Aerobic sediment


Nutrients f







Anaerobic sediment







Figure 6 Schematic of a typical integrated ecological and biogeochemical model. Some components (e.g., herbivorous and carnivorous zooplankton), reactions (e.g., DOM excretion), and transport pathways (e.g., phytoplankton mixing) are omitted from this illustration for simplicity. POM, particulate organic matter; DOM, dissolved organic matter; Graz., grazing; Min., mineralization; Hydr., hydrolysis; Part. Mix., particle mixing.

model accounts for nutrients, phytoplankton, zooplankton, particulate organic matter (POM), and dissolved organic matter (DOM). The number of state variables is typically larger than the number of boxes shown in Figure 6. That is because the models typically track various elements (C, N, P, and Si) individually in each of the components. For P, for example, the state variables can be the concentrations of PO4 (nutrient), phytoplank-ton P, zooplankton P, particulate organic P (POM), and dissolved organic P (DOM). Dissolved oxygen and sulfide are also often simulated. Important transport pathways include phytoplankton and POM settling from the epi-limnion to the hypolimnion where they decay to DOM and then nutrients. The nutrients are mixed back into the epilimnion when the lake overturns. Phytoplankton and POM also deposit to the sediment bed, where they decay to DOM and nutrients, which diffuse out of the sediment. Important reactions include phytoplankton uptake and excretion of nutrients. The phytoplankton are grazed by zooplankton, which results in the phytoplankton biomass being assimilated by the zooplankton or excreted as detritus (POM). Zooplankton die by predation from higher organisms, which also produces POM. POM hydrolyzes to DOM, which mineralizes to nutrients.

Note that this model includes a zooplankton state variable, which is unusual. Most operational eutrophica-tion models do not explicitly consider zooplankton, but rather implicitly include their effect on the algae by assigning a seasonally varying grazing rate. This is due to the functional complexity of zooplankton, which can, for example, enter stages of diapause or dormancy at various stages of their life cycle that can last from a month to over a decade.

Example 2: Terrestrial/Soil Environment

Linked biogeochemical and ecological models also exist for the terrestrial environment. The development and application of those models is motivated by the desire to understand how terrestrial ecosystems respond to changes in management (e.g., crop rotation, fertilization) and/or climate (e.g., increased CO2, temperature). A typical P flow diagram for a terrestrial model is presented in Figure 7. Live plant P is divided into above and below




Standing dead





Strongly sorbed







Figure 7 Schematic of the CENTURY integrated ecological and biogeochemical model for the terrestrial environment. Some transport pathways (e.g., leaching, harvesting) are omitted for clarity.

(i.e., roots) ground pools. Upon death above ground, P is moved to a standing dead pool, which can fall to become surface litter in structural or metabolic pools (different decay rates). A surface microbial pool is associated with the surface litter. Upon death below ground, P is moved to belowground, structural or metabolic pools. Other soil organic matter is divided into active, slow, and passive pools with different decomposition rates.

Various inorganic forms of P are simulated. Labile P is in equilibrium with sorbed P, and sorbed P is in equilibrium with strongly sorbed P, which in turn is lost to occluded P. Weathering of parent material P (e.g., apatite) results in labile and sorbed P.

Consider, for example, a potential history of a P molecule leached from the parent material. It enters the labile/ sorbed pool, where it may become absorbed by the roots of a tree, transported above ground and incorporated into a branch. When the tree dies, it may stand for some time (standing dead), but eventually it will fall and the P molecule may become part of the surface structural pool. If it is not decomposed by surface microbes, it will become part of the belowground, slow organic pool. Below ground it may be absorbed by microbes and enter the active organic pool, which may die and release the molecule back to the labile/sorbed pool.

Past, Present, and Future of Integrated Ecological and Biogeochemical Modeling

This section reviews the past, present, and future of integrated ecological and biogeochemical models. The Redfield relation introduced above can be considered to be the simplest integrated ecological/biogeochemical model. Following the work of Redfield, significant improvements were made to this model in basically two dimensions: (1) spatial and temporal resolution and (2) functional complexity, as illustrated in Figure 8. A similar perspective on the advancement of ecological modeling in Saginaw Bay is shared by V. Bierman:

Model development is proceeding along two parallel pathways. The first of these involves the development of research-oriented process models, which include biological and chemical detail but which, for simplicity, do not include any spatial detail. The second pathway involves the development of an engineering-oriented water quality model that mimics, as closely as practicable, the actual physical system, including spatial detail. At any given point in time, the water quality model will contain those chemical and biological processes that have previously been investigated and developed using the spatially-simplified model. There is constant feedback between these two pathways and constant interaction between the entire modeling effort and an ongoing sampling effort on Saginaw Bay.


Cell quota


Bierman, Canale and Auer, Nyholm, Amano et al. Thebault and Qotbi




Spatial and temporal resolution

Figure 8 Resolution and complexity of integrated ecological and biogeochemical models. For simplicity only selected models are included. More detailed information on the specific models is included in the references listed in the Further Reading section.

It is useful to mention a number of models in Figure 8, because they constitute significant milestones. The introduction of the WASP (Water Quality Analysis and Simulation Program) model represented a significant improvement in model resolution. Further advances in resolution, and the state of the science of today, are represented by the RCA (Row-Column Aesop) and BLOOM models. These models are typically set up with a high spatial and temporal resolution (thousands of mass balance compartments). Functional model complexity varies mainly in the way nutrient uptake and cell composition are simulated. At the lowest level, there is net uptake (uptake -excretion) of nutrients and a fixed 'Redfield' cell composition. Departures from this model were motivated by the realization that phytoplankton composition is variable, and as a result various 'variable stoichiometry' or 'variable composition' models were developed. At an intermediate level of complexity one variable (e.g., total algal P) is used to describe the composition of the algae. Those models are commonly called 'cell quota' models, where the cell quota is the mass of nutrient per cell. At a high level of complexity, models explicitly account for uptake and excretion and various species of the nutrients (e.g., PO4, polyphosphates, etc.) and reactions in the algal cells. The phosphate interaction model (PIM), for example, has three state variables for intracellular P (soluble inorganic P, polyphosphate, structural and soluble organic P). Variable phytoplankton composition models have been integrated with spatially and temporally explicit models, as exemplified by the Bierman and other similar models. Increasing the spatial and temporal resolution and functional complexity of models can be problematic and for that reason, IBMs, like iAlgae, are being constructed as a potential alternative to the traditional population-level models. Individual-based modeling of algae and bacteria is a current research topic and will likely be a significant factor in future modeling in the area of integrated ecological and biogeochemical modeling.

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