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Year of publication
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- English (23)
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- subdwarfs (3)
- acute kidney injury (2)
- adaptive processes (2)
- analysis (2)
- aquatic (2)
- bifurcation (2)
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Leach, Taylor H. ; Beisner, Beatrix E. ; Carey, Cayelan C. ; Pernica, Patricia ; Rose, Kevin C. ; Huot, Yannick ; Brentrup, Jennifer A. ; Domaizon, Isabelle ; Grossart, Hans-Peter ; Ibelings, Bastiaan W. ; Jacquet, Stephan ; Kelly, Patrick T. ; Rusak, James A. ; Stockwell, Jason D. ; Straile, Dietmar ; Verburg, Piet
The vertical distribution of chlorophyll in stratified lakes and reservoirs frequently exhibits a maximum peak deep in the water column, referred to as the deep chlorophyll maximum (DCM). DCMs are ecologically important hot spots of primary production and nutrient cycling, and their location can determine vertical habitat gradients for primary consumers. Consequently, the drivers of DCM structure regulate many characteristics of aquatic food webs and biogeochemistry. Previous studies have identified light and thermal stratification as important drivers of summer DCM depth, but their relative importance across a broad range of lakes is not well resolved. We analyzed profiles of chlorophyll fluorescence, temperature, and light during summer stratification from 100 lakes in the Global Lake Ecological Observatory Network (GLEON) and quantified two characteristics of DCM structure: depth and thickness. While DCMs do form in oligotrophic lakes, we found that they can also form in eutrophic to dystrophic lakes. Using a random forest algorithm, we assessed the relative importance of variables associated with light attenuation vs. thermal stratification for predicting DCM structure in lakes that spanned broad gradients of morphometry and transparency. Our analyses revealed that light attenuation was a more important predictor of DCM depth than thermal stratification and that DCMs deepen with increasing lake clarity. DCM thickness was best predicted by lake size with larger lakes having thicker DCMs. Additionally, our analysis demonstrates that the relative importance of light and thermal stratification on DCM structure is not uniform across a diversity of lake types.
Mooij, Wolf M. ; Trolle, Dennis ; Jeppesen, Erik ; Arhonditsis, George B. ; Belolipetsky, Pavel V. ; Chitamwebwa, Deonatus B. R. ; Degermendzhy, Andrey G. ; DeAngelis, Donald L. ; Domis, Lisette Nicole de Senerpont ; Downing, Andrea S. ; Elliott, J. Alex ; Fragoso Jr., Carlos Ruberto ; Gaedke, Ursula ; Genova, Svetlana N. ; Gulati, Ramesh D. ; Håkanson, Lars ; Hamilton, David P. ; Hipsey, Matthew R. ; ‘t Hoen, Jochem ; Hülsmann, Stephan ; Los, F. Hans ; Makler-Pick, Vardit ; Petzoldt, Thomas ; Prokopkin, Igor G. ; Rinke, Karsten ; Schep, Sebastiaan A. ; Tominaga, Koji ; Van Dam, Anne A. ; Van Nes, Egbert H. ; Wells, Scott A. ; Janse, Jan H.
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.
Mooij, Wolf M. ; Trolle, Dennis ; Jeppesen, Erik ; Arhonditsis, George B. ; Belolipetsky, Pavel V. ; Chitamwebwa, Deonatus B. R. ; Degermendzhy, Andrey G. ; DeAngelis, Donald L. ; Domis, Lisette Nicole de Senerpont ; Downing, Andrea S. ; Elliott, J. Alex ; Fragoso Jr, Carlos Ruberto ; Gaedke, Ursula ; Genova, Svetlana N. ; Gulati, Ramesh D. ; Håkanson, Lars ; Hamilton, David P. ; Hipsey, Matthew R. ; ‘t Hoen, Jochem ; Hülsmann, Stephan ; Los, F. Hans ; Makler-Pick, Vardit ; Petzoldt, Thomas ; Prokopkin, Igor G. ; Rinke, Karsten ; Schep, Sebastiaan A. ; Tominaga, Koji ; Van Dam, Anne A. ; Van Nes, Egbert H. ; Wells, Scott A. ; Janse, Jan H.
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.