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  - THES
A1  - Huber, Veronika Emilie Charlotte
T1  - Climate impact on phytoplankton blooms in shallow lakes
T1  - Der Einfluss des Klimas auf Algenblüten in Flachseen
N2  - Lake ecosystems across the globe have responded to climate warming of recent decades. However, correctly attributing observed changes to altered climatic conditions is complicated by multiple anthropogenic influences on lakes. This thesis contributes to a better understanding of climate impacts on freshwater phytoplankton, which forms the basis of the food chain and decisively influences water quality. The analyses were, for the most part, based on a long-term data set of physical, chemical and biological variables of a shallow, polymictic lake in north-eastern Germany (Müggelsee), which was subject to a simultaneous change in climate and trophic state during the past three decades. Data analysis included constructing a dynamic simulation model, implementing a genetic algorithm to parameterize models, and applying statistical techniques of classification tree and time-series analysis. Model results indicated that climatic factors and trophic state interactively determine the timing of the phytoplankton spring bloom (phenology) in shallow lakes. Under equally mild spring conditions, the phytoplankton spring bloom collapsed earlier under high than under low nutrient availability, due to a switch from a bottom-up driven to a top-down driven collapse. A novel approach to model phenology proved useful to assess the timings of population peaks in an artificially forced zooplankton-phytoplankton system. Mimicking climate warming by lengthening the growing period advanced algal blooms and consequently also peaks in zooplankton abundance. Investigating the reasons for the contrasting development of cyanobacteria during two recent summer heat wave events revealed that anomalously hot weather did not always, as often hypothesized, promote cyanobacteria in the nutrient-rich lake studied. The seasonal timing and duration of heat waves determined whether critical thresholds of thermal stratification, decisive for cyanobacterial bloom formation, were crossed. In addition, the temporal patterns of heat wave events influenced the summer abundance of some zooplankton species, which as predators may serve as a buffer by suppressing phytoplankton bloom formation. This thesis adds to the growing body of evidence that lake ecosystems have strongly responded to climatic changes of recent decades. It reaches beyond many previous studies of climate impacts on lakes by focusing on underlying mechanisms and explicitly considering multiple environmental changes. Key findings show that climate impacts are more severe in nutrient-rich than in nutrient-poor lakes. Hence, to develop lake management plans for the future, limnologists need to seek a comprehensive, mechanistic understanding of overlapping effects of the multi-faceted human footprint on aquatic ecosystems.
N2  - Weltweit haben Seeökosysteme auf den Klimawandel der letzten Jahrzehnte reagiert. Beobachtete Veränderungen eindeutig dem Klimawandel zuzuordnen, wird jedoch häufig dadurch erschwert, dass Seen gleichzeitig vielfachen anthropogenen Einflüssen ausgesetzt sind. Diese Arbeit trägt zu einem besseren Verständnis des Klimaeinflusses auf Algen bei, die am Anfang der Nahrungskette stehen und maßgeblich die Wasserqualität eines Sees beeinflussen können. Zum größten Teil stützt sich die Arbeit auf eine dreißigjährige Datenreihe eines unregelmäßig geschichteten Flachsees im Nordosten von Deutschland (Müggelsee), in dem sowohl steigende Wassertemperaturen als auch sinkende Nährstoffeinträge zu verzeichnen waren. Bei der Datenanalyse wurde ein neu erstelltes dynamisches Simulationsmodell, genetische Algorithmen zur Parametrisierung von Modellen, und statistische Methoden der Klassifizierung und Zeitreihenanalyse genutzt. Ergebnisse dieser Arbeit zeigen, dass nicht nur klimatische Faktoren sondern auch die Nährstoffverfügbarkeit im See den Zeitpunkt der Algenfrühjahrsblüte (Phänologie) beeinflussen. Durch eine Veränderung der Mechanismen, die zum Kollaps der Blüte führen, trat diese trotz ähnlich milder Winterbedingungen bei hoher Nährstoffverfügbarkeit früher auf als bei niedriger. Ein neuentwickelter Ansatz zur Modellierung von Phänologie erwies sich als geeignet, um vorherzusagen, wann Algen und ihre Räuber in einem künstlich periodisch angetriebenen Laborsystem ihre Populationshöhepunkte erreichten. Eine Verlängerung der Wachstumsperiode führte dazu, dass diese früher auftraten. Die Untersuchung, warum sich Blaualgen im betrachteten See während jüngster Hitzewellenereignisse überraschend unterschiedlich entwickelt hatten, ergab, dass ungewöhnlich warmes Wetter nicht wie häufig vermutet generell förderlich für ihre Entwicklung ist. Der Zeitpunkt und die Dauer der Hitzewellen waren entscheidend dafür, ob für Blaualgen kritische Schwellenwerte der thermischen Schichtung im See überschritten wurden. Zudem zeigte sich, dass saisonale Erwärmungsmuster einen bedeutenden Einfluss auf Räuber nahmen, die das Auftreten von Algenblüten verhindern können. Diese Arbeit reiht sich in eine wachsende Anzahl von Studien ein, die zeigen, dass Seeökosysteme bereits stark auf die Klimaveränderungen der letzen Jahrzehnte reagiert haben. Mit ihrem Fokus auf Mechanismen und der expliziten Berücksichtigung simultaner anthropogener Einflüsse geht diese Arbeit gleichzeitig über viele bisherige Studien hinaus, die sich auf reine Beobachtung und die Betrachtung klimatischer Faktoren beschränkten. Kernergebnisse deuten daraufhin, dass Klimafolgen in nährstoffreichen Seen stärker ausfallen als in nährstoffarmen Seen. Nur mit einem umfassenden, mechanistischen Verständnis des vielfältigen anthropogenen Einflusses wird eine hohe Wasserqualität in Seen auch in Zukunft aufrechtzuerhalten sein.
KW  - Klimawandel
KW  - Gewässer
KW  - Phytoplankton
KW  - Phänologie
KW  - Modellierung
KW  - climate change
KW  - freshwater
KW  - phytoplankton
KW  - phenology
KW  - modelling
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-42346
ER  -