@misc{SommerAdrianDomisetal.2012, author = {Sommer, Ulrich and Adrian, Rita and Domis, Lisette Nicole de Senerpont and Elser, James J. and Gaedke, Ursula and Ibelings, Bas and Jeppesen, Erik and Lurling, Miquel and Molinero, Juan Carlos and Mooij, Wolf M. and van Donk, Ellen and Winder, Monika}, title = {Beyond the Plankton Ecology Group (PEG) Model mechanisms driving plankton succession}, series = {Annual review of ecology, evolution, and systematics}, volume = {43}, journal = {Annual review of ecology, evolution, and systematics}, number = {2-4}, editor = {Futuyma, DJ}, publisher = {Annual Reviews}, address = {Palo Alto}, isbn = {978-0-8243-1443-9}, issn = {1543-592X}, doi = {10.1146/annurev-ecolsys-110411-160251}, pages = {429 -- 448}, year = {2012}, abstract = {The seasonal succession of plankton is an annually repeated process of community assembly during which all major external factors and internal interactions shaping communities can be studied. A quarter of a century ago, the state of this understanding was described by the verbal plankton ecology group (PEG) model. It emphasized the role of physical factors, grazing and nutrient limitation for phytoplankton, and the role of food limitation and fish predation for zooplankton. Although originally targeted at lake ecosystems, it was also adopted by marine plankton ecologists. Since then, a suite of ecological interactions previously underestimated in importance have become research foci: overwintering of key organisms, the microbial food web, parasitism, and food quality as a limiting factor and an extended role of higher order predators. A review of the impact of these novel interactions on plankton seasonal succession reveals limited effects on gross seasonal biomass patterns, but strong effects on species replacements.}, language = {en} } @article{JanssenArhonditsisBeusenetal.2015, author = {Janssen, Annette B. G. and Arhonditsis, George B. and Beusen, Arthur and Bolding, Karsten and Bruce, Louise and Bruggeman, Jorn and Couture, Raoul-Marie and Downing, Andrea S. and Elliott, J. Alex and Frassl, Marieke A. and Gal, Gideon and Gerla, Daan J. and Hipsey, Matthew R. and Hu, Fenjuan and Ives, Stephen C. and Janse, Jan H. and Jeppesen, Erik and Joehnk, Klaus D. and Kneis, David and Kong, Xiangzhen and Kuiper, Jan J. and Lehmann, Moritz K. and Lemmen, Carsten and Oezkundakci, Deniz and Petzoldt, Thomas and Rinke, Karsten and Robson, Barbara J. and Sachse, Rene and Schep, Sebastiaan A. and Schmid, Martin and Scholten, Huub and Teurlincx, Sven and Trolle, Dennis and Troost, Tineke A. and Van Dam, Anne A. and Van Gerven, Luuk P. A. and Weijerman, Mariska and Wells, Scott A. and Mooij, Wolf M.}, title = {Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective}, series = {Aquatic ecology : the international forum covering research in freshwater and marine environments}, volume = {49}, journal = {Aquatic ecology : the international forum covering research in freshwater and marine environments}, number = {4}, publisher = {Springer}, address = {Dordrecht}, issn = {1386-2588}, doi = {10.1007/s10452-015-9544-1}, pages = {513 -- 548}, year = {2015}, abstract = {Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.}, language = {en} } @article{QuintanaArimBadosaetal.2015, author = {Quintana, Xavier D. and Arim, Matias and Badosa, Anna and Maria Blanco, Jose and Boix, Dani and Brucet, Sandra and Compte, Jordi and Egozcue, Juan J. and de Eyto, Elvira and Gaedke, Ursula and Gascon, Stephanie and Gil de Sola, Luis and Irvine, Kenneth and Jeppesen, Erik and Lauridsen, Torben L. and Lopez-Flores, Rocio and Mehner, Thomas and Romo, Susana and Sondergaard, Martin}, title = {Predation and competition effects on the size diversity of aquatic communities}, series = {Aquatic sciences : research across boundaries}, volume = {77}, journal = {Aquatic sciences : research across boundaries}, number = {1}, publisher = {Springer}, address = {Basel}, issn = {1015-1621}, doi = {10.1007/s00027-014-0368-1}, pages = {45 -- 57}, year = {2015}, abstract = {Body size has been widely recognised as a key factor determining community structure in ecosystems. We analysed size diversity patterns of phytoplankton, zooplankton and fish assemblages in 13 data sets from freshwater and marine sites with the aim to assess whether there is a general trend in the effect of predation and resource competition on body size distribution across a wide range of aquatic ecosystems. We used size diversity as a measure of the shape of size distribution. Size diversity was computed based on the Shannon-Wiener diversity expression, adapted to a continuous variable, i.e. as body size. Our results show that greater predation pressure was associated with reduced size diversity of prey at all trophic levels. In contrast, competition effects depended on the trophic level considered. At upper trophic levels (zooplankton and fish), size distributions were more diverse when potential resource availability was low, suggesting that competitive interactions for resources promote diversification of aquatic communities by size. This pattern was not found for phytoplankton size distributions where size diversity mostly increased with low zooplankton grazing and increasing nutrient availability. Relationships we found were weak, indicating that predation and competition are not the only determinants of size distribution. Our results suggest that predation pressure leads to accumulation of organisms in the less predated sizes, while resource competition tends to favour a wider size distribution.}, language = {en} } @misc{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 = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1326}, issn = {1866-8372}, doi = {10.25932/publishup-42983}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429839}, pages = {35}, 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{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} }