scholarly journals Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability

2019 ◽  
Vol 150 ◽  
pp. 107247 ◽  
Author(s):  
Emmanuel Anane ◽  
Diana C. López C ◽  
Tilman Barz ◽  
Gurkan Sin ◽  
Krist V. Gernaey ◽  
...  
Keyword(s):  
Growth Models ◽  
Uncertain Parameter ◽  
Parameter Estimates ◽  
Output Uncertainty ◽  
Model Reliability ◽  
Dynamic Growth

Age and growth dynamics of spiny dogfish, Squalus acanthias, in the Adriatic Sea (Eastern Mediterranean Sea)

2016 ◽  
Vol 67 (3) ◽  
pp. 357 ◽  
Author(s):  
Romana Gračan ◽  
Scott A. Heppell ◽  
Gordana Lacković ◽  
Bojan Lazar
Keyword(s):  
Adriatic Sea ◽  
Growth Models ◽  
Eastern Mediterranean ◽  
Growth Dynamics ◽  
Fishing Effort ◽  
Age And Growth ◽  
Percentage Error ◽  
Parameter Estimates ◽  
Spiny Dogfish ◽  
Average Percentage

This research provides the first information on age and growth estimates for the endangered Mediterranean subpopulation of spiny dogfish, a commercially exploited shark, highly sensitive to overexploitation. We collected samples from 206 specimens caught by commercial bottom trawls in the Adriatic Sea, and utilising three ageing protocols achieved good agreement between the readings (average percentage error=1.65%). Four growth models were fitted to length-at-age and weight-at-age data, for each sex separately. The Gompertz growth model produced the statistically best fit resulting in the following parameters: k values for males and females were 0.09 and 0.04 year–1, size-at-birth ranged from 22.9 to 24.1-cm total length, with a theoretical asymptotic length of 103.3cm for males and 173.3cm for females. The age at 50% maturity was 10.5 years for males and 20.1 years for females. The maximum age was estimated at 23 years for males and 36 years for females, with natural mortality estimates of 0.12 for males and 0.07 for females. As a result of reported demographic parameter estimates, high fishing effort and particularly low resilience of the species to exploitation, it is important to produce proper species-specific management strategy for the spiny dogfish in the region.

scholarly journals Age, growth, and maturity of the whitebrow skate, Bathyraja minispinosa, from the eastern Bering Sea

2011 ◽  
Vol 68 (7) ◽  
pp. 1426-1434 ◽  
Author(s):  
Shaara M. Ainsley ◽  
David A. Ebert ◽  
Gregor M. Cailliet
Keyword(s):  
Bering Sea ◽  
Growth Models ◽  
Gompertz Model ◽  
Parameter Estimates ◽  
Population Declines ◽  
Age At Maturity ◽  
Von Bertalanffy Model ◽  
Eastern Bering Sea ◽  
Best Fit ◽  
The Bering Sea

Abstract Ainsley, S. M., Ebert, D. A., and Cailliet, G. M. 2011. Age, growth, and maturity of the whitebrow skate, Bathyraja minispinosa, from the eastern Bering Sea. – ICES Journal of Marine Science, 68: 1426–1434. Skates are a common bycatch in groundfish fisheries in the Bering Sea; however, their life-history characteristics are not well known. The study is the first to investigate the age, growth, and age at maturity of Bathyraja minispinosa. Ages were estimated using sectioned vertebrae and several growth models were compared. The Gompertz model was the best fit and no significant differences were detected between sexes for any model. The maximum age estimated was 37 years, and parameter estimates generated from the three-parameter von Bertalanffy model were k = 0.02 year−1 and L∞ = 146.9 cm total length (TL). Males reached their size at 50% maturity larger than females (70.1 and 67.4 cm, respectively), but no significant differences in the estimated size or age at maturity were found. Whereas B. minispinosa is smaller than many skate species in the eastern Bering Sea, it has a considerably longer estimated lifespan, indicating that size may not be a reliable method of estimating the vulnerability of a rajid species to population declines in the eastern North Pacific.

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 Maternal and paternal influences on the developmental trajectory of childhood anxiety symptoms: a genetically sensitive investigation

2017 ◽  
Author(s):  
Andy Peter Field ◽  
Kathryn J. Lester ◽  
Sam Cartwright-Hatton ◽  
Gordon Harold ◽  
Daniel S. Shaw ◽  
...  
Keyword(s):  
Growth Models ◽  
Environmental Influence ◽  
Child Anxiety ◽  
Childhood Anxiety ◽  
Anxiety Symptoms ◽  
Developmental Trajectory ◽  
Adoptive Parent ◽  
Parameter Estimates ◽  
Adopted Children ◽  
Mothers And Fathers

One theory suggests that anxious fathers may pose a greater environmental influence on childhood anxiety than anxious mothers. This study uses the Early Growth and Development Study (EGDS) to test rearing parent anxiety influences from mothers and fathers on child anxiety symptoms between 18 months and 4.5, while considering inherited influences. The EGDS is a longitudinal, multisite study of adopted children recruited through US adoption agencies, and their adoptive and birth parents. Bayesian latent growth models of the trajectory of child anxiety symptoms over 3 years predicted from inherited (birth parent anxiety) and adoptive parent anxiety influences were compared for maternal and paternal measures. Parameter estimates and their HPD intervals provided evidence that the slope for anxiety symptoms between 18 and 54 months is trivially affected by both rearing parent anxiety and inherited influences from both mothers and fathers. Similarly, rearing parental anxiety and inherited influence from both mothers and fathers had only a very small effect on the intercept for growth (anxiety symptoms at 18 months old). The evidence for differences between mothers and fathers for any of these parameters was, at best, weak. Contrary to theoretical predictions, anxiety in the rearing father is unlikely to have a more important role in fostering child anxiety symptoms than that in the rearing mother.

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.

scholarly journals Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Dora Matzke ◽  
Alexander Ly ◽  
Ravi Selker ◽  
Wouter D. Weeda ◽  
Benjamin Scheibehenne ◽  
...  
Keyword(s):  
Measurement Error ◽  
Bayesian Modeling ◽  
Pearson Correlation ◽  
Psychological Research ◽  
Uncertain Parameter ◽  
Parameter Estimates ◽  
Data Sets ◽  
Data Set ◽  
Bayesian Hierarchical ◽  
Estimation Uncertainty

Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Bayesian hierarchical model proposed by Behseta, Berdyyeva, Olson, and Kass (2009) that allows researchers to infer the underlying correlation between error-contaminated observations. We show that this approach may be also applied to obtain the underlying correlation between uncertain parameter estimates as well as the correlation between uncertain parameter estimates and noisy observations. We illustrate the Bayesian modeling of correlations with two empirical data sets; in each data set, we first infer the posterior distribution of the underlying correlation and then compute Bayes factors to quantify the evidence that the data provide for the presence of an association.

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