TY - JOUR A1 - Janssen, Annette B. G. A1 - Arhonditsis, George B. A1 - Beusen, Arthur A1 - Bolding, Karsten A1 - Bruce, Louise A1 - Bruggeman, Jorn A1 - Couture, Raoul-Marie A1 - Downing, Andrea S. A1 - Elliott, J. Alex A1 - Frassl, Marieke A. A1 - Gal, Gideon A1 - Gerla, Daan J. A1 - Hipsey, Matthew R. A1 - Hu, Fenjuan A1 - Ives, Stephen C. A1 - Janse, Jan H. A1 - Jeppesen, Erik A1 - Joehnk, Klaus D. A1 - Kneis, David A1 - Kong, Xiangzhen A1 - Kuiper, Jan J. A1 - Lehmann, Moritz K. A1 - Lemmen, Carsten A1 - Oezkundakci, Deniz A1 - Petzoldt, Thomas A1 - Rinke, Karsten A1 - Robson, Barbara J. A1 - Sachse, Rene A1 - Schep, Sebastiaan A. A1 - Schmid, Martin A1 - Scholten, Huub A1 - Teurlincx, Sven A1 - Trolle, Dennis A1 - Troost, Tineke A. A1 - Van Dam, Anne A. A1 - Van Gerven, Luuk P. A. A1 - Weijerman, Mariska A1 - Wells, Scott A. A1 - Mooij, Wolf M. T1 - Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective JF - Aquatic ecology : the international forum covering research in freshwater and marine environments N2 - 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. KW - Water quality KW - Ecology KW - Geochemistry KW - Hydrology KW - Hydraulics KW - Hydrodynamics KW - Physical environment KW - Socio-economics KW - Model availability KW - Standardization KW - Linking Y1 - 2015 U6 - https://doi.org/10.1007/s10452-015-9544-1 SN - 1386-2588 SN - 1573-5125 VL - 49 IS - 4 SP - 513 EP - 548 PB - Springer CY - Dordrecht 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 - TY - GEN 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 T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1326 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 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429839 SN - 1866-8372 IS - 1326 ER - TY - JOUR A1 - Quintana, Xavier D. A1 - Arim, Matias A1 - Badosa, Anna A1 - Maria Blanco, Jose A1 - Boix, Dani A1 - Brucet, Sandra A1 - Compte, Jordi A1 - Egozcue, Juan J. A1 - de Eyto, Elvira A1 - Gaedke, Ursula A1 - Gascon, Stephanie A1 - Gil de Sola, Luis A1 - Irvine, Kenneth A1 - Jeppesen, Erik A1 - Lauridsen, Torben L. A1 - Lopez-Flores, Rocio A1 - Mehner, Thomas A1 - Romo, Susana A1 - Sondergaard, Martin T1 - Predation and competition effects on the size diversity of aquatic communities JF - Aquatic sciences : research across boundaries N2 - 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. KW - Phytoplankton KW - Zooplankton KW - Fish KW - Size distribution KW - Predation KW - Competition KW - Compositional data analysis Y1 - 2015 U6 - https://doi.org/10.1007/s00027-014-0368-1 SN - 1015-1621 SN - 1420-9055 VL - 77 IS - 1 SP - 45 EP - 57 PB - Springer CY - Basel ER - TY - JOUR A1 - Sommer, Ulrich A1 - Adrian, Rita A1 - Domis, Lisette Nicole de Senerpont A1 - Elser, James J. A1 - Gaedke, Ursula A1 - Ibelings, Bas A1 - Jeppesen, Erik A1 - Lurling, Miquel A1 - Molinero, Juan Carlos A1 - Mooij, Wolf M. A1 - van Donk, Ellen A1 - Winder, Monika ED - Futuyma, DJ T1 - Beyond the Plankton Ecology Group (PEG) Model mechanisms driving plankton succession JF - Annual review of ecology, evolution, and systematics JF - Annual Review of Ecology Evolution and Systematics N2 - 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. KW - lakes KW - oceans KW - seasonal patterns KW - pelagic zone KW - light KW - overwintering KW - grazing KW - parasitism KW - food quality Y1 - 2012 SN - 978-0-8243-1443-9 U6 - https://doi.org/10.1146/annurev-ecolsys-110411-160251 SN - 1543-592X VL - 43 IS - 2-4 SP - 429 EP - 448 PB - Annual Reviews CY - Palo Alto ER -