<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author><author><style face="normal" font="default" size="100%">Thomas, K. A.</style></author><author><style face="normal" font="default" size="100%">Stine, P. A.</style></author><author><style face="normal" font="default" size="100%">Odion, D.</style></author><author><style face="normal" font="default" size="100%">Borchert, M. I.</style></author><author><style face="normal" font="default" size="100%">Thorne, J. H.</style></author><author><style face="normal" font="default" size="100%">Gray, M. V.</style></author><author><style face="normal" font="default" size="100%">Walker, R. E.</style></author><author><style face="normal" font="default" size="100%">Warner, K.</style></author><author><style face="normal" font="default" size="100%">Graae, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The California Gap Analysis Project-Final Report</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1998</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University of California, Santa Barbara</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A GIS framework for modelling wildlife species distributions</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">evidence</style></keyword><keyword><style  face="normal" font="default" size="100%">expert system</style></keyword><keyword><style  face="normal" font="default" size="100%">GIS</style></keyword><keyword><style  face="normal" font="default" size="100%">inference</style></keyword><keyword><style  face="normal" font="default" size="100%">scale</style></keyword><keyword><style  face="normal" font="default" size="100%">wild pigs</style></keyword><keyword><style  face="normal" font="default" size="100%">wildlife modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Maps of wildlife species distributions are a fundamental display of data in biogeography, and increasingly GIS methods are used to develop models of distributions. This dissertation examines some of the major issues in constructing predictive maps of species, focusing on the capability of GIS to relate environmental factors to distributions through logical or mathematical inference. The dissertation is structured in three parts. The first part considers how a variety of data sources may be aggregated to build up a picture of a distribution, using the example of the orange-throated whiptail, a lizard species living in southern California. It discusses how structuring these data on a hierarchy of spatial scales can lead to new inferences about distributions and habitat relationships. The second and third sections elaborate this theme of data availability and spatial scale in distribution modelling, using the example of the feral pig in central California. The second section presents a case study of developing an expert system to predict relative pig abundance at a regional scale. It illustrates how an expert system provides a formal treatment of aggregation of evidence, and how increasing the degree of interaction with a GIS can lead to elicitation of better models from domain experts. The third section presents a habitat model for the feral pig at a local scale. The grain size of this model is very finely resolved with respect to the home range of a pig, so this model integrates habitat elements over the home range size of the animal to create a spatially sensitive model of habitat quality. This model is tested against observation data at a number of different spatial scales, the results illustrating that it is important to recognize the spatial scale of a habitat model when it is applied.</style></abstract><work-type><style face="normal" font="default" size="100%">phdPh.D.</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author><author><style face="normal" font="default" size="100%">Bueno, M. J.</style></author><author><style face="normal" font="default" size="100%">Church, R. L.</style></author><author><style face="normal" font="default" size="100%">Okin, W. J.</style></author><author><style face="normal" font="default" size="100%">Gerrard, R. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Spatial Modeling and Decision Support System for Conservation of Biological Diversity</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September 30, 19</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University of California, Santa Barbara</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Final Report to IBM Environmental Research Program</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Goodchild, M. F.</style></author><author><style face="normal" font="default" size="100%">Steyaert, L. T.</style></author><author><style face="normal" font="default" size="100%">Parks, B. O.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical representation of species distributions for biological survey and monitoring</style></title><secondary-title><style face="normal" font="default" size="100%">GIS and Environmental Modeling: Progress and Research Issues</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data hypercube, orange-throated whiptail, GIS modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1996</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">GIS World Books</style></publisher><pub-location><style face="normal" font="default" size="100%">Fort Collins, Colorado</style></pub-location><pages><style face="normal" font="default" size="100%">445-449</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Spatial and temporal axes of domain, grain, and sampling intensity can serve as a framework to discuss opportunities for integrating spatial biodiversity data into richer, more complex representations of species distributions. This conceptual framework also highlights many of the problems in integrating data of different spatial, temporal and thematic properties. A recent analysis of the distribution of the orange-throated whiptail lizard in southern California is reviewed as an example of integration of datasets. Comparison of representations resulting from different data sources makes biases evident, highlights areas of inadequate sampling, and can lead to new inferences about habitat relationships through convergence of evidence. Improvements in the technology needed to facilitate better integration of distribution models with GIS in the areas of data entry, linkages to tools outside traditional GIS functionality, and new GIS tools to integrate existing datasets are discussed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Goodchild, M. F.</style></author><author><style face="normal" font="default" size="100%">Steyaert, L. T.</style></author><author><style face="normal" font="default" size="100%">Parks, B. O.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical representation of species distributions for biological survey and monitoring</style></title><secondary-title><style face="normal" font="default" size="100%">GIS and Environmental Modeling: Progress and Research Issues</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data hypercube</style></keyword><keyword><style  face="normal" font="default" size="100%">GIS modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">orange-throated whiptail</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><publisher><style face="normal" font="default" size="100%">GIS World Books</style></publisher><pub-location><style face="normal" font="default" size="100%">Fort Collins, Colorado</style></pub-location><pages><style face="normal" font="default" size="100%">445-449</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Spatial and temporal axes of domain, grain, and sampling intensity can serve as a framework to discuss opportunities for integrating spatial biodiversity data into richer, more complex representations of species distributions. This conceptual framework also highlights many of the problems in integrating data of different spatial, temporal and thematic properties. A recent analysis of the distribution of the orange-throated whiptail lizard in southern California is reviewed as an example of integration of datasets. Comparison of representations resulting from different data sources makes biases evident, highlights areas of inadequate sampling, and can lead to new inferences about habitat relationships through convergence of evidence. Improvements in the technology needed to facilitate better integration of distribution models with GIS in the areas of data entry, linkages to tools outside traditional GIS functionality, and new GIS tools to integrate existing datasets are discussed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Stine, P. A.