Growth model selection for juvenile blacklip abalone (Haliotis rubra): assessing statistical and biological validity

2012 ◽  
Vol 63 (1) ◽  
pp. 23 ◽  
Author(s):  
Fay Helidoniotis ◽  
Malcolm Haddon
Keyword(s):  
Linear Model ◽  
Growth Model ◽  
Growth Rates ◽  
Shell Length ◽  
Linear Models ◽  
Marine Organism ◽  
Growth Models ◽  
Von Bertalanffy ◽  
Haliotis Rubra ◽  
Selection Of

Accurate estimates of marine organism growth are important for modelling the dynamics of populations and rely on the selection of an appropriate growth model. However, there is no assurance that the statistically optimum model will also be biologically plausible. Three growth models (von Bertalanffy, Gompertz and a linear model) were fitted to a dataset consisting of two cohorts of juvenile size classes of blacklip abalone (Haliotis rubra). Results show that the non-seasonal Gompertz was statistically better than the non-seasonal von Bertalanffy and linear models. There was a persistent seasonal signal through the juvenile size range, with slow growth in winter and fast growth during summer. When a seasonal term was formally incorporated, the model fits were greatly improved, particularly for the linear and von Bertalanffy models. The seasonal-Gompertz predicted growth rates that were biologically implausible for juveniles of 2 mm shell length; 107 μm day–1 for one cohort and 24 μm day–1 for the other. These rates are inconsistent with published growth rates observed under both controlled and wild conditions. In contrast, the seasonal-linear model predicted growth rates of 60 μm day–1 for animals of 2 mm shell length, consistent with published findings. The selection of a growth model based solely on statistical criteria may not take into account the complex processes that influence growth of juveniles.

Dynamic Predictive Model for Growth of Bacillus cereus from Spores in Cooked Beans

2017 ◽  
Vol 81 (2) ◽  
pp. 308-315 ◽  
Author(s):  
Vijay K. Juneja ◽  
Abhinav Mishra ◽  
Abani K. Pradhan
Keyword(s):  
Bacillus Cereus ◽  
Growth Rates ◽  
Linear Models ◽  
Growth Models ◽  
Maximum Growth ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Three Phase ◽  
Primary Model

ABSTRACT Kinetic growth data for Bacillus cereus grown from spores were collected in cooked beans under several isothermal conditions (10 to 49°C). Samples were inoculated with approximately 2 log CFU/g heat-shocked (80°C for 10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol–egg yolk–polymyxin agar and incubating at 30°C for 24 h. Data were fitted into Baranyi, Huang, modified Gompertz, and three-phase linear primary growth models. All four models were fitted to the experimental growth data collected at 13 to 46°C. Performances of these models were evaluated based on accuracy and bias factors, the coefficient of determination (R2), and the root mean square error. Based on these criteria, the Baranyi model best described the growth data, followed by the Huang, modified Gompertz, and three-phase linear models. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95 to 0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates for all four primary models. The acceptable prediction zone (APZ) approach also was used for validation of the model with observed data collected during single and two-step dynamic cooling temperature protocols. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations for the exponential single rate cooling were within the APZ, which was set between −0.5 and 1 log CFU/g; 26 of 28 predictions for the two-step cooling profiles also were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under various temperature conditions or during extended chilling of cooked beans.

Statistical modeling of baleen and body length at age in bowhead whales (Balaena mysticetus)

2012 ◽  
Vol 90 (8) ◽  
pp. 915-931 ◽  
Author(s):  
S.C. Lubetkin ◽  
J.E. Zeh ◽  
J.C. George
Keyword(s):  
Body Length ◽  
Growth Model ◽  
Growth Models ◽  
Information Criterion ◽  
Single Stage ◽  
Von Bertalanffy ◽  
Balaena Mysticetus ◽  
Bowhead Whales ◽  
Multi Stage ◽  
Age Estimates

We used baleen lengths and age estimates from 175 whales and body lengths and age estimates from 205 whales to test which of several single- and multi-stage growth models best characterized age-specific baleen and body lengths for bowhead whales ( Balaena mysticetus L., 1758) with the goal of determining which would be best for predicting whale age based on baleen or body length. Previous age estimates were compiled from several techniques, each of which is valid over a relatively limited set of physical characteristics. The best fitting single-stage growth model was a variation of the von Bertalanffy growth model for both baleen and body length data. Based on Bayesian information criterion, the two- and three-stage versions of the von Bertalanffy model fit the data better than did the single-stage models for both baleen and body length. The best baleen length models can be used to estimate expected ages for bowhead whales with up to 300–325 cm baleen, depending on sex, which correspond to age estimates approaching 60 years. The best body length models can be used to estimate expected ages for male bowhead whales up to 14 m, and female bowheads up to 15.5 m or ages up to approximately 40 years.

