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Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

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dc.contributor.authorLofton, Mary E.en
dc.contributor.authorBrentrup, Jennifer A.en
dc.contributor.authorBeck, Whitney S.en
dc.contributor.authorZwart, Jacob A.en
dc.contributor.authorBhattacharya, Ruchien
dc.contributor.authorBrighenti, Ludmila S.en
dc.contributor.authorBurnet, Sarah H.en
dc.contributor.authorMcCullough, Ian M.en
dc.contributor.authorSteele, Bethel G.en
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorCottingham, Kathryn L.en
dc.contributor.authorDietze, Michael C.en
dc.contributor.authorEwing, Holly A.en
dc.contributor.authorWeathers, Kathleen C.en
dc.contributor.authorLaDeau, Shannon L.en
dc.date.accessioned2022-06-09T19:07:10Zen
dc.date.available2022-06-09T19:07:10Zen
dc.date.issued2022-03en
dc.description.abstractNear-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.en
dc.description.notesAuburn Water District/Lewiston Water Division; Bates College; Dartmouth College; Division of Biological Infrastructure, Grant/Award Numbers: 0434684, 1933016; Division of Computer and Network Systems, Grant/Award Number: 1737424; Division of Emerging Frontiers, Grant/Award Numbers: 0842112, 0842125, 0842267; Division of Environmental Biology, Grant/Award Numbers: 0749022, 1010862, 1137327, 1638577, 1702991, 1753639, MSB-1638575, MSB-1926050; Lake Sunapee Protective Assocation; Midge Eliassen Fellowship; NSF, United States National Science Foundation, Grant/Award Numbers: CNS-1737424, DBI-0434684, DEB-0749022, DEB-1010862, EF-0842112, EF-0842125, EF-0842267, EF-1137327, EF-1638577, EF-1702991, ICER-1517823, MSB EF-1638575; Virginia Polytechnic Institute and State University; Virginia Water Resources Research Centeren
dc.description.sponsorshipAuburn Water District/Lewiston Water Division; Bates College; Dartmouth College; Division of Biological Infrastructure [0434684, 1933016]; Division of Computer and Network Systems [1737424]; Division of Emerging Frontiers [0842112, 0842125, 0842267]; Division of Environmental Biology [0749022, 1010862, 1137327, 1638577, 1702991, 1753639, MSB-1638575, MSB-1926050]; Lake Sunapee Protective Assocation; Midge Eliassen Fellowship; NSF, United States National Science Foundation [CNS-1737424, DBI-0434684, DEB-0749022, DEB-1010862, EF-0842112, EF-0842125, EF-0842267, EF-1137327, EF-1638577, EF-1702991, ICER-1517823, MSB EF-1638575]; Virginia Polytechnic Institute and State University; Virginia Water Resources Research Centeren
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/eap.2590en
dc.identifier.eissn1939-5582en
dc.identifier.issn1051-0761en
dc.identifier.othere2590en
dc.identifier.pmid35343013en
dc.identifier.urihttp://hdl.handle.net/10919/110534en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectalgaeen
dc.subjectBayesian modelen
dc.subjectbloomsen
dc.subjectecological forecastingen
dc.subjecthindcasten
dc.subjectlakeen
dc.subjectoligotrophicen
dc.subjectphytoplanktonen
dc.subjectscumsen
dc.subjectstate-space modelen
dc.subjectuncertainty partitioningen
dc.subjectvariance partitioningen
dc.titleUsing near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial densityen
dc.title.serialEcological Applicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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