996 resultados para ENVIRONMENTAL SCIENCES


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From 19 authoritative lists with 164 entries of ‘endangered’ Australian mammal species, 39 species have been reported as extinct. When examined in the light of field conditions, the 18 of these species thought to be from Queensland consist of (a) species described from fragmentary museum material collected in the earliest days of exploration, (b) populations inferred to exist in Queensland by extrapolation from distribution records in neighbouring States or countries, (c) inhabitants of remote and harsh locations where search effort is extraordinarily difficult (especially in circumstances of drought or flooding). and/or (d) individuals that are clearly transitory or peripheral in distribution. ‘Rediscovery’ of such scarce species - a not infrequent occurrence - is nowadays attracting increasing attention. Management in respect of any scarce wildlife in Queensland presently derives from such official lists. The analyses here indicate that this method of prioritizing action needs review. This is especially so because action then tends to be centred on species chosen out of the lists for populist reasons and that mostly addresses Crown lands. There is reason to believe that the preferred management may lie private lands where casual observation has provided for rediscovery and where management is most desirable and practicable.

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The process of offsetting land against unavoidable disturbance of development sites in Queensland will benefit from a method that allows the best possible selection to be made of alternative lands. With site selection now advocated through a combination of Regional Ecosystem and Land Capability classifications state-wide, a case study has determined methods of assessing the functional lift – that is, measures of net environmental gain – of such action. Outcomes with potentially high functional lift are determined, that offer promise not only for endangered ecosystems but also for managing adjacent conservation reserves.

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Australian climate, soils and agricultural management practices are significantly different from those of the northern hemisphere nations. Consequently, experimental data on greenhouse gas production from European and North American agricultural soils and its interpretation are unlikely to be directly applicable to Australian systems. A programme of studies of non-CO2 greenhouse gas emissions from agriculture has been established that is designed to reduce uncertainty of non-CO2 greenhouse gas emissions in the Australian National Greenhouse Gas Inventory and provide outputs that will enable better on-farm management practices for reducing non-CO2 greenhouse gas emissions, particularly nitrous oxide. The systems being examined and their locations are irrigated pasture (Kyabram Victoria), irrigated cotton (Narrabri, NSW), irrigated maize (Griffith, NSW), rain-fed wheat (Rutherglen, Victoria) and rain-fed wheat (Cunderdin, WA). The field studies include treatments with and without fertilizer addition, stubble burning versus stubble retention, conventional cultivation versus direct drilling and crop rotation to determine emission factors and treatment possibilities for best management options. The data to date suggest that nitrous oxide emissions from nitrogen fertilizer, applied to irrigated dairy pastures and rain-fed winter wheat, appear much lower than the average of northern hemisphere grain and pasture studies. More variable emissions have been found in studies of irrigated cotton/vetch/wheat rotation and substantially higher emissions from irrigated maize.

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Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250 kg N ha−1, with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650 € ha−1 for the 200 N and 1485 to 2875 € ha−1 for the 250 N. N leaching varied between 16.4 and 19.3 kg N ha−1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N2O emissions varied between 0.28 kg N2O ha−1 for the 50 kg N ha−1 rate to a maximum of 1.41 kg N2O ha−1 for the 250 kg N ha−1 rate.

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The period of developmental vulnerability to toxicants begins at conception and extends through gestation, parturition, infanthood and childhood to adolescence. The concern is that children: (1) may experience quantitatively and qualitatively different exposures, and (2) may have different sensitivity to chemical pollutants. Traditional toxicological studies are inappropriate for assessing the results of chronic exposure at very low levels during critical periods of development. This paper will discuss (1) the health effects associated with exposure to selected emerging organic pollutants, including brominated flame retardants, perfluorinated compounds, organophosphate pesticides and bisphenol A; (2) difficulties in monitoring these substances in children, and (3) suggest techniques and strategies for overcoming these difficulties. Such biomonitoring data can be used to identify where policies should be directed in order to reduce exposure, and to document policies that have successfully reduced exposure.

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This overview article for the special series “Bayesian Networks in Environmental and Resource Management” reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource management. We highlight progress in computational methods, best-practices for model design and model communication. We review several research challenges to the use of BNs in environmental and resource management that we think may find a solution in the near future with further research attention.

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Environmental variation is a fact of life for all the species on earth: for any population of any particular species, the local environmental conditions are liable to vary in both time and space. In today's world, anthropogenic activity is causing habitat loss and fragmentation for many species, which may profoundly alter the characteristics of environmental variation in remaining habitat. Previous research indicates that, as habitat is lost, the spatial configuration of remaining habitat will increasingly affect the dynamics by which populations are governed. Through the use of mathematical models, this thesis asks how environmental variation interacts with species properties to influence population dynamics, local adaptation, and dispersal evolution. More specifically, we couple continuous-time continuous-space stochastic population dynamic models to landscape models. We manipulate environmental variation via parameters such as mean patch size, patch density, and patch longevity. Among other findings, we show that a mixture of high and low quality habitat is commonly better for a population than uniformly mediocre habitat. This conclusion is justified by purely ecological arguments, yet the positive effects of landscape heterogeneity may be enhanced further by local adaptation, and by the evolution of short-ranged dispersal. The predicted evolutionary responses to environmental variation are complex, however, since they involve numerous conflicting factors. We discuss why the species that have high levels of local adaptation within their ranges may not be the same species that benefit from local adaptation during range expansion. We show how habitat loss can lead to either increased or decreased selection for dispersal depending on the type of habitat and the manner in which it is lost. To study the models, we develop a recent analytical method, Perturbation expansion, to enable the incorporation of environmental variation. Within this context, we use two methods to address evolutionary dynamics: Adaptive dynamics, which assumes mutations occur infrequently so that the ecological and evolutionary timescales can be separated, and via Genotype distributions, which assume mutations are more frequent. The two approaches generally lead to similar predictions yet, exceptionally, we show how the evolutionary response of dispersal behaviour to habitat turnover may qualitatively depend on the mutation rate.