@article{SpijkermanWackerWeithoffetal.2012, author = {Spijkerman, Elly and Wacker, Alexander and Weithoff, Guntram and Leya, Thomas}, title = {Elemental and fatty acid composition of snow algae in Arctic habitats}, series = {Frontiers in microbiology}, volume = {3}, journal = {Frontiers in microbiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-302X}, doi = {10.3389/fmicb.2012.00380}, pages = {15}, year = {2012}, abstract = {Red, orange or green snow is the macroscopic phenomenon comprising different eukaryotic algae. Little is known about the ecology and nutrient regimes in these algal communities. Therefore, eight snow algal communities from five intensively tinted snow fields in western Spitsbergen were analysed for nutrient concentrations and fatty acid (FA) composition. To evaluate the importance of a shift from green to red forms on the FA-variability of the field samples, four snow algal strains were grown under nitrogen replete and moderate light (+N+ML) or N-limited and high light (-N+HL) conditions. All eight field algal communities were dominated by red and orange cysts. Dissolved nutrient concentration of the snow revealed a broad range of NH4+ (<0.005-1.2 mg NI-1) and only low PO43- (< 18 mu g P I-1) levels. The external nutrient concentration did not reflect cellular nutrient ratios as C:N and C:P ratios of the communities were highest at locations containing relatively high concentrations of NH4- and PO43-. Molar N:P ratios ranged from 11 to 21 and did not suggest clear limitation of a single nutrient. On a per carbon basis, we found a 6-fold difference in total FA content between the eight snow algal communities, ranging from 50 to 300 mg FA g C-1. In multivariate analyses total FA content opposed the cellular N:C quota and a large part of the FA variability among field locations originated from the abundant FAs C181n-9, C18 2n-6, and C183n-3. Both field samples and snow algal strains grown under -N+HL conditions had high concentrations of C181n-9. FAs possibly accumulated due to the cessation of growth. Differences in color and nutritional composition between patches of snow algal communities within one snow field were not directly related to nutrient conditions. We propose that the highly patchy distribution of snow algae within and between snow fields may also result from differences in topographical and geological parameters such as slope, melting water rivulets, and rock formation.}, language = {en} } @article{PerillonPoeschkeLewandowskietal.2017, author = {P{\´e}rillon, C{\´e}cile and P{\"o}schke, Franziska and Lewandowski, J{\"o}rg and Hupfer, Michael and Hilt, Sabine}, title = {Stimulation of epiphyton growth by lacustrine groundwater discharge to an oligo-mesotrophic hard-water lake}, series = {Freshwater Science}, volume = {36}, journal = {Freshwater Science}, publisher = {Univ. of Chicago Press}, address = {Chicago}, issn = {2161-9549}, doi = {10.1086/692832}, pages = {555 -- 570}, year = {2017}, abstract = {Periphyton is a major contributor to aquatic primary production and often competes with phytoplankton and submerged macrophytes for resources. In nutrient-limited environments, mobilization of sediment nutrients by groundwater can significantly affect periphyton (including epiphyton) development in shallow littoral zones and may affect other lake primary producers. We hypothesized that epiphyton growth in the littoral zone of temperate oligomesotrophic hard-water lakes could be stimulated by nutrient (especially P) supply via lacustrine groundwater discharge (LGD). We compared the dry mass, chlorophyll a (chl a), and nutrient content of epiphyton grown on artificial substrates at different sites in a groundwater-fed lake and in experimental chambers with and without LGD. During the spring-summer periods, epiphyton accumulated more biomass, especially algae, in littoral LGD sites and in experimental chambers with LGD compared to controls without LGD. Epiphyton chl a accumulation reached up to 46 mg chl a/m(2) after 4 wk when exposed to LGD, compared to a maximum of 23 mg chl a/m(2) at control (C) sites. In the field survey, differences in epiphyton biomass between LGD and C sites were most pronounced at the end of summer, when epilimnetic P concentrations were lowest and epiphyton C:P ratios indicated P limitation. Groundwater-borne P may have facilitated epiphyton growth on macrophytes and periphyton growth on littoral sediments. Epiphyton stored up to 35 mg P/m(2) in 4 wk (which corresponds to 13\% of the total P content of the littoral waters), preventing its use by phytoplankton, and possibly contributing to the stabilization of a clear-water state. However, promotion of epiphyton growth by LGD may have contributed to an observed decline in macrophyte abundance caused by epiphyton shading and a decreased resilience of small charophytes to drag forces in shallow littoral areas of the studied lake in recent decades.