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: superindividual 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.
Highlights The General Lake Model (GLM) is stress tested against 32 globally distributed lakes. There was low correlation between input data uncertainty and model performance. Model performance related to lake-morphometry, light extinction and flow regime; deep, clear lakes with high residence times had the lowest model error.
Here, we communicate a point of departure in the development of aquatic ecosystem models, namely a new community-based framework, which supports an enhanced and transparent union between the collective expertise that exists in the communities of traditional ecologists and model developers. Through a literature survey, we document the growing importance of numerical aquatic ecosystem models while also noting the difficulties, up until now, of the aquatic scientific community to make significant advances in these models during the past two decades. Through a common forum for aquatic ecosystem modellers we aim to (i) advance collaboration within the aquatic ecosystem modelling community, (ii) enable increased use of models for research, policy and ecosystem-based management, (iii) facilitate a collective framework using common (standardised) code to ensure that model development is incremental, (iv) increase the transparency of model structure, assumptions and techniques, (v) achieve a greater Handling editor: Boping Han understanding of aquatic ecosystem functioning, (vi) increase the reliability of predictions by aquatic ecosystem models, (vii) stimulate model inter-comparisons including differing model approaches, and (viii) avoid 're-inventing the wheel', thus accelerating improvements to aquatic ecosystem models. We intend to achieve this as a community that fosters interactions amongst ecologists and model developers. Further, we outline scientific topics recently articulated by the scientific community, which lend themselves well to being addressed by integrative modelling approaches and serve to motivate the progress and implementation of an open source model framework.
To explore the role that fish play in aquatic ecosystems, a hydrodynamic–ecological model (DYRESM–CAEDYM) was coupled to a novel fish population model. The coupled model (DYCD–FISH) combined two modelling approaches: a complex dynamic model and an individual-based model. The coupled model simulates fish growth population dynamics and predicts fish impacts on various ecosystem components, including nutrients and lower trophic levels. The model was employed to explore the role of the dominant fish in Lake Kinneret (Israel), Acanthobrama terraesanctae (Kinneret bleak, or in Hebrew, lavnun ha’kinneret; hereafter lavnun). Model results suggested that the lavnun has a significant impact (p < 0.05) on the magnitude of output variables including its prey food (the predatory and microzooplankton), major nutrients such as ammonium (NH4) and phosphate (PO4), and on several phytoplankton species, but not on the seasonality of any of the output variables. Since the model incorporates trophic levels from nutrients to fish, it revealed the nonlinear dynamic impacts of fish on different ecosystem components and in particular has led to quantitative insights into the relative influence of top-down control on water quality attributes. Besides being an ecosystem research tool, DYCD–FISH can also be employed as a fishery management tool, and in particular facilitate ecosystem-based fishery management.
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