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Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA

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Abstract

Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and “zones” of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.

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References

  • Aboal, M., Puig, M. A., Ríos, H., & López-Jiménez, E. (2000). Relationship between macroinvertebrate diversity and toxicity of Cyanophyceae (Cyanobacteria) in some streams from Eastern Spain. Verhandlungen des Internationalen Verein Limnologie, 27, 555–559.

    Google Scholar 

  • Andres, A. S., Main, C. R., Pettay, D. T., & Ullman, W. J. (2019). Hydrophysical and hydrochemical controls of cyanobacterial blooms in Coursey Pond, Delaware (USA). Journal of Environmental Quality, 48, 73–82.

    Google Scholar 

  • Benavides, M., Martias, C., Elifantz, H., Berman-Frank, I., Dupouy, C., & Bonnet, S. (2018). Dissolved organic matter influences N2 fixation in the New Caledonian lagoon (Western Tropical South Pacific). Frontiers in Marine Science. https://doi.org/10.3389/fmars.2018.00089.

  • Beutler, M., Wiltshire, K. H., Reineke, C., & Hansen, U. P. (2004). Algorithms and practical fluorescence models of the photosynthetic apparatus of red cyanobacteria and Cryptophyta designed for the fluorescence detection of red cyanobacteria and cryptophytes. Aquatic Microbial Ecology, 35, 115–129.

    Google Scholar 

  • Bittencourt-Oliveira, C., Cordeiro-Araújo, M. K., Chia, M. A., Arruda-Neto, J. D. T., Oliveira, E. T., & Santos, F. (2016). Lettuce irrigated with contaminated water: photosynthetic effects, antioxidative response and bioaccumulation of microcystin congeners. Ecotoxicology and Environmental Safety, 128, 83–90.

    CAS  Google Scholar 

  • Bouma-Gregson, K., Power, M. E., & Bormans, M. (2017). Rise and fall of toxic benthic freshwater cyanobacteria (Anabaena spp.) in the Eel River: buoyancy and dispersal. Harmful Algae, 66, 79–87.

    Google Scholar 

  • Breiman, L. (2017). Classification and regression trees. New York: Routledge. https://doi.org/10.1201/9781315138470.

    Book  Google Scholar 

  • Brient, L., Lengronne, M., Bertrand, E., Rolland, D., Sipel, A., Steinmann, D., Baudin, I., Legeas, M., & Bormans, M. (2007). A phycocyanin probe as a tool for monitoring cyanobacteria in freshwater bodies. Journal of Environmental Monitoring, 10(2), 248–255.

    Google Scholar 

  • Buratti, F. M., Manganelli, M., Vichi, S., Stefanelli, M., Scardala, S., Testai, E., & Funari, E. (2017). Cyanotoxins: producing organisms, occurrence, toxicity, mechanism of action and human health toxicological risk evaluation. Archives of Toxicology, 91, 1049–1130.

    CAS  Google Scholar 

  • Butitta, V. L., Carpenter, S. R., Loken, L. C., Pace, M. L., & Stanley, E. H. (2017). Spatial early warning signals in a lake manipulation. Ecosphere, 8(10), 1–11.

    Google Scholar 

  • Carmichael, W. W. (1994). The toxins of cyanobacteria. Scientific American, 270(1), 78–86.

    CAS  Google Scholar 

  • Carmichael, W. W. (2001). Health effects of toxin-producing cyanobacteria: “the CyanoHABs”. Human and Ecological Risk Assessment, 7(5), 1393–1407.

    Google Scholar 

  • Chau, K. (2006). A review on integration of artificial intelligence into water quality monitoring. Marine Pollution Bulletin, 52, 726–733.

    CAS  Google Scholar 

  • Chorus, I., & Bartram, J. (1999). Toxic cyanobacteria in water—a guide to their public health consequences, monitoring, and management. New York: E & FN Spon, published on behalf of the World Health Organization 400p.

