Modelling fish growth: model selection, multi-model inference and model selection uncertainty

By Stelios Katsanevakis. 2006

334 citations recorded by the source corpus.

Subfields: Aquatic Science, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/302270ba2cdcd157794353751cdc6fed59e424f2

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In Aquatic Science

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Analysing the growth of turbot (Psetta maxima) in a commercial recirculation system with the use of three different growth models (2011, result): “This has often been criticized since the use of VBGM could lead to overestimation and underestimation or even unrealistic estimates of the growth parameters and therefore to incorrect conclusions regarding the biological interpretation of the growth parameters (Katsanevakis 2006; Katsanevakis and Ma”
Age and growth of longtail tuna (Thunnus tonggol) in tropical and temperate waters of the central Indo-Pacific (2010, methodology): “The most appropriate model for describing growth of longtail tuna was assessed using a bias-corrected form of Akaike’s Information Criterion (AICc) described by Katsanevakis (2006).”
Growth and mortality rates of the fan mussel Pinna nobilis in Lake Vouliagmeni (Korinthiakos Gulf, Greece): a generalized additive modelling approach (2007, methodology): “ Katsanevakis (2006b) proposed the use of many candidate growth models and a multi-model inference (MMI) approach, based on information theory (Burnham and Anderson 2002), as a more robust approach to study absolute growth and deal with model selection uncertainty.”
Avoiding fishy growth curves (2013, methodology): “…von Bertalanffy models as well as others, such as Gompertz and logistic), comparing them using (AIC), and reporting parameter estimates of all candidate models or a single set of estimates frommulti-model averaging (Katsanevakis 2006; Katsanevakis & Maravelias 2008; Thorson & Simpfendorfer 2009).”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, background): “…in which commensurable models exist and are being averaged in a variety of ways (e.g., Stanley and Burnham 1998, Brodziak and Legault 2005, Brandon and Wade 2006, Katsanevakis 2006, Hill et al. 2007, Katsanevakis and Maravelias 2008, Hollowed et al. 2009, Jiao et al. 2009, Brodziak and Piner 2010).”
Strengthening statistical usage in marine ecology (2012, methodology): “…this approach in marine biology was in a series of papers, which quietly revolutionized the modeling of growth and allometry in marine ectotherms (Katsanevakis, 2006; Katsanevakis and Maravelias, 2008; Katsanevakis et al., 2007a), and also contained concise explanations of information theory and…”
Modelling growth and body composition in fish nutrition: where have we been and where are we going? (2010, background): “…(2003) and Thornley and France (2007). von Bertalanffy equation The equation of von Bertalan¡y (1957) stands as the most studied and applied growth function to predict growth of ¢sh and other ectotherms (Ricker 1979; Hernandez-Llamas & Ratkowsky 2004; De Graaf & Prein 2005; Katsanevakis 2006).”
Comparison of six statistical approaches in the selection of appropriate fish growth models (2009, methodology): “The four models mentioned previously are special cases of the Schnute-Richards model with properly selected values for the dimensionless parameter δ, ν, and γ (Quinn et al., 1999; Katsanevakis, 2006 ).”