3 resultados para Maximum entropy

em eResearch Archive - Queensland Department of Agriculture


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Given the limited resources available for weed management, a strategic approach is required to give the best bang for your buck. The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species invasive potential.

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Different degrees of severity of threshing were imposed during combine-harvesting of seed of Gatton panic, a cultivar of Panicum maximum , to determine effects of degree of threshing damage on subsequent properties of seed. Threshing cylinder peripheral speeds and concave clearances covering the normal range employed commercially were varied experimentally in the harvest of 2 crops grown in north Queensland. Harvested seed was dried and cleaned, then stored under ambient conditions. The extent of physical damage was measured, and samples were tested at intervals for viability, germination, dormancy and seedling emergence from soil in a glasshouse and in the field over the 2 seasons following harvest. Physical damage increased as peripheral rotor speed rose and (though less markedly) as concave clearance was reduced. As the level of damage increased, viability was progressively reduced, life expectancy was shortened, and dormancy was broken. When the consequences were measured as seedling emergence from soil, the adverse effects on viability tended to cancel out the benefits of dormancy-breaking, leaving few net differences attributable to the degree of threshing severity. We concluded that there would be no value in trying to manipulate the quality of seed produced for normal commercial use through choice of cylinder settings, but that deliberate light or heavy threshing could benefit special-purpose seed, destined, respectively, for long-term storage or immediate use.

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It is common to model the dynamics of fisheries using natural and fishing mortality rates estimated independently using two separate analyses. Fishing mortality is routinely estimated from widely available logbook data, whereas natural mortality estimations have often required more specific, less frequently available, data. However, in the case of the fishery for brown tiger prawn (Penaeus esculentus) in Moreton Bay, both fishing and natural mortality rates have been estimated from logbook data. The present work extended the fishing mortality model to incorporate an eco-physiological response of tiger prawn to temperature, and allowed recruitment timing to vary from year to year. These ecological characteristics of the dynamics of this fishery were ignored in the separate model that estimated natural mortality. Therefore, we propose to estimate both natural and fishing mortality rates within a single model using a consistent set of hypotheses. This approach was applied to Moreton Bay brown tiger prawn data collected between 1990 and 2010. Natural mortality was estimated by maximum likelihood to be equal to 0.032 ± 0.002 week−1, approximately 30% lower than the fixed value used in previous models of this fishery (0.045 week−1).