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

Fishes ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 60
Author(s):  
Sergio G. Castillo-Vargasmachuca ◽  
Eugenio Alberto Aragón-Noriega ◽  
Guillermo Rodríguez-Domínguez ◽  
Leonardo Martínez-Cárdenas ◽  
Eulalio Arámbul-Muñoz ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Curves ◽  
Gompertz Model ◽  
Information Criterion ◽  
Weight Growth ◽  
Length Data ◽  
Size At Age ◽  
The Right

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)

Crustaceana ◽  
2021 ◽  
Vol 94 (8) ◽  
pp. 981-992
Author(s):  
Sergio G. Castillo-Vargasmachuca ◽  
E. Alberto Aragón-Noriega ◽  
Jesús T. Ponce-Palafox ◽  
Guillermo Rodríguez-Domínguez ◽  
Raúl Claro De los Santos
Keyword(s):  
Standard Deviation ◽  
Growth Parameters ◽  
Growth Models ◽  
Information Criterion ◽  
Pacific White Shrimp ◽  
White Shrimp ◽  
Penaeus Vannamei ◽  
Von Bertalanffy ◽  
Sigmoid Curve ◽  
The Right

Abstract 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.

scholarly journals Mathematical models to describe the growth curves of Vietnamese Ri chicken

2023 ◽  
Vol 83 ◽  
Author(s):  
T. H. Nguyen ◽  
C. X. Nguyen ◽  
M. Q. Luu ◽  
A. T. Nguyen ◽  
D. H. Bui ◽  
...  
Keyword(s):  
Growth Curve ◽  
Model Comparison ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Chicken Breed ◽  
Richards Model ◽  
Genetics And Genomics ◽  
Backyard Chicken

Abstract Ri chicken is the most popular backyard chicken breed in Vietnam, but little is known about the growth curve of this breed. This study compared the performances of models with three parameters (Gompertz, Brody, and Logistic) and models containing four parameters (Richards, Bridges, and Janoschek) for describing the growth of Ri chicken. The bodyweight of Ri chicken was recorded weekly from week 1 to week 19. Growth models were fitted using minpack.lm package in R software and Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and root mean square error (RMSE) were used for model comparison. Based on these criteria, the models having four parameters showed better performance than the ones with three parameters, and the Richards model was the best one for males and females. The lowest and highest value of asymmetric weights (α) were obtained by Bridges and Brody models for each of sexes, respectively. Age and weight estimated by the Richard model were 8.46 and 7.51 weeks and 696.88 and 487.58 g for males and for females, respectively. Differences in the growth curves were observed between males and female chicken. Overall, the results suggested using the Richards model for describing the growth curve of Ri chickens. Further studies on the genetics and genomics of the obtained growth parameters are required before using them for the genetic improvement of Ri chickens.

scholarly journals Description of blackberry fruit growth by nonlinear regression models

2020 ◽  
Vol 42 (2) ◽  
Author(s):  
Édipo Menezes da Silva ◽  
Maraísa Hellen Tadeu ◽  
Victor Ferreira da Silva ◽  
Rafael Pio ◽  
Tales Jesus Fernandes ◽  
...  
Keyword(s):  
Statistical Models ◽  
Regression Models ◽  
Least Squares Method ◽  
Growth Curves ◽  
Gompertz Model ◽  
Information Criterion ◽  
Financial Return ◽  
Nonlinear Regression Models ◽  
Small Fruit ◽  
Small Producers

Abstract Blackberry is a small fruit with several properties beneficial to human health and its cultivation is an alternative for small producers due to its fast and high financial return. Studying the growth of fruits over time is extremely important to understand their development, helping in the most appropriate crop management, avoiding post-harvest losses, which is one of the aggravating factors of blackberry cultivation, being a short shelf life fruit. Thus, growth curves are highlighted in this type of study and modeling through statistical models helps understanding how such growth occurs. Data from this study were obtained from an experiment conducted at the Federal University of Lavras in 2015. The aim of this study was to adjust nonlinear, double Logistic and double Gompertz models to describe the diameter growth of four blackberry cultivars (‘Brazos’, ‘Choctaw’, ‘Guarani’ and ‘Tupy’). Estimations of parameters were obtained using the least squares method and the Gauss-Newton algorithm, with the “nls” and “glns” functions of the R statistical software. The comparison of adjustments was made by the Akaike information criterion (AICc), residual standard deviation (RSD) and adjusted determination coefficient (R2 aj). The models satisfactorily described data, choosing the Logistic double model for ‘Brazos’ and ‘Guarani’ cultivars and the double Gompertz model for ‘Tupy’ and ‘Choctaw’ cultivars.

scholarly journals Mathematical models to describe the growth curves of white-egg layers

2018 ◽  
Vol 39 (3) ◽  
pp. 1327
Author(s):  
Cleber Franklin Santos de Oliveira ◽  
João Marcos Novais Tavares ◽  
Gerusa Da Silva Salles Corrêa ◽  
Bruno Serpa Vieira ◽  
Silvana Alves Pedrozo Vitalino Barbosa ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Mean Squared Error ◽  
Computer Software ◽  
Logistic Models ◽  
Growth Curves ◽  
Gompertz Model ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Population Densities