    Google Scholar 

  • Corbel, S., Mougin, C., & Bouaïcha, N. (2014). Cyanobacterial toxins: modes of actions, fate in aquatic and soil ecosystems, phytotoxicity and bioaccumulation in agricultural crops. Chemosphere, 96, 1–15.

    CAS  Google Scholar 

  • Corbel, S., Mougin, C., Nélieu, S., Delarue, G., & Bouaïcha, N. (2016). Evaluation of the transfer and the accumulation of microcystins in tomato (Solanum lycopersicum cultivar MicroTom) tissues using a cyanobacterial extract containing microcystins and the radiolabeled microcystin-LR ((14)C-MC-LR). Science of the Total Environment, 541, 1052–1058.

    CAS  Google Scholar 

  • Davis, T. W., Berry, D. L., Boyer, G. L., & Gobler, C. J. (2009). The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae, 8(5), 715–725.

    CAS  Google Scholar 

  • Dia, S. A. (2016). Efficacy of three algaecides on algal blooms in hypereutrophic lakes. Master’s Thesis, American University of Beirut, Beirut, Lebanon. 68p.

  • Douterelo, I., Perona, E., & Mateo, P. (2004). Use of cyanobacteria to assess water quality in running waters. Environmental Pollution, 127(3), 377–384.

    CAS  Google Scholar 

  • Effler, S. W., Litten, S., Field, S. D., Tong-Ngork, T., Hale, F., Meyer, M., & Quirk, M. (1980). Whole lake responses to low level copper sulfate treatment. Water Research, 14(10), 1489–1499.

    CAS  Google Scholar 

  • Elder, J. F., & Horne, A. J. (1978). Copper cycles and CuSO4 algicidal capacity in two California lakes. Environmental Management, 2(1), 17–30.

    CAS  Google Scholar 

  • EPA. (1997). Method 445.0 in vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence. EPA-600R15006 . 22p.

  • EPA. (2019). Cyanobacteria and cyanotoxins: information for drinking water systems. EPA-810F11001. 12p.

  • Foster, G. M., Graham, J. L., & King, L. R. (2019). Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016: U.S. Geological Survey Scientific Investigations Report 2018–5166, 36 p.

  • Giardino, C., Candiani, G., Bresciani, M., Lee, Z., Gagliano, S., & Pepe, M. (2012). BOMBER: a tool for estimating water quality and bottom properties from remote sensing images. Computers & Geosciences, 45, 313–318.

    CAS  Google Scholar 

  • Gitelson, A., Dall’Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., Gurlin, D., & Holz, J. (2008). A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: validation. Remote Sensing of Environment, 112, 3582–3593.

    Google Scholar 

  • Greenfield, D. I., Duquette, A., Goodson, A., Keppler, C. J., Williams, S. H., Brock, L. M., Stackley, K. D., White, D., & Wilde, S. B. (2014). The effects of three chemical algaecides on cell numbers and toxin content of the cyanobacteria Microcystis aeruginosa and Anabaenopsis sp. Environmental Management, 54, 1110–1120.

    Google Scholar 

  • Hagerthey, S. E., & Kerfoot, W. C. (2005). Spatial variation in groundwater-related resource supply influences freshwater benthic algal assemblage composition. Journal of the North American Benthological Society, 24, 807–819.

    Google Scholar 

  • Harris, T. D., & Graham, J. L. (2017). Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking water reservoir using a 14-year dataset. Lake and Reservoir Management, 33, 32–48.

    CAS  Google Scholar 

  • Havens K.E. (2008). Cyanobacteria blooms: effects on aquatic ecosystems. In Hudnell, H.K. (ed.) Cyanobacterial harmful algal blooms: state of the science and research needs. Advances in experimental medicine and biology, vol 619. Springer, New York, NY. pp. 733–747.

  • Havens, K. E., James, R. T., East, T. L., & Smith, V. H. (2003). N:P ratios, light limitation, and cyanobacterial dominance in a subtropical lake impacted by non-point source nutrient pollution. Environmental Pollution, 122(3), 379–390.