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 Estimation of annual compound growth rates of guava (Psidium guajava L.) fruit in Haryana using Non linear model

2019 ◽  
Vol 11 (4) ◽  
pp. 778-784
Author(s):  
Pardeep Panghal ◽  
Manoj Kumar ◽  
Sarita Rani
Keyword(s):  
Growth Rate ◽  
Linear Model ◽  
Growth Rates ◽  
Logistic Model ◽  
Linear Models ◽  
Psidium Guajava ◽  
Annual Growth Rate ◽  
Guava Fruit ◽  
Non Linear ◽  
Best Fit

Computation of growth rates plays an important role in agricultural and economic research to study growth pattern of a various commodities. Many of the research workers used the parametric approach for computation of annual growth rate but not use the concept of non-linear model.  In this paper, an attempt has been made to study growth rates of guava for three districts (Hisar, and Kurukshetra) and Haryana state as a whole using different non-linear models. The time series data on annual area and production of guava (Psidium guajava L.) in different districts of Haryana from 1990-91 to 2015-16 were collected to fit non linear models. Growth rates were computed through best fitted non-linear models. It was found that Logistic model could be best fit for computation of growth rates of area for guava fruit in Hisar and Kurukshetra district and Haryana state as a whole whereas Gompertz model was best fit for Yamunanagar district based on high R2 and least MSE and RMSE values. It was also observed that monomolecular model was best fit for production of guava fruits in Hisar and Yamunanagar district whereas Logistic model was best fit for production of guava fruit in Kurukshetra and Haryana state as a whole because of high R2 and least MSE and RMSE values. R and excel software have been used for fitting the non linear model and computation of growth rates for area and production of guava fruit for the year 1990-91 to 2015-16. None has been used the non linear model growth model for computation of annual growth rate of guava fruit for area and production of Haryana state. But in this work non linear growth model has been used for computation of growth rate instead of parametric approaches.

scholarly journals An Overview of Population Growth Trends of Nepal

2015 ◽  
Vol 19 (1) ◽  
pp. 57-61 ◽  
Author(s):  
Laxman Kumar Regmi
Keyword(s):  
Population Growth ◽  
Growth Model ◽  
Growth Rates ◽  
Exponential Growth ◽  
Rural Areas ◽  
Population Change ◽  
Population Policy ◽  
Growth Models ◽  
Growth Trends ◽  
Time Period

This paper aims to estimate population growth rates of Nepal and also to estimate required time period for doubling population. For this, arithmetic, geometric and exponential growth models are applied. The data are taken from the recent national censuses of Nepal. Population growth trends were 2.10% in 1971, 2.60% in 1981, 2.10% in 1991, 2.25% in 2001 and 1.35% annually in 2011. The trends of urban populations were about 4% in 1971, 6% in 1981, 9% in 1991, 14% in 2001 and 17% in 2011. The population density rose from 79 in 1971 to 181 in 2011. Urban growth rate was 7% whereas it was 2% for rural areas. The population change was found to be 40% in urban whereas 11% in rural areas during 2001-2011. However, overall change was found to be 14% during 2001-2011. The estimated growth rates were found to be 1.44%, 1.35% and 1.35% by using arithmetic, geometric and exponential respectively. The estimated time period for doubling populations was found to be 67 year by arithmetic growth model and 50 years by geometric and exponential growth model. The findings of this paper may help policy-makers and planners for designing population policy of Nepal.Journal of Institute of Science and Technology, 2014, 19(1): 52-61

scholarly journals The influence of sample distribution on growth model output for a highly-exploited marine fish, the Gulf Corvina (Cynoscion othonopterus)

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5582 ◽  
Author(s):  
Derek G. Bolser ◽  
Arnaud Grüss ◽  
Mark A. Lopez ◽  
Erin M. Reed ◽  
Ismael Mascareñas-Osorio ◽  
...  
Keyword(s):  
Growth Model ◽  
Marine Fish ◽  
Growth Models ◽  
Growth Patterns ◽  
Von Bertalanffy ◽  
Model Output ◽  
Age Classes ◽  
Equal Distribution ◽  
Sample Distribution ◽  
Richards Model

