Calibrating process-based marine ecosystem models: An example case using Atlantis

Type Article
Date 2019-11
Language English
Author(s) Pethybridge Heidi1, Weijerman Mariska2, Perrymann Holly3, Audzijonyte Asta4, Porobic Javier1, McGregor Vidette5, Girardin RaphaelORCID6, Bulman Cathy1, Ortega-Cisneros Kelly7, Sinerchia Matteo8, Hutton Trevor1, Lozano-Montes Hector1, Mori Mao4, Novaglio Camilla1, Fay Gavin9, Gorton Rebecca1, Fulton Elizabeth1
Affiliation(s) 1 : CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia
2 : NOAA Fisheries, Pacific Islands Fisheries Science Centre, Honolulu, HI, 96818, United States
3 : Institute of Marine Research (IMR), Nordnesgaten 33, 5005, Bergen, Norway
4 : Institute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Hobart, TAS, 7001, Australia
5 : National Institute of Water and Atmospheric Research (NIWA), 301 Evans Bay Parade, Greta Point, Wellington, New Zealand
6 : IFREMER, Channel and North Sea Fisheries Research Unit, 150 Quai Gambetta, BP 699, 62321, Boulogne-sur-mer, France
7 : Department of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South Africa
8 : CNR-IAS Institute for the Anthropic impacts and Sustainability in marine environment. Torregrande, Oristano, Italy
9 : Department of Fisheries Oceanography, School for Marine Science and Technology, University of Massachusetts Dartmouth, 200 Mill Rd., Fairhaven, MA 02719, United States
Source Ecological Modelling (0304-3800) (Elsevier BV), 2019-11 , Vol. 412 , P. 108822 (13p.)
DOI 10.1016/j.ecolmodel.2019.108822
WOS© Times Cited 10
Keyword(s) Best practices, Model diagnostics, Food web, Pedigree, Parameter estimation

Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers.

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Pethybridge Heidi, Weijerman Mariska, Perrymann Holly, Audzijonyte Asta, Porobic Javier, McGregor Vidette, Girardin Raphael, Bulman Cathy, Ortega-Cisneros Kelly, Sinerchia Matteo, Hutton Trevor, Lozano-Montes Hector, Mori Mao, Novaglio Camilla, Fay Gavin, Gorton Rebecca, Fulton Elizabeth (2019). Calibrating process-based marine ecosystem models: An example case using Atlantis. Ecological Modelling, 412, 108822 (13p.). Publisher's official version : , Open Access version :