Vertebral size-at-age heterogeneity in gummy shark harvested off southern Australia

Stochastic parametrization of growth heterogeneity was applied to investigate the distribution of vertebral band radius-at-age in three populations of gummy shark Mustelus antarcticus Günther harvested with very different fishing effort and different mesh-sizes of gillnet. Three examples of four parameter growth models were developed where the random von Bertalanffy growth rate K is represented alternatively by three positive distributions to avoid negative tails in size-at-age distribution. Models with gamma and log-normal distributions of K fitted the data equally well and both fitted better than a model with the Weibull distribution. Various results are presented from the model developed with the gamma distribution of K. Heterogeneity in vertebral growth is presented as a series of quantiles of distribution of band radius-at-age. Probability density functions of band radius are presented for sharks at four selected ages, and cross-sections of these probability densities against age are presented for three selected values of band radius. Heterogeneity of growth rate K in a population is presented as tables of 10% quantiles and as graphs of probability densities. The differences in the patterns of vertebral growth for male and female sharks separately, between Bass Strait during 1973–1976, Bass Strait during 1986–1987 and South Australia during 1986–1987 are generally consistent with those determined from shark length-at-age in other published studies. However, the stochastic modelling approach adopted in the present study avoids having to make any assumptions about vertebral growth patterns of individual sharks and embraces heterogeneity in vertebral radius-at-age in the models which allows for better evaluation of alternative hypotheses for explaining the observed differences in growth patterns.     

Probability density functions useful for parametrization of heterogeneity in growth and allometry data

This paper addresses the problem of modelling heterogeneous individual characteristics in a population. A flexible unified approach for stochastic parametrization dynamics of the distribution in population data is proposed. To approximate data with multiple observations per individual, models based on Markov processes are constructed. The method can be applied to scalar or multivariate characteristics, and its application to growth and allometry data is considered. Different stochastic versions of known growth and allometry functions are developed, which enable wide applicability. Simple informative growth indices are calculated as the moments of distribution. The three-parameter Gompertz growth model for size-at-age data was reparametrized to a size-increment data model with two parameters. An erratum to this article is available at .     

Industrial workwear for hot workplace environments: thermal management attributes

International Journal of Biometeorology - Personal protective clothing (PPC) is critical for worker safety and wellbeing from both protection and thermal management perspectives, particularly as...     

Cormorant Catch Concerns for Fishers: Estimating the Size-Selectivity of a Piscivorous Bird

Conflict arises in fisheries worldwide when piscivorous birds target fish species of commercial value. This paper presents a method for estimating size selectivity functions for piscivores and uses it to compare predation selectivities of Great Cormorants (Phalacrocorax carbo sinensis L. 1758) with that of gill-net fishing on a European perch (Perca fluviatilis L. 1758) population in the Curonian Lagoon, Lithuania. Fishers often regard cormorants as an unwanted “satellite species”, but the degree of direct competition and overlap in size-specific selectivity between fishers and cormorants is unknown. This study showed negligible overlap in selectivity between Great Cormorants and legal-sized commercial nets. The selectivity estimation method has general application potential for use in conjunction with population dynamics models to assess fish population responses to size-selective fishing from a wide range of piscivorous predators.     

Use of Bayes theorem to correct size-specific sampling bias in growth data

The bayesian decomposition of posterior distribution was used to develop a likelihood function to correct bias in the estimates of population parameters from data collected randomly with size-specific selectivity. Positive distributions with time as a parameter were used for parametrization of growth data. Numerical illustrations are provided. The alternative applications of the likelihood to estimate selectivity parameters are discussed.     

Parameterization of European perch Perca fluviatilis length-at-age data using stochastic Gompertz growth models

Three stochastic versions of the Gompertz growth model were used to parameterize total length (L(T) )-at-age data for perch Perca fluviatilis, an important target species for commercial and recreational fishers and a food species for predatory fishes and aquatic birds. Each model addresses growth heterogeneity by incorporating random parameters from a specific positive distribution: Weibull, gamma or log-normal. The modelling outputs for each version of the model provide L(T) distributions for selected ages and percentiles of L(T) at age for both males and females. The results highlight the importance of using a stochastic approach and the logistic-like growth pattern for analysing growth data for P. fluviatilis in Curonian Lagoon (Lithuania). Outputs from this modelling can be extended to a stochastic analysis of fish cohort dynamics, incorporating all length-based biological relationships, and the selectivity-related interactions between fish cohorts and fishing gear.