FN Archimer Export Format
PT J
TI Calibrating process-based marine ecosystem models: An example case using Atlantis
BT 
AF 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
AS 1:1;2:2;3:3;4:4;5:1;6:5;7:6;8:1;9:7;10:8;11:1;12:1;13:4;14:1;15:9;16:1;17:1;
FF 1:;2:;3:;4:;5:;6:;7:PDG-RBE-HMMN-LRHBL;8:;9:;10:;11:;12:;13:;14:;15:;16:;17:;
C1 CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia
   NOAA Fisheries, Pacific Islands Fisheries Science Centre, Honolulu, HI, 96818, United States
   Institute of Marine Research (IMR), Nordnesgaten 33, 5005, Bergen, Norway
   Institute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Hobart, TAS, 7001, Australia
   National Institute of Water and Atmospheric Research (NIWA), 301 Evans Bay Parade, Greta Point, Wellington, New Zealand
   IFREMER, Channel and North Sea Fisheries Research Unit, 150 Quai Gambetta, BP 699, 62321, Boulogne-sur-mer, France
   Department of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South Africa
   CNR-IAS Institute for the Anthropic impacts and Sustainability in marine environment. Torregrande, Oristano, Italy
   Department of Fisheries Oceanography, School for Marine Science and Technology, University of Massachusetts Dartmouth, 200 Mill Rd., Fairhaven, MA 02719, United States
C2 CSIRO, AUSTRALIA
   NOAA, USA
   IMR (BERGEN), NORWAY
   UNIV TASMANIA, AUSTRALIA
   NIWA, NEW ZEALAND
   IFREMER, FRANCE
   UNIV RHODES, SOUTH AFRICA
   CNR, ITALY
   UNIV MASSACHUSETTS DARTMOUTH, USA
SI BOULOGNE
SE PDG-RBE-HMMN-LRHBL
IN WOS Ifremer UPR
   copubli-europe
   copubli-int-hors-europe
   copubli-sud
IF 2.497
TC 19
UR https://archimer.ifremer.fr/doc/00516/62723/67160.pdf
LA English
DT Article
DE ;Best practices;Model diagnostics;Food web;Pedigree;Parameter estimation
AB 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. 
PY 2019
PD NOV
SO Ecological Modelling
SN 0304-3800
PU Elsevier BV
VL 412
UT 000494885900004
DI 10.1016/j.ecolmodel.2019.108822
ID 62723
ER

EF