    CAS  Google Scholar 

  • Hilborn, E. D., & Beasley, V. R. (2015). One health and cyanobacteria in freshwater systems: animal illnesses and deaths are sentinel events for human health risks. Toxins, 7, 1374–1395.

    CAS  Google Scholar 

  • Horváth, H., Kovács, A. W., Riddick, C., & Présing, M. (2013). Extraction methods for phycocyanin determination in freshwater filamentous cyanobacteria and their application in a shallow lake. European Journal of Phycology, 48(3), 278–286.

    Google Scholar 

  • Hu, C., Muller-Karger, F. E., & Swarzenski, P. W. (2006). Hurricanes, submarine groundwater discharge, and Florida’s red tides. Geophysical Research Letters, 33, L11601. https://doi.org/10.1029/2005GL025449.

    Article  Google Scholar 

  • Hudon, C., De Sève, M., & Cattaneo, A. (2014). Increasing occurrence of the benthic filamentous cyanobacterium Lyngbya wollei: a symptom of freshwater ecosystem degradation. Freshwater Science, 33, 606–618.

    Google Scholar 

  • Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J. M. H., & Visser, P. M. (2018). Cyanobacterial blooms. Nature Reviews Microbiology, 16(8), 471–483.

    CAS  Google Scholar 

  • Izydorczyka, K., Carpentier, C., Mrówczyńskia, J., Wagenvoort, A., Jurczak, T., & Tarczyńska, M. (2009). Establishment of an Alert Level Framework for cyanobacteria in drinking water resources by using the Algae Online Analyser for monitoring cyanobacterial chlorophyll a. Water Research, 43, 989–996.

    Google Scholar 

  • Ji, G., & Havens, K. (2019). Periods of extreme shallow depth hinder but do not stop long-term improvements of water quality in Lake Apopka, Florida (USA). Water, 11, 538. https://doi.org/10.3390/w11030538.

    Article  CAS  Google Scholar 

  • John, D. M., Whitton, B. A., & Brook, A. J. (2002). The freshwater algal flora of the British Isles: an identification guide to freshwater and terrestrial algae (702p). Cambridge University Press.

  • Kasinak, J. M. E., Holt, B. M., Chislock, M. F., & Wilson, A. E. (2015). Benchtop fluorometry of phycocyanin as a rapid approach for estimating cyanobacterial biovolume. Journal of Plankton Research, 37(1), 248–257.

    CAS  Google Scholar 

  • Kislik, C., Dronova, I., & Kelly, M. (2018). UAVs in support of algal bloom research: a review of current applications and future opportunities. Drones, 2, 35.

    Google Scholar 

  • Kittler, K., Schreiner, M., Krumbein, A., Manzei, S., Koch, M., Rohn, S., & Maul, R. (2012). Uptake of the cyanobacterial toxin cylindrospermopsin in Brassica vegetables. Food Chemistry, 133, 875–879.

    CAS  Google Scholar 

  • Komárek, J., Kaštovský, J., Mareš, J., & Johansen, J. R. (2014). Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia, 86, 295–335.

    Google Scholar 

  • Kong, Y., Lou, I., Zhang, Y., Lou, C. U., & Mok, K. M. (2014). Using an online phycocyanin fluorescence probe for rapid monitoring of cyanobacteria in Macau freshwater reservoir. Hydrobiologia, 741(1), 33–49.

    CAS  Google Scholar 

  • Konopka, A., & Brock, T. D. (1978). Effect of temperature on blue-green algae (Cyanobacteria) in Lake Mendota. Applied and Environmental Microbiology, 36(4), 572–576.

    CAS  Google Scholar 

  • Kosten, S., Huszar, V. L. M., Bécares, E., Costa, L. S., van Donk, E., Hansson, L. A., Jeppesen, E., Kruk, C., Lacerat, G., Mazzeo, N., Meester, L. D., Moss, B., Lürling, M., Nõges, T., Romo, S., & Scheffer, M. (2011). Warmer climates boost cyanobacterial dominance in shallow lakes. Global Change Biology, 18(1), 118–126.

