A length-based hierarchical model of brown trout (Salmo trutta fario) growth and production
Summary (1 min read)
Introduction
- Abundance is modeled as a mixture of Gaussian cohorts; cohorts centers and standard deviations are related by a von Bertalanffy growth function; time of emergence and growth rate are functions of water temperature; water temperature is predicted from air temperature; biomass, production, and P/B are subsequently computed.
- The trend is to construct such statistical models within a Bayesian framework (Congdon, 2006).
- The authors primary objective is to provide a layout to compute growth parameters of brown trout populations by using accessible data (namely removal sampling and air temperature data).
- Time scale is daily, spans from January 1st of the year of the first campaign to December 31st of the year of the last campaign, with a total of T days.
Emergence
- Cardinal temperatures are minimum (ymin), optimum (yopt), and maximum (ymax) temperatures required for growth as well as minimum (y0) and optimum (y1) temperatures required for hatching.
- CE50 is the critical value leading to the emergence of 50% of the fry.
- See text for values of multidimensionnal parameters (Li; to; tovio;k).
- Are illustrated in Fig. 1: abundance and growth submodels depend on common parameters (mo;k and so;k, defined later), growth depends on the time of emergence and growth rate, these quantities further depend on the water temperature, fish biomass is the cross-product of fish weight and abundance, and the combination of growth and biomass parameters lead to production.
Weight
- Parameters are shape and rate for gamma distributions, expectation and variance for normal and lognormal distributions, and boundaries for uniform distributions.
- Units which are provided with precisions (e.g. 1=s2l) are units of respective standard deviations (e.g. sl).
- Standard deviations are related to random errors across campaigns (sl, st, sa, sb, sZ, sz), among individuals (sN, s0), and residual (sm, sT, sW).
Abundance
- The abundance submodel is briefly presented and is more thoroughly investigated by Ruiz and Laplanche (2010).
- The motivation to include this additional error term in the model is illustrated and discussed later.
- Model alternatives are defined whether cohort standard deviations so;k (M1 4) and centers mo;k (M1 3) are constrained with a VBGF and whether growth rate Gt (M1 2) and date of emergence temo;k (M1) are temperature-dependent.
- Nevertheless, in the aim of illustrating the modeling of temperature-dependent time of emergence and growth rate, following results are computed by using baseline (M1).
- The model at the current state applies to riverine brown trout (S. trutta fario).
Conflict of interest
- The authors have declared no conflict of interest.
- Growth with seasonally varying temperatures – an expansion of the von Bertalanffy growth model.
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References
262 citations
"A length-based hierarchical model o..." refers background in this paper
...As a result of such adaptation capabilities, brown trout has successfully colonized freshwaters to a world-wide distribution (Elliott, 1994; Klemetsen et al., 2003)....
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227 citations
"A length-based hierarchical model o..." refers background in this paper
...…been related to environmental factors such as temperature (Mallet et al., *Corresponding author: e-mail: laplanche@gmail.com, Tel: 133-534-323-973, Fax: 133-534-323-901 r 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1999) and stream flow (Jensen and Johnsen 1999; Daufresne and Renault, 2006)....
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210 citations
"A length-based hierarchical model o..." refers methods in this paper
...2.8 Constant parameters, data sets, and priors 2.8.1 Constant parameters We use cardinal temperatures (ymin, ymax, yopt) suggested by Elliott et al. (1995) for S. trutta fario....
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165 citations
"A length-based hierarchical model o..." refers background in this paper
...Therefore, various authors have included seasonal variability in the VBGF parameters to consider the effects of water temperature on growth (Taylor, 1960; Somers, 1988; Mallet et al., 1999)....
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161 citations
"A length-based hierarchical model o..." refers background in this paper
...Some contributions have shown, however, that the use of more advanced statistical models is recommendable in the aim of lowering estimation bias (Peterson et al., 2004; Riley and Fausch, 1992)....
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