The aim of this study was to compare mathematical models describing growth curves of white-egg layers at different population densities. To fit the models, 4,000 growing white-egg layers were utilized. The experimental design was completely randomized, with population densities of 71, 68, 65, 62, and 59 birds per cage in the starter phase and 19, 17, 15, 13, and 11 birds per cage in the grower phase, with 10 replicates each. Birds were weighed weekly to determine the average body weight and the weight gain. Gompertz and Logistic models were utilized to estimate their growth. The data analysis was carried out using the PROC NLMIXED procedure of the SAS® statistical computer software to estimate the parameters of the equation because mixed models were employed. The mean squared error, the coefficient of determination, and Akaike’s information criterion were used to evaluate the quality of fit of the models. The studied models converged for the description of the growth of the birds at the different densities studied, showing that they were appropriate for estimating the growth of white-egg layers housed at different population densities. The Gompertz model showed a better fit than the Logistic model.

scholarly journals Selecting the best growth model for elasmobranches

2015 ◽  
Author(s):  
Kwang-Ming Liu ◽  
Chiao-Bin Wu ◽  
Shoou-Jeng Joung ◽  
Wen-Pei Tsai
Keyword(s):  
Growth Model ◽  
Growth Models ◽  
Stock Assessment ◽  
Gompertz Model ◽  
Information Criterion ◽  
Age And Growth ◽  
Deep Waters ◽  
Extended Longevity ◽  
Best Fit ◽  
Two Parameter

Age and growth information is essential for accurate stock assessment of fish, and growth model selection may influence the result of stock assessment. Previous descriptions of the age and growth of elasmobranches relied mainly on the von Bertalanffy growth model (VBGM). However, it has been noted that sharks, skates and rays exhibit significant variety in size, shape, and life-history traits. Given this variation, the VBGM may not necessarily provide the best fit for all elasmobranches. This study attempts to improve the accuracy of age estimates by testing four growth models—the VBGM, two-parameter VBGM, Robertson (Logistic) and Gompertz models—to fit observed and simulated length-at-age data for 37 species of elasmobranches. The best growth model was selected based on corrected Akaike’s Information Criterion (AICc), the AICc difference, and the AICc weight. The VBGM and two-parameter VBGM provide the best fit for species with slow growth and extended longevity (L∞ > 100 cm TL, 0.05 < k < 0.15 yr-1), such as pelagic sharks. For fast-growing small sharks (L∞ < 100 cm TL, kr or kg > 0.2 yr-1) in deep waters and for small-sized demersal skates/rays, the Robertson and the Gompertz models provide the best fit. The best growth models for small sharks in shallow waters are the two-parameter VBGM and the Robertson model, while all the species best fit by the Gompertz model are skates and rays.

scholarly journals Age estimation and the best growth model selection of the tentacled blenny Parablennius tentacularis (Brünnich, 1768) in the southeastern Black Sea

2021 ◽  
Vol 38 (2) ◽  
pp. 229-236
Author(s):  
Ayşe Van ◽  
Aysun Gümüş ◽  
Melek Özpiçak ◽  
Serdar Süer
Keyword(s):  
Growth Model ◽  
Black Sea ◽  
Growth Parameters ◽  
Growth Models ◽  
Information Criterion ◽  
Current Data ◽  
Logistic Growth ◽  
Data Set ◽  
Frequency Distributions ◽  
Selection Of

By the study's coverage, 522 individuals of tentacled blenny (Parablennius tentacularis (Brünnich, 1768)), were caught with the bottom trawl operations (commercial fisheries and scientific field surveys) between May 2010 and March 2012 from the southeastern Black Sea. The size distribution range of the sample varied between 4.8-10.8 cm. The difference between sex length (K-S test, Z=3.729, P=0.000) and weight frequency distributions (K-S test, Z=3.605, P=0.000) was found to be statistically significant. The length-weight relationship models were defined as isometric with W = 0.009L3.034 in male individuals and positive allometric with W = 0.006L3.226 in female individuals. Otolith and vertebra samples were compared for the selection of the most accurate hard structure that can be used to determine the age. Otolith was chosen as the most suitable hard structure. The current data set was used to predict the best growth model. For this purpose, the growth parameters were estimated with the widely used von Bertalanffy, Gompertz and Logistic growth functions. Akaike's Information Criterion (AIC), Lmak./L∞ ratio, and R2 criteria were used to select the most accurate growth models established through these functions. Model averaged parameters were calculated with multi-model inference (MMI): L'∞ = 15.091 cm, S.E. (L'∞) = 3.966, K'= 0.232 year-1, S.E. (K') = 0.122.

scholarly journals Prediksi Akhir Pandemi COVID-19 di Indonesia dengan Simulasi Berbasis Model Pertumbuhan Parametrik

