Flexible von Bertalanffy growth models incorporating Bayesian splines

Chambers, Mark S.; Sidhu, Leesa A.; O'Neill, Ben; Sibanda, Nokuthaba

doi:10.1016/j.ecolmodel.2017.03.026

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…However, one complication posed by a Bayesian approach is that parameters differ between models. This requires different priors to be specified; a complication which had no previous solution [ 57 ]. Therefore, many of multi-model Bayesian applications have compared models that were similar or variants of the same model shape (i.e.…”

Section: Discussionmentioning

confidence: 99%

Modernising fish and shark growth curves with Bayesian length-at-age models

Smart

Grammer

2021

PLoS ONE

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Growth modelling is a fundamental component of fisheries assessments but is often hindered by poor quality data from biased sampling. Several methods have attempted to account for sample bias in growth analyses. However, in many cases this bias is not overcome, especially when large individuals are under-sampled. In growth models, two key parameters have a direct biological interpretation: L0, which should correspond to length-at-birth and L∞, which should approximate the average length of full-grown individuals. Here, we present an approach of fitting Bayesian growth models using Markov Chain Monte Carlo (MCMC), with informative priors on these parameters to improve the biological plausibility of growth estimates. A generalised framework is provided in an R package ‘BayesGrowth’, which removes the hurdle of programming an MCMC model for new users. Four case studies representing different sampling scenarios as well as three simulations with different selectivity functions were used to compare this Bayesian framework to standard frequentist growth models. The Bayesian models either outperformed or matched the results of frequentist growth models in all examples, demonstrating the broad benefits offered by this approach. This study highlights the impact that Bayesian models could provide in age and growth studies if applied more routinely rather than being limited to only complex or sophisticated applications.

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Section: Discussionmentioning

confidence: 99%

Modernising fish and shark growth curves with Bayesian length-at-age models

Smart

Grammer

2021

PLoS ONE

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“…(These two variables, length and weight, can easily be converted into each other using a simple formula [20].) The majority of existing growth models are built upon the von Bertalanffy model [21,22], which utilizes the age of the fish to predict its growth. However, being a non-probabilistic model, it can't be applied to a population of fish without assuming uniformity in growth parameters among all fish.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Modeling of Fish Growth in Smart Aquaculture Systems

2023

KSII TIIS

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We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

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“…An alternative way of combining data is to increase the age of fish according to the time of year at which the fish was surveyed (Chambers, Sidhu, O'Neil, & Sibanda, ; Sparre & Venema, ). In effect, this assumes that every fish spawned on January 1st, and its age is the number of winters survived plus the time of year at which it was caught, expressed as proportion.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative way of combining data is to increase the age of fish according to the time of year at which the fish was surveyed (Chambers, Sidhu, O'Neil, & Sibanda, 2017;Sparre & Venema, 1998).…”

Section: Introductionmentioning

confidence: 99%

Making the most of survey data: Incorporating age uncertainty when fitting growth parameters

Spence

Turtle

2017

Ecology and Evolution

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Individual growth is an important parameter and is linked to a number of other biological processes. It is commonly modeled using the von Bertalanffy growth function (VBGF), which is regularly fitted to age data where the ages of the animals are not known exactly but are binned into yearly age groups, such as fish survey data. Current methods of fitting the VBGF to these data treat all the binned ages as the actual ages. We present a new VBGF model that combines data from multiple surveys and allows the actual age of an animal to be inferred. By fitting to survey data for Atlantic herring (Clupea harengus) and Atlantic cod (Gadus morhua), we compare our model with two other ways of combining data from multiple surveys but where the ages are as reported in the survey data. We use the fitted parameters as inputs into a yield‐per‐recruit model to see what would happen to advice given to management. We found that each of the ways of combining the data leads to different parameter estimates for the VBGF and advice for policymakers. Our model fitted to the data better than either of the other models and also reduced the uncertainty in the parameter estimates and models used to inform management. Our model is a robust way of fitting the VBGF and can be used to combine data from multiple sources. The model is general enough to fit other growth curves for any taxon when the age of individuals is binned into groups.

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Flexible von Bertalanffy growth models incorporating Bayesian splines

Cited by 7 publications

References 36 publications

Modernising fish and shark growth curves with Bayesian length-at-age models

Modernising fish and shark growth curves with Bayesian length-at-age models

Probabilistic Modeling of Fish Growth in Smart Aquaculture Systems

Making the most of survey data: Incorporating age uncertainty when fitting growth parameters

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