scholarly journals Global synthesis of sea turtle von Bertalanffy growth parameters through Bayesian hierarchical modeling

2021 ◽  
Vol 657 ◽  
pp. 191-207
Author(s):  
MD Ramirez ◽  
T Popovska ◽  
EA Babcock
Keyword(s):  
Population Dynamics ◽  
Parameter Estimation ◽  
Body Size ◽  
Sea Turtles ◽  
Estimation Method ◽  
Growth Parameter ◽  
Sea Turtle ◽  
Parameter Estimates ◽  
Von Bertalanffy ◽  
Bayesian Hierarchical

Knowledge of sea turtle demographic rates is central to modeling their population dynamics, but few studies have quantitatively synthesized existing data globally. Here, we used a Bayesian hierarchical model to conduct a meta-analysis of published von Bertalanffy growth curve parameters (growth coefficient, K; asymptotic length, L∞) for chelonid sea turtles. We identified 34 studies for 5 of 6 extant chelonids that met minimum selection criteria. We implemented a suite of models that included a multivariate normal likelihood on the log-transformed values of the 2 parameters to evaluate the influence of species, population (regional management unit, RMU), parameter estimation method (mark-recapture, skeletochronology, length-frequency analysis), latitude, and sampled body size range (all sizes, no large, no small, no large or small) on growth parameter estimates. According to information criteria, the best model included a random effect of species. The second best model also included latitude as a fixed effect, but RMU, parameter estimation method, latitude, and sampled body size ultimately did not strongly influence the means or variances of K and L∞ among studies. The apparent lack of RMU effect on parameter estimates within species may be an artifact of the small number of RMUs with published growth parameter estimates. The species-specific, and in some cases RMU-specific, posterior means and standard deviations of K and L∞ from this study would be appropriate priors for future studies of growth in chelonid sea turtles or for models of population dynamics. We highlight the need for expanded study and synthesis of sea turtle somatic growth rates.

scholarly journals A QUASI-LIKELIHOOD APPROACH TO PARAMETER ESTIMATION FOR SIMULATABLE STATISTICAL MODELS

2014 ◽  
Vol 33 (2) ◽  
pp. 107 ◽  
Author(s):  
Markus Baaske ◽  
Felix Ballani ◽  
Karl Gerald Van den Boogaart
Keyword(s):  
Parameter Estimation ◽  
Statistical Models ◽  
Estimation Method ◽  
Score Function ◽  
Boolean Model ◽  
Parameter Estimates ◽  
Random Errors ◽  
Likelihood Functions ◽  
Simulation Based ◽  
Likelihood Approach

This paper introduces a parameter estimation method for a general class of statistical models. The method exclusively relies on the possibility to conduct simulations for the construction of interpolation-based metamodels of informative empirical characteristics and some subjectively chosen correlation structure of the underlying spatial random process. In the absence of likelihood functions for such statistical models, which is often the case in stochastic geometric modelling, the idea is to follow a quasi-likelihood (QL) approach to construct an optimal estimating function surrogate based on a set of interpolated summary statistics. Solving these estimating equations one can account for both the random errors due to simulations and the uncertainty about the meta-models. Thus, putting the QL approach to parameter estimation into a stochastic simulation setting the proposed method essentially consists of finding roots to a sequence of approximating quasiscore functions. As a simple demonstrating example, the proposed method is applied to a special parameter estimation problem of a planar Boolean model with discs. Here, the quasi-score function has a half-analytical, numerically tractable representation and allows for the comparison of the model parameter estimates found by the simulation-based method and obtained from solving the exact quasi-score equations.

Development of growth parameter estimation method about plant micro fluidic system

2019 ◽  
Vol 2019.10 (0) ◽  
pp. 19pm5PN346
Author(s):  
Ryo Miyake ◽  
Toshihiro Kasama ◽  
Gononoga Maia ◽  
Yoshishige Endo ◽  
JUangang Guan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Growth Parameter ◽  
Parameter Estimation Method

scholarly journals Growth Parameters and Spawning Season Estimation of Four Important Flyingfishes in the Kuroshio Current Off Taiwan and Implications From Comparisons With Global Studies

2022 ◽  
Vol 8 ◽  
Author(s):  
Shui-Kai Chang ◽  
Tzu-Lun Yuan ◽  
Simon D. Hoyle ◽  
Jessica H. Farley ◽  
Jen-Chieh Shiao
Keyword(s):  
Growth Parameters ◽  
Growth Models ◽  
Growth Parameter ◽  
Optimal Growth ◽  
Kuroshio Current ◽  
Parameter Estimates ◽  
Special Importance ◽  
Von Bertalanffy ◽  
Multiple Model ◽  
The Kuroshio

