9 resultados para picture

em eResearch Archive - Queensland Department of Agriculture


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Australia’s rangelands are the extensive arid and semi-arid grazing lands that cover approximately 70% of the Australian continent. They are characterised by low and generally variable rainfall, low productivity and a sparse population. They support a number of industries including mining and tourism, but pastoralism is the primary land use. In some areas, the rangelands have a history of biological decline (Noble 1997), with erosion, loss of perennial native grasses and incursion of woody vegetation commonly reported in the scientific and lay literature. Despite our historic awareness of these trends, the establishment of systems to measure and monitor degradation, has presented numerous problems. The size and accessibility of Australia’s rangeland often mitigates development of extensive monitoring programs. So, too, securing on-going commitment from Government agencies to fund rangeland monitoring activities have led to either abandonment or a scaled-down approach in some instances (Graetz et al. 1986; Holm 1993). While a multiplicity of monitoring schemes have been developed for landholders at the property scale, and some have received promising initial uptake, relatively few have been maintained for more than a few years on any property without at least some agency support (Pickup et al. 1998). But, ironically, such property level monitoring tools can contribute significantly to local decisions about stock, infrastructure and sustainability. Research in recent decades has shown the value of satellites for monitoring change in rangelands (Wallace et al. 2004), especially in terms of tree and ground cover. While steadily improving, use of satellite data as a monitoring tool has been limited by the cost of the imagery, and the equipment and expertise needed to extract useful information from it. A project now under way in the northern rangelands of Australia is attempting to circumvent many of the problems through a monitoring system that allows property managers to use long-term satellite image sequences to quickly and inexpensively track changes in land cover on their properties

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Objective: To identify nematodes seen in histological sections of brains of flying foxes (fruit bats) and describe the associated clinical disease and pathology. Proceedures: Gross and histological examination of brains from 86 free-living flying foxes with neurological disease was done as part of an ongoing surveillance program for Australian bat lyssavirus. Worms were recovered, or if seen in histological sections, extracted by maceration of half the brain and identified by microscopic examination. Histological archives were also reviewed. Results: There was histological evidence of angiostrongylosis in 16 of 86 recently submitted flying foxes with neurological disease and in one archival case from 1992. In 10 flying foxes, worms were definitively identified as Angiostrongylus cantonensis fifth-stage larvae. A worm fragment and third stage larvae were identified as Angiostrongylus sp, presumably A cantonensis, in a further three cases. The clinical picture was dominated by paresis, particularly of the hindlimbs, and depression, with flying foxes surviving up to 22 days in the care of wildlife volunteers. Brains containing fifthstage larvae showed a moderate to severe eosinophilic and granulomatous meningoencephalitis (n = 14), whereas there was virtually no inflammation of the brains of bats which died when infected with only smaller, third-stage larvae (n = 3). There was no histological evidence of pulmonary involvement. Conclusion: This is the first report of the recovery and identification of A cantonensis from free-living Australian wildlife. While angiostrongylosis is a common cause of paresis in flying foxes, the initial clinical course cannot be differentiated from Australian bat lyssavirus infection, and wildlife carers should be urged not to attempt to rehabilitate flying foxes with neurological disease.

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Ecological and genetic studies of marine turtles generally support the hypothesis of natal homing, but leave open the question of the geographical scale of genetic exchange and the capacity of turtles to shift breeding sites. Here we combine analyses of mitochondrial DNA (mtDNA) variation and recapture data to assess the geographical scale of individual breeding populations and the distribution of such populations through Australasia. We conducted multiscale assessments of mtDNA variation among 714 samples from 27 green turtle rookeries and of adult female dispersal among nesting sites in eastern Australia. Many of these rookeries are on shelves that were flooded by rising sea levels less than 10 000 years (c. 450 generations) ago. Analyses of sequence variation among the mtDNA control region revealed 25 haplotypes, and their frequency distributions indicated 17 genetically distinct breeding stocks (Management Units) consisting either of individual rookeries or groups of rookeries in general that are separated by more than 500 km. The population structure inferred from mtDNA was consistent with the scale of movements observed in long-term mark-recapture studies of east Australian rookeries. Phylogenetic analysis of the haplotypes revealed five clades with significant partitioning of sequence diversity (Φ = 68.4) between Pacific Ocean and Southeast Asian/Indian Ocean rookeries. Isolation by distance was indicated for rookeries separated by up to 2000 km but explained only 12% of the genetic structure. The emerging general picture is one of dynamic population structure influenced by the capacity of females to relocate among proximal breeding sites, although this may be conditional on large population sizes as existed historically across this region.

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The selection of different patch types for grazing by cattle in tropical savannas is well documented. Advances in high resolution satellite imagery and computing power now allow us to identify patch types over an entire paddock, combined with GPS collars as a non instrusive method of capturing positional data, an accurate and comprehensive picture of landscape use by cattle can be quantified.

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Patch selection by grazing animals is difficult to quantify, particularly in large, extensive paddocks like those in northern Australia. However, advances in high resolution satellite imagery now allow identification of patch types over an entire paddock which combined with GPS collars to capture positional data, can give an accurate and comprehensive picture of landscape use by cattle.

