Stock assessment of school shark, Galeorhinus galeus, based on a spatially explicit population dynamics model

2000 ◽  
Vol 51 (3) ◽  
pp. 205 ◽  
Author(s):  
André E. Punt ◽  
Fred Pribac ◽  
Terence I. Walker ◽  
Bruce L. Taylor ◽  
Jeremy D. Prince
Keyword(s):  
Population Dynamics ◽  
Spatial Structure ◽  
Stock Assessment ◽  
Dynamics Model ◽  
Point Estimates ◽  
Population Dynamics Model ◽  
Equilibrium Size ◽  
Assessment Approach ◽  
The Status ◽  
Galeorhinus Galeus

The school shark (Galeorhinus galeus) resource off southern Australia is assessed by use of an assessment approach that takes account of the spatial structure of the population. The population dynamics model underlying the assessment considers the spatial as well as the age-specific characteristics of school shark. It allows for a series of fisheries (each based on a different gear type), explicitly models the pupping/recruitment process, and allows for multiple stocks. The values for the parameters of this model are determined by fitting it to catch-rate data and information from tagging studies. The point estimates of the pup production at the start of 1997 range from 12% to 18% of the pre-exploitation equilibrium size, depending on the specifications of the assessment. Allowing for spatial structure and incorporating tag release–recapture data lead to reduced uncertainty compared with earlier assessments. The status of the resource, as reflected by the ratio of present to virgin pup production and total (1+) biomass, is sensitive to the assumed level of movement between the stocks in New Zealand and those in Australia, with lower values corresponding to higher levels of movement.

Stock assessment and risk analysis for the school shark (Galeorhinus galeus) off southern Australia

1998 ◽  
Vol 49 (7) ◽  
pp. 719 ◽  
Author(s):  
André E. Punt ◽  
Terence I. Walker
Keyword(s):  
Stock Assessment ◽  
Catch Rate ◽  
Data Series ◽  
Structured Population ◽  
Population Dynamics Model ◽  
Equilibrium Size ◽  
Structured Population Dynamics ◽  
Pup Survival ◽  
Galeorhinus Galeus ◽  
Selection Of

A spatially aggregated age- and sex-structured population dynamics model was fitted to standardized catch-rate data from the school shark resource off southern Australia. The model incorporates the peculiarities of shark populations and fisheries, including the pupping process and the selectivity characteristics of gill-nets. Estimates are determined by a Bayesian approach that incorporates prior distributions for virgin biomass, the parameter that determines productivity, and the variation in pup survival. Tests of sensitivity include changing the data series used, varying the value of adult natural mortality, and changing the prior distribution for the productivity parameter. The point estimates of the mature biomass at the start of 1995 range from 13% to 45% of the pre-exploitation equilibrium size, depending on the specifications of the assessment. The results are notably sensitive to the selection of a catch-rate series. Results suggest that the current fishing intensity will lead to further declines in abundance, that a reduction of ~20% in fishing mortality would achieve a 0.5 probability of not declining further, and that a reduction of 42% would achieve with a probability of 0.8 the management goal of not being below the 1996 mature biomass at the start of 2011. Extra keyword: CPUE.

scholarly journals General state-space population dynamics model for Bayesian stock assessment

2015 ◽  
Vol 72 (8) ◽  
pp. 2209-2222 ◽  
Author(s):  
Samu H. P. Mäntyniemi ◽  
Rebecca E. Whitlock ◽  
Tommi A. Perälä ◽  
Paul A. Blomstedt ◽  
Jarno P. Vanhatalo ◽  
...  
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
Stock Assessment ◽  
Life Stage ◽  
Individual Case ◽  
Biological Knowledge ◽  
Model Parameters ◽  
Biological Processes ◽  
Dynamics Model ◽  
Population Dynamics Model

Abstract This study presents a state-space modelling framework for the purposes of stock assessment. The stochastic population dynamics build on the notion of correlated survival and capture events among individuals. The correlation is thought to arise as a combination of schooling behaviour, a spatially patchy environment, and common but unobserved environmental factors affecting all the individuals. The population dynamics model isolates the key biological processes, so that they are not condensed into one parameter but are kept separate. This approach is chosen to aid the inclusion of biological knowledge from sources other than the assessment data at hand. The model can be tailored to each case by choosing appropriate models for the biological processes. Uncertainty about the model parameters and about the appropriate model structures is then described using prior distributions. Different combinations of, for example, age, size, phenotype, life stage, species, and spatial location can be used to structure the population. To update the prior knowledge, the model can be fitted to data by defining appropriate observation models. Much like the biological parameters, the observation models must also be tailored to fit each individual case.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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