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dc.contributor.authorRigosi, Annaen_NZ
dc.contributor.authorHanson, Paulen_NZ
dc.contributor.authorHamilton, David P.en_NZ
dc.contributor.authorHipsey, Matthew R.en_NZ
dc.contributor.authorRusak, James A.en_NZ
dc.contributor.authorBois, Julieen_NZ
dc.contributor.authorSparber, Karinen_NZ
dc.contributor.authorChorus, Ingriden_NZ
dc.contributor.authorWatkinson, Andrew J.en_NZ
dc.contributor.authorQin, Boqiangen_NZ
dc.contributor.authorKim, Bomchulen_NZ
dc.contributor.authorBrookes, Justin D.en_NZ
dc.coverage.spatialUnited Statesen_NZ
dc.date.accessioned2015-06-16T21:28:02Z
dc.date.available2015-01-01en_NZ
dc.date.available2015-06-16T21:28:02Z
dc.date.issued2015-01-01en_NZ
dc.identifier.citationRigosi, A., Hanson, P., Hamilton, D. P., Hipsey, M., Rusak, J. A., Bois, J., … Brookes, J. D. (2015). Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems. Ecological Applications, 25(1), 186–199.en
dc.identifier.issn1051-0761en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/9414
dc.description.abstractA Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherEcological Society of Americaen_NZ
dc.relation.urihttp://www.esajournals.org/toc/ecap/25/1en_NZ
dc.rights© 2015 by the Ecological Society of America
dc.subjectBayesian networken_NZ
dc.subjectclimate changeen_NZ
dc.subjectcyanobacterial bloomsen_NZ
dc.subjectmultiple systemsen_NZ
dc.subjectnutrientsen_NZ
dc.subjectrisk assessmenten_NZ
dc.subjectuncertaintyen_NZ
dc.titleDetermining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systemsen_NZ
dc.typeJournal Article
dc.identifier.doi10.1890/13-1677.1en_NZ
dc.relation.isPartOfEcological Applicationsen_NZ
pubs.begin-page186
pubs.elements-id119261
pubs.end-page199
pubs.issue1en_NZ
pubs.notesEBSCOhost confirms peer revieweden_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FSEN
pubs.organisational-group/Waikato/FSEN/School of Science
pubs.publisher-urlhttp://www.esajournals.org/doi/pdf/10.1890/13-1677.1en_NZ
pubs.volume25en_NZ


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