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Analysis of multispecies microcosm experiments

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dc.contributor.authorMercante, Donald Eugeneen
dc.contributor.committeechairSmith, Eric P.en
dc.contributor.committeememberHinkelmann, Klausen
dc.contributor.committeememberLentner, Marvinen
dc.contributor.committeememberReynolds, Marion R. Jr.en
dc.contributor.committeememberFoutz, Robert V.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T21:20:56Zen
dc.date.adate2005-10-13en
dc.date.available2014-03-14T21:20:56Zen
dc.date.issued1990en
dc.date.rdate2005-10-13en
dc.date.sdate2005-10-13en
dc.description.abstractTraditionally, single species toxicity tests have been the primary tool for assessment of hazard of toxic substances in aquatic ecosystems. These tests are inadequate for accurately reflecting the impact of toxicants on the community structure inherent in ecosystems. Multispecies microcosm experiments are gaining widespread acceptance as an important vehicle in understanding the nature and magnitude of effects for more complex systems. Microcosm experiments are complex and costly to conduct. Consequently, sample sizes are typically small (8-20 replicates). In addition, these experiments are difficult to analyze due to their multivariate and repeated measures nature. Working under the constraint of small sample sizes, we develop inferential as well as diagnostic methods that detect and measure community changes as a result of an intervention (i.e. toxicant), and assess the importance of individual species. A multi-factorial simulation analysis is used to compare several methods. The Multi-Response Permutation Procedure (MRPP) and a regression method incorporating a correlation structure are found to be the most powerful procedures for detecting treatment differences. The MRPP is particularly suited to experiments with replication and when the response variable may not be normally distributed. The regression model for dissimilarity data has the advantage of enabling direct estimation of many parameters not possible with the MRPP as well as the magnitude of treatment effects. A stepwise dependent variable selection algorithm with a selection criterion based on a conditional p-value argument is proposed and applied to a real data set. It is seen to have advantages over other methods for assessing species importance.en
dc.description.degreePh. D.en
dc.format.extentxiii, 141 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-10132005-152505en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10132005-152505/en
dc.identifier.urihttp://hdl.handle.net/10919/39798en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1990.M473.pdfen
dc.relation.isformatofOCLC# 22252252en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1990.M473en
dc.subject.lcshMicrocosm and macrocosm -- Researchen
dc.subject.lcshMultivariate analysisen
dc.titleAnalysis of multispecies microcosm experimentsen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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