{"id":{"repo_id":"salford","oai_identifier":"oai:salford-repository.worktribe.com:1337046"},"canonical_url":"https://search.dev.ndltd.org/etd/salford/oai:salford-repository.worktribe.com:1337046","repository":{"repo_id":"salford","name":"U. of Salford","base_url":"https://salford-repository.worktribe.com/oaiprovider"},"display":{"title":"Spatial modelling of small mammal distributions in relation to parasite transmission in western China","abstract":"This research investigates the spatial distributions of small mammal species which actas intermediate host vectors for the parasitic tapeworm Echinococcus multilocularis, whichcauses a significant burden of human disease in western China. Small mammal distributionsare modelled in relation to landscape characteristics derived from multiple Landsat TM,Landsat ETM+ and MODIS satellite-derived datasets. Statistical models are used to identifythe landscape variables influencing the small mammal spatial distributions, and to describethe nature of these relationships. They are then used predictively to determine probable smallmammal distributions over large areas. Assessment of landscape change in two study areas,over a thirty year period, and its impact on small mammal communities is also modelled.Results indicate that some small mammal distributions are significantly related to thespatial distribution of the degraded grassland habitat, with landscape metrics analysisindicating that these distributions are also positively related to degraded grassland patch size.Small mammal distributions are also shown to be negatively related to single-date vegetationindex values, although the strength of these relationships is lower. When modelled againsttime-series MODIS vegetation index data, small mammal distributions show strongerrelationships than when using the single-date Landsat ETM+ imagery. These validated timeseriesmodels are used to predict small mammal distributions to a high level of accuracy.Although successful for locations where both MODIS and small mammal transect datasets areavailable, these models are not temporally or geographically transferable. Long-termlandscape change assessed using Landsat MSS and ETM+ imagery showed that large scalelandscape degradation occurred at the Serxu site between 1977 and 2001, increasing the areaof degraded grassland, and the probability of small mammal presence. At the Yili site nooverall landscape change trends were displayed. Probability maps of small mammaldistributions have been produced, allowing the identification of potential Echinococcusmultilocularis transmission foci. This has potentially significant applications in aiding thedevelopment of future disease control programmes.","abstract_html":"This research investigates the spatial distributions of small mammal species which actas intermediate host vectors for the parasitic tapeworm Echinococcus multilocularis, whichcauses a significant burden of human disease in western China. Small mammal distributionsare modelled in relation to landscape characteristics derived from multiple Landsat TM,Landsat ETM+ and MODIS satellite-derived datasets. Statistical models are used to identifythe landscape variables influencing the small mammal spatial distributions, and to describethe nature of these relationships. They are then used predictively to determine probable smallmammal distributions over large areas. Assessment of landscape change in two study areas,over a thirty year period, and its impact on small mammal communities is also modelled.Results indicate that some small mammal distributions are significantly related to thespatial distribution of the degraded grassland habitat, with landscape metrics analysisindicating that these distributions are also positively related to degraded grassland patch size.Small mammal distributions are also shown to be negatively related to single-date vegetationindex values, although the strength of these relationships is lower. When modelled againsttime-series MODIS vegetation index data, small mammal distributions show strongerrelationships than when using the single-date Landsat ETM+ imagery. These validated timeseriesmodels are used to predict small mammal distributions to a high level of accuracy.Although successful for locations where both MODIS and small mammal transect datasets areavailable, these models are not temporally or geographically transferable. Long-termlandscape change assessed using Landsat MSS and ETM+ imagery showed that large scalelandscape degradation occurred at the Serxu site between 1977 and 2001, increasing the areaof degraded grassland, and the probability of small mammal presence. At the Yili site nooverall landscape change trends were displayed. Probability maps of small mammaldistributions have been produced, allowing the identification of potential Echinococcusmultilocularis transmission foci. This has potentially significant applications in aiding thedevelopment of future disease control programmes.","abstract_has_math":false,"creators":["Marston, C"],"institution":null,"degree_name":null,"degree_level":"Doctoral (Level 8)","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-24T04:26:09Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:salford-repository.worktribe.com:1337046"],"render_values":[{"text":"oai:salford-repository.worktribe.com:1337046","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.sponsor","label":"Sponsor","values":["National Institutes of Health (USA)"]},{"key":"dc:creator","label":"Author","values":["Marston, C"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2008-11-01"]},{"key":"dc:date.issued","label":"Date","values":["2008"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://salford-repository.worktribe.com/output/1337046"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral (Level 8)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:salford-repository.worktribe.com:1337046"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://salford-repository.worktribe.com/file/1337046/1/11139145.