Growth shapes the life history of fishes. Establishing appropriate aging procedures and selecting representative growth models are important steps in developing stock assessments. Flyingfishes (Exocoetidae) have ecological, economic, and cultural importance to many coastal countries including Taiwan. There are 29 species of flyingfishes found in the Kuroshio Current off Taiwan and adjacent waters, comprising 56% of the flyingfishes taxa recorded worldwide. Among the six dominant species in Taiwan, four are of special importance. This study reviews aging data of these four species, documents major points of the aging methods to address three aging issues identified in the literature, and applies multi-model inference to estimate sex-combined and sex-specific growth parameters for each species. The candidate growth models examined included von Bertalanffy, Gompertz, Logistic, and Richards models, and the resulting optimal model tended to be the von Bertalanffy model for sex-combined data and Gompertz and von Bertalanffy models for sex-specific cases. The study also estimates hatch dates from size data collected from 2008 to 2017; the results suggest that the four flyingfishes have two spawning seasons per year. Length-weight relationships are also estimated for each species. Finally, the study combines the optimal growth estimates from this study with estimates for all flyingfishes published globally, and statistically classifies the estimates into clusters by hierarchical clustering analysis of logged growth parameters. The results demonstrate that aging materials substantially affect growth parameter estimates. This is the first study to estimate growth parameters of flyingfishes with multiple model consideration. This study provides advice for aging flyingfishes based on the three aging issues and the classification analysis, including a recommendation of using the asterisci for aging flyingfishes to avoid complex otolith processing procedures, which could help researchers from coastal countries to obtain accurate growth parameters for many flyingfishes.

Application of the Weighted Least Squares Parameter Estimation Method to the Robot Calibration

1994 ◽  
Vol 116 (3) ◽  
pp. 890-893 ◽  
Author(s):  
G. Zak ◽  
B. Benhabib ◽  
R. G. Fenton ◽  
I. Saban
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Kinematic Model ◽  
Measurement Data ◽  
Estimation Method ◽  
Kinematic Parameter ◽  
Measuring System ◽  
Parameter Estimates ◽  
Robot Calibration

Significant attention has been paid recently to the topic of robot calibration. To improve the robot’s accuracy, various approaches to the measurement of the robot’s position and orientation (pose) and correction of its kinematic model have been proposed. Little attention, however, has been given to the method of estimation of the kinematic parameters from the measurement data. Typically, a least-squares solution method is used to estimate the corrections to the parameters of the model. In this paper, a method of kinematic parameter estimation is proposed where a standard least-squares estimation procedure is replaced by weighted least-squares. The weighting factors are calculated based on all the a priori available statistical information about the robot and the pose-measuring system. By giving greater weight to the measurements made where the standard deviation of the noise in the data is expected to be lower, a significant reduction in the error of the kinematic parameter estimates is made possible. The improvement in the calibration results was verified using a calibration simulation algorithm.

Estimating taxonomic durations and preservation probability

Paleobiology ◽  
1997 ◽  
Vol 23 (3) ◽  
pp. 278-300 ◽  
Author(s):  
Mike Foote
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Parameter Estimates ◽  
Extinction Rate ◽  
Mammal Species ◽  
The Face ◽  
Stratigraphic Ranges ◽  
Binned Data ◽  
Preservation Rate ◽  
Discrete Time Models

Paleontological completeness and stratigraphic ranges depend on extinction rate, origination rate, preservation rate, and the length of the interval of time over which observations can be made. I derive expressions for completeness and the distribution of durations and ranges as functions of these parameters, considering both continuous- and discrete-time models.Previous work has shown that, if stratigraphic ranges can be followed indefinitely forward, and if extinction and preservation occur at stochastically constant rates, then extinction rate and preservability can be estimated from (1) discrete (binned) stratigraphic ranges even if data on occurrences within ranges are unknown, and (2) continuous ranges if the number of occurrences within each range is known. I show that, regardless of whether the window of observation is finite or infinite, extinction and preservation rates can also be estimated from (3) continuous ranges when the number of occurrences is not known, and (4) discrete ranges when the number of occurrences is not known. One previous estimation method for binned data involves a sample-size bias. This is circumvented by using maximum likelihood parameter estimation. It is worth exploiting data on occurrences within ranges when these are available, since they allow preservation rate to be estimated with less variance. The various methods yield comparable parameter estimates when applied to Cambro-Ordovician trilobite species and Cenozoic mammal species.Stratigraphic gaps and variable preservation affect stratigraphic ranges predictably. In many cases, accurate parameter estimation is possible even in the face of these complications. The distribution of stratigraphic ranges can be used to estimate the sizes of gaps if their existence is known.

