TY - JOUR A1 - Ehrlich, Elias A1 - Kath, Nadja Jeanette A1 - Gaedke, Ursula T1 - The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton JF - The ISME journal N2 - Theory predicts that trade-offs, quantifying costs of functional trait adjustments, crucially affect community trait adaptation to altered environmental conditions, but empirical verification is scarce. We evaluated trait dynamics (antipredator defense, maximum growth rate, and phosphate affinity) of a lake phytoplankton community in a seasonally changing environment, using literature trait data and 21 years of species-resolved high-frequency biomass measurements. The trait data indicated a concave defense-growth trade-off, promoting fast-growing species with intermediate defense. With seasonally increasing grazing pressure, the community shifted toward higher defense levels at the cost of lower growth rates along the trade-off curve, while phosphate affinity explained some deviations from it. We discuss how low fitness differences of species, inferred from model simulations, in concert with stabilizing mechanisms, e.g., arising from further trait dimensions, may lead to the observed phytoplankton diversity. In conclusion, quantifying trade-offs is key for predictions of community trait adaptation and biodiversity under environmental change. KW - coexistence KW - community ecology KW - diversity KW - evolution KW - fitness KW - functional traits KW - lake KW - maintenance KW - mechanisms KW - plankton Y1 - 2020 U6 - https://doi.org/10.1038/s41396-020-0619-1 SN - 1751-7362 SN - 1751-7370 VL - 14 IS - 6 SP - 1451 EP - 1462 PB - Nature Publishing Group CY - London ER - TY - JOUR A1 - Wurzbacher, Christian A1 - Warthmann, Norman A1 - Bourne, Elizabeth Charlotte A1 - Attermeyer, Katrin A1 - Allgaier, Martin A1 - Powell, Jeff R. A1 - Detering, Harald A1 - Mbedi, Susan A1 - Großart, Hans-Peter A1 - Monaghan, Michael T. T1 - High habitat-specificity in fungal communities in oligo-mesotrophic, temperate Lake Stechlin (North-East Germany) JF - MycoKeys N2 - Freshwater fungi are a poorly studied ecological group that includes a high taxonomic diversity. Most studies on aquatic fungal diversity have focused on single habitats, thus the linkage between habitat heterogeneity and fungal diversity remains largely unexplored. We took 216 samples from 54 locations representing eight different habitats in the meso-oligotrophic, temperate Lake Stechlin in North-East Germany. These included the pelagic and littoral water column, sediments, and biotic substrates. We performed high throughput sequencing using the Roche 454 platform, employing a universal eukaryotic marker region within the large ribosomal subunit (LSU) to compare fungal diversity, community structure, and species turnover among habitats. Our analysis recovered 1027 fungal OTUs (97% sequence similarity). Richness estimates were highest in the sediment, biofilms, and benthic samples (189-231 OTUs), intermediate in water samples (42-85 OTUs), and lowest in plankton samples (8 OTUs). NMDS grouped the eight studied habitats into six clusters, indicating that community composition was strongly influenced by turnover among habitats. Fungal communities exhibited changes at the phylum and order levels along three different substrate categories from littoral to pelagic habitats. The large majority of OTUs (> 75%) could not be classified below the order level due to the lack of aquatic fungal entries in public sequence databases. Our study provides a first estimate of lake-wide fungal diversity and highlights the important contribution of habitat heterogeneity to overall diversity and community composition. Habitat diversity should be considered in any sampling strategy aiming to assess the fungal diversity of a water body. KW - Freshwater fungi KW - aquatic fungi KW - metabarcoding KW - LSU