Environmental predictors of phytoplankton chlorophyll-a in Great Lakes coastal wetlands |
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| Affiliation: | 1. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, United States;2. Institute for Great Lakes Research and CMU Biological Station, Central Michigan University, Mt. Pleasant, MI 48859, United States;3. Canadian Wildlife Service, Environment and Climate Change Canada, Toronto, ON M3H 5T4, Canada;4. Department of Biology, New Mexico Institute of Mining and Technology, Soccoro, NM 87801, United States;5. Aquatic Research Laboratory, Lake Superior State University, Sault Ste. Marie, MI 49783, United States;6. Annis Water Resources Institute, Grand Valley State University, Muskegon, MI 49441, United States;1. Tokyo Institute of Technology, 2-12-1-M1-4, Ookayama, Meguro-ku, Tokyo 152-8552, Japan;2. University of Toyama, 3190 Gofuku, Toyama 930-8555, Japan;3. Institute of Technology of Cambodia, Russian Federation Blvd., P.O. BOX 86, Phnom Penh 12156, Cambodia;4. Ministry of Industry, Science, Technology and Innovation of Cambodia, Preah Norodom Boulevard, Sangkat Phsar Thmey III, Khan Daun Penh, Phnom Penh 120203, Cambodia;5. Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan;1. University at Buffalo, Graduate Program in Evolution, Ecology and Behavior, 602 Clemens Hall, Buffalo, NY 14260, USA;2. Buffalo State College, Great Lakes Center, 1300 Elmwood Ave., Buffalo, NY 14222, USA;3. University at Buffalo, Department of Geology, 126 Cooke Hall, Buffalo, NY 14260, USA;1. Research Park of St. Petersburg State University, St. Petersburg, VO, Dekabristov Lane, 16, St. Petersburg 199155, Russia;2. Institute of Limnology of the Russian Academy of Sciences, Sevastianova St. 9, St. Petersburg 196105 Russia;3. N. Laverov Federal Center for Integrated Arctic Research, Ural Branch of the Russian Academy of Sciences, Northern Dvina Emb. 23, Arkhangelsk 163000, Russia;1. Department of Medicinal Plants, Amol University of Special Modern Technologies, Amol, Iran;2. Department of Environmental Science, University of Tehran, Tehran, Iran;3. Department of Fisheries, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran;4. Department of Environment Sciences, Faculty of Natural Resources, University of Zabol, Sistan, Iran;5. Iran Fisheries Organization, Tehran, Iran;6. Department of Fisheries, University of Tehran, Karaj, Iran;1. Purdue University, Department of Forestry and Natural Resources, 195 Marsteller Street, West Lafayette, IN 48105, USA;2. Illinois-Indiana Sea Grant, Department of Forestry and Natural Resources, Purdue University, 195 Marsteller Street, West Lafayette, IN 48105, USA;3. Tetra Tech, 10 Post Office Square, Suite 1100, Boston, MA 02109, USA;4. National Oceanographic and Atmospheric Administration, Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI 48108-9719, USA |
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| Abstract: | Coastal wetlands of the Laurentian Great Lakes are diverse and productive ecosystems that provide many ecosystem services, but are threatened by anthropogenic factors, including nutrient input, land-use change, invasive species, and climate change. In this study, we examined one component of wetland ecosystem structure – phytoplankton biomass – using the proxy metric of water column chlorophyll-a measured in 514 coastal wetlands across all five Great Lakes as part of the Great Lakes Coastal Wetland Monitoring Program. Mean chlorophyll-a concentrations increased from north-to-south from Lake Superior to Lake Erie, but concentrations varied among sites within lakes. To predict chlorophyll-a concentrations, we developed two random forest models for each lake – one using variables that may directly relate to phytoplankton biomass (“proximate” variables; e.g., dissolved nutrients, temperature, pH) and another using variables with potentially indirect effects on phytoplankton growth (“distal” variables; e.g., land use, fetch). Proximate and distal variable models explained 16–43% and 19–48% of variation in chlorophyll-a, respectively, with models developed for lakes Erie and Michigan having the highest amount of explanatory power and models developed for lakes Ontario, Superior, and Huron having the lowest. Land-use variables were important distal predictors of chlorophyll-a concentrations across all lakes. We found multiple proximate predictors of chlorophyll-a, but there was little consistency among lakes, suggesting that, while chlorophyll-a may be broadly influenced by distal factors such as land use, individual lakes and wetlands have unique characteristics that affect chlorophyll-a concentrations. Our results highlight the importance of responsible land-use planning and watershed-level management for protecting coastal wetlands. |
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| Keywords: | Great Lakes Coastal wetlands Land use Water quality Random forest |
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