A length‐based hierarchical model of brown trout (Salmo trutta fario) growth and production

Abstract

We present a hierarchical Bayesian model (HBM) to estimate the growth parameters, production, and production over biomass ratio (P/B) of resident brown trout (Salmo trutta fario) populations. The data which are required to run the model are removal sampling and air temperature data which are conveniently gathered by freshwater biologists. The model is the combination of eight submodels: abundance, weight, biomass, growth, growth rate, time of emergence, water temperature, and production. 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. We illustrate the capabilities of the model by investigating the growth and production of a brown trout population (Neste d'Oueil, Pyrénées, France) by using data collected in the field from 2005 to 2010.

Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data

“…using the negative binomial distribution, or expressing dispersion as a function of physical habitat covariates. We chose to model growth rate with a power function instead of the more widely used von Bertalanffy growth function (He & Bence, 2007;Lecomte & Laplanche, 2012 (Pitcher, 2002), thus providing a low amount of information on trout growth. Information on individuals, either from laboratory experiments or in situ via capture-recapture (Tang et al, 2014), seems more appropriate.…”

“…The HBM framework also allows for 'internal' errors or random effects, which account for additional sources of variability. We did not use internal, additive errors for the cohort mean sizes predicted by the growth model, as Lecomte & Laplanche (2012) did.…”

“…One main reason for the renewed interest is that the HBM framework offers the ability to build observation models that are connected to ecological models, both possibly advanced (e.g., more than what dedicated tools such as MARK can offer), as integrated models (Letcher et al, 2015). More specifically, HBMs have proven to effectively model EFRS observations (Rivot et al, 2008), multimodal length distributions (Ruiz & Laplanche, 2010), growth (He & Bence, 2007;Bal et al, 2011;Lecomte & Laplanche, 2012;Sigourney et al, 2012;Dortel et al, 2013), and population dynamics (Kanno et al, 2015;Bret et al, 2017). Other reasons for the growing popularity of HBMs include their ability to propagate uncertainty from observations to parameter estimates and to compare competing models to test hypotheses (Lunn et al, 2013).…”

“…While raw EFRS length data have been used to infer somatic growth (Lecomte & Laplanche, 2012), and pre-processed length data (into mean-length-at-age and density-atage estimates) have been used to model either growth or population dynamics separately (e.g., He et al (2008); Laplanche et al (2018)), to our knowledge, raw EFRS length data have never been used to infer growth and population dynamics at the same time as an integrated model. We thus present an integrated HBM that models observations, somatic growth, and basic population dynamics.…”

Optimal flow for brown trout: Habitat – prey optimization

“…In field studies, a variety of factors such as competition for space and predation (Ayllón et al, 2013;Kaspersson et al, 2013), temperature (Lecomte and Laplanche, 2012;Moore et al, 2012), climate change (Comte et al, 2013), presence of waterfalls or dams and flow variability (Fjeldstad et al, 2012), changes in habitat structures (Muotka and Syrjänen, 2007) and stochasticity are shown to affect the abundance and the distribution of fish. Each of these factors can influence the relationship among the density of organisms and the habitat characteristics, inflating variability.…”