FN Archimer Export Format
PT J
TI Comparison of individual-based model output to data using a model of walleye pollock early life history in the Gulf of Alaska
BT 
AF HINCKLEY, Sarah
   PARADA, Carolina
   HORNE, John K.
   MAZUR, Michael
   WOILLEZ, Mathieu
AS 1:1;2:2,3;3:4;4:5;5:6;
FF 1:;2:;3:;4:;5:PDG-RBE-STH-LBH;
C1 Alaska Fisheries Sci Ctr, 7600 Sand Point Way NE, Seattle, WA 98115 USA.
   Univ Concepcion, Dept Geofis, Concepcion, Chile.
   Univ Concepcion, IMO, Concepcion, Chile.
   Univ Washington, Sch Aquat & Fisheries Sci, Box 355020, Seattle, WA 98195 USA.
   US Fish & Wildlife Serv, 170 North First St, Lander, WY 82520 USA.
   IFREMER, Sci & Technol Halieut, Brest, France.
C2 ALASKA FISHERIES SCI CTR NOAA, USA
   UNIV CONCEPCION, CHILE
   UNIV CONCEPCION, CHILE
   UNIV WASHINGTON, USA
   US FISH & WILDLIFE SERV, USA
   IFREMER, FRANCE
SI BREST
SE PDG-RBE-STH-LBH
IN WOS Ifremer jusqu'en 2018
   copubli-int-hors-europe
   copubli-sud
IF 1.713
TC 13
UR https://archimer.ifremer.fr/doc/00327/43775/43332.pdf
LA English
DT Article
DE ;Mathematical models;Statistical analysis;Marine fish;Spatial distributions;Spatial analysis;Individual-based models;USA gulf of alaska;Walleye pollock
AB Biophysical individual-based models (IBMs) have been used to study aspects of early life history of marine fishes such as recruitment, connectivity of spawning and nursery areas, and marine reserve design. However, there is no consistent approach to validating the spatial outputs of these models. In this study, we hope to rectify this gap. We document additions to an existing individual-based biophysical model for Alaska walleye pollock (Gadus chalcogrammus), some simulations made with this model and methods that were used to describe and compare spatial output of the model versus field data derived from ichthyoplankton surveys in the Gulf of Alaska. We used visual methods (e.g. distributional centroids with directional ellipses), several indices (such as a Normalized Difference Index (NDI), and an Overlap Coefficient (OC), and several statistical methods: the Syrjala method, the Getis-Ord Gi⁎ statistic, and a geostatistical method for comparing spatial indices. We assess the utility of these different methods in analyzing spatial output and comparing model output to data, and give recommendations for their appropriate use. Visual methods are useful for initial comparisons of model and data distributions. Metrics such as the NDI and OC give useful measures of co-location and overlap, but care must be taken in discretizing the fields into bins. The Getis-Ord Gi⁎ statistic is useful to determine the patchiness of the fields. The Syrjala method is an easily implemented statistical measure of the difference between the fields, but does not give information on the details of the distributions. Finally, the geostatistical comparison of spatial indices gives good information of details of the distributions and whether they differ significantly between the model and the data. We conclude that each technique gives quite different information about the model-data distribution comparison, and that some are easy to apply and some more complex. We also give recommendations for a multistep process to validate spatial output from IBMs.
PY 2016
PD OCT
SO Deep-sea Research Part Ii-topical Studies In Oceanography
SN 0967-0645
PU Pergamon-elsevier Science Ltd
VL 132
UT 000385598700017
BP 240
EP 262
DI 10.1016/j.dsr2.2016.04.007
ID 43775
ER

EF