A Bayesian approach to estimating mortality rates using hazard functions: Application to an Australian halfbeak, Hyporhamphus australis fishery

Kienzle, Marco; Broadhurst, Matt K.; Pletzer, A.; Stewart, John

doi:10.1016/j.fishres.2021.106066

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their findings illustrated the superior performance of MCMC in forecasting seasonal and intermittent demand with overall lower accuracy metrics. Besides the above-mentioned studies, Bayesian MCMC approach has also become more prevalent in dynamic model estimation (Liesenfeld and Richard, 2004;Li et al, 2017;Billio et al, 2018;Brix et al, 2018;Akbar et al, 2019;Kostrzewski and Kostrzewska, 2019;Yang, 2019;Zhong et al, 2019a;Zhong et al, 2019b;Leng and Li, 2020;Maldonado et al, 2020;Kienzle et al, 2021;, wind forecasting (Pang et al, 2001;Erto et al, 2010;Li and Shi, 2010;Karatepe and Corscadden, 2013;Moghaddass and Sheng, 2019;García et al, 2020;Lepore et al, 2020;Kikumoto et al, 2021;Mbuvha et al, 2021), water quality modelling (Samanta et al, 2007;Liu et al, 2008;Cheng et al, 2014;Hantush and Chaudhary, 2014;Lu et al, 2014;Wellen et al, 2014;Camacho et al, 2015;Zheng and Han, 2015;Chaudhary and Hantush, 2017), and uncertainty analysis (Keats et al, 2007;Lehuger et al, 2009;Wang and Chen, 2013;Wang and Jin, 2013;Liang et al, 2016;Berger et al, 2016;Folgoc et al, 20...…”

Section: Review On Bayesian Probabilistic Forecasting Methodsmentioning

confidence: 99%

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

Liu¹

View full text Add to dashboard Cite

CHAPTER 2 LITERATURE REVIEW……………………………………………10 2.1 Introduction………………………………………….…….…………………10 2.2 Literature review approach…………………………………………..……….11 2.2.1 Description………………….……………...…………….………………11 2.2.2 Bibliometric statistics………………………………...……….………….12 2.3 Literature review in ship emission accounting……………………….……....14 2.3.1 Top-down ship emission accounting method………………….………….14 2.3.2 Bottom-up ship emission accounting method…………….………………16 2.3.2.1 Temporal and spatial resolution of ship movements………………....18 2.3.2.2 Data sources for ship specification information……….……..…..…..20 2.3.2.3 Approaches to estimate emission factors…………….……………….22 2.3.3 Research topics in bottom-up emission inventories………….………..….24 2.3.3.1 Ship emission inventories with different boundaries…………………24 2.3.3.2 Ship emission inventories with different emission sources……..….…27 2.4 Other data modelling techniques………………………………….…………..29 III

show abstract

Section: Review On Bayesian Probabilistic Forecasting Methodsmentioning

confidence: 99%

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

Liu¹

View full text Add to dashboard Cite

show abstract

Using otolith weights to estimate age for eastern sea garfish, Hyporhamphus australis

Stewart,

Broadhurst,

Tolhurst

2024

Front. Fish Sci.

View full text Add to dashboard Cite

The most common method of estimating teleost ages is via sectioned otoliths. With diminishing funding and policies around cost-recovery from fishing industries, exploring cost-effective methods of estimating ages is warranted. The present study used 18 years of size-at-age data collected from monitoring of the commercial halfbeak (Hyporhamphus australis) fishery off New South Wales, Australia, to predict age classes from otolith weights, while considering other sources of variability such as sex, fish length, and year, month, and location of capture. We observed a significant linear relationship between age class and mean otolith weight. A generalized linear mixed model predicted 1-year olds with an 82% success rate; but was less successful for other ages. Year of sampling explained the greatest variability in the model and the distributions of otolith weights for each age class had considerable overlap. We conclude that substantial inter-annual variability in the age-class to otolith weight relationship, in addition to the relatively low precision when aging H. australis by counting annuli in sectioned otoliths, limits the predictive capacity of this model for future monitoring. Nevertheless, substantial cost savings could be made through recalibrating the model for new samples through direct aging of a subset of otoliths each year. The population of H. australis is continuing to rebuild from a previously overfished state, with an expectation that older fish will become more abundant in the fishery. Age estimation from counting annuli in sectioned otoliths is likely to be the most reliable method of identifying older individuals.

show abstract

A Bayesian approach to estimating mortality rates using hazard functions: Application to an Australian halfbeak, Hyporhamphus australis fishery

Cited by 2 publications

References 19 publications

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

Using otolith weights to estimate age for eastern sea garfish, Hyporhamphus australis

Contact Info

Product

Resources

About