@article{EhrlichKathGaedke2020,
  author    = {Ehrlich, Elias and Kath, Nadja Jeanette and Gaedke, Ursula},
  title     = {The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton},
  series = {The ISME journal},
  volume    = {14},
  journal   = {The ISME journal},
  number    = {6},
  publisher = {Nature Publishing Group},
  address   = {London},
  issn      = {1751-7362},
  doi       = {10.1038/s41396-020-0619-1},
  pages     = {1451 -- 1462},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{WurzbacherWarthmannBourneetal.2016,
  author    = {Wurzbacher, Christian and Warthmann, Norman and Bourne, Elizabeth Charlotte and Attermeyer, Katrin and Allgaier, Martin and Powell, Jeff R. and Detering, Harald and Mbedi, Susan and Großart, Hans-Peter and Monaghan, Michael T.},
  title     = {High habitat-specificity in fungal communities in oligo-mesotrophic, temperate Lake Stechlin (North-East Germany)},
  series = {MycoKeys},
  volume    = {41},
  journal   = {MycoKeys},
  publisher = {Pensoft Publ.},
  address   = {Sofia},
  issn      = {1314-4057},
  doi       = {10.3897/mycokeys.16.9646},
  pages     = {17 -- 44},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@article{SeilervanVelzenNeuetal.2017,
  author    = {Seiler, Claudia and van Velzen, Ellen and Neu, Thomas R. and Gaedke, Ursula and Berendonk, Thomas U. and Weitere, Markus},
  title     = {Grazing resistance of bacterial biofilms: a matter of predators' feeding trait},
  series = {FEMS microbiology ecology},
  volume    = {93},
  journal   = {FEMS microbiology ecology},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0168-6496},
  doi       = {10.1093/femsec/fix112},
  pages     = {9},
  year      = {2017},
  abstract  = {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.},
  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}
}