Challenges and opportunities for integrating lake ecosystem modelling approaches

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 trait-based 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 model

Algal and aquatic plant carbon concentrating mechanisms in relation to environmental change

Carbon dioxide concentrating mechanisms (also known as inorganic carbon concentrating mechanisms; both abbreviated as CCMs) presumably evolved under conditions of low CO2 availability. However, the timing of their origin is unclear since there are no sound estimates from molecular clocks, and even if there were, there are no proxies for the functioning of CCMs. Accordingly, we cannot use previous episodes of high CO2 (e.g. the Palaeocene-Eocene Thermal Maximum) to indicate how organisms with CCMs responded. Present and predicted environmental change in terms of increased CO2 and temperature are leading to increased CO2 and HCO3- and decreased CO32- and pH in surface seawater, as well as decreasing the depth of the upper mixed layer and increasing the degree of isolation of this layer with respect to nutrient flux from deeper waters. The outcome of these forcing factors is to increase the availability of inorganic carbon, photosynthetic active radiation (PAR) and ultraviolet B radiation (UVB) to aquatic photolithotrophs and to decrease the supply of the nutrients (combined) nitrogen and phosphorus and of any non-aeolian iron. The influence of these variations on CCM expression has been examined to varying degrees as acclimation by extant organisms. Increased PAR increases CCM expression in terms of CO2 affinity, while increased UVB has a range of effects in the organisms examined; little relevant information is available on increased temperature. Decreased combined nitrogen supply generally increases CO2 affinity, decreased iron availability increases CO2 affinity, and decreased phosphorus supply has varying effects on the organisms examined. There are few data sets showing interactions among the observed changes, and even less information on genetic (adaptation) changes in response to the forcing factors. In freshwaters, changes in phytoplankton species composition may alter with environmental change with consequences for frequency of species with or without CCMs. The information available permits less predictive power as to the effect of the forcing factors on CCM expression than for their overall effects on growth. CCMs are currently not part of models as to how global environmental change has altered, and is likely to further alter, algal and aquatic plant primary productivity