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Atlantis an ecosystem model that considers all parts of marine ecosystems - biophysical, economic and social. Originally focused on the biophysical world and then fisheries it has grown to begin to be used for multiple use and climate questions.
Atlantis is a deterministic biogeochemical whole of ecosystem model. It's overall structure is based around the Management Strategy Evaluation (MSE) approach, where there is a sub-model (or module) for each of the major steps in the adaptive management cycle (Figure 1).
At the core of Atlantis is a deterministic biophysical sub-model, coarsely spatially-resolved in three dimensions, which tracks nutrient (usually N and S) flows through the main biological groups in the system. The primary ecological processes modelled are consumption, production, waste production, migration, predation, recruitment, habitat dependency, and mortality. The trophic resolution is typically at the functional group level. Invertebrates are typically represented as biomass pools, while vertebrates are represented using an explicit age-structured formulation. The physical environment is also represented explicitly, via a set of polygons matched to the major geographical and bioregional features of the simulated marine system (e.g. Figure 2). Biological model components are replicated in each depth layer of each of these polygons. Movement between the polygons is by advective transfer or by directed movements depending on the variable in question.
Atlantis also includes a detailed industry (or exploitation) sub-model. This model deals not only with the impact of pollution, coastal development and broad-scale environmental (e.g. climate) change, but is focused on the dynamics of fishing fleets. It allows for multiple fleets, each with its own characteristics of gear selectivity, habitat association, targeting, effort allocation and management structures. At its most complex, it includes explicit handling of economics, compliance decisions, exploratory fishing and other complicated real world concerns such as quota trading. All forms of fishing maybe represented, including recreational fishing (which is based on the dynamically changing human population in the area).
The exploitation model interacts with the biotic part of the ecosystem, but also supplies ‘simulated data’ to the sampling and assessment sub-model. The sampling and assessment sub-model in Atlantis is designed to generate sector dependent and independent data with realistic levels of measurement uncertainty evaluated as bias and variance. These simulated data are based on the outputs from the biophysical and exploitation sub-models, using with a user-specified monitoring scheme. The data are then fed into the same assessment models used in the real world, and the output of these is input to a management sub-model. This last sub-model is typically a set of decision rules and management actions (currently only detailed for the fisheries sector), which can be drawn from an extensive list of fishery management instruments, including: gear restrictions, days at sea, quotas, spatial and temporal zoning, discarding restrictions, size limits, bycatch mitigation, and biomass reference points.
Atlantis has been used in 19+ systems, primarily in the temperate regions of Australia and North America (Figure 3).
The modelling approach has primarily been used to address fisheries management questions (e.g. appropriate strategic management options for regional fisheries), but is increasingly being used to consider other facets of how marine systems function or are used.
|Port Phillip Bay (Australia)|
|Eastern Tasmania (Australia)|
|New South Wales shelf (Australia)|
|Clarence River (Australia)|
|Northeast United States|
|Chesapeake Bay (USA)|
|California Current (USA and Canada)|
|Central California (USA)|
|Sea of Cortez (Gulf of California) (Mexico)|
|Strait of Georgia and Puget Sound|
|Barents Sea |
|Northeastern Australian waters |
|Derwent River and Storm Bay (Australia)|
|Mediterranean (Spainish coast) |
|Basque waters (Bay Biscay)|
|Gulf of Mexico|
|__________________________Key||Main purpose||Secondary purpose||Preliminary efforts||Planned|
Kaplan, I.C., Levin P.S., Burden M. and Fulton, E.A., in press. Fishing Catch Shares in the Face of Global Change: A Framework for Integrating Cumulative Impacts and Single Species Management. Canadian Journal Fisheries and Aquatic Sciences
Link, J.S., Fulton, E.A. and Gamble, R.J. in press. The Northeast US Application of ATLANTIS: An full system model exploring marine ecosystem dynamics in a living marine resource management context. Progress in Oceanography
Fulton, E.A., Smith A.D.M., Smith D.C. and van Putten, I.E., 2010. Human behaviour - the neglected source of uncertainty in fisheries management. Fish and Fisheries. DOI: 10.1111/j.1467-2979.2010.00371.x
Fulton, E.A., 2010. Approaches to end to end ecosystem models. Journal of Marine Systems. doi:10.1016/j.jmarsys.2009.12.012
Fulton, E.A., Smith, A.D.M. and Smith, D.C., 2007. Alternative Management Strategies for Southeast Australian Commonwealth Fisheries: Stage 2: Quantitative Management Strategy Evaluation. Australian Fisheries Management Authority Report. (9 MB pdf)
Fulton E. A., Smith, A. D. M., and Punt, A. E., 2005. Which ecological indicators can robustly detect effects of fishing? ICES Journal of Marine Science 62:540 – 551.
Fulton, E.A., Smith, A.D.M. and Johnson C.R., 2003. Effect of complexity on marine ecosystem models. Marine Ecology Progress Series 253: 1 – 16.