On the issue of limiting nutrient and predictions of cyanobacteria in aquatic systems

Abstract

This study aims at bridging the gap between freshwater and marine eutrophication studies by presenting (1) a cross-system analysis of the relationship between chlorophyll and the total nitrogen (TN) to total phosphorus (TP) ratio (2) a general model to predict concentrations of cyanobacteria from data on TP, the TN/TP-ratio, salinity and temperature, and (3) a general trophic level classification for aquatic systems based on chlorophyll classes (for oligo-, meso-, eu-and hypertrophic systems). The data compiled in this study concerns more than 500 lakes and coastal areas covering a very wide domain in terms of nutrient concentrations and salinity. There was no simple relationship between the TN/TP-ratio and empirical chlorophyll concentrations or concentrations of cyanobacteria. Variations in TP rather than TN generally seem to be more important to predict variations among systems in chlorophyll-a and cyanobacteria. Different "bioavailable" forms of the nutrients (DIN, DIP, phosphate, nitrate, etc.) have been shown to have very high coefficients of variation (CV), which means that many samples are needed to obtain reliable empirical data which are necessary in models aiming for high predictive power and practical usefulness.

The relationships of meteorological factors and nutrient levels with phytoplankton biomass in a shallow eutrophic lake dominated by cyanobacteria, Lake Dianchi from 1991 to 2013

“…Nutrients (nitrogen and phosphorus) are typically considered the principal factors contributing to cyanobacterial bloom formation (Håkanson et al 2007;Heisler et al 2008;Paerl et al 2014). However, cyanobacterial blooms have also been reported to be controlled by physical factors when nutrient levels are sufficient (Qin et al 2010;Zhang et al 2012).…”

“…The dynamics of cyanobacterial blooms involve the combined effects of physical (e.g., light, temperature, turbulence and mixing, and water residence time), chemical (e.g., nutrients, dissolved carbon, and salinity), and biological (e.g., grazing, microbial interactions, and allelopathy) factors and are also affected by the cyanobacterium itself (Dokulil and Teubner 2000;Heisler et al 2008;O'Neil et al 2012;Paerl and Otten 2013). Nutrients (particularly nitrogen and phosphorus) play vital roles in bloom formation (Håkanson et al 2007;Heisler et al 2008;Paerl et al 2014), and meteorological factors, climate change (Hu et al 2009;Paerl and Huisman 2008;Verspagen et al 2014;Zhang et al 2012), and their indirect effects (Callieri et al 2014;Paerl and Huisman 2009;Posch et al 2012) have also been identified as contributors to the expansion of cyanobacterial blooms.…”

Nitrous oxide fluxes and dissolved N gases (N2 and N2O) within riparian zones along the agriculturally impacted San Joaquin River

“…of this primary limiting nutrient to freshwater and marine ecosystems (Vitousek et al 1997;Caraco and Cole 2001;Howarth and Marino 2006;Hakanson et al 2007). In particular, N fertilization has led to degradation of water quality, depletion of oxygen and decreases in species abundance and richness (Rabalais et al 2002;Morrisey et al 2003;USEPA 2013).…”

Challenges and opportunities for integrating lake ecosystem modelling approaches

“…Other simple regression models include relationships between TP, TN and/or lake depth versus, respectively, bird numbers and richness (Hoyer and Canfield 1994), fish biomass and/or production (Hanson and Leggett 1982;Downing et al 1990;Randall et al 1995;Bachmann et al 1996), zoobenthos biomass (Hanson and Peters 1984), macrophyte coverage and plant volume present (Bachmann et al 2002;Søndergaard et al 2010), zooplankton biomass (Hanson and Peters 1984;Jeppesen et al 1997Jeppesen et al , 2005, zooplankton:phytoplankton biomass ratio (Jeppesen et al 2005), phytoplankton biovolume at the class level (Downing et al 2001;Jeppesen et al 2005;Håkanson et al 2007) and bacterioplankton biomass and production (Hardy et al 1986;Roland et al 2010). Some empirical models have linked measures of biodiversity (e.g., species richness or richness of native species) in lakes to external factors (e.g., Leibold 1999;Jeppesen et al 2000;Alkemade et al 2010).…”