Contact : Paul Marchal (Paul.Marchal@ifremer.fr)
Development of a formal procedure to optimize the calibration and strengthen the robustness of a complex ecosystem model: Atlantis
The evaluation and implementation of ecosystem-based management requires the support of mature ecosystem models. Atlantis is one such model, for which Ifremer has developed and calibrated an Eastern English Channel application (Atlantis-EEC). One major limitation of all complex ecosystem models, and of Atlantis-EEC in particular, is the lack of a formal mathematical protocol to optimize/calibrate the huge amount of parameters and also the poor knowledge of how parameters’ uncertainty is actually driving the model’s response. This project will develop methods to formalize the calibration procedure and to assess the sensitivity of the Eastern English Channel Atlantis ecosystem model application to parameter uncertainty. Successfully addressing that dual challenge, in collaboration with mathematicians, will boost Atlantis-EEC to the confines of academia, stepping inside advice-giving territory. The methodological developments brought about by this project will thus benefit the ecosystem modelers’ community, as well as managers in charge of mitigating the effects of mankind on marine ecosystems.
Keywords: ecosystem model, Atlantis, calibration, black box optimization, sensitivity analysis, large dimension
Applicants should have a strong mathematical background and hold a PhD in that discipline. Applicants should have established expertise in applied mathematics (statistics, optimisation, machine learning), computing, engineering, and should demonstrate their capacity in scientific programming (e.g., Java, C++, R, SAS,…). Some basic knowledge in marine ecology would be appreciated.
Drawing lessons from past failures and successes (Daw and Gray, 2005; Hilborn, 2004), fisheries science has gradually been moving from traditional single-species considerations (Ludwig, 2002; Rosenberg, 2002) towards a comprehensive, ecosystem-based management (EBM) approach (Botsford et al., 1997; Browman and Stergiou, 2004). Over the last few decades, interest in ecosystem modelling has thus grown, and several “end-to-end” models have been developed to emulate the dynamics of marine ecosystems (Plagányi, 2007). One of the first ecosystem models that included a large set of species groups and trophic connections was Ecopath with Ecosim (EwE) (Christensen and Walters, 2004). Recently, other models have emerged focusing on the spatial dynamics of fish and
fisheries distributions, e.g., ISIS-Fish (Mahévas and Pelletier, 2004), and/or taking into account nutrient cycling, food web dynamics, and environmental variability, e.g., OSMOSE (Shin and Cury, 2004; Travers et al., 2009), APECOSM (Maury et al., 2007), NEMURO.FISH (Kishi et al., 2011), SEAPODYM (Lehodey et al., 2008) and Atlantis (Fulton et al., 2011). Atlantis is currently one of the most comprehensive and up-to-date ecosystem model (Plagányi, 2007), and it has successfully been applied to a number of case studies worldwide (Ainsworth et al., 2012; Kaplan et al., 2012; Savina et al., 2013), including the Eastern English Channel (Atlantis-EEC; Girardin et al., 2016). One major limitation of all complex ecosystem models mentioned above, and of the Atlantis-EEC application in particular, is the lack of a formal mathematical protocol to optimize/calibrate the huge amount of parameters and also the poor knowledge of how parameters’ uncertainty is actually driving the model’s response.
The overarching objective of our postdoctoral project will be to address this double challenge, building on the existing Atlantis-EEC application.
• Methods, expected results, possible collaborations
In a first step, an optimization framework will be developed to calibrate Atlantis-EEC. Given the numerous parameters to estimate, the
project will consider methods aiming at reducing the complexity by either identifying the most sensitive ones (Morris method, group
screening), or making explicit the relationships between variables. Different calibration methods will then be explored, including some that have already been used for models of similar complexity, e.g. evolutionary strategies as already used for Osmose (Duboz et al, 2010), Bayesian approaches (Kennedy and O’Hagan, 2001; Andrianakis et al., 2015). The adjustment of metamodels is also envisaged to get around the computational costs constraints. A key task here will be the definition of the objective function, which should account for multiple objectives and for the heterogeneous quality of available data. In a second step, an analysis of uncertainty and/or sensitivity of the Atlantis-EEC model will also be carried out to highlight the key uncertain parameters for which a refined tuning would be necessary. An important step forward would hence be to identify and analyse the main sources of uncertainty in our model. Due to the numerous parameters considered in Atlantis, the application of sensitivity/uncertainty propagation analyses would require using, (i) meta-models (Grace et al., 2010), (ii) experimental plans to reduce the number of simulations based on, e.g., the Morris methods or optimized factorial designs (Lehuta et al., 2013), Latin Hypercube Sampling (Gasche et al., 2013), (iii) sobol indices (Sobol’, 2001), to explore the uncertainty in our application or, (iv) other techniques such as adaptive screening already tested on Atlantis (Pantus, 2007).
The first benefit of this project will be to reduce the computational burden and more generally the time needed to calibrate Atlantis-EEC, but also any Atlantis applications developed around the world.
An additional outcome of the project will then be an identification of the most important parameters that should be best informed as a matter of priority, and the resulting impact on data collection. This is particularly important for Atlantis but also for any complex model where the priority is often given to parameters for which data is abundant, although these are not necessarily those having the largest influence on the model response.
Consolidating the estimation procedure by making it more objective and transparent will also contribute to the general credibility of the
model. The science developed within this project will thus allow advancing Atlantis-EEC beyond academic confines, and eventually stepping inside advice-giving territory. Atlantis-EEC could thus be applied to evaluate the ecosystem effects of scenarios combining climate change, human activities (including fishing and non-fishing sectors), and of their management.
Several contacts have already been made within the Atlantis community, and several colleagues from the NOAA (USA) and CSIRO (Australia) have already expressed interest in collaborating on that particular topic. Importantly also, because the successful achievement of that project will require strong mathematical developments, guidance will be provided by Victor Picheny (INRA, Unité de Mathématiques et Informatique Appliquées de Toulouse), and other colleagues from the MEXICO network (Méthodes pour l’EXploration Informatique des modèles Complexes, http://reseau-mexico.fr/), who will also help to identify potential postdoctoral candidates with the appropriate numerate background.
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