Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary — Danubian Phytoplankton Growth Model (DPGM)

Abstract

Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of Göd, Hungary (at 1669 river kilometer -hereafter referred to as "rkm"). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L −1 ) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979-1990 has a higher level of nutrient excess compared with that of the 1991-2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass.

Functional diversity of phytoplankton highlights long‐term gradual regime shift in the middle section of the Danube River due to global warming, human impacts and oligotrophication

“…The slightly decreasing water discharge, the altered seasonality in river flow and the increasing water temperature in the middle section of the Danube are all in good agreement with regional climate models (Sipkay, Kiss, Vadadi-F€ ul€ op, Homor odi, & Hufnagel, 2012;Stagl & Hattermann, 2015;Webb & Nobilis, 2007). As also predicted for the Rhine River (Shabalova, Van Deursen, & Buishand, 2003), annual maximum discharge values seem to increase in magnitude in the middle Danube section.…”

Climate change and plankton phenology in freshwater: current trends and future commitments

“…Predator-prey models were applied to simulate the onset of the CWP with climatic forcing (Scheffer et al, 2001) and to understand how overwintering vs diapausing strategies in Daphnia determine match-mismatch in zooplankton-algae interactions . Sipkay et al (2012) validated a strategic model of a theoretical algal community to field data and then developed a tactical model for phytoplankton communities in the Danube River by considering temperatures and light only. Such simple tactical models (Sipkay et al, 2008;VadadiFülöp et al, 2009) do not aimed to shed new light on a theory, rather favour considerable applicability.…”

“…Whether climate or nutrients have a stronger impact on plankton phenologies is a question that has not yet been answered: emerging evidence exists for either cases (Thackeray et al, 2008;Feuchtmayr et al, 2010;Desortová and Puncochár, 2011;Shimoda et al, 2011;Sipkay et al, 2012;Zhang et al, 2012).…”

Climate change and freshwater zooplankton: what does it boil down to?

“…Such an approach neglects some ultimate drivers and tends to focus on the core of the process, still it can provide fairly significant pieces of a puzzle when the overall system behavior is to be disentangled. Such models do not aimed at serving biological interpretation of mathematical operations, rather stress predictions of considerable applicability (e.g., Hufnagel and Gaál 2005;Sipkay et al 2008Sipkay et al , 2009Sipkay et al , 2012Vadadi-Fülöp et al 2009). …”

“…That is why ecologists by necessity make a compromise between exactitude and reality and create tactical models aiming at underlining the most important variables accounting for the major variation in community dynamics. One sophisticated example is to validate a strategic model of a theoretical community to field data and then develop a tactical model (e.g., Sipkay et al 2012). The choice also depends on whether we are interested in the interpretation of some a priori time series (tactical models) or the objective is to shed some new light on a theory (strategic models).…”