Temporal changes of phytoplankton community at different depths of a shallow hypertrophic reservoir in relation to environmental variables

Kwon, Yong‐Su; Hwang, Soon‐Jin; Park, KuSung; Kim, Ho‐Seob; Kim, Baik‐Ho; Shin, Kyung‐Hoon; An, KwangGuk; Song, Young Joo; Young-Seuk, Park

doi:10.1051/limn/2009014

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…The NMDS technique appears to be superior to other ordination techniques when applied to ecological data (Kenkel and Orloci, 1986;Bettinetti et al, 2000;Kwon et al, 2009;Paule et al, 2009). NMDS was performed using PC-ORD (McCune and Mefford, 1999), based on the Bray-Curtis distance measure and 50 runs with a maximum of 100 iterations per run.…”

Section: Data Analysesmentioning

confidence: 99%

Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams

Tang

Cai

et al. 2010

Ann. Limnol. - Int. J. Lim.

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-We characterized the responses of benthic macroinvertebrates to heavy metals released from mines in high mountain streams of the Gangqu River in the Shangrila Gorge, China. Benthic macroinvertebrates were collected with a kick-net at 32 sampling sites. In addition, 25 environmental variables including heavy metal concentrations were measured at each sampling site. Although the concentrations of heavy metals were not seriously high, their effects were reflected in the changes of community composition of benthic macroinvertebrates. Total abundance and species richness decreased with increasing heavy metal concentrations. Species richness of Plecoptera and Trichoptera, Margalef richness index, and percentage of scrapers in functional feeding groups were negatively correlated with heavy metal concentrations. A high variation was observed in the response of different taxa to heavy metals. Heavy metals mainly affected the sensitive taxa of Plecoptera, Ephemeroptera, and Trichoptera. However, some tolerant taxa were observed in Trichoptera (such as Hydropsyche sp.) and Dugesia sp. Cluster analysis and a non-metric multidimensional scaling analysis were applied to characterize specific macroinvertebrate taxa composition. The analyses revealed the effects of different environmental factors such as geographical, hydromorphological, physical, and chemical factors including heavy metals on the distribution of benthic macroinvertebrates in high mountain streams. Although the effects were compounded with different factors such as altitude, temperature, stream width, turbidity, and heavy metals, the influence of heavy metals on benthic macroinvertebrate communities was clearly identified (decreased species richness, changes in species composition). Even though the contamination of heavy metals was low in the sampling area, the consequences were clear, indicating that longterm exposure to heavy metals could seriously impact aquatic communities in high mountain streams.

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Section: Data Analysesmentioning

confidence: 99%

Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams

Tang

Cai

et al. 2010

Ann. Limnol. - Int. J. Lim.

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“…Microcystis sp., M. marginata , M. incerta , and M. flos-aquae. were found not only in brackish waters [ 30 , 67 ], but also in marine waters with high salinity [ 26 ], as well as in fresh waters [ 37 , 61 , 68 , 69 ], indicating they could adapt to a broad salinity range. That these species of Microcystis thrived in the studied area was related to their salt-tolerant characteristic and the adaptable growth environment, such as nutrients [ 26 ], dissolved organic matter [ 64 ], and sunlight.…”

Section: Discussionmentioning

confidence: 99%

Phytoplankton Community Response to Environmental Factors along a Salinity Gradient in a Seagoing River, Tianjin, China

et al. 2022

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A river-estuary ecosystem usually features a distinct salinity gradient and a complex water environment, so it is enormously valuable to study the response mechanism of living organisms to multiple abiotic factors under salinity stress. Phytoplankton, as an important part of aquatic microorganisms, has always been of concern for its crucial place in the aquatic ecosystem. In this study, phytoplankton data and 18 abiotic factors collected from 15 stations in Duliujian River, a seagoing river, were investigated in different seasons. The results showed that the river studied was of a Cyanophyta-dominant type. Salinity (SAL) was the key control factor for phytoplankton species richness, while water temperature (WT) was critical not only for species richness, but also community diversity, and the abundance and biomass of dominant species. Apart from WT, the abundance and biomass of dominant species were also driven by total nitrogen (TN), nitrate (NO3−), pH, and water transparency (SD). Moreover, total dissolved phosphorus (TDP), pH, and chemical oxygen demand (COD) were crucial for community diversity and evenness. The bloom of dominant species positively associated with TDP led to lower diversity and evenness in autumn. In addition, when available nitrogen was limited, Pseudoanabaena sp. could obtain a competitive advantage through the N2 fixation function. Increased available nitrogen concentration could favor the abundance of Chlorella vulgaris to resist the negative effect of WT. The results show that Oscillatoria limosa could serve as an indicator of organic contamination, and nutrient-concentration control must be effective to inhibit Microcystis bloom. This could help managers to formulate conservation measures.

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“…Furthermore, turbidity, which was relatively high in terms of importance in the bottom layer, exhibited such high importance due to its relationship with the light environment for algae growth [ 4 ]. These depth-variable environmental variables are likely to be the key factors driving changes in the composition of the phytoplankton community [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Geosmin at Different Depths of Lake Using Machine Learning Techniques

Kwon

Cho

Kim

et al. 2021

IJERPH

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Geosmin is a major concern in the management of water sources worldwide. Thus, we predicted concentration categories of geosmin at three different depths of lakes (i.e., surface, middle, and bottom), and analyzed relationships between geosmin concentration and factors such as phytoplankton abundance and environmental variables. Data were collected monthly from three major lakes (Uiam, Cheongpyeong, and Paldang lakes) in Korea from May 2014 to December 2015. Before predicting geosmin concentration, we categorized it into four groups based on the boxplot method, and multivariate adaptive regression splines, classification and regression trees, and random forest (RF) were applied to identify the most appropriate modelling to predict geosmin concentration. Overall, using environmental variables was more accurate than using phytoplankton abundance to predict the four categories of geosmin concentration based on AUC and accuracy in all three models as well as each layer. The RF model had the highest predictive power among the three SDMs. When predicting geosmin in the three water layers, the relative importance of environmental variables and phytoplankton abundance in the sensitivity analysis was different for each layer. Water temperature and abundance of Cyanophyceae were the most important factors for predicting geosmin concentration categories in the surface layer, whereas total abundance of phytoplankton exhibited relatively higher importance in the bottom layer.

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Temporal changes of phytoplankton community at different depths of a shallow hypertrophic reservoir in relation to environmental variables

Cited by 7 publications

References 33 publications

Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams

Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams

Phytoplankton Community Response to Environmental Factors along a Salinity Gradient in a Seagoing River, Tianjin, China

Prediction of Geosmin at Different Depths of Lake Using Machine Learning Techniques

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