Effects of intrapopulation variability on von Bertalanffy growth parameter estimates from equal mark–recapture intervals

1997 ◽  
Vol 54 (9) ◽  
pp. 2025-2032
Author(s):  
E B Smith ◽  
F M Williams ◽  
C R Fisher
Keyword(s):  
Growth Parameters ◽  
Growth Parameter ◽  
Parameter Estimates ◽  
Growth Equation ◽  
Size Distributions ◽  
Von Bertalanffy ◽  
Initial Size ◽  
Estimation Errors ◽  
Mark Recapture ◽  
Intrapopulation Variability

The effects of intrapopulation variability on the parameter estimates of the von Bertalanffy growth equation have received discussion in the literature. Here we evaluated the effects of intrapopulation variability, using computer simulations, on four commonly used methods for estimating the von Bertalanffy growth parameters: the Ford-Walford plot, Ricker's method, Bayley's method, and Fabens' method. Intrapopulation variability in growth rates (k) and maximum sizes ( L infinity ) plus initial size distributions and measurement error, were tested for their effects on the accuracy of the parameter estimates using simulated mark-recapture data with equal recapture intervals. Fabens' method and a modified Ford-Walford plot provided the most accurate estimates in all cases, but when intrapopulation variability was large, they performed poorly. With moderate intrapopulation variability, the bias in estimates was small although between-sample variance was quite large. Biased initial size distributions without either small or large size classes cause a magnification of the estimation errors. Without knowledge of the degree of intrapopulation variability in a natural population, large errors of unknown magnitude in parameter estimation can result, and care should be taken when interpreting these estimates. However, if this variability can be quantified, then approximate parameter estimate errors can be obtained.

scholarly journals A time-dependent parameter estimation framework for crop modeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Faezeh Akhavizadegan ◽  
Javad Ansarifar ◽  
Lizhi Wang ◽  
Isaiah Huber ◽  
Sotirios V. Archontoulis
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Estimation Method ◽  
Time Dependent ◽  
Bayesian Optimization ◽  
Parameter Estimates ◽  
Crop Modeling ◽  
Bayesian Optimization Algorithm ◽  
Crop Models ◽  
Dependent Parameter

AbstractThe performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits’ trends over 34 years (1985–2018).

Effect of temporal changes in size composition on estimates of von Bertalanffy growth parameters for Gemfish, Rexa solandri (Cuvier), Gempylidae

1992 ◽  
Vol 43 (5) ◽  
pp. 1229 ◽  
Author(s):  
K.R. Rowling ◽  
DD Reid
Keyword(s):  
Likelihood Ratio ◽  
Growth Parameters ◽  
Growth Parameter ◽  
Size Composition ◽  
Likelihood Ratio Tests ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Von Bertalanffy ◽  
Different Populations ◽  
Best Estimates

Estimates of von Bertalanffy growth parameters were made for mature (>3-year-old) gemfish (Rexea solandri), using otoliths sampled biennially from commercial catches between 1980 and 1986. During this period, there was a significant decline in the mean length of mature gemfish in the catch. Large variations in growth-parameter estimates were found over the period sampled (e.g. L∞ ranged between 87.3 and 130.3 cm for males and between 113.1 and 134.7 cm for females). Likelihood-ratio tests showed many of the growth-parameter estimates to be significantly different between the years sampled. Inclusion in the analyses of data for juvenile (1- to 3-year-old) fish considerably reduced both the standard errors of the parameter estimates and the significance of the variations between them. Comparison of parameters estimated by following the growth of individual cohorts spawned in 1975 and 1981 suggested that the variations in the parameters for the 1980-86 data were caused by differences in the length composition of the samples rather than by changes in the growth rate of the fish. The results suggest the need for care when comparing growth parameters estimated for different populations, especially when there are large differences in length composition. The best estimates of growth parameters for gemfish were considered to be those derived from the aggregate data for the whole of the period sampled, including the data for juveniles. For male gemfish these best estimates (with asymptotic standard errors in parentheses) were L∞ =97.5 (0.8) cm, K=0.212 (0.005) year-1 and to = - 0.54 (0.05) years, and for females the estimates were L∞ = 109.4 (0.6) cm, K= 0.180 (0.003) year-1 and to= -0.63 (0.04) years. Likelihood-ratio tests showed these estimates of L∞ and K to be significantly different between the sexes.

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