Estimating the growth of fishes is critical to understanding their life history and conducting fisheries assessments. It is imperative to sufficiently sample each size and age class of fishes to construct models that accurately reflect biological growth patterns, but this may be a challenging endeavor for highly-exploited species in which older fish are rare. Here, we use the Gulf Corvina (Cynoscion othonopterus), a vulnerable marine fish that has been persistently overfished for two decades, as a model species to compare the performance of several growth models. We fit the von Bertalanffy, Gompertz, logistic, Schnute, and Schnute–Richards growth models to length-at-age data by nonlinear least squares regression and used simple indicators to reveal biased data and ensure our results were biologically feasible. We then explored the consequences of selecting a biased growth model with a per-recruit model that estimated female spawning-stock-biomass-per-recruit and yield-per-recruit. Based on statistics alone, we found that the Schnute–Richards model described our data best. However, it was evident that our data were biased by a bimodal distribution of samples and underrepresentation of large, old individuals, and we found the Schnute–Richards model output to be biologically implausible. By simulating an equal distribution of samples across all age classes, we found that sample distribution distinctly influenced model output for all growth models tested. Consequently, we determined that the growth pattern of the Gulf Corvina was best described by the von Bertalanffy growth model, which was the most robust to biased data, comparable across studies, and statistically comparable to the Schnute–Richards model. Growth model selection had important consequences for assessment, as the per-recruit model employing the Schnute–Richards model fit to raw data predicted the stock to be in a much healthier state than per-recruit models employing other growth models. Our results serve as a reminder of the importance of complete sampling of all size and age classes when possible and transparent identification of biased data when complete sampling is not possible.

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.

Maturity and growth characteristics of a commercially exploited stingray, Dasyatis dipterura

2007 ◽  
Vol 58 (1) ◽  
pp. 54 ◽  
Author(s):  
Wade D. Smith ◽  
Gregor M. Cailliet ◽  
Everardo Mariano Melendez
Keyword(s):  
Growth Rates ◽  
Growth Models ◽  
Information Criteria ◽  
Growth Characteristics ◽  
Von Bertalanffy ◽  
Common Component ◽  
Disc Width ◽  
Marginal Increment ◽  
Akaike’S Information Criteria ◽  
Age Estimates

Maturity and growth characteristics were estimated for Dasyatis dipterura from western Mexico, where it is a common component of artisanal elasmobranch fisheries. Median disc width at maturity was estimated as 57.3 cm for females (n = 126) and 46.5 cm for males (n = 55) respectively. Age estimates were obtained from 304 fishery-derived specimens (169 female, 135 male). An annual pattern of band-pair deposition was validated through modified centrum edge and marginal increment analyses. Gompertz, polynomial and von Bertalanffy growth models were fit to disc width and weight-at-age data. Resulting models were evaluated based on biological rationale, standard error of model estimates, and Akaike’s information criteria. Growth characteristics differed significantly between females and males. Maximum age estimates were 28 years for females and 19 years for males. Three-parameter von Bertalanffy growth models of disc width-at-age data generated the most appropriate fits and produced relatively low estimates of instantaneous growth rates for females (DW∞ = 92.4 cm, k = 0.05, t0 = –7.61, DW0 = 31.4 cm) and males (DW∞ = 62.2 cm, k = 0.10, t0 = –6.80, DW0 = 31.3 cm). These values are the lowest reported for myliobatiform stingrays and indicate slow growth rates in comparison with elasmobranchs in general.

Growth of the abalone Haliotis roei gray

1989 ◽  
Vol 40 (2) ◽  
pp. 199 ◽  
Author(s):  
JK Keesing ◽  
FE Wells
Keyword(s):  
Growth Model ◽  
Growth Rates ◽  
Maximum Size ◽  
Growth Characteristics ◽  
Minimum Size ◽  
First Year ◽  
Von Bertalanffy ◽  
Commercial Fishery ◽  
Von Bertalanffy Growth Model ◽  
Second Year

The growth characteristics of the abalone Haliotis roei from Western Australia are described. Abalone grow rapidly to over 40 mm in their first year. In their second year, they reach 60 mm, the minimum size that can be legally taken by amateur fishermen. They are recruited to the commercial fishery at 70 mm during their third year. The parameters of the von Bertalanffy growth model were K = 0.67 year-1 and L∞ = 85.2 mm; however, differences occur between intertidal and subtidal habitats, with abalone of the subtidal population achieving faster growth rates and a larger maximum size. No significant differences in growth were detected between sexes.

scholarly journals Which Microbial Growth Model Best Fits to Fusarium graminearum?

2018 ◽  
Author(s):  
Edgar Cambaza
Keyword(s):  
Linear Model ◽  
Fusarium Graminearum ◽  
Growth Model ◽  
Yeast Extract ◽  
Microbial Growth ◽  
Growth Models ◽  
Head Blight ◽  
Actual Growth ◽  
Imagej Software ◽  
Yeast Extract Agar

Fusarium graminearum causes head blight in wheat and corn, and produces chemicals harmful for humans and other animals. It is important to understand how it grows in order to prevent outbreaks. There are 3 well-known growth models for microorganisms and they seem applicable to molds: linear, Gompertz and Baranyi. This study aimed to see which could better represent F. graminearum growth. Three replicates were grown in yeast extract agar (YEA) for 20 days. The Feret’s radius was measured in ImageJ software, and then related to the models. Linear model was the most closely correlated to the actual growth. Thus, considering that it was the most representative of the reality and it is easier to use, it seems to be the best logical choice for F. graminearum growth studies.

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