@mastersthesis {444,
	title = {A GIS framework for modelling wildlife species distributions},
	year = {1998},
	type = {phdPh.D.},
	abstract = {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.},
	keywords = {evidence, expert system, GIS, inference, scale, wild pigs, wildlife modeling},
	author = {Hollander, A. D.}
}
@inbook {391,
	title = {A spatial analytical hierarchy for Gap Analysis},
	booktitle = {Gap Analysis: A Landscape Approach to Biodiversity Planning},
	year = {1996},
	pages = {15-24},
	publisher = {American Society for Photogrammetry and Remote Sensing},
	organization = {American Society for Photogrammetry and Remote Sensing},
	address = {Bethesda, MD},
	abstract = {Representation in the nature reserve system is determined by comparing the distribution of vegetation and vertebrates with that of land ownership and management over a region of interest. Locating potential places to increase representation is typically done by a systematic selection algorithm over a set of spatial units larger than the landscape units of the vegetation map. The landscape is thus the basic spatial unit at which biodiversity data are compiled for Gap Analysis. However, it is only one of four levels of spatial entity that must be explicitly defined in order to complete a Gap Analysis. We refer to these entities as the planning region, the planning unit, the landscape, and the landscape feature. The objective of this paper is to describe a spatial analytical hierarchy for Gap Analysis based on these four entities. Within this broader theme we also present results of a more focused analysis on the effect of planning unit size on the selection of priority conservation areas in southwestern California.},
	keywords = {gap analysis, reserve selection, scale},
	author = {Davis, F. W. and Stoms, D. M.},
	editor = {Scott, J. M. and Tear, T. H. and Davis, F. W.}
}
@article {763,
	title = {Effects of habitat map generalization in biodiversity assessment},
	journal = {Photogrammetric Engineering and Remote Sensing},
	volume = {58},
	year = {1992},
	month = {1992},
	pages = {1587-1591},
	abstract = {Species richness is being mapped as part of an inventory of biological diversity in California (i.e., gap analysis). Species distributions are modeled with a GIS on the basis of maps of each species{\textquoteright} preferred habitats. Species richness is then tallied in equal-area sampling units. A GIS sensitivity analysis examined the effects of the level of generalization of the habitat map on the predicted distribution of species richness in the southern Sierra Nevada. As the habitat map was generalized, the number of habitat types mapped within grid cells tended to decrease with a corresponding decline in numbers of species predicted. Further, the ranking of grid cells in order of predicted numbers of species changed dramatically between levels of generalization. Areas predicted to be of greatest conservation value on the basis of species richness may therefore be sensitive to GIS data resolution.},
	keywords = {habitat suitability, scale, sensitivity analysis, species richness},
	url = {<Go to ISI>://A1992JV67200007},
	author = {Stoms, D. M.}
}
