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 Parameter identifiability and model selection for sigmoid population growth models

2021 ◽  
Author(s):  
Matthew J Simpson ◽  
Alexander Browning ◽  
David James Warne ◽  
Oliver J Maclaren ◽  
Ruth E Baker
Keyword(s):  
Model Selection ◽  
Growth Model ◽  
Growth Models ◽  
Logistic Growth ◽  
Parameter Estimates ◽  
Sufficient Information ◽  
Data Set ◽  
Parameter Identifiability ◽  
Continental Scale ◽  
Statistical Assumptions

Sigmoid growth models, such as the logistic and Gompertz growth models, are widely used to study various population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and precise parameter estimation are critical if these models are to be used to make useful inferences about underlying ecological mechanisms. However, the question of parameter identifiability for these models -- whether a data set contains sufficient information to give unique or sufficiently precise parameter estimates for the given model -- is often overlooked; We use a profile-likelihood approach to systematically explore practical parameter identifiability using data describing the re-growth of hard coral cover on a coral reef after some ecological disturbance. The relationship between parameter identifiability and checks of model misspecification is also explored. We work with three standard choices of sigmoid growth models, namely the logistic, Gompertz, and Richards' growth models; We find that the logistic growth model does not suffer identifiability issues for the type of data we consider whereas the Gompertz and Richards' models encounter practical non-identifiability issues, even with relatively-extensive data where we observe the full shape of the sigmoid growth curve. Identifiability issues with the Gompertz model lead us to consider a further model calibration exercise in which we fix the initial density to its observed value, neglecting its uncertainty. This is a common practice, but the results of this exercise suggest that parameter estimates and fundamental statistical assumptions are extremely sensitive under these conditions; Different sigmoid growth models are used within subdisciplines within the biology and ecology literature without necessarily considering whether parameters are identifiable or checking statistical assumptions underlying model family adequacy. Standard practices that do not consider parameter identifiability can lead to unreliable or imprecise parameter estimates and hence potentially misleading interpretations of the underlying mechanisms of interest. While tools in this work focus on three standard sigmoid growth models and one particular data set, our theoretical developments are applicable to any sigmoid growth model and any relevant data set. MATLAB implementations of all software available on GitHub.

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

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 Optimal exponent-pairs for the Bertalanffy-Pütter growth model

2018 ◽  
Author(s):  
Katharina Renner-Martin ◽  
Norbert Brunner ◽  
Manfred Kühleitner ◽  
Werner-Georg Nowak ◽  
Klaus Scheicher
Keyword(s):  
Differential Equation ◽  
Growth Model ◽  
Explanatory Power ◽  
Information Criterion ◽  
Optimal Growth ◽  
Growth Function ◽  
Data Fitting ◽  
Model Parameters ◽  
Data Set ◽  
Von Bertalanffy Growth Function

The Bertalanffy-Pütter growth model describes mass m at age t by means of the differential equation dm/dt = p⋅ma−q⋅mb. The special case using the Bertalanffy exponent-pair a=2/3 and b=1 is most common (it corresponds to the von Bertalanffy growth function VBGF for length in fishery literature). For data fitting using general exponents, five model parameters need to be optimized, the pair a<b of non-negative exponents, the non-negative constants p and q, and a positive initial value m0 for the differential equation. For the case b=1 it is known that for most fish data any exponent a<1 could be used to model growth without affecting the fit to the data significantly (when the other parameters p, q, m0 were optimized). Thereby, data fitting used the method of least squares, minimizing the sum of squared errors (SSE). It was conjectured that the optimization of both exponents would result in a significantly better fit of the optimal growth function to the data and thereby reduce SSE. This conjecture was tested for a data set for the mass-growth of Walleye (Sander vitreus), a fish from Lake Erie, USA. Compared to the Bertalanffy exponent-pair the optimal exponent-pair achieved a reduction of SSE by 10%. However, when the optimization of additional parameters was penalized, using the Akaike information criterion (AIC), then the optimal exponent-pair model had a higher (worse) AIC, when compared to the Bertalanffy exponent-pair. Thereby SSE and AIC are different ways to compare models. SSE is used, when predictive power is needed alone, and AIC is used, when simplicity of the model and explanatory power are needed.

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.