</style></author><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Borchert, M. I.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gap analysis of the actual vegetation of California: 1. The Southwestern Region</style></title><secondary-title><style face="normal" font="default" size="100%">Madrono</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">California</style></keyword><keyword><style  face="normal" font="default" size="100%">gap analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">vegetation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">40-78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Gap Analysis is a method of conservation risk assessment that evaluates the protection status of plant communities, animal species and vertebrate species richness by overlay of biological distribution data on a map of existing biological reserves. The National Biological Survey has undertaken a national Gap Analysis that is being conducted by individual states but that will eventually produce regional and national assessments. Given California&#039;s size and complexity, we are conducting separate Gap Analyses for each of the state&#039;s 10 ecological regions, as delineated in The Jepson Manual. Here we summarize our findings on the distribution of plant communities and dominant plant species in the Southwestern Region of California, exclusive of the Channel Islands. We tabulate and discuss regional distribution patterns, management status and patterns of land ownership for 76 dominant woody species and 62 natural communities. Nineteen of 62 mapped communities appear to be at risk, as determined by their poor representation in existing reserves, parks or wilderness areas. Communities restricted largely to the lower elevations, such as non-native grasslands and coastal sage scrub types, are clearly at considerable risk. A majority of the lands at these elevations have already been converted to agricultural or urban uses and most of the remaining lands are threatened with future urbanization. Areas that appear to be of highest priority for conservation action based on agreement between our analysis and a recent assessment by The Nature Conservancy include the Santa Margarita River, San Mateo Creek, Miramar Mesa, Santa Clara floodplain near Fillmore, Sespe and Piru Canyons, and Tejon Pass.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An integrated geographic information system for modelling wildlife species distributions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1995</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University of California, Santa Barbara</style></publisher><pages><style face="normal" font="default" size="100%">30</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An integrated geographic information system for modelling wildlife species distributions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">University of California, Santa Barbara</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Final report prepared for the California Dept. of Fish and Game</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Miller, R. I.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical representation of species distributions using maps, images, and sighting data</style></title><secondary-title><style face="normal" font="default" size="100%">Mapping the Diversity of Nature</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data hypercube</style></keyword><keyword><style  face="normal" font="default" size="100%">orange-throated whiptail</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall</style></publisher><pub-location><style face="normal" font="default" size="100%">London</style></pub-location><pages><style face="normal" font="default" size="100%">71-88</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Geographic Information Systems technology permits the generation of complex representations of species distributions, while most of the data underlying these patterns are coarse. This suggests the importance of structuring such data along axes of differing data extent, tiling schemes, themes, and time, and displaying different representations of distributions, the philosophy being that comparison of multiple representations provides a sense of the actual distribution through convergence of evidence. We present an example using a lizard, the orange-throated whiptail (Cnemidophorus hyperythrus), which is native to southern California. The analysis was hierarchically structured by first mapping overall lizard range limits, then suitable habitats within the range, and then habitats over a local extent. Data sources include a generalized range outline, museum records, and field observations, as well as climate data, vegetation maps, and satellite imagery to serve as associated environmental variables. Comparison of representations resulting from these different data sources makes biases evident, highlights areas of inadequate sampling, and can lead to new inferences about habitat relationships. Finally, we discuss forthcoming improvements in the technology that will facilitate creation and display of families of models.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hollander, A. D.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Miller, R. I.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical representation of species distributions using maps, images, and sighting data</style></title><secondary-title><style face="normal" font="default" size="100%">Mapping the Diversity of Nature</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data hypercube, orange-throated whiptail</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1994</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall</style></publisher><pub-location><style face="normal" font="default" size="100%">London</style></pub-location><pages><style face="normal" font="default" size="100%">71-88</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Geographic Information Systems technology permits the generation of complex representations of species distributions, while most of the data underlying these patterns are coarse. This suggests the importance of structuring such data along axes of differing data extent, tiling schemes, themes, and time, and displaying different representations of distributions, the philosophy being that comparison of multiple representations provides a sense of the actual distribution through convergence of evidence. We present an example using a lizard, the orange-throated whiptail (Cnemidophorus hyperythrus), which is native to southern California. The analysis was hierarchically structured by first mapping overall lizard range limits, then suitable habitats within the range, and then habitats over a local extent. Data sources include a generalized range outline, museum records, and field observations, as well as climate data, vegetation maps, and satellite imagery to serve as associated environmental variables. Comparison of representations resulting from these different data sources makes biases evident, highlights areas of inadequate sampling, and can lead to new inferences about habitat relationships. Finally, we discuss forthcoming improvements in the technology that will facilitate creation and display of families of models.</style></abstract></record></records></xml>