There is growing interest in models of marine ecosystems that deal with the effects of climate change through the higher trophic levels. Such end-to-end models combine physicochemical oceanographic descriptors and organisms ranging from microbes to higher-trophic-level (HTL) organisms, including humans, in a single modeling framework. The demand for such approaches arises from the need for quantitative tools for ecosystem-based management, particularly models that can deal with bottom-up and top-down controls that operate simultaneously and vary in time and space and that are capable of handling the multiple impacts expected under climate change. End-to-end models are now feasible because of improvements in the component submodels and the availability of sufficient computing power. We discuss nine issues related to the development of end-to-end models. These issues relate to formulation of the zooplankton submodel, melding of multiple temporal and spatial scales, acclimation and adaptation, behavioral movement, software and technology, model coupling, skill assessment, and interdisciplinary challenges. We urge restraint in using end-to-end models in a true forecasting mode until we know more about their performance. End-to-end models will challenge the available data and our ability to analyze and interpret complicated models that generate complex behavior. End-to-end modeling is in its early developmental stages and thus presents an opportunity to establish an open-access, community-based approach supported by a suite of true interdisciplinary efforts

[1] We have used a marine food-web model, an atmosphere-ocean general circulation model (GCM), and an empirical dimethylsulfide (DMS) algorithm to predict the DMS seawater concentration and the DMS sea-to-air flux in 10°latitude bands from 70°N to 70°S under contemporary and enhanced greenhouse conditions. The DMS empirical algorithm utilizes the food-web model predictions of surface chlorophyll and the GCM's simulation of oceanic mixed layer depth. The food-web model was first calibrated to contemporary climate conditions using satellite-derived chlorophyll data and meteorological forcings. For the climate change simulations, the meteorological forcings were derived from a transient simulation of the CSIRO Mark 2 GCM, using the IPCC/IS92a radiative forcing scenario to the period of equivalent CO 2 tripling (2080). The globally integrated DMS flux perturbation is predicted to be +14%; however, we found strong latitudinal variation in the perturbation. The greatest perturbation to DMS flux is simulated at high latitudes in both hemispheres, with little change predicted in the tropics and sub-tropics. The largest change in annual integrated flux (+106%) is simulated in the Southern Hemisphere between 50°S and 60°S. At this latitude, the DMS flux perturbation is most influenced by the GCM-simulated changes in the mixed layer depth. The results indicate that future increases in stratification in the polar oceans will play a critical role in the DMS cycle and climate change.

Abstract. Mathematical biology/ecology teaching for undergraduates has generally relied on the Lotka-Volterra competition and predator-prey models to introduce students to population dynamics. Students are provided with an understanding of the application of dynamical system theory in simulating and understanding the behavior of the natural world, and they are provided with opportunities to practice phase plane analysis techniques such as determining the stability of equilibrium points and bifurcation analysis. This paper outlines a course in ecological modeling suitable for all students in the life sciences. The course is based on realistic ecological principles, such as using nutrient concentration to measure populations together with explicit resource availability to constrain population growth, and it considers simple Lotka-Volterra systems within this theoretical framework. An advantage of this approach is that the widely experimentally observed models of mixotrophy and mutualism can be naturally and simply introduced and analyzed. Continuous variation of models across a trophic level is now possible. Competitors can smoothly change to mutualist/mixotroph populations, which can further smoothly change to become predators, synthesizing in simple terms the relationships among trophic interactions within the LotkaVolterra framework. Standard texts on mathematical ecology do not include mixotrophy, which is central to understanding trophic interactions.

[1] A new one-dimensional model of DMSP/DMS dynamics (DMOS) is developed and applied to the Sargasso Sea in order to explain what drives the observed dimethylsulfide (DMS) summer paradox: a summer DMS concentration maximum concurrent with a minimum in the biomass of phytoplankton, the producers of the DMS precursor dimethylsulfoniopropionate (DMSP). Several mechanisms have been postulated to explain this mismatch: a succession in phytoplankton species composition towards higher relative abundances of DMSP producers in summer; inhibition of bacterial DMS consumption by ultraviolet radiation (UVR); and direct DMS production by phytoplankton due to UVR-induced oxidative stress. None of these hypothetical mechanisms, except for the first one, has been tested with a dynamic model. We have coupled a new sulfur cycle model that incorporates the latest knowledge on DMSP/DMS dynamics to a preexisting nitrogen/carbon-based ecological model that explicitly simulates the microbial-loop. This allows the role of bacteria in DMS production and consumption to be represented and quantified. The main improvements of DMOS with respect to previous DMSP/DMS models are the explicit inclusion of: solar-radiation inhibition of bacterial sulfur uptakes; DMS exudation by phytoplankton caused by solar-radiation-induced stress; and uptake of dissolved DMSP by phytoplankton. We have conducted a series of modeling experiments where some of the DMOS sulfur paths are turned ''off'' or ''on,'' and the results on chlorophyll-a, bacteria, DMS, and DMSP (particulate and dissolved) concentrations have been compared with climatological data of these same variables. The simulated rate of sulfur cycling processes are also compared with the scarce data available from previous works. All processes seem to play a role in driving DMS seasonality. Among them, however, solar-radiation-induced DMS exudation by phytoplankton stands out as the process without which the model is unable to produce realistic DMS simulations and reproduce the DMS summer paradox.

The Subantarctic Southern Ocean is a high‐nutrient low‐chlorophyll region, and it has been suggested that primary production is limited by deep mixing and the availability of iron. Australian dust is high in iron content and can be transported over the Subantarctic Southern Ocean, particularly during the austral spring and summer when dust storm frequency in southern Australia is maximal. We present evidence for a coupling between satellite‐derived (SeaWiFS) aerosol optical thickness and chlorophyll concentration in the upper ocean. The coupling is evident at monthly, weekly and daily timescales. Although the monthly coherence is likely to be due to other covarying factors, the coupling at weekly and daily timescales supports the hypothesis that the episodic atmospheric delivery of iron is stimulating phytoplankton growth. We also discuss the impact of oceanic dimethylsulfide production on aerosol concentration in the study region.