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

Expert web

By placing a component of the marine environment under controlled conditions, mesocosms provide important links between field studies and laboratory experiments. Project Leader Dr Paolo Simonelli and scientific site coordinators Drs Elin Lindehoff, Jamileh Javidpour, Romain Pete, Stella Berger and Tatiana Tsagaraki explain how they are opening up access to these unique resources for European scientist

Item-Focused Trees for the Detection of Differential Item Functioning in Partial Credit Models

Various methods to detect differential item functioning (DIF) in item response models are available. However, most of these methods assume that the responses are binary, and so for ordered response categories available methods are scarce. In the present article, DIF in the widely used partial credit model is investigated. An item-focused tree is proposed that allows the detection of DIF items, which might affect the performance of the partial credit model. The method uses tree methodology, yielding a tree for each item that is detected as DIF item. The visualization as trees makes the results easily accessible, as the obtained trees show which variables induce DIF and in which way. In the present paper, the new method is compared with alternative approaches and simulations demonstrate the performance of the method

A multigroup diffusion solver using pseudo transient continuation for a radiation-hydrodynamic code with patch-based AMR

We present a scheme to solve the nonlinear multigroup radiation diffusion (MGD) equations. The method is incorporated into a massively parallel, multidimensional, Eulerian radiation-hydrodynamic code with adaptive mesh refinement (AMR). The patch-based AMR algorithm refines in both space and time creating a hierarchy of levels, coarsest to finest. The physics modules are time-advanced using operator splitting. On each level, separate level-solve packages advance the modules. Our multigroup level-solve adapts an implicit procedure which leads to a two-step iterative scheme that alternates between elliptic solves for each group with intra-cell group coupling. For robustness, we introduce pseudo transient continuation (PTC). We analyze the magnitude of the PTC parameter to ensure positivity of the resulting linear system, diagonal dominance and convergence of the two-step scheme. For AMR, a level defines a subdomain for refinement. For diffusive processes such as MGD, the refined level uses Dirichet boundary data at the coarse-fine interface and the data is derived from the coarse level solution. After advancing on the fine level, an additional procedure, the sync-solve (SS), is required in order to enforce conservation. The MGD SS reduces to an elliptic solve on a combined grid for a system of G equations, where G is the number of groups. We adapt the partial temperature scheme for the SS; hence, we reuse the infrastructure developed for scalar equations. Results are presented. (Abridged)Comment: 46 pages, 14 figures, accepted to JC

High CO2 concentration and iron availability determine the metabolic inventory in an Emiliania huxleyi-dominated phytoplankton community

Ocean acidification (OA), a consequence of anthropogenic carbon dioxide (CO2) emissions, strongly impacts marine ecosystems. OA also influences iron (Fe) solubility, affecting biogeochemical and ecological processes. We investigated the interactive effects of CO2 and Fe availability on the metabolome response of a natural phytoplankton community. Using mesocosms we exposed phytoplankton to ambient (390 μatm) or future CO2 levels predicted for the year 2100 (900 μatm), combined with ambient (4.5 nM) or high (12 nM) dissolved iron (dFe). By integrating over the whole phytoplankton community, we assigned functional changes based on altered metabolite concentrations. Our study revealed the complexity of phytoplankton metabolism. Metabolic profiles showed three stages in response to treatments and phytoplankton dynamics. Metabolome changes were related to the plankton group contributing respective metabolites, explaining bloom decline and community succession. CO2 and Fe affected metabolic profiles. Most saccharides, fatty acids, amino acids and many sterols significantly correlated with the high dFe treatment at ambient pCO2. High CO2 lowered the abundance of many metabolites irrespective of Fe. However, sugar alcohols accumulated, indicating potential stress. We demonstrate that not only altered species composition but also changes in the metabolic landscape affecting the plankton community may change as a consequence of future high-CO2 oceans.publishedVersio