Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.

Competition of sorghum cultivars and densities with Japanese millet (Echinochloa esculenta).

Field studies were conducted at two locations in southern Queensland, Australia during the 2003-2004 and 2004-2005 growing seasons to determine the differential competitiveness of sorghum (Sorghum bicolor L. Moench) cultivars and crop densities against weeds and the sorghum yield loss due to weeds. Weed competition was investigated by growing sorghum in the presence or absence of a model grass weed, Japanese millet (Echinochloa esculenta). The correlation analyses showed that the early growth traits (height, shoot biomass, and daily growth rate of the shoot biomass) of sorghum adversely affected the height, biomass, and seed production of millet, as measured at maturity. "MR Goldrush" and "Bonus MR" were the most competitive cultivars, resulting in reduced weed biomass, weed density, and weed seed production. The density of sorghum also had a significant effect on the crop's ability to compete with millet. When compared to the density of 4.5 plants per m2, sorghum that was planted at 7.5 plants per m2 suppressed the density, biomass, and seed production of millet by 22%, 27% and 38%, respectively. Millet caused a significant yield loss in comparison with the weed-free plots. The combined weed-suppressive effects of the competitive cultivars, such as MR Goldrush, and high crop densities minimized the yield losses from the weeds. These results indicate that sorghum competition against grass weeds can be improved by choosing competitive cultivars and by using a high crop density of > 7.5 plants per m2. These non-chemical options should be included in an integrated weed management program for better weed management, particularly where the control options are limited by the evolution of herbicide resistance.

Nutrient levels in experimental tanks supplied with prawn pond effluent: the effect of artificial substrate and different densities of the banana prawn Penaeus merguiensis (de Man)

To experimentally investigate the effect of vertical artificial substrate and different densities of the banana prawn Penaeus (Fenneropenaeus) merguiensis on nutrient levels in prawn pond effluent, a time series experiment was conducted in a replicated tank system supplied periodically with discharge from a prawn production pond. Few differences (P>0.05) were detected between tanks without prawns, and tanks with low densities (5 prawns in 1700 litres) of prawns (10-12 g), in terms of nitrogen and phosphorus in the water column over the 28-day experimental period. Higher densities of prawns (starting at 25 or 50 per tank) caused an elevation of these macronutrients in the water column. This was partly due to prawn biomass losses from mortalities and weight reductions in the tank system. The survival and condition of prawns was significantly (P<0.05) reduced in tanks at these higher densities. The presence of artificial substrate (2 m2 tank-1) did not affect (P>0.05) the levels of nutrients in tank water columns, but significantly (P<0.05) increased the amount of nitrogen in tank residues left at the end of the trial when no prawns were present. The prawns had obviously been grazing on surfaces inside the tanks, and their swimming actions appeared to keep light particulate matter in suspension. Higher prawn densities increased microalgal blooms, which presumably kept ammonia levels low, and it is suggested that this association may provide the means for improved remediation of prawn farm effluent in the future.

Evaluating and validating abundance monitoring methods in the absence of populations of known size: review and application to a passive tracking index

Rarely is it possible to obtain absolute numbers in free-ranging populations and although various direct and indirect methods are used to estimate abundance, few are validated against populations of known size. In this paper, we apply grounding, calibration and verification methods, used to validate mathematical models, to methods of estimating relative abundance. To illustrate how this might be done, we consider and evaluate the widely applied passive tracking index (PTI) methodology. Using published data, we examine the rationality of PTI methodology, how conceptually animal activity and abundance are related and how alternative methods are subject to similar biases or produce similar abundance estimates and trends. We then attune the method against populations representing a range of densities likely to be encountered in the field. Finally, we compare PTI trends against a prediction that adjacent populations of the same species will have similar abundance values and trends in activity. We show that while PTI abundance estimates are subject to environmental and behavioural stochasticity peculiar to each species, the PTI method and associated variance estimate showed high probability of detection, high precision of abundance values and, generally, low variability between surveys, and suggest that the PTI method applied using this procedure and for these species provides a sensitive and credible index of abundance. This same or similar validation approach can and should be applied to alternative relative abundance methods in order to demonstrate their credibility and justify their use.

Predictive modelling to aid the regional-scale management of a vertebrate pest

Extensive resources are allocated to managing vertebrate pests, yet spatial understanding of pest threats, and how they respond to management, is limited at the regional scale where much decision-making is undertaken. We provide regional-scale spatial models and management guidance for European rabbits (Oryctolagus cuniculus) in a 260,791 km(2) region in Australia by determining habitat suitability, habitat susceptibility and the effects of the primary rabbit management options (barrier fence, shooting and baiting and warren ripping) or changing predation or disease control levels. A participatory modelling approach was used to develop a Bayesian network which captured the main drivers of suitability and spread, which in turn was linked spatially to develop high resolution risk maps. Policy-makers, rabbit managers and technical experts were responsible for defining the questions the model needed to address, and for subsequently developing and parameterising the model. Habitat suitability was determined by conditions required for warren-building and by above-ground requirements, such as food and harbour, and habitat susceptibility by the distance from current distributions, habitat suitability, and the costs of traversing habitats of different quality. At least one-third of the region had a high probability of being highly suitable (support high rabbit densities), with the model supported by validation. Habitat susceptibility was largely restricted by the current known rabbit distribution. Warren ripping was the most effective control option as warrens were considered essential for rabbit persistence. The anticipated increase in disease resistance was predicted to increase the probability of moderately suitable habitat becoming highly suitable, but not increase the at-risk area. We demonstrate that it is possible to build spatial models to guide regional-level management of vertebrate pests which use the best available knowledge and capture fine spatial-scale processes.