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Title: Distribution modelling, macroecology and conservation : cacti of the Chihuahuan Desert Region
Author: Cabello, Barbara Karen Lucfa Goettsch
ISNI:       0000 0001 3513 4875
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2007
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The literature has seen an astounding increase in the use of formal statistical/mathematical models to predict the potential distributions of species. In part this is because the possible applications of such models are diverse, from theoretical ecology to the future implications of global climate change. In particular, they have become important tools in conservation planning (e.g. establishment of protected areas), filling gaps in the known distributions of species. This said, many gaps remain in understanding of how well different kinds of models predict the distributions of species in many higher taxa and regions, and what some of the consequences of those predictions might be. In this thesis, I explore such issues using as a study group the Cactaceae from the Chihuahuan Desert Region (CDR), which is highly endangered and in need of conservation. Models were constructed for a number of species representing a range of biologies and distributions, and te'sted using common statistical methodologies (e.g. data partitioning to apply X2 tests, ROC curves), expert opinion, and directly in the field. Field testing was done not only considering the presence-absence of species but their densities at sites. Important macroecological patterns of cacti that have implications for conservation strategies were also studied; i) density distributions, ii) density-area of occupancy relationship, iii) environmental variables related with distributions, and iv) patterns of species richness. Finally, I couple predictive models and an advanced method of landscape prioritisation to find areas of conservation importance for cacti in the CDR. The study demonstrates the potential of predictive models to aid conservation planning and their usefulness in the selection of prospective areas for sampling. The importance of careful interpretation of such predictions when they are to be applied to solving practical problems (e.g. reserve selection programs) is discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available