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dc.contributor.advisorIllian, Janine
dc.contributor.advisorBurslem, David
dc.contributor.advisorLaw, Richard
dc.contributor.authorBrown, Calum
dc.coverage.spatial181en_US
dc.date.accessioned2012-09-20T20:14:34Z
dc.date.available2012-09-20T20:14:34Z
dc.date.issued2012-11
dc.identifier.urihttp://hdl.handle.net/10023/3084
dc.description.abstractThe use of spatial statistics to investigate ecological processes in plant communities is becoming increasingly widespread. In diverse communities such as tropical rainforests, analysis of spatial structure may help to unravel the various processes that act and interact to maintain high levels of diversity. In particular, a number of contrasting mechanisms have been suggested to explain species coexistence, and these differ greatly in their practical implications for the ecology and conservation of tropical forests. Traditional first-order measures of community structure have proved unable to distinguish these mechanisms in practice, but statistics that describe spatial structure may be able to do so. This is of great interest and relevance as spatially explicit data become available for a range of ecological communities and analysis methods for these data become more accessible. This thesis investigates the potential for inference about underlying ecological processes in plant communities using spatial statistics. Current methodologies for spatial analysis are reviewed and extended, and are used to characterise the spatial signals of the principal theorised mechanisms of coexistence. The sensitivity of a range of spatial statistics to these signals is assessed, and the strength of such signals in natural communities is investigated. The spatial signals of the processes considered here are found to be strong and robust to modelled stochastic variation. Several new and existing spatial statistics are found to be sensitive to these signals, and offer great promise for inference about underlying processes from empirical data. The relative strengths of particular processes are found to vary between natural communities, with any one theory being insufficient to explain observed patterns. This thesis extends both understanding of species coexistence in diverse plant communities and the methodology for assessing underlying process in particular cases. It demonstrates that the potential of spatial statistics in ecology is great and largely unexplored.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subjectSpatial patternen_US
dc.subjectTropical rainforesten_US
dc.subjectSpatial point processen_US
dc.subjectEcological modellingen_US
dc.subjectStatistical ecologyen_US
dc.subject.lccQH541.15S62C2
dc.subject.lcshSpatial ecologyen_US
dc.subject.lcshSpatial analysis (Statistics)en_US
dc.subject.lcshRain forest ecologyen_US
dc.titleSpatial patterns and species coexistence : using spatial statistics to identify underlying ecological processes in plant communitiesen_US
dc.typeThesisen_US
dc.contributor.sponsorMicrosoft Research Ltden_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.publisher.departmentCentre for Research into Ecological and Environmental Modellingen_US


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