934 resultados para SPECIES DISTRIBUTION MODELS
Resumo:
Includes bibliographical references (v. 1, p. 106-116; v. 2, p. 106-114) and indexes.
Resumo:
Includes bibliographical references (v.1, p. 101-115) and indexes.
Resumo:
Includes index.
Resumo:
Mode of access: Internet.
Resumo:
Stafleu and Cowan, 2d ed.
Resumo:
Mode of access: Internet.
Resumo:
Mode of access: Internet.
Resumo:
Indexed in Annual report for 1912, p. 148-164, 201-203.
Resumo:
The morphology of the exine of Late Cretaceous and Tertiary specimens of Tricolpites reticulatus previously documented from Kerguelen, the Antarctic Peninsula, and the Otway Basin of southeastern Australia has been re-examined and compared with the three pollen types identified in the genus Gunnera. An Antarctic specimen of T reticulatus (Maastrichtian) has a uniform reticulum with elongated lumina, similar to that characterising pollen type 3a of Gunnera macrophylla (subgenus Pseudogunnera). Late Cretaceous (Maastrichtian) Australian specimens of T reticulatus differ; specimens from McNamara resemble pollen of subgenera Pseudogunnera and Milligania of type 3a or type 3b, while specimens of T reticulatus from Princes show more rounded and equidimensional lumina and are therefore tentatively attributed to pollen type 2 found in subgenera Gunnera, Misandra and Panke. Kerguelen Island T reticulatus (Miocene) are distinct from Vega Island specimens: a closer resemblance of Kerguelen T reticulatus and pollen type 2 of extant Gunnera is hypothesised. A comparison between specimens of the North American Tricolpites reticulatus/microreticulatus and pollen of Gunnera is also made. The clear similarity of the North American specimens of Tricolpites microreticulatus and pollen of Gunnera in shape and in the exine surface features of pollen suggests that this taxon should not be separated from T reticulatus but should be treated as a synonym of this species. (C) 2004 Elsevier B.V. All rights reserved.
Resumo:
Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hübner)(Lepidoptera: Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.
Resumo:
Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.
Resumo:
Wet woodlands have been recognised as a priority habitat and have featured in the UK BAP since 1994. Although this has been acknowledged in a number of UK policies and guidelines, there is little information relating to their detailed ecology and management. This research, focusing on lowland Alnus glutinosa woodlands, aimed to address this data paucity through the analysis of species requirements and to develop a methodology to guide appropriate management for this habitat for the benefit of wildlife. To achieve these aims data were collected from 64 lowland Alnus glutinosa woodlands and a review of the literature was undertaken to identify species associated with the target habitat. The groundflora species found to be associated with lowland Alnus glutinosa woodland were assessed in relation to their optimal environmental conditions (Ellenberg indicator values) and survival strategies (Grime CSR-Strategy) to determine the characteristics (Characters of a Habitat; CoaHs) and range of intra-site conditions (Niches of a Habitat; NoaH). The methodologies, using CSR and Ellenberg indicator values in combination, were developed to determine NoaHs and were tested both quantitatively and qualitatively at different lowland Alnus glutinosa sites. The existence of CoaHs and NoaHs in actual sites was verified by detailed quadrat data gathered at three Alnus glutinosa woodlands at Stonebridge Meadows, Warwickshire, UK and analysed using TWINSPAN and DCA ordination. The CoaHs and NoaHs and their component species were confirmed to have the potential to occur in a particular woodland. Following a literature search relating to the management of small wet woodlands within the UK, in conjunction with the current research, broad principles and strategies were identified for the management of lowland Alnus glutinosa woodland. Using the groundflora composition, an innovative procedure is developed and described for identifying the potential variation within a particular site and determining its appropriate management. Case studies were undertaken on distinct woodlands and the methodology proved effective.
Resumo:
The spatial distribution of self-employment in India: evidence from semiparametric geoadditive models, Regional Studies. The entrepreneurship literature has rarely considered spatial location as a micro-determinant of occupational choice. It has also ignored self-employment in developing countries. Using Bayesian semiparametric geoadditive techniques, this paper models spatial location as a micro-determinant of self-employment choice in India. The empirical results suggest the presence of spatial occupational neighbourhoods and a clear north–south divide in self-employment when the entire sample is considered; however, spatial variation in the non-agriculture sector disappears to a large extent when individual factors that influence self-employment choice are explicitly controlled. The results further suggest non-linear effects of age, education and wealth on self-employment.