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research investigates the spatial distributions of small mammal species which actas intermediate host vectors for the parasitic tapeworm Echinococcus multilocularis, whichcauses a significant burden of human disease in western China. Small mammal distributionsare modelled in relation to landscape characteristics derived from multiple Landsat TM,Landsat ETM+ and MODIS satellite-derived datasets. Statistical models are used to identifythe landscape variables influencing the small mammal spatial distributions, and to describethe nature of these relationships. They are then used predictively to determine probable smallmammal distributions over large areas. Assessment of landscape change in two study areas,over a thirty year period, and its impact on small mammal communities is also modelled.Results indicate that some small mammal distributions are significantly related to thespatial distribution of the degraded grassland habitat, with landscape metrics analysisindicating that these distributions are also positively related to degraded grassland patch size.Small mammal distributions are also shown to be negatively related to single-date vegetationindex values, although the strength of these relationships is lower. When modelled againsttime-series MODIS vegetation index data, small mammal distributions show strongerrelationships than when using the single-date Landsat ETM+ imagery. These validated timeseriesmodels are used to predict small mammal distributions to a high level of accuracy.Although successful for locations where both MODIS and small mammal transect datasets areavailable, these models are not temporally or geographically transferable. Long-termlandscape change assessed using Landsat MSS and ETM+ imagery showed that large scalelandscape degradation occurred at the Serxu site between 1977 and 2001, increasing the areaof degraded grassland, and the probability of small mammal presence. At the Yili site nooverall landscape change trends were displayed. Probability maps of small mammaldistributions have been produced, allowing the identification of potential Echinococcusmultilocularis transmission foci. This has potentially significant applications in aiding thedevelopment of future disease control programmes."]},{"key":"dc:title","label":"Title","values":["Spatial modelling of small mammal distributions in relation to parasite transmission in western China"]}]}],"canonical_facts":{"dc:contributor.sponsor":["National Institutes of Health (USA)"],"dc:creator":["Marston, C"],"dc:date":["2008-11-01"],"dc:date.issued":["2008"],"dc:description.abstract":["This research investigates the spatial distributions of small mammal species which actas intermediate host vectors for the parasitic tapeworm Echinococcus multilocularis, whichcauses a significant burden of human disease in western China. Small mammal distributionsare modelled in relation to landscape characteristics derived from multiple Landsat TM,Landsat ETM+ and MODIS satellite-derived datasets. Statistical models are used to identifythe landscape variables influencing the small mammal spatial distributions, and to describethe nature of these relationships. They are then used predictively to determine probable smallmammal distributions over large areas. Assessment of landscape change in two study areas,over a thirty year period, and its impact on small mammal communities is also modelled.Results indicate that some small mammal distributions are significantly related to thespatial distribution of the degraded grassland habitat, with landscape metrics analysisindicating that these distributions are also positively related to degraded grassland patch size.Small mammal distributions are also shown to be negatively related to single-date vegetationindex values, although the strength of these relationships is lower. When modelled againsttime-series MODIS vegetation index data, small mammal distributions show strongerrelationships than when using the single-date Landsat ETM+ imagery. These validated timeseriesmodels are used to predict small mammal distributions to a high level of accuracy.Although successful for locations where both MODIS and small mammal transect datasets areavailable, these models are not temporally or geographically transferable. Long-termlandscape change assessed using Landsat MSS and ETM+ imagery showed that large scalelandscape degradation occurred at the Serxu site between 1977 and 2001, increasing the areaof degraded grassland, and the probability of small mammal presence. At the Yili site nooverall landscape change trends were displayed. Probability maps of small mammaldistributions have been produced, allowing the identification of potential Echinococcusmultilocularis transmission foci. This has potentially significant applications in aiding thedevelopment of future disease control programmes."],"dc:identifier":["oai:salford-repository.worktribe.com:1337046"],"dc:identifier.uri":["https://salford-repository.worktribe.com/file/1337046/1/11139145.pdf"],"dc:language":["en"],"dc:relation.isreferencedby":["https://salford-repository.worktribe.com/output/1337046"],"dc:title":["Spatial modelling of small mammal distributions in relation to parasite transmission in western China"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral (Level 8)"]},"updated_at":"2026-07-24T04:26:09Z"}