Assessing uncertainty of a multispecies size-spectrum model resulting from process and observation errors
Author(s) -
Chongliang Zhang,
Yong Chen,
Yiping Ren
Publication year - 2015
Publication title -
ices journal of marine science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.348
H-Index - 117
eISSN - 1095-9289
pISSN - 1054-3139
DOI - 10.1093/icesjms/fsv086
Subject(s) - fishing , uncertainty analysis , scale (ratio) , environmental science , fisheries management , process (computing) , sampling (signal processing) , statistical model , ecosystem , replicate , computer science , environmental resource management , econometrics , ecology , statistics , mathematics , geography , simulation , biology , cartography , filter (signal processing) , computer vision , operating system
Ecosystem models, specifically multispecies dynamic models, have been increasingly used to project impacts of fishing activity on the trophodynamics of ecosystems to support ecosystem-based fisheries management. Uncertainty is unavoidable in modelling processes and needs to be recognized and properly quantified before models are utilized. Uncertainty was assessed in this study for a multispecies size-spectrum model that quantifies community structure and ecological characteristics. The uncertainty was assumed to result from errors in fish life-history and metabolic scale parameters, environmental variability, fishing variability, and sampling errors. Given the same level of imprecision, metabolic scale parameters had the dominant influence on the uncertainty of the size spectrum modelling results, followed by life-history parameters. Both types of errors led to “scenario uncertainty”, suggesting the possible existence of alternative states of community structure. Environmental variability, fishing variability, and observation errors resulted in “statistical uncertainty”, implying that such uncertainty can be described adequately in statistical terms. The results derived from such a simulation study can provide guidance for identifying research priorities to help narrow the gap in scientific knowledge and reduce the uncertainty in fisheries management.
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