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

Abstract

Growth is a fundamental ecological process of stream-dwelling salmonids which is strongly interrelated to critical life history events (emergence, mortality, sexual maturity, smolting, spawning). The ability to accurately model growth becomes critical when making population predictions over large temporal (multi-decadal) and spatial (meso) scales, e.g., investigating the effect of global change. Body length collection by removal sampling is a widely-used practice for monitoring fish populations over such large scales. Such data can be efficiently integrated into a Hierarchical Bayesian Model (HBM) and lead to interesting findings on fish dynamics. We illustrate this approach by presenting an integrated HBM of brown trout (Salmo trutta) growth, population dynamics, and removal sampling data collection processes using large temporal and spatial scales data (20 years; 48 sites placed along a 100 km latitudinal gradient). Growth and population dynamics are modelled by ordinary differential equations with parameters bound together in a hierarchical structure. The observation process is modelled with a combination of a Poisson error, a binomial error, and a mixture of Gaussian distributions. Absolute fit is measured using posterior predictive checks, which results indicate that our model fits the data well. Results indicate that growth rate is positively correlated to catchment area. This result corroborates those of other studies (laboratory, exploratory) that identified factors besides water temperature that are related to daily ration and have a significant effect on stream-dwelling salmonid growth at a large scale. Our study also illustrates the value of integrated HBM and

Coarse woody habitat use by local fish species and structural integrity of enhancements over time in a shallow northern boreal lake assessed in a Bayesian modelling approach

“…Results can be reported in a standardized fashion and different reporting guidelines, ensuring reproducibility and clarity, already exist and can be readily used (Rindskopf, 2020). Many studies in Ecology face problems of scale and time dependency and sensitivity with both often being expensive or in some cases outright not feasible from a logistical standpoint (Laplanche et al, 2019; Lemoine et al, 2016; Shaw et al, 2021). Treatment replicates are often low in cases of remote locations or when concerning endangered and threatened species or ecosystems while sampling and study design often introduces dependencies and pseudoreplication by default (Laplanche et al, 2019; Lemoine et al, 2016; Shaw et al, 2021).…”

“…Many studies in Ecology face problems of scale and time dependency and sensitivity with both often being expensive or in some cases outright not feasible from a logistical standpoint (Laplanche et al, 2019; Lemoine et al, 2016; Shaw et al, 2021). Treatment replicates are often low in cases of remote locations or when concerning endangered and threatened species or ecosystems while sampling and study design often introduces dependencies and pseudoreplication by default (Laplanche et al, 2019; Lemoine et al, 2016; Shaw et al, 2021). The same applies for enhancement and restoration measures where a single river stretch or lake is targeted without adequate reference sites or replicates available, especially in remote areas like the northern boreal.…”

“…The same applies for enhancement and restoration measures where a single river stretch or lake is targeted without adequate reference sites or replicates available, especially in remote areas like the northern boreal. Our study highlights the current and future use for a Bayesian approach when dealing with such issues while retaining informative values of observations in the form of posterior means and probabilities of treatment differences (Laplanche et al, 2019;Lazic et al, 2020).…”

“…Coarse woody habitat introduction in the littoral zone of shallow lakes has shown to be beneficial for multiple fish species typical for northern boreal lake systems, with increases in catch predic- Utilizing a Bayesian modelling framework with non-informative priors in combination with spatial autocorrelation considerations seems to provide a feasible alternative to a frequentist approach and might be especially valuable for studies dealing with pseudoreplication concerns and treatment size issues which are more and more common in remote and impaired ecosystems or in cases of endangered species (Laplanche et al, 2019;Lemoine et al, 2016;Shaw et al, 2021).…”

Effect of restoration measures in Atlantic rivers: A 25‐year overview of sea and riverine brown trout populations in the River Bidasoa

“…During the application of a management measure (considering other possible co‐variables constant), first a progressive increase in the number of fish is expected until reaching a maximum (Figure 2a), and, following its application, a progressive decrease will occur. Based on this behaviour, the period of influence of management measures was extended by two years as it is the approximate time for fish to reach 20 cm FL (Laplanche et al., 2019) and be detected in the capture station (93% and 99% of riverine and sea trout, respectively, presented FL ≥ 20 cm, with a median FL of 29 cm and 37 cm and interquartile minima–maxima of 26.1–32.0 cm and 33.1–42.8 cm for riverine and sea trout, respectively).…”