We have considered U(P,N) in the form of a Michaelis-Menten relation in N and proportional to P, ie, U(P,N)=VPv n N/(k n +N). Grazing, G(P,Z) is regarded as proportional to Z, and has been considered either as Michaelis-Menten in P or proportional to P (G(P,Z)=(1-  )VZvpP /(k p +P) or G(P,Z)=(1-  )VvpPZ ). The nutrient recycling term R(P,Z) is proportional to G, ie R(P,Z)=  /(1-  ) G(P,Z). The grazing loss to phytoplankton biomass is G+U, ie either VvpPZ /(k p +P) or VvpPZ. ABSTRACT There is a very large range of estuarine biological responses to nitrogen loadings and other anthropogenic “driving variables”, determined in part by the magnitude, frequency, and other characteristics of the drivers, but also by intrinsic characteristics of the estuarine systems. Such intrinsic characteristics can include both physical/chemical factors (depth, salinity, water residence time, etc) and biological factors (nature of ecological communities, trophic interactions, etc). To address the richness of estuarine response to driving variables, we aim to establish a simple estuarine classification scheme, at least for a river- dominated subset of estuarine systems. Toward this goal, we are investigating a class of models, the nutrient-phytoplankton-zooplankton (NPZ) models, which have been used to examine a range of subjects including effects of nutrient limitation and zooplankton predation on phytoplankton dynamics (eg, Steele and Henderson, 1981) and fish predation (eg, Scheffer et al., 2000), and can admit a wide range of behavior, including multiple steady states and oscillatory behavior (Edwards and Brindley, 1999). Conclusions and future challenges References For those interested in the full citations of publications referred to herein, or in a copy of the poster, please tack your address to the poster and we will them to you! TOWARD A MODEL-BASED SYSTEM OF ESTUARINE CLASSIFICATION D.P. Swaney 1,R.W. Howarth 1, R.M. Marino 1, D. Scavia 2, M. Alber 3 and E.W. Boyer 4 1 Dept of Ecology & Evolutionary Biology, Cornell University, Ithaca, NY, School of Natural Resources & Environment, University of Michigan, Ann Arbor, MI, Dept of Marine Sciences, University of Georgia, Athens, GA Faculty of Forest & Natural Resources Management, SUNY-ESF, Syracuse, NY Acknowledgements This work has been supported by an EPA STAR grant, “Developing regional-scale stressor models for managing eutrophication in coastal marine ecosystems, including interactions of nutrients, sediments, land-use change, and climate variability and change,” EPA Grant Number R830882, R.W. Howarth, P.I. The above system can be written as mass-balance equations in the following form: The equations can be solved numerically to determine the steady-state values of N,P, and Z, if such a state exists (fig 1). Increasing load or changing parameter values may result in oscillatory or other non-steady state behavior. Fig 2 shows the effect of increasing N concentration in load from 5 to 15 mg/l. “Switching on” denitrification removes the oscillatory behavior, effectively reducing the increased load (fig 3). More complex behavior can result by changing values of model parameters. Even simple models of estuarine system biology can exhibit a variety of behaviors in response to different levels of environmental drivers, such as freshwater flow or nutrient load. These include various steady-states, regular oscillations, and irregular fluctuations that may serve as a basis for classification of coastal ecosystems. To date, we have explored only a few functional forms of grazing and have focused on the response of phytoplankton to changes in residence time and N load. Other nutrients (P, Si) and physical factors (light, temperature) may be important as well. Future investigations will more fully explore these relationships as well as effects of seasonality and other time-varying characteristics of driving variables. What are the apparent relationships between flow,  and effect of nutrient loads? Nutrient loads can result from flow dependent sources (riverine flows, groundwater seepage, precipitation) or be essentially independent of terrestrial flows and atmospheric water flows (“point sources”). Fig 1 Fig 4. shows the effect of generating irregular fluctuations in phytoplankton by reducing the recycling of nutrients from the grazing interaction from 0.7 to 0.3 Fig 2 Fig 4 Fig 3 Steady-states of N, P, and Z simulations under the assumption of Michaelis-Menten phytoplankton limited grazing and denitrification as characterized by Nixon et al (1996), ie Figures 5, 6, and 7 show steady state behavior of N, P, and Z over a range of tau (ie residence time, or more properly “freshwater flushing” time) for five different N loading levels. Here, loads are assumed to be independent of freshwater flow. Dashed lines in Figures 5a, 6a, and 7a indicate the NOAA-defined breakpoints of 5, 20, and 60 ugChl/l as definitions of thresholds between Low, Medium, High, and Hyper Eutrophic conditions, translated into phytoplankton nitrogen equivalents. Fig 5a Fig 5b Fig 5cFig 6c Fig 7c Fig 6b Fig 7b Fig 6a Fig 7a where  is freshwater flushing time (days). Denitrification suppresses phytoplankton zooplankton levels below the baseline case, presumably due to N limitation. Steady-states of N, P, and Z simulations under the assumption of Michaelis-Menten phytoplankton limited grazing and no denitrification (baseline case). Plankton density increases and levels off as  increases above 100; N levels decline as P and Z increase, and are suppressed at all load levels above  ~ 30 days. Steady-states of N, P, and Z simulations under the assumption of grazing jointly proportional to phytoplankton and zooplankton, and no denitrification. At  less than 100 days, the phytoplankton response is similar to that of the other scenarios. At longer time scales, phytoplankton density is higher than in the other scenarios. Zooplankton and N are suppressed (note changes of scale) Phytoplankton density apparently increases with  when nitrogen load is independent of flow Phytoplankton density apparently decreases with  when nitrogen load is based on fixed-concentration riverine loads because  increases with decreasing flow. concentrations In fact, flow and N load are correlated, but not perfectly so (NOAA dataset, S.V. Smith, 2003; Loads derived from SPARROW model.) Log(L) = Log (Q) R2 = 0.60 mg/l Fig 8a Fig 8b Fig 8c