen similarity index between the two lakes was 38% only. The phytoplankton biomass in both lakes was low, increasing from spring to summer. In the Lake Lipovskoe, the phytoplankton biomass varied from 0.45 to 1.9 mg/L, chlorophyll a fluctuated from 3.9 to 7.1 µg/L. In the Lake Beloe, the phytoplankton biomass varied from 0.3 to 1.4 mg/L, chlorophyll a fluctuated from 0.45 to 1.3 µg/L. The periphyton biomass was 20 g/m2  (chlorophyll a being 22 mg/m2 ) and 17 g/m2 (chlorophyll a being 17 mg/m2 ) in the Lake Lipovskoe and Lake Beloe, respectively. Nowadays, the ecological status of both lakes can be considered satisfactory, because based on trophic conditions and species compositions they belong to waterbodies of clean and satisfactory clean conditions (II–III classes of water quality). To preserve the unique flora of the lakes in the Kurgal’sky Nature reserve, nature-protected measures should be strengthened.

New data on aphyllophoraceous fungi (Basidiomycota) of the protected areas of the Leningrad Region. X. State Nature Reserve «Kurgalsky»

2018 ◽  
Vol 52 (1) ◽  
pp. 101-121 ◽  
Author(s):  
V. M. Kotkova
Keyword(s):  
Protected Areas ◽  
Russian Federation ◽  
Protected Area ◽  
Nature Reserve ◽  
Leningrad Region ◽  
State Nature Reserve ◽  
X State ◽  
First Time ◽  
Aphyllophoroid Fungi ◽  
State Nature

The paper provides the data on aphyllophoroid fungi of the State Nature Reserve «Kurgalsky» situated in the Kingisepp District of the Leningrad Region. The list includes 285 species annotated by data on their habitats, substrates and frequency. In total 25 species protected in the Leningrad Region and 3 species protected in Russian Federation were found in the protected area. Chaetodermella luna, Phlebia subochracea and Trechispora stevensonii are published for the first time for the Leningrad Region. The specimens of selected species are kept in the Mycological Herbarium of the Komarov Botanical Institute RAS (LE).

New data on aphyllophoroid fungi (Basidiomycota) of the State Nature Reserve “Kurgalsky” (Leningrad Region)

2020 ◽  
Vol 54 (1) ◽  
pp. 73-86
Author(s):  
V. M. Kotkova
Keyword(s):  
Nature Reserve ◽  
The State ◽  
Leningrad Region ◽  
Botanical Institute ◽  
Komarov Botanical Institute ◽  
State Nature Reserve ◽  
Aphyllophoroid Fungi ◽  
State Nature

The paper provides new data on aphyllophoroid fungi of the State Nature Reserve “Kurgalsky” situated in the Kingisepp District of the Leningrad Region. They were collected on the Kader bog and its vicinity. The list includes 165 species annotated by data on their habitats, substrates and frequency, including 37 species new for the reserve. In total 5 species (Antrodia mellita, Chaetoporellus latitans, Junghuhnia collabens, Rigidoporus crocatus, Sidera lenis) protected in the Leningrad Region and 3 species (Phlebia subserialis, Pseudomerulius montanus, Xenasma pruinosum) new for the Leningrad Region were found in study part of reserve. The specimens of selected species are kept in the Mycological Herbarium of the Komarov Botanical Institute RAS (LE).

Mosses of the State Nature Reserve "Kurgalskiy" (Leningrad Region)

2019 ◽  
Vol 53 (2) ◽  
pp. 369-384
Author(s):  
G. Ya. Doroshina ◽  
E. G. Ginzburg ◽  
L. E. Kurbatova
Keyword(s):  
Protected Area ◽  
Nature Reserve ◽  
The State ◽  
Leningrad Region ◽  
Protected Species ◽  
State Nature Reserve ◽  
First Time ◽  
State Nature ◽  
Area Data

The paper provides the data on mosses of the State Nature Reserve ”Kurgalskiy” situated in the Kingisepp District of the Leningrad Region. The list includes 136 species. Among them Plagiothecium nemorale is new for the Leningrad Region, 83 species are recorded for the first time for the protected area, 12 species are protected in the region, Aulacomnium androgynum is protected in Russia. Of the protected species, Plagiothecium latebricola is recorded for the first time for the protected area. Data on habitats, substrates and frequency of every species are provided.

In Vivo Fluorescence of Chlorophyll A: Estimation of Phytoplankton Biomass and Activity in Římov Reservoir (Czech Republic)

1993 ◽  
Vol 28 (6) ◽  
pp. 29-33 ◽  
Author(s):  
V. Vyhnálek ◽  
Z. Fišar ◽  
A. Fišarová ◽  
J. Komárková
Keyword(s):  
Czech Republic ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
In Vivo Fluorescence ◽  
Fluorescence Of Chlorophyll A ◽  
Římov Reservoir ◽  
Natural Phytoplankton

The in vivo fluorescence of chlorophyll a was measured in samples of natural phytoplankton taken from the Římov Reservoir (Czech Republic) during the years 1987 and 1988. The fluorescence intensities of samples either with or without addition of 3-(3,4-dichlorophenyl)-1,1-dimethylurea (diuron, DCMU) were found reliable for calculating the concentration of chlorophyll a during periods when cyanobacteria were not abundant. The correction for background non-chlorophyll fluorescence appeared to be essential. No distinct correlation between a DCMU-induced increase of the fluorescence and primary production of phytoplankton was found.