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 Prediction of mono and multiphasic growth parameters of broilers (Short Communication)

2010 ◽  
Vol 53 (1) ◽  
pp. 101-107
Author(s):  
M. Mendeş
Keyword(s):  
Goodness Of Fit ◽  
Short Communication ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Function ◽  
Logistic Growth ◽  
Model Parameters ◽  
Male And Female ◽  
Body Weights ◽  
Males And Females

Abstract. The main objective of this study was to predict mono and multiphasic growth model parameters of broilers. For this purpose daily body weights-age data of 106 male and female chickens reared under different stocking densities (GR1=11 birds/m2 , GR2=17 birds/m2 and GR3=25 birds/m2) were used. Results of mono and multiphasic (diphasic and triphasic) growth curve analyses showed that defining the growth of birds using multiphasic growth models instead of monophasic growth models, displays more detailed and reliable results. Based on goodness-of-fit criteria, lead to the choice of a triphasic logistic growth function for GR1 and GR2, and diphasic function for GR3 males and females.

Age and growth estimates of the jumbo flying squid (Dosidicus gigas) off Peru

2019 ◽  
Vol 32 ◽  
pp. 7
Author(s):  
Carlos Goicochea-Vigo ◽  
Enrique Morales-Bojórquez ◽  
Viridiana Y. Zepeda-Benitez ◽  
José Ángel Hidalgo-de-la-Toba ◽  
Hugo Aguirre-Villaseñor ◽  
...  
Keyword(s):  
Growth Model ◽  
Growth Pattern ◽  
Growth Models ◽  
Information Criterion ◽  
Growth Patterns ◽  
Age And Growth ◽  
Dosidicus Gigas ◽  
Asymptotic Growth ◽  
Schnute Model ◽  
Gompertz Growth Model

Mantle length (ML) and age data were analyzed to describe the growth patterns of the flying jumbo squid, Dosidicus gigas, in Peruvian waters. Six non-asymptotic growth models and four asymptotic growth models were fitted. Length-at-age data for males and females were analysed separately to assess the growth pattern. Multi-model inference and Akaike's information criterion were used to identify the best fitting model. For females, the best candidate growth model was the Schnute model with L∞ = 106.96 cm ML (CI 101.23–110.27 cm ML, P < 0.05), age at growth inflection 244.71 days (CI 232.82–284.86 days, P < 0.05), and length at growth inflection 57.26 cm ML (CI 55.42–58.51 cm ML, P < 0.05). The growth pattern in males was best described by a Gompertz growth model with L∞ = 127.58 cm ML (CI 115.27–131.80 cm ML, P < 0.05), t0 = 21.8 (CI 20.06–22.41, P < 0.05), and k = 0.007 (CI 0.006–0.007, P < 0.05). These results contrast with the growth model previously reported for D. gigas in the region, where the growth pattern was identified as non-asymptotic.

scholarly journals Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

2006 ◽  
Vol 72 (7) ◽  
pp. 4862-4870 ◽  
Author(s):  
R. Lindqvist
Keyword(s):  
Staphylococcus Aureus ◽  
Growth Rates ◽  
Growth Parameters ◽  
Growth Models ◽  
Viable Count ◽  
Detection Methods ◽  
Accurate Estimation ◽  
Data Set ◽  
Time To Detection ◽  
Strain Variability

ABSTRACT Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17°C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.

scholarly journals Comparison and selection of growth models using the Schnute model

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 188-196 ◽  
Author(s):  
Y. Lei ◽  
S. Y Zhang
Keyword(s):  
Functional Form ◽  
Growth Models ◽  
A Priori ◽  
Data Sets ◽  
Forest Species ◽  
Empirical Relationships ◽  
Data Set ◽  
Schnute Model ◽  
Functional Forms ◽  
Selection Of

Forestmodellers have long faced the problem of selecting an appropriate mathematical model to describe tree ontogenetic or size-shape empirical relationships for tree species. A common practice is to develop many models (or a model pool) that include different functional forms, and then to select the most appropriate one for a given data set. However, this process may impose subjective restrictions on the functional form. In this process, little attention is paid to the features (e.g. asymptote and inflection point rather than asymptote and nonasymptote) of different functional forms, and to the intrinsic curve of a given data set. In order to find a better way of comparing and selecting the growth models, this paper describes and analyses the characteristics of the Schnute model. This model has both flexibility and versatility that have not been used in forestry. In this study, the Schnute model was applied to different data sets of selected forest species to determine their functional forms. The results indicate that the model shows some desirable properties for the examined data sets, and allows for discerning the different intrinsic curve shapes such as sigmoid, concave and other curve shapes. Since no suitable functional form for a given data set is usually known prior to the comparison of candidate models, it is recommended that the Schnute model be used as the first step to determine an appropriate functional form of the data set under investigation in order to avoid using a functional form a priori.

Sign in / Sign up

Export Citation Format

Share Document