Phytoplankton production and growth rate in Lake Tanganyika: evidence of a decline in primary productivity in recent decades

Stenuite, Stéphane; Pirlot, Samuel; Hardy, Marie-Astrid; Sarmento, Hugo; Tarbe, Anne-Laure; Leporcq, Bruno; Descy, Jean-Pierre

doi:10.1111/j.1365-2427.2007.01829.x

Cited by 56 publications

(65 citation statements)

References 37 publications

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“…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. So, to a large extent, the variation of I k in deep tropical lakes reflects seasonal variations of the environmental factors that contribute to determine the phytoplankton assemblage (Stenuite et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…comm. ), (2) volumetric Chla from Descy et al (2005), areal Chla and CNP data from Descy et al (2006), PP data from Stenuite et al (2007); (3) Bergamino et al (2010) and unpublished results (Nadia Bergamino, pers. comm.…”

Section: Discussionmentioning

confidence: 99%

“…Examples of this approach can be found in Lewis (1974) in Lake Lanao (Philippines), in Talling (1965) for several East African lakes, in Hecky and Fee (1981), Sarvala et al (1999) and Stenuite et al (2007) for Lake Tanganyika, in Guildford et al (2007) for Lake Malawi and in Silsbe et al (2006) for Lake Victoria. Such studies derived photosynthetic parameters (P Bm , the maximum specific photosynthetic rate, and Ik or α, a measure of the photosynthetic efficiency) from in situ incubations.…”

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: Discussionmentioning

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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“…3), from which subsequently averagek and standard deviation σ k were calculated ( Table 2). The higherk observed at Ishungu relative to Kigoma and Mpulungu is caused by the higher phytoplankton biomass (represented by chlorophyll a concentrations) in Lake Kivu (2.02 ± 0.78 mg m −3 ; Sarmento et al, 2012) compared to Lake Tanganyika (0.67 ± 0.25 mg m −3 ; Stenuite et al, 2007). Note that, since an uncertainty remains associated with the exact value of k, its value was allowed to vary within given bounds in the different simulations (see Sect.…”

Section: 3 Water Transparency and Temperature Profilesmentioning

confidence: 99%

Understanding the performance of the FLake model over two African Great Lakes

Thiery

Martynov

Darchambeau³

et al. 2014

Geosci. Model Dev.

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Abstract. The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from ∼ 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

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“…Like many oligotrophic environments (e.g. Li et al 1992, Zubkov et al 2000, Grob et al 2007, LT also harbours a plankton community highly dominated by picoplankton -both autotrophic and heterotrophic , Stenuite et al 2007. Flow cytometry counts allowed detection of picoeukaryote populations of around 2 × 10 3 cells ml -1 (Stenuite et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Molecular characterisation of the small-eukaryote community in a tropical Great Lake (Lake Tanganyika, East Africa)

Tarbe

Stenuite

Balagué

et al. 2011

Aquat. Microb. Ecol.

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In aquatic environments, small eukaryotes (mainly algae and protozoa of 1 to 5 µm in size) are a key link in the carbon transfer to higher trophic levels, e.g. through primary production and grazing of picoplankton. However, the diversity of these microorganisms remains poorly investigated in freshwater habitats, and is still unknown in tropical aquatic systems. In this study, we investigated the small-eukaryote diversity in the oligotrophic Lake Tanganyika, one of the African Great Lakes, at different depths in the water column using denaturing gradient gel electrophoresis (DGGE) and gene clone libraries based on 18S rRNA genes. Each sample produced complex DGGE fingerprints clearly discriminating the epilimnion from the metalimnion. Analysis, using genetic libraries, confirmed the high level of small-eukaryote diversity in Lake Tanganyika. Organisms from 5 taxonomic groups (Stramenopiles, Alveolata, Cryptophyta, Kinetoplastea and Choanoflagellida) were dominant among the species detected. Some sequences were nearly identical to those recovered in temperate freshwaters in North America and Europe, suggesting a high dispersal ability in some small-eukaryote lineages. However, 49% of sequences were < 95% similar to any sequence in GenBank. This may result from undersampling of freshwater systems, but also raises the possibility that perennially warm tropical waters harbour particular assemblages of planktonic small eukaryotes.KEY WORDS: Small-eukaryote community · Tropical lake · 18S rRNA gene libraries Resale or republication not permitted without written consent of the publisher

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Phytoplankton production and growth rate in Lake Tanganyika: evidence of a decline in primary productivity in recent decades

Cited by 56 publications

References 37 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

Understanding the performance of the FLake model over two African Great Lakes

Molecular characterisation of the small-eukaryote community in a tropical Great Lake (Lake Tanganyika, East Africa)

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