Numerous models have been developed for predicting various ecological processes in streams and rivers. The origins of today's models can be traced back to the relatively simple water quality models of Streeter and Phelps of biochemical oxygen demand and the resulting DO concentrations in the Ohio River, published in 1925. This initial water quality model was extended over the years to include the nutrient (e.g., nitrogen and phosphorus) cycles and their impact on algal concentrations, which in turn impacted the DO concentrations and the organisms that depend on this supply of oxygen to reproduce and survive. Today one can simulate the effects of nitrates, phosphate, turbidity, pH, temperature, ammonia, alkalinity, carbondioxide, and light on the growth of algae. Algal photosynthesis can be simulated under different temperatures, different carbon dioxide levels, and for light-adapted and shade-adapted communities. Some models extend the simulation of the micro- and macro-algal concentrations to include the macrophyte and zooplankton yields along with the nutrient and oxygen fluxes, the dissolved and particulate dead organic matter, and sediment accumulation processes.
It is common practice today to incorporate menu-driven, graph-based, user interfaces (GUIs) that serve to facilitate the definition and operation of river ecological models (see http://www.wiz.uni-kassel.de/model_db/ models.html). Such interfaces permit users to specify the conceptual model containing a set of compartments, which can be connected to each other by links. For example, the available compartment types could include mixed reactors, biofilm reactors, advective-diffusive reactors (e.g., plug flow with or without dispersion), and river sections (describing water flow and substance transport and transformation in open channels). Compartments can be connected by advective links that represent water flow and substance transport between compartments. Diffusive links can represent boundary layers or membranes, which can be penetrated selectively by certain substances. Users can specify any set of state variables and transformation processes to be active within the compartments. Such models typically are able to perform simulations, sensitivity analyses, and parameter estimations using measured data.
Some river ecological models attempt to model fish populations and movement. This includes the prediction of population and bioaccumulation dynamics of age-structured fish exposed to hydrophobic organic pollutants and metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). Bioaccumulation algorithms are often based on diffusion kinetics and coupled to process-based models for the growth of individual fish. Exchange algorithms consider both biological attributes of fishes and physi-cochemical properties of the chemicals of concern that determine diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics can include the fish's gill morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and structural organic content).
The growth of individual fish can be simulated using a standard mass balance, bioenergetic model (i.e., growth = ingestion — egestion — respiration — specific dynamic action — excretion). A fish's realized ingestion is typically calculated from its maximum consumption rate adjusted for the availability of prey of the appropriate size and taxonomy. The community's food web is specified by defining one or more foraging classes for each fish species based on either its body weight, its body length, or its age. The dietary composition of each of these feeding classes can be specified as a combination of benthos, incidental terrestrial insects, periphyton/attached algae, phyto-plankton, zooplankton, and one or more fish species. Population dynamics can be modeled considering migration, predatory mortalities defined by community's food web and standing stocks, size-dependent physiological mortality rates, the maximum longevity of species, and toxicological responses to chemical exposures.
Understanding movements of individual fish is also important for understanding population dynamics. Identifying underlying mechanisms that influence spatial patterns in populations improves forecasts of alternative management strategies on the spatial dynamics of populations critical for assessing and managing fisheries and improving water resource management. In many systems, the spatial pattern of individuals is driven by environmental factors which can be evaluated separately from biological interactions.
For example, the need to understand fish movements is particularly acute in the Columbia-Snake River system in the Pacific Northwest of the Unites States, where tens of millions of juvenile salmon and steelhead (migrants) migrate downstream through eight dams. This migration consists of dozens of runs, of which 12 are listed under the Endangered Species Act. Since migrants passing through turbines may experience significant mortality (5-15%), research efforts over several decades have been devoted to diverting migrants over spillways and through bypass systems. However, bypass systems have achieved only limited and variable success at considerable cost. Recently, developed models have been sufficiently accurate to be of value in engineering design of bypasses.
Computational fluid dynamics models can now describe hydrodynamic patterns at scales meaningful to fish, and laboratory studies have defined many sensory abilities of fish to distinguish elements of hydrodynamic fields. However, mathematical methods linking fish trajectories to hydrodynamic patterns in terms of fish sensory and behavioral elements remain a challenge.
See also: Adaptive Management and Integrative assessments; Antibiotics in Aquatic and Terrestrial Ecosystems; Climate Change Models.
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