Modelling the effect of vertical mixing on bottle incubations for determining in situ phytoplankton dynamics. I. Growth rates

Abstract

Reliable estimates of in situ phytoplankton growth rates are central to understanding the dynamics of aquatic ecosystems. A common approach for estimating in situ growth rates is to incubate natural phytoplankton assemblages in clear bottles at fixed depths or irradiance levels and measure the change in chlorophyll a (Chl) over the incubation period (typically 24 h). Using a modelling approach, we investigate the accuracy of these Chl-based methods focussing on 2 aspects: (1) in a freely mixing surface layer, the cells are typically not in balanced growth, and with photoacclimation, changes in Chl may yield different growth rates than changes in carbon; and (2) the in vitro methods neglect any vertical movement due to turbulence and its effect on the cells' light history. The growth rates thus strongly depend on the incubation depth and are not necessarily representative of the depth-integrated in situ growth rate in the freely mixing surface layer. We employ an individual based turbulence and photosynthesis model, which also accounts for photoacclimation and photoinhibition, to show that the in vitro Chl-based growth rate can differ both from its carbon-based in vitro equivalent and from the in situ value by up to 100%, depending on turbulence intensity, optical depth of the mixing layer, and incubation depth within the layer. We make recommendations for choosing the best depth for single-depth incubations. Furthermore we demonstrate that, if incubation bottles are being oscillated up and down through the water column, these systematic errors can be significantly reduced. In the present study, we focus on Chl-based methods only, while productivity measurements using carbon-based techniques (e.g. 14 C) are discussed in Ross et al. (2011; Mar Ecol Prog Ser 435:33-45).

Modelling the effect of vertical mixing on bottle incubations for determining in situ phytoplankton dynamics. II. Primary production

“…1 shows a comparison of the 2 productivities for a range of environmental conditions. In agreement with our findings regarding the growth rates (Ross et al 2011), the error in the productivity scales with both τ m and ξ SML (Fig. 1a-d (c,d), are for a shallow and deep surface mising layer (SML) respectively and the parameters are chosen such that the optical depth ξ SML = 5.4 and the mixing time scale τ m = 100 h are the same for both set-ups.…”

“…For each depth bin, we therefore calculate the carbon production per unit volume during the fixeddepth incubations, P incub (z), and compare it with the production in the freely mixing SML, P mixed (z). The difference is displayed as the percent over-or underestimation (1) or depth integrated as Table 2 in Ross et al (2011) and Table 1 in Ross & Geider (2009 ). As we are not interested in absolute magnitudes but only in differences between the mixed and fixed-depth scenarios, we display those units as 'arbitrary' in the plots.…”

“…The biological model used for the comparison is based on the RG I model in Ross & Geider (2009) and the full set of equations and associated parameters is contained in Ross et al (2011;their Appendix 1). To quantify the errors we focus on how primary production is affected by the fixed-depth incubation procedure.…”

“…We present possible explanations for how previous authors could have arrived at contradictory results and discuss whether they might be artefacts related to the particular sampling protocol used. We discuss the errors associated with chlorophyll-based incubation methods for determining in situ phytoplankton growth rates in Ross et al (2011; Mar Ecol Prog Ser 435:13-31). …”

In situ video and fluorescence analysis (VFA) of marine particles: applications to phytoplankton ecological studies.

“…For instance, our in situ video observations highlighted the lower emission intensity of the cells due to photo-adaptation mechanisms in the stratified surface layer of the coastal stations. Variations in the environmental conditions (temperature, light intensity, nutrient concentration, vertical mixing…) result in changes in the photosynthesis activity and in the growth rates of phytoplankton populations (Macintyre et al 2000;Kononen et al 2003;Franklin et al 2009;Ross et al 2011a;Ross et al 2011b). In situ observation of these changes is now available by measuring the individual optical properties (size, fluorescence intensity) of phytoplankton cells, thanks to fluorescence and imaging methods.…”