2016
DOI: 10.1007/s10750-016-2954-9
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Natural water brownification as a shift in the phytoplankton community in a deep hard water lake

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Cited by 22 publications
(21 citation statements)
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“…This indicates that the interactive effects of multiple lake type factors are important in shaping the response of cyanobacteria to multiple stressors and that a lake type analytical approach could help better predict responses to future environmental change. Differences in the response among lake types is not surprising as the biovolume of cyanobacteria is not just affected by factors that affect the amount of phytoplankton such as phosphorus, temperature and retention time but also by factors that shape community composition such as alkalinity, colour and mixing depth (Lenard & Ejankowski, 2017;Maileht et al, 2013;Ptacnik et al, 2008 T A B L E 2 Linear regression mixed effect models explaining cyanobacteria biovolume and chlorophyll-a concentration. The models explain cyanobacterial biovolume (natural log, mm 3 /L) and chlorophyll-a concentration (natural log, μg/L) in different lake types and result from backward stepwise selection, starting with a model with full interactions between the independent variables: mean monthly total phosphorus (TP, μg/L), mean monthly air temperature (°C) and monthly retention time (days).…”
Section: The Sensitivity Of Cyanobacteria To Multiple Stressors Varmentioning
confidence: 99%
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“…This indicates that the interactive effects of multiple lake type factors are important in shaping the response of cyanobacteria to multiple stressors and that a lake type analytical approach could help better predict responses to future environmental change. Differences in the response among lake types is not surprising as the biovolume of cyanobacteria is not just affected by factors that affect the amount of phytoplankton such as phosphorus, temperature and retention time but also by factors that shape community composition such as alkalinity, colour and mixing depth (Lenard & Ejankowski, 2017;Maileht et al, 2013;Ptacnik et al, 2008 T A B L E 2 Linear regression mixed effect models explaining cyanobacteria biovolume and chlorophyll-a concentration. The models explain cyanobacterial biovolume (natural log, mm 3 /L) and chlorophyll-a concentration (natural log, μg/L) in different lake types and result from backward stepwise selection, starting with a model with full interactions between the independent variables: mean monthly total phosphorus (TP, μg/L), mean monthly air temperature (°C) and monthly retention time (days).…”
Section: The Sensitivity Of Cyanobacteria To Multiple Stressors Varmentioning
confidence: 99%
“…Colour as an additional lake type factor is an important inclusion, not only because changes in colour can strongly alter phytoplankton biomass and community structure (e.g. Lenard & Ejankowski, 2017) but also because humic substances have increased in lakes in past decades (Monteith et al, 2007). It is interesting that synergistic effects of temperature and phosphorus were only detected in humic lakes (polymictic, humic types for cyanobacteria and chlorophyll-a as well as stratified, medium-high alkalinity and humic type for chlorophyll-a).…”
Section: Modelmentioning
confidence: 99%
“…The winter phytoplankton may be an inoculum for spring phytoplankton development and cause an early depletion of nutrients, and may also affect the food web in aquatic ecosystems during spring and summer [36,37]. Thus, the role of winter periods in shaping the phytoplankton community in a lake for the whole of the subsequent year has recently been widely discussed [38][39][40][41]. Simultaneously, there have also been some experimental studies which pointed out that selected chlorophytes and cyanobacteria can have similar optimum growth conditions.…”
Section: Introductionmentioning
confidence: 99%
“…Above a concentration threshold of 5-15 mg L -1 , the negative influence of DOC shading on autochthonous primary production exceeds the positive effects of DOC on resource availability via direct supply of fixed C and potential fertilization of autochthonous production (Karlsson et al 2009;Jones et al, 2012;Seekell et al, 2015;Kelly et al 2018). Additionally, increased DOC concentrations can alter phytoplankton species composition and diversity (Lenard and Ejankowski, 2017;Urrutia-Cordero et al, 2017), which in turn can affect the aquatic food web (McGowen et al, 2005).…”
Section: Introductionmentioning
confidence: 99%
“…Boreal lakes with a water retention time between one and three years are particularly vulnerable to precipitation-induced browning as climate change models predicting increasing precipitation in this region indicate that many of these lakes will continue to experience browning in the foreseeable future (Weyhenmeyer et al, 2016). As with streams, brownification in lakes can lead to anoxia and have strong effects on water chemistry, algal community composition, biomass and productivity, as well as the mortality of macrozoobenthos and fish (Sadro and Melack, 2012;Brothers et al, 2014;Lenard and Ejankowski, 2017). Due to longer water residence time in lakes, the effects of sudden brownification events on water chemistry and biota are expected to last longer than in rivers.…”
Section: Introductionmentioning
confidence: 99%