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The growth of the Australian turfgrass industry has significantly expanded over recent decades. One reason for this occurring has been with development of better suited or higher quality turfgrass cultivars for Australia’s harsh climatic conditions. In recent years drought has widely affected the turfgrass industry and as such, greater drought tolerant C4 grasses such as Cynodon spp. have been used. In 2008, as part of the 24th Australian Turfgrass Conference Proceedings, Peter McMaugh wrote an extensive article on the couch grass breeding history in Australia. This paper contains an extension to his work detailing the current (1950s to 2010) Cynodon species found in Australia. Detailed information has been sourced in relation to the origin and development of the grasses which are suitable for turfgrass use. Such detail provides an interesting picture of the source of proliferation of newer cultivars and how the Australian industry has evolved with the introduction of overseas and Australian selected cultivars. The information adds to the preceding work, including morphological and agronomic attributes and how closely each selection or cultivar is related. The cultivars discussed in this article (listed alphabetically) are derived from one of the four classifications identified by the breeder/author, being (i) Cynodon sp. (although the cultivar contained within the taxa fits best being classified as a Cynodon hybrid), (ii) Cynodon dactylon x C. transvaalensis (Cynodon hybrid), (iii) Cynodon dactylon (green couch) and (iv) Cynodon dactylon x C. magenissii.

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Understanding the host range for all of the fruit fly species within the South Pacific region is vital to establishing trade and quarantine protocols. This is important for the countries within the region and their trade partners. A significant aspect of the Australian Centre for International Agricultural Research (ACIAR) and Regional Fruit Fly Projects (RFFP) has been host fruit collecting which has provided information on fruit fly host records in the seven participating countries. This work is still continuing in all project countries at different intensities. In the Cook Islands, Fiji, Tonga and Western Samoa, fruit surveys have assumed a quarantine surveillance role, with a focus on high risk fruits, such as guava, mango, citrus, bananas, cucurbits and solanaceous fruits. In the Solomon Islands, Vanuatu and the Federated States of Micronesia (FSM), fruit surveys are still at the stage where host ranges are far from complete. By the end of the current project a more complete picture of the fruit fly hosts in these countries will have been gained. A brief summary of the data collected to date is as follows: 23 947 fruit samples collected to date; 2181 positive host fruit records; 31 fruit fly species reared from fruit; 12 species reared from commercial fruit. A commercial fruit is classed as an edible fruit with potential for trade at either a local or international level. This allows for the inclusion of endemic fruit species that have cultural significance as a food source. On the basis of these results, there are fruit fly species of major economic importance in the South Pacific region. However, considerably more fruit survey work is required in order to establish a detailed understanding of all the pest species.

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Development of new agricultural industries in northern Australia is often perceived as a solution to changes in water availability that have occurred within southern Australia as a result of changes to government policy in response to and exacerbated by climate change. This report examines the likely private, social and community costs and benefits associated with the establishment of a cotton industry in the Burdekin. The research undertaken covers three spatial scales by modelling the response of cotton and to climate change at the crop and farm scale and linking this to regional scale modelling of the economy. Modelling crop growth as either a standalone crop or as part of a farm enterprise provides the clearest picture of how yields and water use will be affected under climate change. The alternative to this is to undertake very costly trials in environmental chambers. For this reason it is critical that funding for model development especially for crops being crop in novel environments be seen as a high priority for climate change and adaptation studies. Crop level simulations not only provide information on how the crop responds to climate change, they also illustrate that that these responses are the result of complex interactions and cannot necessarily be derived from the climate information alone. These simulations showed that climate change would lead to decreased cotton yields in 2030 and 2050 without the affect of CO2 fertilisation. Without CO2 fertilisation, yields would be decreased by 3.2% and 17.8%. Including CO2 fertilisation increased yields initially by 5.9%, but these were reduced by 3.6% in 2050. This still represents a major offset and at least ameliorates the impact of climate change on yield. To cope with the decreased in-crop rainfall (4.5% by 2030 and 15.8% in 2050) and an initial increase in evapotranspiration of 2% in 2030 and

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The mango industry in Australia is worth in excess of $150 million annually with the Kensington Pride (KP) cultivar capturing 60% of the domestic market. Valued by consumers for desirable taste and colour characteristics, KP has been used extensively as a parent in the Department of Agriculture and Fisheries’ (Queensland, Australia) mango breeding program with over 400 hybrid trees sharing KP as the male parent. In order to gain a better understanding of Australia’s most significant mango variety, Horticulture Innovation Australia had led an international collaboration between the Queensland Department of Agriculture and Fisheries (Australia), the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT, India) and the Beijing Genomics Institute (China) to sequence the KP genome. Preliminary de novo assembly of illumina short read sequence data suggests that the KP genome is highly heterozygous and has an estimated genome size of 407 Mb. As refinements and additional sequence data are added to the assembly, a more complete picture of the mango genome will be elucidated.