Original data for article: Comparison of epifluorescence microscopy and flow cytometry in counting freshwater picophytoplankton

Salmi, Pauliina; Mäki, Anita; Mikkonen, Anu; Puupponen, Veli-Mikko; Vuorio, Kristiina; Tiirola, Marja

doi:10.17011/jyx/dataset/66278

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“…Also, pico-phytoplankton (< 2 µm) cannot be differentiated accurately via the traditional Utermöhl (1958) microscopic counting techniques (Booth 1993). These challenges, however, can be partly overcome by assessing phytoplankton community composition via epifluorescence (Callieri & Stockner 2002;Crosbie et al 2003;Salmi et al 2021) or DNA metabarcoding (Groendahl et al 2017;MacKeigan et al 2022). Advantages and disadvantages of these methods are further described in the Supplementary Material.…”

Section: Introductionmentioning

confidence: 99%

Pigment and fluorescence proxies to estimate functional diversity of phytoplankton communities

Ilić¹,

Walden²,

Hammerstein³

et al. 2023

fal

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Given the global loss of biodiversity, trait-based studies are needed to assess the associated consequences for ecosystem functions and services. Many studies focus on the assessment of functional diversity of natural communities as a mechanistic link between biodiversity and ecosystem functioning. In freshwater ecosystems, diversity of primary producers is crucial for resource use efficiency and trophic transfer of energy. Furthermore, one indicator of the ecological status of surface waters is the composition of natural phytoplankton communities. The number of available techniques for the quantification and discrimination of different phytoplankton groups have increased in recent years. For example, phytoplankton community composition can indirectly be assessed via CHEMTAX, a matrix factorization program, which calculates the contribution of different phytoplankton taxa to the total chlorophyll-a using concentrations of pigments analysed via liquid chromatography. A more direct, in vivo assessment can be achieved with instruments based on spectral fluorometry, such as the Algae Lab Analyser, which allows for a differentiation of four phytoplankton groups depending on spectral fluorescence signatures. In this study, we compared both methods by analyses of phytoplankton biomass and functional diversity from phytoplankton communities of three lakes of different trophic states, while a subset of biomass and diversity estimates derived from microscopic counts served as a reference. We found marked differences in biomass estimates of all assessed phytoplankton groups, with cyanobacteria being significantly underestimated by the Algae Lab Analyser. Furthermore, we show that the level of agreement between the methods somewhat depends on the trophic state of the lake. We conclude that both methods are suitable to estimate phytoplankton functional diversity with specific advantages and disadvantages. Here we provide users with a flow chart to help them find the most suitable method for their respective purposes.

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