Evaluation of the most effective measures of infectivity of a precocious line of Eimeria acervulina in poultry

Coccidiosis is an economically important parasitic disease of chickens that, in Australia, is caused by seven species of the genus Eimeria.1 The disease has traditionally been controlled by prophylactic drugs, but vaccination with attenuated lines of the parasites2–4 is rapidly gaining acceptance world wide. Live Eimeria vaccines are produced in batches which are not frozen and have a limited shelf life. The per cent infectivity of vaccine seed stocks and the vaccines produced from them must therefore be accurately monitored using standardised dose dependant assays to ensure that shelf life, quality control and vaccine release specifications are met. Infectivity for the chicken host cannot readily be determined by microscopic observation of oocysts or sporocyst hatching.5 Dose dependent parameters such as body weight gain, feed conversion ratio, visual lesion scores, mortality, oocysts production, clinical symptoms and microscopic lesion counts could be used as measures of infectivity.6–11 These parameters show significant dose dependant effects with field strains, but lines of vaccine parasites that have been selected for precocious development with associated reduced virulence and reproductive capability may not have the same effect.3,4 The aim of this trial was to determine which parameters provide the most effective measures of infective dose in birds inoculated with a precocious vaccine strain.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.