2020 ◽  
Vol 9 (2) ◽  
pp. 63-68
Author(s):  
Fransiscus Rian Pratikto
Keyword(s):  
Least Squares Method ◽  
Growth Models ◽  
Growth Curves ◽  
Nonlinear Least Squares ◽  
Gompertz Model ◽  
Population Data ◽  
Logistic Growth ◽  
Social Restriction ◽  
Nonlinear Least Squares Method

This research aims to predict the end of the COVID-19 pandemic in Indonesia based on parametric growth models. The models are chosen by considering their fitness with the data of Taiwan which is believed to have passed over the peak of the pandemic and have gone through all phases in the growth curves. The models are parameterized using the nonlinear least squares method. The deviation and confidence interval of each parameter is estimated using the k-fold cross-validation and the bootstrap techniques. Using the total cases per million population data from March 2 to June 18, 2020, it was found that two growth models fit the data, i.e. logistic and modified Gompertz, where the latter performs better. Using the information about the deviation of each model parameter, a simulation model is developed to predict the time at which the total cases curve starts to flatten, which is an indication of the end of the pandemic. It was found with 95% confidence level that based on the modified Gompertz model the pandemic will end somewhere between March 9 – September 7, 2021 with total cases per million of 206 - 555. Meanwhile, based on the logistic growth model, the end of the pandemic is between August 28 – September 23, 2020 with total cases per million of 180 - 375. This model can be extended by making comparative scenario with Taiwan based on measures that represent the quality of the pandemic mitigation such as test ratio and the intensity of social restriction.

Are Growth Parameters Estimated from Tagging and Age–Length Data Comparable?

1988 ◽  
Vol 45 (6) ◽  
pp. 936-942 ◽  
Author(s):  
R. I. C. C. Francis
Keyword(s):  
Growth Rate ◽  
Growth Rates ◽  
Growth Parameters ◽  
Growth Models ◽  
Maximum Length ◽  
Fish Growth ◽  
Data Sets ◽  
Von Bertalanffy ◽  
Length Data ◽  
The Mean

The two most common ways of estimating fish growth use age–length data and tagging data. It is shown that growth parameters estimated from these two types of data have different meanings and thus are not directly comparable. In particular, the von Bertalanffy parameter l∞ means asymptotic mean length at age for age–length data, and maximum length for tagging data, when estimated by conventional methods. New parameterizations are given for the von Bertalanffy equation which avoid this ambiguity and better represent the growth information in the two types of data. The comparison between growth estimates from these data sets is shown to be equivalent to comparing the mean growth rate of fish of a given age with that of fish of length equal to the mean length at that age. How much these growth rates may differ in real populations remains unresolved: estimates for two species of fish produced markedly different results, neither of which could be reproduced using growth models. Existing growth models are shown to be inadequate to answer this question.

Weight and feather growth of nestling tree swallows

1982 ◽  
Vol 60 (5) ◽  
pp. 1080-1090 ◽  
Author(s):  
Reto Zach ◽  
Keith R. Mayoh
Keyword(s):  
Brood Size ◽  
Logistic Model ◽  
Growth Curves ◽  
Gompertz Model ◽  
Weather Conditions ◽  
Growth Rate Constant ◽  
Tree Swallows ◽  
Primary Feather ◽  
Weight Growth ◽  
Feather Growth

Six models were tested for weight and feather growth of nestling tree swallows (Iridoprocne bicolor). The logistic model, with an asymptotic weight A of 22.4 g and a growth-rate constant K of 0.51 day−1, was most appropriate for weight growth. Observed curves for individual birds were of diverse shape and some lacked weight recession. Growth of primary feather 9 and rectrix feather 6 followed the Gompertz model, with A and K values of 80.9 and 49.6 mm, and 0.21 and 0.20 day−1, respectively. Fitting of the models to feather length was complicated because growth curves for individual birds did not coincide in time, and fledging occurred before completion of growth. Long nestling times were associated with slow weight and feather growth, and A and K were consistently negatively correlated. The variation of A, K, and nestling time among broods ranged from 49.2 to 95.1%, and brood size could not account for much of the observed variability. Presumably, various nest-box variables and weather conditions as related to the age of nestlings are important. Primary and rectrix growth were similar and closely linked but apparently unrelated to weight growth. Growth of feather vanes was closely linked to feather growth. Birds that failed to fledge had depressed weight, feather, and vane growth. Weight growth and recession seem to be particularly well suited for investigating natural and man-made environmental stresses.

scholarly journals Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tarylee Reddy ◽  
Ziv Shkedy ◽  
Charl Janse van Rensburg ◽  
Henry Mwambi ◽  
Pravesh Debba ◽  
...  
Keyword(s):  
South Africa ◽  
Growth Models ◽  
National Level ◽  
Growth Curves ◽  
Gompertz Model ◽  
Logistic Growth ◽  
Short Term ◽  
Provincial Level ◽  
Maximum Period ◽  
Data Driven Approach

Abstract Background The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. Methods In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. Results We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551–26,702 cases in 5 days and an additional 47,449–57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145–437 COVID-19 deaths in 5 days and an additional 243–947 deaths in 10 days. Conclusions By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

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