scholarly journals Importance of modelling heteroscedasticity of survey index data in fishery stock assessments

2014 ◽  
Vol 72 (1) ◽  
pp. 130-136 ◽  
Author(s):  
Saang-Yoon Hyun ◽  
Mark N. Maunder ◽  
Brian J. Rothschild
Keyword(s):  
Coefficient Of Variation ◽  
Likelihood Function ◽  
Error Model ◽  
Fish Stock ◽  
Additional Parameter ◽  
Traditional Practice ◽  
Multiplicative Error ◽  
Additive Error ◽  
Stochastic Error ◽  
Stock Assessments

Abstract Many fish stock assessments use a survey index and assume a stochastic error in the index on which a likelihood function of associated parameters is built and optimized for the parameter estimation. The purpose of this paper is to evaluate the assumption that the standard deviation for the difference in the log-transformed index is approximately equal to the coefficient of variation of the index, and also to examine the homo- and heteroscedasticity of the errors. The traditional practice is to assume a common variance of the index errors over time for estimation convenience. However, if additional information is available about year-to-year variability in the errors, such as year-to-year coefficient of variation, then we suggest that the heteroscedasticity assumption should be considered. We examined five methods with the assumption of a multiplicative error in the survey index and two methods with that of an additive error in the index: M1, homoscedasticity in the multiplicative error model; M2, heteroscedasticity in the multiplicative error model; M3, M2 with approximate weighting and an additional parameter for scaling variance; M4–M5, pragmatic practices; M6, homoscedasticity in the additive error model; M7, heteroscedasticity in the additive error model. M1–M2 and M6–M7 are strictly based on statistical theories, whereas M3–M5 are not. Heteroscedasticity methods M2, M3, and M7 consistently outperformed the other methods. However, we select M2 as the best method. M3 requires one more parameter than M2. M7 has problems arising from the use of the raw scale as opposed to the logarithm transformation. Furthermore, the fitted survey index in M7 can be negative although its domain is positive.

scholarly journals Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

2017 ◽  
Vol 14 (18) ◽  
pp. 4295-4314 ◽  
Author(s):  
Dan Lu ◽  
Daniel Ricciuto ◽  
Anthony Walker ◽  
Cosmin Safta ◽  
William Munger
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Likelihood Function ◽  
Terrestrial Ecosystem ◽  
Error Model ◽  
Model Parameters ◽  
Posterior Distributions ◽  
Ecosystem Models ◽  
Bayesian Calibration

Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

A Study on Estimation Model of Incidence Factor of the Thermal Bridge Using In-Situ Measurement Infrared Thermography

2021 ◽  
Author(s):  
Eunho Kang ◽  
Hyomoon Lee ◽  
Dongsu Kim ◽  
Jongho Yoon
Keyword(s):  
Infrared Thermography ◽  
Coefficient Of Variation ◽  
Performance Indicator ◽  
Likelihood Function ◽  
Estimation Method ◽  
Estimation Model ◽  
Thermal Bridge ◽  
Convergence Results ◽  
Bridge Performance

Abstract Practical thermal bridge performance indicators (ITBs) of existing buildings may differ from calculated thermal bridge performance derived theoretically due to actual construction conditions, such as effect of irregular shapes and aging. To fill this gap in a practical manner, more realistic quantitative evaluation of thermal bridge at on-site needs to be considered to identify thermal behaviors throughout exterior walls and thus improve overall insulation performance of buildings. In this paper, the model of a thermal bridge performance indicator is developed based on an in-situ Infrared thermography method, and a case study is then carried out to evaluate thermal performance of an existing exterior wall using the developed model. For the estimation method in this study, the form of the likelihood function is used with the Bayesian method to constantly reflect the measured data. Subsequently, the coefficient of variation is applied to analyze required times for the assumed convergence. Results from the measurement for three days show that thermal bridge under the measurement has more heat losses, including 1.14 times, when compared to the non-thermal bridge. In addition, the results present that it takes about 40 hours to reach 1% of the variation coefficient. Comparison of the ITB estimated at coefficient of variation 1% (40 hours point) with the ITB estimated at end-of-experiment (72 hours point) results in 0.9% of a relative error.

How much evidence is required for acceptance of productivity regime shifts in fish stock assessments: Are we letting managers off the hook?

2015 ◽  
Vol 168 ◽  
pp. 49-55 ◽  
Author(s):  
Neil L. Klaer ◽  
Robert N. O’Boyle ◽  
Jonathan J. Deroba ◽  
Sally E. Wayte ◽  
L. Richard Little ◽  
...  
Keyword(s):  
Regime Shifts ◽  
Fish Stock ◽  
Stock Assessments

Accounting for marine reserves using spatial stock assessments

2015 ◽  
Vol 72 (2) ◽  
pp. 262-280 ◽  
Author(s):  
Carey R. McGilliard ◽  
André E. Punt ◽  
Richard D. Methot ◽  
Ray Hilborn
Keyword(s):  
Spatial Heterogeneity ◽  
Stock Assessment ◽  
Marine Reserves ◽  
Movement Patterns ◽  
Catch Rate ◽  
Fish Stock ◽  
Rate Data ◽  
Stock Assessments ◽  
Age Structured ◽  
Movement Parameters

Some fish stock assessments are conducted in regions that contain no-take marine reserves (NTMRs). NTMRs are expected to lead to spatial heterogeneity in fish biomass by allowing a buildup of biomass inside their borders while fishing pressure occurs outside. Stock assessments do not typically account for spatial heterogeneity caused by NTMRs, which may lead to biased estimates of biomass. Simulation modeling is used to analyze the ability of several stock assessment configurations to estimate current biomass after the implementation of a single, large NTMR. Age-structured spatial operating models with three patterns of ontogenetic movement are used to represent the “true” population dynamics. Results show that assessing populations as a single stock with use of fishery catch-rate data and without accounting for the NTMR results in severe underestimation of biomass for two of the movement patterns. Omitting fishery catch-rate data or allowing time-varying dome-shaped selectivity after NTMR implementation leads to improved estimates of current biomass, but severe bias in estimated trends in biomass over time. Performing separate assessments for fished areas and NTMRs leads to improved estimation performance in the absence of movement among assessment areas, but can severely overestimate biomass otherwise. Performing a spatial assessment with estimation of movement parameters among areas was found to be the best way to assess a species, even when movement patterns were unknown. However, future work should explore the performance of spatial assessments when catchability varies among areas.

Fish stock assessments in Lake Konnevesi

Hydrobiologia ◽  
1982 ◽  
Vol 86 (1-2) ◽  
pp. 219-222 ◽  
Author(s):  
J. Toivonen ◽  
H. Auvinen ◽  
P. Valkeaj�rvi
Keyword(s):  
Fish Stock ◽  
Stock Assessments
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