Differences between Nearshore and Offshore Phytoplankton Communities in Lake Ontario

1987 ◽  
Vol 44 (12) ◽  
pp. 2155-2163 ◽  
Author(s):  
I. M. Gray
Keyword(s):  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Algal Biomass ◽  
Phytoplankton Community ◽  
Seasonal Pattern ◽  
Principal Component ◽  
Lake Ontario ◽  
Dominant Group ◽  
Phytoplankton Communities ◽  
Eutrophic Species

Differences between nearshore and offshore phytoplankton biomass and composition were evident in Lake Ontario in 1982. Phytoplankton biomass was characterized by multiple peaks which ranged over three orders of magnitude. Perhaps as a consequence of the three times higher current velocities at the northshore station, phytoplankton biomass ranged from 0.09 to 9.00 g∙m−3 compared with 0.10 to 2.40 g∙m−3 for the midlake station. Bacillariophyceae was the dominant group at the northshore station until September when Cyanophyta contributed most to the biomass (83%). Although Bacillariophyceae was the principal component of the spring phytoplankton community at the midlake station, phytoflagellates (49%) and Chlorophyceae (25%) were responsible for summer biomass, with the Chlorophyceae expanding to 80% in the fall. The seasonal pattern of epilimnetic chlorophyll a correlated with temperature. While chlorophyll a concentrations were similar to values from 1970 and 1972, algal biomass had declined and a number of eutrophic species (Melosira binderana, Stephanodiscus tenuis, S. hantzschii var. pusilla, and S. alpinus) previously found were absent in 1982.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

scholarly journals Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

2018 ◽  
Vol 15 (9) ◽  
pp. 1881 ◽  
Author(s):  
Sina Keller ◽  
Philipp Maier ◽  
Felix Riese ◽  
Stefan Norra ◽  
Andreas Holbach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Green Algae ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Inland Waters ◽  
Learning Approaches ◽  
River Elbe ◽  
Regression Framework

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Extraction and quantification of chlorophyll a from freshwater green algae

1998 ◽  
Vol 32 (7) ◽  
pp. 2220-2223 ◽  
Author(s):  
David Simon ◽  
Stuart Helliwell
Keyword(s):  
Chlorophyll A ◽  
Green Algae

The Deep Chlorophyll Maximum: Comparing Vertical Profiles of Chlorophyll a

1982 ◽  
Vol 39 (5) ◽  
pp. 791-803 ◽  
Author(s):  
John J. Cullen
Keyword(s):  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Physiological Adaptation ◽  
Organic Carbon Content ◽  
Deep Chlorophyll Maximum ◽  
Vertical Profiles ◽  
In Vivo Fluorescence ◽  
Chlorophyll Maximum ◽  
Vertical Distributions

The relationship between chlorophyll a and phytoplankton biomass (organic carbon content) is highly variable as is the yield of in vivo fluorescence per unit chlorophyll. Thus, vertical profiles of chlorophyll or in vivo fluorescence must be interpreted with caution if their ecological significance is to be established. Although the variability of carbon-to-chlorophyll ratios and fluorescence yield is large, much of it can be anticipated, corrected for, and usefully interpreted. Vertical profiles from different regions of the sea are presented; each has a deep chlorophyll maximum, but the probable mechanisms of their formation and maintenance differ widely. Most vertical distributions of chlorophyll can be explained by the interaction between hydrography and growth, behavior, or physiological adaptation of phytoplankton with no special consideration of grazing by herbivores, even though vertical distributions of epizooplankton are not uniform. The interaction between vertical profiles of zooplankton and chlorophyll will be better understood when the relationships between chlorophyll and phytoplankton biomass in those profiles is determined.Key words: chlorophyll a, fluorescence, phytoplankton, vertical structure

Change in biomass of benthic and planktonic algae along a disturbance gradient for 24 Great Lakes coastal wetlands

2003 ◽  
Vol 60 (6) ◽  
pp. 676-689 ◽  
Author(s):  
Sheila A McNair ◽  
Patricia Chow-Fraser
Keyword(s):  
Water Quality ◽  
Great Lakes ◽  
Chlorophyll A ◽  
Total Variation ◽  
Coastal Wetlands ◽  
Phytoplankton Biomass ◽  
Wetland Management ◽  
Percent Cover ◽  
Planktonic Algae ◽  
Wide Range

We quantified the chlorophyll a content of planktonic algae and benthic algae in periphyton on acrylic rods and in epiphyton growing on macrophytes in 24 coastal wetlands in all five Laurentian Great Lakes. Sites were selected to represent a wide range of environmental conditions ranging from nutrient-poor, clear-water marshes with abundant macrophytes to nutrient-enriched, turbid systems devoid of aquatic vegetation. Water quality and species and percent cover of submergent macrophytes were measured in each wetland. Principal components analysis (PCA) showed that total phosphorus, turbidity, and suspended solids, variables associated with human-induced degradation, were most strongly correlated with PC axis 1 (PC1), accounting for 69% of the total variation. The PC1 site score was significantly related to both periphyton and phytoplankton biomass, respectively accounting for 54 and 70% of the total variation in periphyton and phytoplankton data, whereas PC1 only accounted for 18% of the variation in epiphyton biomass. Periphytic and epiphytic biomass were negatively correlated with percent cover and species richness of submergent macrophytes, but phytoplankton biomass was not. We conclude that periphytic and planktonic chlorophyll a biomass are good indicators of human-induced water-quality degradation and recommend that both benthic and planktonic algal biomass should be routinely monitored as part of an effective wetland management program.

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