Modelling the distribution and relative abundance of feral camels in the Northern Territory using count data

The objectives of this study were to predict the potential distribution, relative abundance and probability of habitat use by feral camels in southern Northern Territory. Aerial survey data were used to model habitat association. The characteristics of ‘used’ (where camels were observed) v. ‘unused’ (pseudo-absence) sites were compared. Habitat association and abundance were modelled using generalised additive model (GAM) methods. The models predicted habitat suitability and the relative abundance of camels in southern Northern Territory. The habitat suitability maps derived in the present study indicate that camels have suitable habitat in most areas of southern Northern Territory. The index of abundance model identified areas of relatively high camel abundance. Identifying preferred habitats and areas of high abundance can help focus control efforts.

Mixture Effects of Benzene, Toluene, Ethylbenzene and Xylenes (BTEX) to Lung Carcinoma Cells via a Hanging Drop Air Exposure System

A recently developed hanging drop air exposure system for toxicity studies of volatile chemicals was applied to evaluate the cell viability of lung carcinoma A549 cells after 1 h and 24 h of exposure to benzene, toluene, ethylbenzene and xylenes (BTEX) as individual compounds and mixtures of 4 or 6 components. The cellular chemical concentrations causing 50% reduction of cell viability (EC50) were calculated use a mass balance model and came to 17, 12, 11, 9, 4 and 4 mmol/kg cell dry weight for benzene, toluene, ethylbenzene, m-xylene, o-xylene and p-xylene respectively after 1 h of exposure. The EC50 decreased by a factor of four after 24 h of exposure. All mixture effects were best described by the mixture toxicity model of concentration addition, which is valid for chemicals with the same mode of action. Good agreement with the model predictions were found for benzene, toluene, ethylbenzene and m-xylene at four different representative fixed concentration ratios after 1 h of exposure but lower agreement to mixture prediction was obtained after 24 h of exposure. A recreated car exhaust mixture, which involved the contribution of the more toxic p-xylene and o-xylene, yielded an acceptable but lower quality prediction as well.

Effects of exposure to Bovine viral diarrhoea virus 1 on risk of bovine respiratory disease in Australian feedlot cattle

Viruses play a key role in the complex aetiology of bovine respiratory disease (BRD). Bovine viral diarrhoea virus 1 (BVDV-1) is widespread in Australia and has been shown to contribute to BRD occurrence. As part of a prospective longitudinal study on BRD, effects of exposure to BVDV-1 on risk of BRD in Australian feedlot cattle were investigated. A total of 35,160 animals were enrolled at induction (when animals were identified and characteristics recorded), held in feedlot pens with other cattle (cohorts) and monitored for occurrence of BRD over the first 50 days following induction. Biological samples collected from all animals were tested to determine which animals were persistently infected (PI) with BVDV-1. Data obtained from the Australian National Livestock Identification System database were used to determine which groups of animals that were together at the farm of origin and at 28 days prior to induction (and were enrolled in the study) contained a PI animal and hence to identify animals that had probably been exposed to a PI animal prior to induction. Multi-level Bayesian logistic regression models were fitted to estimate the effects of exposure to BVDV-1 on the risk of occurrence of BRD.Although only a total of 85 study animals (0.24%) were identified as being PI with BVDV-1, BVDV-1 was detected on quantitative polymerase chain reaction in 59% of cohorts. The PI animals were at moderately increased risk of BRD (OR 1.9; 95% credible interval 1.0-3.2). Exposure to BVDV-1 in the cohort was also associated with a moderately increased risk of BRD (OR 1.7; 95% credible interval 1.1-2.5) regardless of whether or not a PI animal was identified within the cohort. Additional analyses indicated that a single quantitative real-time PCR test is useful for distinguishing PI animals from transiently infected animals.The results of the study suggest that removal of PI animals and/or vaccination, both before feedlot entry, would reduce the impact of BVDV-1 on BRD risk in cattle in Australian feedlots. Economic assessment of these strategies under Australian conditions is required. © 2016 Elsevier B.V.

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.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.