Integrating size-structured assessment and bioeconomic management advice in Australia's northern prawn fishery

2010 ◽  
Vol 67 (8) ◽  
pp. 1785-1801 ◽  
Author(s):  
André E. Punt ◽  
Roy A. Deng ◽  
Catherine M. Dichmont ◽  
Tom Kompas ◽  
William N. Venables ◽  
...  
Keyword(s):  
Population Model ◽  
Net Present Value ◽  
Fishing Effort ◽  
Bioeconomic Model ◽  
Short Term ◽  
Time Step ◽  
Structured Population Model ◽  
Penaeus Semisulcatus ◽  
Management Advice ◽  
Northern Prawn Fishery

Abstract Punt, A. E., Deng, R. A., Dichmont, C. M., Kompas, T., Venables, W. N., Zhou, S., Pascoe, S., Hutton, T., Kenyon, R., van der Velde, T., and Kienzle, M. 2010. Integrating size-structured assessment and bioeconomic management advice in Australia's northern prawn fishery. – ICES Journal of Marine Science, 67: 1785–1801. Three species in Australia's northern prawn fishery (Penaeus semisulcatus, P. esculentus, and Metapenaeus endeavouri) are assessed using a size-structured population model that operates on a weekly time-step. The parameters of this multispecies population model are estimated using data on catches, catch rates, length frequency data from surveys and the fishery, and tag release–recapture data. The model allows for the technical interaction among the three species. The results from the multispecies stock assessment are used to calculate the time-series of catches and levels of fishing effort that maximize net present value. The bioeconomic model takes into account costs which are proportional to catches and those which are proportional to fishing effort, as well as fixed costs. The sensitivity of the results is examined by changing the assumptions regarding the values for the economic parameters of the bioeconomic model as well as those on which the assessment are based. The results suggest that fishing effort needs to be reduced in the short term to achieve economic goals, although most stocks are estimated currently to be above the stock size corresponding to maximum sustainable yield. Short-term catches and effort levels are sensitive to model assumptions, and in particular, to trends in prices and costs.

Stock-recruitment relationships of the tiger prawns (Penaeus esculentus and Penaeus semisulcatus) in the Australian northern prawn fishery

1996 ◽  
Vol 47 (1) ◽  
pp. 87 ◽  
Author(s):  
YG Wang ◽  
D Die
Keyword(s):  
Population Model ◽  
Fishing Effort ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Commercial Catch ◽  
Penaeus Semisulcatus ◽  
Spawning Stock ◽  
Tiger Prawn ◽  
Stock Recruitment ◽  
Northern Prawn Fishery

This paper investigates the stock-recruitment and equilibrium yield dynamics for the two species of tiger prawns (Penaeus esculentus and Penaeus semisulcatus) in Australia's most productive prawn fishery: the Northern Prawn Fishery. Commercial trawl logbooks for 1970-93 and research surveys are used to develop population models for these prawns. A population model that incorporates continuous recruitment is developed. Annual spawning stock and recruitment indices are then estimated from the population model. Spawning stock indices represent the abundance of female prawns that are likely to spawn; recruitment indices represent the abundance of all prawns less than a certain size. The relationships between spawning stock and subsequent recruitment (SRR), between recruitment and subsequent spawning stock (RSR), and between recruitment and commercial catch were estimated through maximum-likelihood models that incorporated autoregressive terms. Yield as a function of fishing effort was estimated by constraining to equilibrium the SRR and RSR. The resulting production model was then used to determine maximum sustainable yield (MSY) and its corresponding fishing effort (fMSY). Long-term yield estimates for the two tiger prawn species range between 3700 and 5300 t. The fishing effort at present is close to the level that should produce MSY for both species of tiger prawns. However, current landings, recruitment and spawning stock are below the equilibrium values predicted by the models. This may be because of uncertainty in the spawning stock-recruitment relationships, a change in carrying capacity, biased estimates of fishing effort, unreliable catch statistics, or simplistic assumptions about stock structure. Although our predictions of tiger prawn yields are uncertain, management will soon have to consider new measures to counteract the effects of future increases in fishing effort.

scholarly journals Improving catch prediction for tiger prawns in the Australian northern prawn fishery

2014 ◽  
Vol 72 (1) ◽  
pp. 117-129 ◽  
Author(s):  
Roy A. Deng ◽  
André E. Punt ◽  
Catherine M. Dichmont ◽  
Rik C. Buckworth ◽  
Charis Y. Burridge
Keyword(s):  
Population Model ◽  
Frequency Data ◽  
Length Frequency ◽  
Structured Population Model ◽  
Structured Population ◽  
Penaeus Semisulcatus ◽  
Tiger Prawn ◽  
Length Frequency Data ◽  
Northern Prawn Fishery ◽  
Quantities Of Interest

Abstract Population models form the basis for the assessments of species in the tiger prawn component of Australia's northern prawn fishery. Penaeus semisulcatus and P. esculentus are assessed using a size-structured population model. These assessments form the basis for a control rule which predicts future total allowable catches (TACs) for P. semisulcatus and P. esculentus so that the discounted profit from the fishery is maximized. However, there are concerns with this approach: (i) the TAC predictions have consistently overpredicted actual catches and (ii) the assessment for one of the species exhibits a retrospective pattern. A series of analyses was conducted to explore the causes of these observations. Results indicate that catch, effort, and recruitment prediction can be improved substantially by changing the assumed selectivity pattern for one of the surveys, changing how the length frequency data are assembled from the raw data collected, changing the constraints on the minimum amount of effort by target fleet, modifying how the distribution of effort by week is forecasted, and dropping the length frequency data from the most recent recruitment survey. More generally, the analyses illustrate how retrospective analysis can be used to improve how assessments and projections are undertaken when the quantities of interest are known retrospectively.

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

scholarly journals Sensitivity equations for a size-structured population model

2009 ◽  
Vol 67 (4) ◽  
pp. 627-660 ◽  
Author(s):  
H. T. Banks ◽  
Stacey L. Ernstberger ◽  
Shuhua Hu
Keyword(s):  
Population Model ◽  
Structured Population Model ◽  
Structured Population ◽  
Sensitivity Equations
Sign in / Sign up

Export Citation Format

Share Document