Modeling photosynthesis‐irradiance curves: Effects of temperature, dissolved silica depletion, and changing community assemblage on community photosynthesis

Abstract

Phytoplankton primary production plays a crucial role in a range of local and global phenomena. In the quest for understanding the dynamics of algal growth and its associated processes (nutrient uptake, temperature dependence, photoacclimation, etc.), considerable effort is put into both data gathering and modeling. Two main approaches can be distinguished: on the one hand, carefully designed laboratory experiments allow the growth of selected species to be studied under full control, and more or less complex dynamical models are built and calibrated on the data set (e.g., Geider et al. 1997;Davidson and Gurney 1999;Flynn and Martin-Jezequel 2000). This approach increases our mechanistic understanding (Baklouti et al. 2006), although it is acknowledged that existing data sets are not adequate for the evaluation of detailed models (Flynn and Martin-Jezequel 2000). On the other hand, ecosystem scale studies of whole plankton communities are performed by incubating samples retrieved from the field at ambient conditions and varying light intensities E, Modeling photosynthesis-irradiance curves: Effects of temperature, dissolved silica depletion, and changing community assemblage on community photosynthesis AbstractSets of photosynthesis-irradiance (P-I) curves yield more information about community photosynthesis when analyzed with proper models in mind. Based on ecosystem-specific considerations regarding the factors that explain spatial and temporal patterns of photosynthesis, the Webb model of photosynthesis can be extended and fitted to P-I data. We propose a method based on a series of nested models of increasing complexity to test whether supposed effects of environmental factors are reflected in the P-I data, whether more complex models fit the data significantly better than more simple models, and whether parameters describing the presumed dependencies can be estimated from the data set. We compare a direct approach, fitting the extended model to all P-I data at once, with a two-step approach in which photosynthetic efficiencies and maximum photosynthetic rates of individual P-I curves are determined first, and then related to environmental variables. A nested model approach prevents overfitting of multiparameter models. Monte Carlo analysis sheds light on the error structure of the model, by separating parameter and model uncertainty, and provides an assessment of the performance of the formulations used in ecosystem models. We demonstrate that the two-step approach underperforms when used to compute photosynthetic rates. We apply the proposed method to an extensive P-I data set from the Schelde estuary, where spatiotemporal patterns of photosynthesis arise from a combination of seasonality, silica depletion, phytoplankton community composition, and salinity effects.

Planktonic primary production in the western Dutch Wadden Sea

“…From the abiotic variables that can be estimated at present from Earth Observation data, P B max can be indirectly estimated from SST (and underwater light-climate) (Behrenfeld & Falkowski 1997, Cox et al 2010, while α B can be derived from P B max. The low variability in α B and P B max explained by environmental variables impedes the estimation of primary production from remotely sensed data.…”

“…If these parameters could be derived as well, this more extensive data set would allow for more extensive monitoring of temporal and spatial variation such as shifts in the timing of phytoplankton blooms, gradients in pelagic production in river outflows and trends in overall productivity (Pereira et al 2013). Modelling of photosynthetic parameters as a function of temperature (Behrenfeld & Falkowski 1997, Cox et al 2010 or of temperature and nutrients (Cox et al 2010) would allow for indirect estimates of pelagic production from satellite data.…”

The impact of temporal variability in light-climate on time-averaged primary production and a phytoplankton bloom in a well-mixed estuary

“…Other parameter values used in our calculations are summarized in Table 2. These parameters were based on the literature Peene, 1995, 2005;Cox et al, 2010;Maris and Meire, 2016). To assess the accuracy of the moment approximation until second-order terms, we compare the results to the output of a numerical integrator of DAP, making use of the same time series.…”

“…Overview of parameter values used in this paper. The values are based on the literature Peene, 1995, 2005;Cox et al, 2010;Maris and Meire, 2016).…”