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dc.contributor.authorWaller, MacKenzieen
dc.date2015-09-28 22:18:44.44
dc.date.accessioned2015-10-03T23:02:57Z
dc.date.available2015-10-03T23:02:57Z
dc.date.issued2015-10-03
dc.identifier.urihttp://hdl.handle.net/1974/13755
dc.descriptionThesis (Master, Environmental Studies) -- Queen's University, 2015-09-28 22:18:44.44en
dc.description.abstractIn recent years, reports of nuisance algae have increased within the Great Lakes Basin. These nuisance algal blooms have been known to include potentially hazardous cyanobacteria. There is a concern that harmful algal blooms are occurring more frequently which could have adverse impacts to wildlife, human activities and public health. However, there is a general lack of information on the distribution, frequency and taxonomic composition of algal blooms within the Great Lakes basin, particularly in eastern Ontario. This thesis addresses this lack of information through intensive sampling by researchers and citizen scientist volunteers. The St. Lawrence River/Lake St. Francis tributaries were used as a model system to evaluate the effects of various stressors (environmental, landscape and water quality) on algal density and frequency of harmful algal blooms. The monitoring program, run in both 2013 and 2014, found that algal assemblages were dominated by diatoms, followed by chlorophytes, and on average only ~ 1% of overall algal composition were cyanobacteria, with even smaller amounts of chrysophytes, euglenophytes, dinophytes, and cryptophytes. Multiple linear regression models were developed to predict both overall algal density and cyanobacteria density from physical and chemical variables; water temperature, total phosphorus and day of year emerged as the best predictors of algal density, and water temperature and depth were the best predictors for cyanobacteria. However, the variance explained by these models, although significant, was low. The between-year variance in algal density and composition was high, likely related to differences in precipitation. The reliability and utility of citizen science for the monitoring of harmful and nuisance algae occurrences was also evaluated. Results suggest that citizen science monitoring programs provide data complementary to traditional monitoring, although challenges associated with volunteer recruitment and retention can affect the overall effectiveness of this approach. Significant dissimilarities in community composition were observed between volunteer and researcher-collected samples, with volunteers collecting more chain forming diatoms and rare taxa. Citizen scientist samples also exhibited higher algal density than researcher-collected samples related to either differences site choice and/or the method of sample collection.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsCreative Commons - Attribution - CC BYen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectCultural Eutrophicationen
dc.subjectEastern Ontarioen
dc.subjectCyanobacteriaen
dc.subjectNuisance Algaeen
dc.subjectCitizen Scienceen
dc.subjectAlgal Bloomsen
dc.subjectRiver Tributariesen
dc.titleUnderstanding the characteristics of algal communities in the tributaries of Lake St. Francis (St. Lawrence River) through intensive sampling and the use of citizen scienceen
dc.typethesisen
dc.description.degreeM.E.S.en
dc.contributor.supervisorCumming, Brian F.en
dc.contributor.supervisorBramburger, Andrew J.en
dc.contributor.departmentEnvironmental Studiesen
dc.degree.grantorQueen's University at Kingstonen


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