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Research about central Lake Erie

Source-linked reports with geographic coverage including central Lake Erie.

2 recordsLinked to original sources

Spatially and temporally variable production pathways support the Lake Erie central basin food web

In large freshwater systems, the dominant production pathways supporting food webs are often spatiotemporally variable. We used stable isotope analysis and analysis of covariance (ANCOVA) models to investigate spatial and interannual variation in the dominant production pathways supporting fish consumers within the central basin of Lake Erie. We examined C and N stable isotope ratios of zooplankton, benthic invertebrates, and four species of fish common to nearshore areas of the central basin (yellow perch, Perca flavescens ; white perch, Morone americana ; rainbow smelt, Osmerus mordax ; and round goby, Neogobius melanostomus ) using tissue samples collected in 2017 and 2019. δ 13 C values varied by location consistent with expected baseline differences in nutrient loading ( 13 C was more enriched in the southern region) in two of six ANCOVA models. Furthermore, δ 15 N values varied with individual fish size and by location in a manner consistent with spatial patterns of nutrient loading from surrounding agricultural landscapes ( 15 N was more enriched in the northern region) and a longitudinal gradient of eutrophication, decreasing from west to east. These patterns were not exhibited by all species and did not necessarily persist across years, suggesting that additional factors (e.g., regional diet differences, river plume dynamics) also contributed to observed δ 13 C and δ 15 N variation. We suggest that spatiotemporal variation of stable isotope ratios should be accounted for in studies of trophic basis of production and food web structure in Lake Erie.

central Lake Erie

Spatio-temporal analysis of hypoxia in the Central Basin of Lake Erie of North America

We develop a spatio-temporal geostatistical interpolation framework to estimate hypoxia extent (dissolved oxygen [DO] concentrations below 2 mg/L) with data from a network of DO loggers. The framework uses empirical orthogonal functions and Bayesian kriging to identify the spatially varying temporal pattern and estimate the distribution of hypoxia, including estimation uncertainty. A prototype web application is also developed in R. The framework is applied to analyze spatio-temporal dynamics of DO in the central basin of Lake Erie in North America using data sampled from a logger network placed on the lake bottom during the summers of 2014, 2015, and 2016. Cross-validation results demonstrate that the framework is capable of capturing the dynamic nature of bottom hypoxia over offshore areas, but nearshore areas have poor interpolation performance due to the impacts of complex physical processes such as seiche events. The findings showed that in the central basin, hypoxia started to emerge in early August of 2014, while in 2015 and 2016 hypoxia began in July. The peak hypoxia extent occurred in late September 2014, mid-August 2015, and early September 2016. The prediction error of the overall spatial extent of hypoxia was as large as 25% of the interpolation area based on current logger deployment. Based on the cross-validation and interpolation error, we suggest placing more loggers in nearshore areas to reduce prediction error near the margins of the hypoxic zone.

central Lake Erie