866 resultados para Compositional data analysis-roots in geosciences


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Thesis--Illinois.

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"PB2005-917005."

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Thesis (Master's)--University of Washington, 2016-06

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The research presented indicates that lucerne crown and root rot caused by Stagonospora meliloti is prevalent in southern New South Wales, whereas Acrocalymma medicaginis is the more commonly observed pathogen in Queensland. Although both pathogens cause reddening of internal root and crown tissue of lucerne, they can be distinguished by symptomatology. S. meliloti causes a diffuse red blotching of the internal tissue accompanied by the presence of an external lesion, whereas A. medicaginis causes red streaking at the extremity of wedge-shaped, dry-rotted tissue. Inoculation of propagules of a susceptible lucerne clone indicated that S. meliloti was the more aggressive pathogen. Although A. medicaginis does not cause leaf disease, there was a strong relationship between the leaf and root reaction of clones to S. meliloti. Inheritance of resistance to S. meliloti in lucerne appeared to be conditioned by a single dominant gene, based on segregations observed in S-1 and F-1 populations, but not in a backcross population from the same family where an excess of susceptible individuals (74% v. expected of 50%) was obtained in a cross of a resistant F-1 individual to the susceptible parent. Resistance appears to be highly heritable, however, and amenable to population improvement by breeding. A conclusion of the research is that breeding for resistance to S. meliloti for lucernes to be grown in southern Australia would appear to be a worthwhile objective. Presently, no highly resistant cultivars exist anywhere in the world.

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Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.