Clustering Response-Stressor Relationships in Ecological Studies
| dc.contributor.author | Gao, Feng | en |
| dc.contributor.committeechair | Smith, Eric P. | en |
| dc.contributor.committeecochair | Prins, Samantha C. Bates | en |
| dc.contributor.committeemember | Terrell, George R. | en |
| dc.contributor.committeemember | Spitzner, Dan J. | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2014-03-14T20:13:26Z | en |
| dc.date.adate | 2008-07-31 | en |
| dc.date.available | 2014-03-14T20:13:26Z | en |
| dc.date.issued | 2007-06-20 | en |
| dc.date.rdate | 2008-07-31 | en |
| dc.date.sdate | 2008-06-21 | en |
| dc.description.abstract | This research is motivated by an issue frequently encountered in water quality monitoring and ecological assessment. One concern for researchers and watershed resource managers is how the biological community in a watershed is affected by human activities. The conventional single model approach based on regression and logistic regression usually fails to adequately model the relationship between biological responses and environmental stressors since the study samples are collected over a large spatial region and the response-stressor relationships are usually weak in this situation. In this dissertation, we propose two alternative modeling approaches to partition the whole region of study into disjoint subregions and model the response-stressor relationships within subregions simultaneously. In our examples, these modeling approaches found stronger relationships within subregions and should help the resource managers improve impairment assessment and decision making. The first approach is an adjusted Bayesian classification and regression tree (ABCART). It is based on the Bayesian classification and regression tree approach (BCART) and is modified to accommodate spatial partitions in ecological studies. The second approach is a Voronoi diagram based partition approach. This approach uses the Voronoi diagram technique to randomly partition the whole region into subregions with predetermined minimum sample size. The optimal partition/cluster is selected by Monte Carlo simulation. We propose several model selection criteria for optimal partitioning and modeling according to the nature of the study and extend it to multivariate analysis to find the underlying structure of response-stressor relationships. We also propose a multivariate hotspot detection approach (MHDM) to find the region where the response-stressor relationship is the strongest according to an R-square-like criterion. Several sets of ecological data are studied in this dissertation to illustrate the implementation of the above partition modeling approaches. The findings from these studies are consistent with other studies. | en |
| dc.description.degree | Ph. D. | en |
| dc.identifier.other | etd-06212008-233109 | en |
| dc.identifier.sourceurl | http://scholar.lib.vt.edu/theses/available/etd-06212008-233109/ | en |
| dc.identifier.uri | http://hdl.handle.net/10919/28094 | en |
| dc.publisher | Virginia Tech | en |
| dc.relation.haspart | FengGaoPhDStatistics1.pdf | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | model selection | en |
| dc.subject | CCA | en |
| dc.subject | RDA | en |
| dc.subject | BCART | en |
| dc.subject | Voronoi diagrams | en |
| dc.subject | Model based clustering | en |
| dc.title | Clustering Response-Stressor Relationships in Ecological Studies | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
Files
Original bundle
1 - 1 of 1