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Dynamics of the stream-lake transitional zone affect littoral lake metabolism

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dc.contributor.authorWard, Nicole K.en
dc.contributor.authorBrentrup, Jennifer A.en
dc.contributor.authorRichardson, David C.en
dc.contributor.authorWeathers, Kathleen C.en
dc.contributor.authorHanson, Paul C.en
dc.contributor.authorHewett, Russell J.en
dc.contributor.authorCarey, Cayelan C.en
dc.date.accessioned2022-06-07T19:16:32Zen
dc.date.available2022-06-07T19:16:32Zen
dc.date.issued2022-07en
dc.description.abstractLake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream-lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake's epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2-2.5 mg O-2 L-1 day(-1) (9-670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream-lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.en
dc.description.notesThis work was financially supported by NSF Grants ICER1517823, DEB-1753639, DBI-1933102, and DBI-1933016, and a Virginia Tech College of Science Make-A-Difference Scholarship.en
dc.description.sponsorshipNSF [ICER1517823, DEB-1753639, DBI-1933102, DBI-1933016]; Virginia Tech College of Science Make-A-Difference Scholarshipen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s00027-022-00854-7en
dc.identifier.eissn1420-9055en
dc.identifier.issn1015-1621en
dc.identifier.issue3en
dc.identifier.other31en
dc.identifier.urihttp://hdl.handle.net/10919/110454en
dc.identifier.volume84en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectEcosystem functionen
dc.subjectMachine learningen
dc.subjectLittoralen
dc.subjectPelagicen
dc.subjectGPPen
dc.subjectRen
dc.titleDynamics of the stream-lake transitional zone affect littoral lake metabolismen
dc.title.serialAquatic Sciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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