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.

GIS-based tools for management of pine plantations, Queensland, Australia

Two examples of GIS-based multiple-criteria evaluations of plantation forests are presented. These desktop assessments use available topographical, geological and pedological information to establish the risk of occurrence of certain environmentally detrimental processes. The first case study is concerned with the risk that chemical additives (i.e. simazine) applied within the forestry landscape may reach the drainage system. The second case study assesses the vulnerability of forested areas to landslides. The subject of the first multiple-criteria evaluation (MCE) was a 4 km2 logging area, which had been recently site-prepared for a Pinus plantation. The criteria considered relevant to the assessment were proximity to creeks, slope, soil depth to the restrictive layer (i.e. potential depth to a perched water table) and soil erodability (based on clay content). The output of the MCE was in accordance with field observations, showing that this approach has the potential to provide management support by highlighting areas vulnerable to waterlogging, which in turn can trigger overland flow and export of pollutants to the local stream network. The subject of the second evaluation was an Araucaria plantation which is prone to landslips during heavy rain. The parameters included in the assessment were drainage system, the slope of the terrain and geological features such as rocks and structures. A good correlation between the MCE results and field observations was found, suggesting that this GIS approach is useful for the assessment of natural hazards. Multiple-criteria evaluations are highly flexible as they can be designed in either vector or raster format, depending on the type of available data. Although tested on specific areas, the MCEs presented here can be easily used elsewhere and assist both management intervention and the protection of the adjacent environment by assessing the vulnerability of the forest landscape to either introduced chemicals or natural hazards.

Round scallops and square meshes: a comparison of four codend types on the catch rates of target species and by-catch in the Queensland (Australia) saucer scallop (Amusium balloti) trawl fishery

Concern over the amount of by-catch from benthic trawl fisheries and research into the problem have increased in recent years. The present paper demonstrated that by-catch rates in the Queensland (Australia) saucer scallop (Amusium balloti) trawl fishery can be reduced by 77% (by weight) using nets fitted with a turtle excluder device (TED) and a square-mesh codend, compared with a standard diamond-mesh codend with no TED. This large reduction was achieved with no significant effect on the legal size scallop catch rate and 39% fewer undersize scallops were caught. In total, 382 taxa were recorded in the by-catch, which was dominated by sponges, portunid crabs, small demersal and benthic fish (e.g. leatherjackets, stingerfish, bearded ghouls, nemipterids, longspine emperors, lizard fish, triggerfish, flounders and rabbitfish), elasmobranchs (e.g. mainly rays) and invertebrates (e.g. sea stars, sea urchins, sea cucumbers and bivalve molluscs). Extremely high reductions in catch rate (i.e. ≥85%) were demonstrated for several by-catch species owing to the square-mesh codend. Square-mesh codends show potential as a means of greatly reducing by-catch and lowering the incidental capture and mortality of undersize scallops and Moreton Bay bugs (Thenus australiensis) in this fishery

Networks of stored grain diseases and pests: Strategies for sampling and mitigation

Pathogens and pests of stored grains move through complex dynamic networks linking fields, farms, and bulk storage facilities. Human transport and other forms of dispersal link the components of this network. A network model for pathogen and pest movement through stored grain systems is a first step toward new sampling and mitigation strategies that utilize information about the network structure. An understanding of network structure can be applied to identifying the key network components for pathogen or pest movement through the system. For example, it may be useful to identify a network node, such as a local grain storage facility, through which grain from a large number of fields will be accumulated and move through the network. This node may be particularly important for sampling and mitigation. In some cases more detailed information about network structure can identify key nodes that link two large sections of the network, such that management at the key nodes will greatly reduce the risk of spread between the two sections. In addition to the spread of particular species of pathogens and pests, we also evaluate the spread of problematic subpopulations, such as subpopulations with pesticide resistance. We present an analysis of stored grain pathogen and pest networks for Australia and the United States.