KW - GMYC KW - habitat specificity KW - Chytridiomycota KW - Cryptomycota KW - Rozellomycota KW - community ecology KW - lake ecosystem KW - biofilm KW - sediment KW - plankton KW - water sample KW - benthos KW - reed KW - fungal diversity Y1 - 2016 U6 - https://doi.org/10.3897/mycokeys.16.9646 SN - 1314-4057 SN - 1314-4049 VL - 41 SP - 17 EP - 44 PB - Pensoft Publ. CY - Sofia ER - TY - JOUR A1 - Seiler, Claudia A1 - van Velzen, Ellen A1 - Neu, Thomas R. A1 - Gaedke, Ursula A1 - Berendonk, Thomas U. A1 - Weitere, Markus T1 - Grazing resistance of bacterial biofilms: a matter of predators’ feeding trait JF - FEMS microbiology ecology N2 - Biofilm formation in bacteria is considered to be one strategy to avoid protozoan grazing. However, this assumption is largely based on experiments with suspension-feeding protozoans. Here we test the hypothesis that grazing resistance depends on both the grazers’ feeding trait and the bacterial phenotype, rather than being a general characteristic of bacterial biofilms. We combined batch experiments with mathematical modelling, considering the bacterium Pseudomonas putida and either a suspension-feeding (i.e. the ciliate Paramecium tetraurelia) or a surface-feeding grazer (i.e. the amoeba Acanthamoeba castellanii). We find that both plankton and biofilm phenotypes were consumed, when exposed to their specialised grazer, whereas the other phenotype remained grazing-resistant. This was consistently shown in two experiments (starting with either only planktonic bacteria or with additional pre-grown biofilms) and matches model predictions. In the experiments, the plankton feeder strongly stimulated the biofilm biomass. This stimulation of the resistant prey phenotype was not predicted by the model and it was not observed for the biofilm feeders, suggesting the existence of additional mechanisms that stimulate biofilm formation besides selective feeding. Overall, our results confirm our hypothesis that grazing resistance is a matter of the grazers’ trait (i.e. feeding type) rather than a biofilm-specific property. KW - protozoa KW - biofilm KW - plankton KW - predator-prey model KW - grazing defence KW - feeding trait Y1 - 2017 U6 - https://doi.org/10.1093/femsec/fix112 SN - 0168-6496 SN - 1574-6941 VL - 93 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Mooij, Wolf M. A1 - Trolle, Dennis A1 - Jeppesen, Erik A1 - Arhonditsis, George B. A1 - Belolipetsky, Pavel V. A1 - Chitamwebwa, Deonatus B. R. A1 - Degermendzhy, Andrey G. A1 - DeAngelis, Donald L. A1 - Domis, Lisette Nicole de Senerpont A1 - Downing, Andrea S. A1 - Elliott, J. Alex A1 - Fragoso Jr, Carlos Ruberto A1 - Gaedke, Ursula A1 - Genova, Svetlana N. A1 - Gulati, Ramesh D. A1 - Håkanson, Lars A1 - Hamilton, David P. A1 - Hipsey, Matthew R. A1 - ‘t Hoen, Jochem A1 - Hülsmann, Stephan A1 - Los, F. Hans A1 - Makler-Pick, Vardit A1 - Petzoldt, Thomas A1 - Prokopkin, Igor G. A1 - Rinke, Karsten A1 - Schep, Sebastiaan A. A1 - Tominaga, Koji A1 - Van Dam, Anne A. A1 - Van Nes, Egbert H. A1 - Wells, Scott A. A1 - Janse, Jan H. T1 - Challenges and opportunities for integrating lake ecosystem modelling approaches JF - Aquatic ecology N2 - 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. KW - aquatic KW - food web dynamics KW - plankton KW - nutrients KW - spatial KW - lake KW - freshwater KW - marine KW - community KW - population KW - hydrology KW - eutrophication KW - global change KW - climate warming KW - fisheries KW - biodiversity KW - management KW - mitigation KW - adaptive processes KW - non-linear dynamics KW - analysis KW - bifurcation KW - understanding KW - prediction KW - model limitations KW - model integration Y1 - 2010 U6 - https://doi.org/10.1007/s10452-010-9339-3 SN - 1573-5125 SN - 1386-2588 VL - 44 SP - 633 EP - 667 PB - Springer Science + Business Media B.V. CY - Dordrecht ER -