}, language = {en} } @article{MooijTrolleJeppesenetal.2010, author = {Mooij, Wolf M. and Trolle, Dennis and Jeppesen, Erik and Arhonditsis, George B. and Belolipetsky, Pavel V. and Chitamwebwa, Deonatus B. R. and Degermendzhy, Andrey G. and DeAngelis, Donald L. and Domis, Lisette Nicole de Senerpont and Downing, Andrea S. and Elliott, J. Alex and Fragoso Jr, Carlos Ruberto and Gaedke, Ursula and Genova, Svetlana N. and Gulati, Ramesh D. and H{\aa}kanson, Lars and Hamilton, David P. and Hipsey, Matthew R. and 't Hoen, Jochem and H{\"u}lsmann, Stephan and Los, F. Hans and Makler-Pick, Vardit and Petzoldt, Thomas and Prokopkin, Igor G. and Rinke, Karsten and Schep, Sebastiaan A. and Tominaga, Koji and Van Dam, Anne A. and Van Nes, Egbert H. and Wells, Scott A. and Janse, Jan H.}, title = {Challenges and opportunities for integrating lake ecosystem modelling approaches}, series = {Aquatic ecology}, volume = {44}, journal = {Aquatic ecology}, publisher = {Springer Science + Business Media B.V.}, address = {Dordrecht}, issn = {1573-5125}, doi = {10.1007/s10452-010-9339-3}, pages = {633 -- 667}, year = {2010}, abstract = {A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.}, language = {en} } @article{KaercherFilstrupBraunsetal.2020, author = {K{\"a}rcher, Oskar and Filstrup, Christopher T. and Brauns, Mario and Tasevska, Orhideja and Patceva, Suzana and Hellwig, Niels and Walz, Ariane and Frank, Karin and Markovic, Danijela}, title = {Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions}, series = {Inland Waters}, volume = {10}, journal = {Inland Waters}, number = {1}, publisher = {Taylor \& Francis}, address = {London}, issn = {2044-2041}, doi = {10.1080/20442041.2019.1689768}, pages = {29 -- 41}, year = {2020}, abstract = {Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.}, language = {en} } @article{KoussoroplisPincebourdeWacker2017, author = {Koussoroplis, Apostolos-Manuel and Pincebourde, Sylvain and Wacker, Alexander}, title = {Understanding and predicting physiological performance of organisms in fluctuating and multifactorial environments}, series = {Ecological monographs : a publication of the Ecological Society of America.}, volume = {87}, journal = {Ecological monographs : a publication of the Ecological Society of America.}, publisher = {Wiley}, address = {Hoboken}, issn = {0012-9615}, doi = {10.1002/ecm.1247}, pages = {178 -- 197}, year = {2017}, abstract = {Understanding how variance in environmental factors affects physiological performance, population growth, and persistence is central in ecology. Despite recent interest in the effects of variance in single biological drivers, such as temperature, we have lacked a comprehensive framework for predicting how the variances and covariances between multiple environmental factors will affect physiological rates. Here, we integrate current theory on variance effects with co-limitation theory into a single unified conceptual framework that has general applicability. We show how the framework can be applied (1) to generate mathematically tractable predictions of the physiological effects of multiple fluctuating co-limiting factors, (2) to understand how each co-limiting factor contributes to these effects, and (3) to detect mechanisms such as acclimation or physiological stress when they are at play. We show that the statistical covariance of co-limiting factors, which has not been considered before, can be a strong driver of physiological performance in various ecological contexts. Our framework can provide powerful insights on how the global change-induced shifts in multiple environmental factors affect the physiological performance of organisms.}, language = {en} } @article{ErlerRiebeBeitzetal.2020, author = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin}, title = {Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s20020418}, pages = {17}, year = {2020}, abstract = {Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.}, language = {en} }