scholarly journals A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis

2018 ◽  
Vol 54 (1) ◽  
pp. 243-255 ◽  
Author(s):  
C. Bracken ◽  
K. D. Holman ◽  
B. Rajagopalan ◽  
H. Moradkhani
Keyword(s):  
Frequency Analysis ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
Hydrologic Frequency Analysis

A Bayesian hierarchical approach to integrating historical and in-season genetic data for real-time assessment of a mixed stock fishery

2020 ◽  
Vol 77 (10) ◽  
pp. 1721-1732
Author(s):  
Lukas B. DeFilippo ◽  
Daniel E. Schindler ◽  
Kyle Shedd ◽  
Kevin L. Schaberg
Keyword(s):  
Real Time ◽  
Sockeye Salmon ◽  
Alternative Methods ◽  
Hierarchical Approach ◽  
Management Tools ◽  
Genetic Stock ◽  
Bayesian Hierarchical ◽  
Fishing Season ◽  
Management Actions ◽  
Mixed Stock

With advances in molecular genetics, it is becoming increasingly feasible to conduct genetic stock identification (GSI) to inform management actions that occur within a fishing season. While applications of in-season GSI are becoming widespread, such programs seldom integrate data from previous years, underutilizing the full breadth of information available for real-time inference. In this study, we developed a Bayesian hierarchical model that integrates historical and in-season GSI data to estimate temporal changes in the composition of a mixed stock of sockeye salmon (Oncorhynchus nerka) returning to Alaska’s Chignik watershed across the fishing season. Simulations showed that even after accounting for time constraints of transporting and analyzing genetic samples, a hierarchical approach can rapidly achieve accurate in-season stock allocation, outperforming alternative methods that rely solely on historical or in-season data by themselves. As the distribution and phenology of fish populations becomes more variable and difficult to predict under climate change, in-season management tools will likely be increasingly relied upon to protect biocomplexity while maximizing harvest opportunity in mixed stock fisheries.

scholarly journals A Bayesian hierarchical approach to estimate growth parameters from length data of narrow spread

2019 ◽  
Vol 77 (2) ◽  
pp. 613-623
Author(s):  
Shijie Zhou ◽  
Sarah Martin ◽  
Dan Fu ◽  
Rishi Sharma
Keyword(s):  
Growth Parameters ◽  
Fish Growth ◽  
Hierarchical Approach ◽  
Length Frequency ◽  
Age Classes ◽  
Bayesian Hierarchical ◽  
Gear Selectivity ◽  
Length Data ◽  
Longtail Tuna ◽  
Length Frequency Data

Abstract Estimating fish growth from length frequency data is challenging. There is often a lack of clearly separated modes and modal progression in the length samples due to a combination of factors, including gear selectivity, slowing growth with increasing age, and spatial segregation of different year classes. In this study, we present an innovative Bayesian hierarchical model (BHM) that enables growth to be estimated where there are few distinguishable length modes in the samples. We analyse and identify the modes in multiple length frequency strata using a multinormal mixture model and then integrate the modes and associated variances into the BHM to estimate von Bertalanffy growth parameters. The hierarchical approach allows the parameters to be estimated at regional levels, where they are assumed to represent subpopulations, as well as at species level for the whole stock. We carry out simulations to validate the method and then demonstrate its application to Indian Ocean longtail tuna (Thunnus tonggol). The results show that the estimates are generally consistent with the range of estimates reported in the literature, but with less uncertainty. The BHM can be useful for deriving growth parameters for other species even if the length data contain few age classes and do not exhibit modal progression.

scholarly journals Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael D Swartz ◽  
Yi Cai ◽  
Wenyaw Chan ◽  
Elaine Symanski ◽  
Laura E Mitchell ◽  
...  
Keyword(s):  
Spina Bifida ◽  
Birth Defects ◽  
Air Toxics ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
Multiple Pollutants

scholarly journals A Bayesian hierarchical approach to multirater correlated ROC analysis

2006 ◽  
Vol 25 (11) ◽  
pp. 1858-1871 ◽  
Author(s):  
Timothy D. Johnson ◽  
Valen E. Johnson
Keyword(s):  
Roc Analysis ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical
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