Individual growth analysis of the Pacific yellowlegs shrimp Penaeus californiensis via multi-criteria approach

Cisneros-Mata³

et al. 2021

Fishes

Self Cite

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Section: Introductionmentioning

confidence: 99%

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Curiel-Bernal¹,

Cisneros-Mata³

et al. 2021

Fishes

Self Cite

“…Residuals are set up according to the curve and variability at age. Variability at age (differences in size of the fishes at same age) has been previously documented and studied [2][3][4][5][6]. Many hypotheses have been proposed to explain this phenomenon, with factors including differences in birthdates, genetic variability, food availability, and competition, among others.…”

Section: Introductionmentioning

confidence: 99%

“…Variability at age is considered with age (depensatory variability [5,7]). If the variability decreases as the age is increases, then the variance is called compensatory [6,8]. It is worth noting that compensatory growth has a different meaning in aquaculture studies.…”

Section: Introductionmentioning

confidence: 99%

“…The authors wrote that "it may happen that younger fish experience considerable variability in growth rate, while older fish tend to reach limiting size." This peculiarity was called "growth compensation" [6]. However, Schnute and Fournier also suggested that "many factors may contribute to size variation among fish of one age," and that it is also possible that individual variability tends to increase with age.…”

Section: Introductionmentioning

confidence: 99%

“…Restrepo et al [7] developed an equation to solve the growth depensation issue and Luquin-Covarrubias et al [5] developed the equations for the other five models. On the other hand, Félix-Ortiz et al [6] developed the equations to compute the growth compensation in five asymptotic models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Standard Deviation Structure as a New Approach to Growth Analysis in Weight and Length Data of Farmed Lutjanus guttatus

Castillo‐Vargasmachuca

Rodríguez‐Domínguez

et al. 2021

Fishes

Self Cite

In the present study, size-at-age data (length and weight) of marine cage-reared spotted rose snapper Lutjanus guttatus were analyzed under four different variance assumptions (observed, constant, depensatory, and compensatory variances) to analyze the robustness of selecting the right standard deviation structure to parametrize the von Bertalanffy, Logistic, and Gompertz models. The selection of the best model and variance criteria was obtained based on the Bayesian information criterion (BIC). According to the BIC results, the observed variance in the present study was the best way to parametrize the three abovementioned growth models, and the Gompertz model best represented the length and weight growth curves. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.

The standard deviation structure as a new approach to growth analysis in the farmed Pacific white shrimp, Penaeus vannamei (Decapoda, Penaeidae)

Castillo‐Vargasmachuca

Ponce‐Palafox

et al. 2021

Crustac.

In the present study, weight-at-age data of reared Pacific white shrimp, Penaeus vannamei Boone, 1931, were analysed under four different assumptions of variance (observed, constant, depensatory, and compensatory variance) in order to analyse the robustness for selecting the right standard deviation structure to parametrize the Von Bertalanffy, Logistic and Gompertz models. Selection of the best model and variance criteria were obtained based on the Bayesian information criterion (BIC). According to the results of the BIC, the observed variance in the present study proved to constitute the best way to parametrize the three above-listed growth models, and the Von Bertalanffy model appeared to be the best to represent the growth curve found. This is an asymptotic sigmoid curve with an inflection point. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.