Environmental variables and plankton communities in the pelagic of lakes: enclosure experiment and comparative lake survey

Most primary production of lakes and oceans occurs in the well-mixed surface layer that is subject to strong seasonal and geographical variation. With increasing mixed surface layer depth average light supply and specific nutrient supply decrease and so do light-dependent production rates and depth-dependent sinking loss rates of phytoplankton. Changes in mixing depth are expected to have important consequences for the dynamics of phytoplankton biomass, algal nutrient stoichiometry, light availability and nutrient retention in the mixed layer. Light absorption by enhanced concentrations of abiotic substances (humic substances, clay particles) furthermore negatively affects light availability and production. I tested the predictions of a dynamical “closed system” model concerning the effects of mixing depth and background turbidity (Kbg) on phytoplankton biomass, light climate and nutrients in a field enclosure experiment. The natural phytoplankton community was exposed to high and low background turbidity along a gradient of mixing depth. For sinking algae, the model predicts that phytoplankton biomass should be most strongly limited by sedimentation losses in shallow mixed layers, by mineral nutrients at intermediate mixing depths and by a lack of light in deep mixed layers. As predicted, phytoplankton volumetric and areal biomasses showed a unimodal relationship to mixing depth and were negatively affected by background attenuation. With increasing Kbg the biomass peak shifted towards shallower mixing depth. The concentrations of dissolved and total nutrients were positively affected by increasing mixing depth but only marginally related to Kbg most likely due to a variable carbon to phosphorus cell quota. For thermally stratified lakes I derived the following predictions from a dynamical “open system” model which includes variable algal cell quota: within a realistic mixing depth range (3-12m) light availability, phytoplankton density, and the carbon:phosphorus ratio of algal biomass should all be negatively related to mixing depth, while algal standing stock should be unimodally related, and total and dissolved nutrients be horizontally or positively related to mixing depth. All these prediction were in qualitatively good agreement with data from 65 central European lakes sampled during summer stratification. Notably, I observed the predicted negative relationship between phytoplankton density and mixing depth in spite of the rather limited range of mixing depths typical for medium sized temperate lakes. Furthermore, I found a strong negative relationship among zooplankton biomass and mixing depth. In a comprehensive comparative lake study of 40 northern German lakes, I sampled the surface mixed layers for a set of variables and focused on the taxonomic composition of phytoplankton and the relationships of taxonomic classes to environmental variables. I used high performance liquid chromatography to analyse the phytoplankton samples for 13 photosynthetic pigments and calculated the contributions of seven algal classes with distinct pigment signatures to total chlorophyll a using CHEMTAX, a matrix factorisation program. In multiple regression analyses, I examined the relationships of phytoplankton biomass and composition to total nitrogen (TN), total phosphorus (TP), total silica (TSi), mixing depth, water temperature, and zooplankton biomass. Total Chl-a was positively related to TN and TP and unimodally related to mixing depth. TN was the factor most strongly related to the biomasses of single taxa. I found positive relationships of chrysophytes, chlorophytes, cryptophytes, and euglenophytes to TN, and of diatoms and chrysophytes to TSi. Diatoms were negatively related to TN. Cryptophytes and chlorophytes were negatively and cyanobacteria positively related to zooplankton. Finally, the relative biomasses of chrysophytes and cryptophytes were negatively related to mixing depth. Most results were consistent with theoretical expectations. Some relationships may, however, have been masked by strong cross-correlations among several environmental variables.