The Ecology of Phytoplankton

Reynolds, C. S.

doi:10.1017/cbo9780511542145

Cited by 1,803 publications

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“…able of developing their maximal photosynthetic rate at low mean irradiance induced by deep mixing, and having accessory photosynthetic pigments. This situation occurs in deep tropical lakes in the dry season conditions, selecting for diatoms, which are also favored by increased nutrient supply and reduced sedimentation losses (Reynolds, 2006b). In contrast, high I k occurred in Lake Kivu when I Zm was high due to stratification of the mixolimnion reducing the mixing depth, selecting for high-light-adapted phytoplankton, able to face N depletion, such as cyanobacteria.…”

Section: Discussionmentioning

confidence: 99%

“…Chla was used as an index of phytoplankton biomass (Reynolds, 2006a). Total phytoplankton biomass and the contribution of the different algal groups were achieved by HPLC analysis of marker pigments.…”

Section: Phytoplankton Biomassmentioning

confidence: 99%

“…In these conditions, phytoplankton is subjected to potential light limitation, leading to diatom dominance (Reynolds, 2006a). By contrast, in the shallow mixed layer of the rainy season, the lower Zm:Zeu ratio selects for high-light adapted phytoplankton such as green algae and cyanobacteria.…”

Section: Introductionmentioning

confidence: 99%

“…At the community level, it has occasionally been demonstrated that larger cells sustain higher P Bm than smaller cells (Cermeño et al, 2005;Peltomaa and Ojala, 2010), but field studies attempting to relate the compound photosynthetic response on the taxonomic composition of the phytoplankton assemblage are rare (Segura et al, 2013). However, phytoplankton composition matters, as shown by experimental studies on pure cultures, which have provided evidence of significant variation of photosynthetic parameters among taxonomic groups (Falkowski and Raven, 2007;Kirk, 1994;Reynolds, 2006a).…”

Section: Introductionmentioning

confidence: 99%

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Primary production in a tropical large lake: The role of phytoplankton composition

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

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• We provide a 7-year dataset of primary production in a tropical great lake.• Specific photosynthetic rate was determined by community composition.• Annual primary production varied between 143 and 278 mg C m − 2 y − 1 .• Pelagic production was highly sensitive to climate variability. Phytoplankton biomass and primary production in tropical large lakes vary at different time scales, from seasons to centuries. We provide a dataset made of 7 consecutive years of phytoplankton biomass and production in Lake Kivu (Eastern Africa). From 2002 to 2008, bi-weekly samplings were performed in a pelagic site in order to quantify phytoplankton composition and biomass, using marker pigments determined by HPLC. Primary production rates were estimated by 96 in situ 14 C incubations. A principal component analysis showed that the main environmental gradient was linked to a seasonal variation of the phytoplankton assemblage, with a clear separation between diatoms during the dry season and cyanobacteria during the rainy season. A rather wide range of the maximum specific photosynthetic rate (P Bm ) was found, ranging between 1.15 and 7.21 g carbon g −1 chlorophyll a h −1 , and was best predicted by a regression model using phytoplankton composition as an explanatory variable. The irradiance at the onset of light saturation (I k ) ranged between 91 and 752 μE m −2 s −1 and was linearly correlated with the mean irradiance in the mixed layer. The inter-annual variability of phytoplankton biomass and production was high, ranging from 53 to 100 mg chlorophyll a m −2 (annual mean) and from 143 to 278 g carbon m −2 y −1 , respectively. The degree of seasonal mixing determined annual production, demonstrating the sensitivity of tropical lakes to climate variability. A review of primary production of other African great lakes allows situating Lake Kivu productivity in the same range as that of lakes Tanganyika and Malawi, even if mean phytoplankton biomass was higher in Lake Kivu. a b s t r a c t a r t i c l e i n f o

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Section: Discussionmentioning

confidence: 99%

Section: Phytoplankton Biomassmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Primary production in a tropical large lake: The role of phytoplankton composition

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

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“…In shallow waters, P released from the sediment can soon be mixed into the entire water column, while in the deep stratified Miyun Reservoir, the released P is trapped in the hypolimnion and is unavailable to the phytoplankton to a large extent (Kolzau et al 2014). Moreover, many phytoplankton species are able to store enough cellular P through Bluxury uptake,^and with this intracellular storage, they are able to support several rounds of cell division when ambient P concentration is insufficient (Reynolds 2006). In our experiment, phytoplankton growth was no longer P limited when ambient TP concentration was ≥0.071 mg P L −1 ; hence, the setting criteria of TP should be targeted lower than this value.…”

Section: Nutrient Thresholds Needed For Controlling Algal Blooms In Tmentioning

confidence: 99%

Critical nutrient thresholds needed to control eutrophication and synergistic interactions between phosphorus and different nitrogen sources

Zeng

Qin

Bao

et al. 2016

Environ Sci Pollut Res

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Eutrophication is one of the greatest threats to global freshwater ecosystems. The phytoplankton responses to nutrient inputs vary in different water bodies, so it is particularly important to determine the nutrient thresholds and synergistic interactions between nutrients in different freshwater ecosystems. Field sampling and bioassay experiments were conducted to determine the thresholds of soluble reactive phosphorus (SRP), nitrate-nitrogen (NO 3 -N), and ammonium-nitrogen (NH 4 -N) in Miyun Reservoir. A separate nutrient addition bioassay was designed to assess the synergistic interactions between these nutrients. Chlorophyll a (Chl a) concentrations were used to estimate phytoplankton biomass. The results showed the following: (1) nutrient threshold bioassay indicated that eutrophication thresholds of SRP, NO 3 -N, and NH 4 -N should be targeted at below 0.04 mg P L −1 , 0.5 mg N L −1 , and 0.3 mg N L −1 , respectively, to limit the growth of phytoplankton. (2) The stimulatory effect of BNH 4 -N plus P^on phytoplankton biomass was greater than (4) Ammonium was an important factor for the growth of phytoplankton and inputs of both NH 4 -N and NO 3 -N should be reduced to control bloom formation. Our findings imply that although P load reduction is important, appropriate reductions of all forms of N in watershed is recommended in the nutrient management strategy for Miyun Reservoir.

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Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

Cha

Park

Kim

et al. 2014

Water Resources Research

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There have been increasing reports of harmful algal blooms (HABs) worldwide. However, the factors that influence cyanobacteria dominance and HAB formation can be site-specific and idiosyncratic, making prediction challenging. The drivers of cyanobacteria blooms in Lake Paldang, South Korea, the summer climate of which is strongly affected by the East Asian monsoon, may differ from those in wellstudied North American lakes. Using the observational data sampled during the growing season in 2007-2011, a Bayesian hurdle Poisson model was developed to predict cyanobacteria abundance in the lake. The model allowed cyanobacteria absence (zero count) and nonzero cyanobacteria counts to be modeled as functions of different environmental factors. The model predictions demonstrated that the principal factor that determines the success of cyanobacteria was temperature. Combined with high temperature, increased residence time indicated by low outflow rates appeared to increase the probability of cyanobacteria occurrence. A stable water column, represented by low suspended solids, and high temperature were the requirements for high abundance of cyanobacteria. Our model results had management implications; the model can be used to forecast cyanobacteria watch or alert levels probabilistically and develop mitigation strategies of cyanobacteria blooms.

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The Ecology of Phytoplankton

Cited by 1,803 publications

References 0 publications

Primary production in a tropical large lake: The role of phytoplankton composition

Primary production in a tropical large lake: The role of phytoplankton composition

Critical nutrient thresholds needed to control eutrophication and synergistic interactions between phosphorus and different nitrogen sources

Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

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