226 resultados para Annual ornamental


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Biomass indices, from commercial catch per unit of effort (CPUE) or random trawl surveys, are commonly used in fisheries stock assessments. Uncertainty in such indices, often ex-pressed as a coefficient of variation (CV), has two components: observation error, and annual variation in catchability. Only the former can be estimated directly. As a result, the CVs used for these indices either ignore the annual-variation component or assume a value for it (often implicitly). Two types of data for New Zealand stocks were examined: 48 sets of residuals and catchability estimates from stock assessments using either CPUE or trawl survey indices; and biomass estimates from 17 time series of trawl surveys with between 4 and 25 species per time series. These data show clear evidence of significant annual variation in catchability. With the trawl survey data, catchability was detectably extreme for many species in about one year in six. The assessment data suggest that this annual variability typically has a CV of about 0.2. For commercial CPUE the variability is slightly less, and a typical total CV (including both components) of 0.15 to 0.2. This is much less than the values of 0.3 to 0.35 that have commonly been assumed in New Zealand. Some estimates of catchability are shown to be implausible.

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General Circulation Models (GCMs) may be useful in estimating the ecological impacts of global climatic change. We analyzed seasonal weather patterns over the conterminous United States and determined that regional patterns of rainfall seasonality appear to control the distributions of the Nation's major biomes. These regional patterns were compared to the output from three GCMs for validation. The models appear to simulate the appropriate seasonal climates in the northern tier of states. However, the spatial extent of these regions is distorted. None of the models accurately portrayed rainfall seasonalities in the southern tier of states, where biomes are primarily influenced by the Bermuda High.

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The appendices include the workshop agenda, a list of poster presentations, and a list of attendees.

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Time series analysis methods have traditionally helped in identifying the role of various forcing mechanisms in influencing climate change. A challenge to understanding decadal and century-scale climate change has been that the linkages between climate changes and potential forcing mechanisms such as solar variability are often uncertain. However, most studies have focused on the role of climate forcing and climate response within a strictly linear framework. Nonlinear time series analysis procedures provide the opportunity to analyze the role of climate forcing and climate responses between different time scales of climate change. An example is provided by the possible nonlinear response of paleo-ENSO-scale climate changes as identified from coral records to forcing by the solar cycle at longer time scales.

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The appendices include the workshop agenda, a list of poster presentations, and a list of attendees.

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The appendices include the workshop agenda, a list of poster presentations, and a list of attendees.

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Abstracts of 24 oral presentations and 25 poster presentations.