Modelling ecosystem structure and trophic interactions in a typical cyanobacterial bloom-dominated shallow Lake Dianchi, China

Shan, Kun; Li, Lin; Wang, Xiaoxiao; Wu, Yanlong; Hu, Lili; Yu, Ge; Song, Lirong

doi:10.1016/j.ecolmodel.2014.07.015

Cited by 41 publications

(23 citation statements)

References 111 publications

(109 reference statements)

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“…Additional, different regions of the lake show substantial variations, and the average value of the entire lake is not completely representative of these regions. In addition to the factors analyzed in this study, food web structures, trophic interactions, and physicochemical environmental factors may affect the outbreak and distribution of algal blooms [23,26,74]. Therefore, it is necessary to further divide Lake Dianchi according to the outbreak degree of algal blooms [24] and then quantitatively study the relationship between the factors in each zone and their influence on algal blooms with in situ data.…”

Section: Discussionmentioning

confidence: 99%

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Jing

Zhang

et al. 2019

Remote Sensing

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Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi.

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Section: Discussionmentioning

confidence: 99%

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Jing

Zhang

et al. 2019

Remote Sensing

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“…Lake Dianchi is located downstream and southwest of Kunming City, and a large amount of pollutants, such as municipal sewage, industrial wastewater, and non-point source nutrients (e.g., nitrogen and phosphorus), are discharged into the lake due to rapid growth resulting from socioeconomic development in the basin. The ecosystem structure and function of the lake are currently simple and vulnerable, e.g., transparency has decreased to 0.6 m, the aquatic macrophyte community covers less than 2.05 % of the lake area, and the phytoplankton and zoobenthos (macroinvertebrate) species richness has significantly decreased Shan et al 2014). For additional physicochemical factors in the lake and its basin, such as the nutrients and metal and nonmetal elements, please refer to Liu et al (2009).…”

Section: Description Of Lake Dianchimentioning

confidence: 99%

“…Climate warming is also a factor at Lake Dianchi (Zhou et al 2015a). Although several studies have examined the cyanobacterial blooms occurring in this lake (Liu et al 2009;Sheng et al 2012;Shan et al 2014;Wang et al 2015), the most influential or primary factor (e.g., meteorological factors or nutrient levels) remains unclear, particularly for interannual analysis based on more than two decades of data. Moreover, this lake is characterized by an obvious division between dry and rainy seasons, and the physical and chemical conditions associated with rainfall patterns may differ between these two seasons, which would affect cyanobacterial dynamics (Paerl and Huisman 2008;Reichwaldt and Ghadouani 2012).…”

Section: Introductionmentioning

confidence: 99%

The relationships of meteorological factors and nutrient levels with phytoplankton biomass in a shallow eutrophic lake dominated by cyanobacteria, Lake Dianchi from 1991 to 2013

Zhou

Zhang

Lin

et al. 2016

Environ Sci Pollut Res

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Long-term interannual and monthly (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) data were analyzed to elucidate the effects of meteorological factors and nutrient levels on phytoplankton biomass in the cyanobacteria-dominated Waihai basin of Lake Dianchi. The interannual ln(chl. a) exhibited positive correlations with the mean air temperature, mean minimum air temperature, and mean maximum air temperature; in addition, a positive relationship between Δln(chl. a) and ΔTP was observed throughout the period. Additionally, ln(chl. a) exhibited a positive correlation with the TP concentration, negative correlations with the sunshine hours and wind speed during the dry season, and positive correlations with the TN and TP concentrations during the rainy season. Furthermore, TP was the most influential factor affecting cyanobacterial bloom dynamics throughout the entire period and during the dry season, and TN and TP were the most important factors during the rainy season, as determined by relative importance analysis. The results of this study based on interannual analysis demonstrated that both meteorological factors and nutrient levels have important roles in controlling cyanobacterial bloom dynamics. The relative importance of these factors may change according to precipitation patterns. Thus, climate change regulation and eutrophication management should be considered in strategies for bloom control. Decreasing the TP load should be prioritized throughout the entire period and during the dry season, and decreasing the TN and TP loads should be considered initially during the rainy season. In addition, further studies of more frequent and complete data acquired over a longer period of time should be conducted in the future.

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“…Thus, the cyanobacteria dynamics are an essential factor in both nutrients cycling and phytoplankton dynamics. Increases in nutrients leading to a dominance of cyanobacteria have been reported by Dokulil and Teubner [78], and in Curuai, Affonso et al [79] they found that the flushing period was the most eutrophic period. Thus, the extent to which the floodplain becomes shallow, and water flow less intense, the cyanobacteria community can be established [80].…”

Section: Nutrients-phytoplankton Relationships Over Hydrological Cyclementioning

confidence: 68%

Unraveling Flooding Dynamics and Nutrients’ Controls upon Phytoplankton Functional Dynamics in Amazonian Floodplain Lakes

Kraus

Bonnet

Nogueira

et al. 2019

Water

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The processes in tropical floodplain lakes enable maintaining phytoplankton nutrient requirements over a hydrological year. The nutrients such as nitrogen, phosphorus and carbon compounds play an essential role in phytoplankton growth. However, the way that nutrients and phytoplankton interact and how this relationship varies seasonally in tropical freshwater ecosystems is not clear. In this study, we evaluate the relationship between phytoplankton–nutrients over the hydrological cycle in Amazonian floodplain lakes and verify if this relationship influences the biomass of cyanobacteria. We also check what factors linked to nutrients act in structuring phytoplankton community. Using the phytoplankton functional approach, we verified how their ability to respond to hydrological and environmental variations reflects the ecological conditions and investigated how these interactions work. The results show that the Amazonian floodplain lakes could maintain long-term nutrient enrichment status. The nutrients input conduces to cyanobacteria dominance, that allied to other factors, play an essential role in supporting the stability of the phytoplankton–nutrients relationship over the hydrological cycle.

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Modelling ecosystem structure and trophic interactions in a typical cyanobacterial bloom-dominated shallow Lake Dianchi, China

Cited by 41 publications

References 111 publications

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

The relationships of meteorological factors and nutrient levels with phytoplankton biomass in a shallow eutrophic lake dominated by cyanobacteria, Lake Dianchi from 1991 to 2013

Unraveling Flooding Dynamics and Nutrients’ Controls upon Phytoplankton Functional Dynamics in Amazonian Floodplain Lakes

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