    Google Scholar 

  • Kutser, T., Hedley, J., Giardino, C., Roelfsema, C., & Brando, V. E. (2020). Remote sensing of shallow waters – a 50-year retrospective and future directions. Remote Sensing of Environment, 240, 111619.

    Google Scholar 

  • Lawton, L., Marsalek, B., Padisák, J., & Chorus, I. (1999). Determination of cyanobacteria in the laboratory. In I. Chorus & J. Bartran (Eds.), Toxic cyanobacteria in water: a guide to their public health consequences, Monitoring and Management (pp. 347–367). E & FN Spon Publishers.

  • Lee, T. A., Rollwagen-Bollens, G., & Bollens, S. M. (2015). The influence of water quality variables on cyanobacterial blooms and phytoplankton community composition in a shallow temperate lake. Environmental Monitoring and Assessment, 187(6), 315. https://doi.org/10.1007/s10661-015-4550-2.

    Article  CAS  Google Scholar 

  • Lee, S., Jiang, X., Manubolu, M., Riedl, K., Ludsin, S. A., Martin, J. F., & Lee, J. (2017). Fresh produce and their soils accumulate cyanotoxins from irrigation water: implications for public health and food security. Food Research International, 102, 234–245.

    CAS  Google Scholar 

  • Li, L., Sengpiel, R. E., Pascual, D. L., Tedesco, L. P., Wilson, J. S., & Soyeux, A. (2010). Using hyperspectral remote sensing to estimate chlorophyll-a and phycocyanin in a mesotrophic reservoir. International Journal of Remote Sensing, 31(15), 4147–4162.

    Google Scholar 

  • Li, L., Li, L., & Song, K. (2015). Remote sensing of freshwater cyanobacteria: an extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin. Remote Sensing of Environment, 157, 9–23.

    CAS  Google Scholar 

  • Loh, W. (2011). Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 1(1), 14–23.

    Google Scholar 

  • Marshall, H. G., & Alden, R. W. (1990). A comparison of phytoplankton assemblages and environmental relationships in three estuarine rivers of the Lower Chesapeake Bay. Estuaries, 13, 287–300.

    CAS  Google Scholar 

  • Marshall, H. G., Burchardt, L., & Lacouture, R. (2005). A review of phytoplankton composition within Chesapeake Bay and its tidal estuaries. Journal of Plankton Research, 27, 1083–1102.

    Google Scholar 

  • McQuaid, N., Zamyadi, A., Prevost, M., Bird, D. F., & Dorner, S. (2010). Use of in vivo phycocyanin fluorescence to monitor potential microcystin producing cyanobacterial biovolume in a drinking water source. Journal of Environmental Monitoring, 13, 455–463.

    Google Scholar 

  • Millie, D. F., Weckman, G. R., Fahnenstiel, G. L., Carrick, H. J., Ardjmand, E., Young II, W. A., Sayers, M. J., & Shuchman, R. A. (2014). Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis–environmental associations within western Lake Erie. Canadian Journal of Fisheries & Aquatic Sciences, 71, 1642–1654.

    CAS  Google Scholar 

  • Mishra, S., Mishra, D. R., Lee, Z., & Tucker, C. S. (2013). Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach. Remote Sensing of Environment, 133, 141–151.

    Google Scholar 

  • O’Neil, J. M., Davis, T. W., Burford, M. A., & Gobler, C. J. (2012). The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae, 14, 313–334.

    Google Scholar 

  • Otsuki, A., Omi, T., Hashimoto, S., Aizaki, M., & Takamura, N. (1994). HPLC fluorometric determination of natural phytoplankton phycocyanin and its usefulness as cyanobacterial biomass in highly eutrophic shallow lake. Water, Air, and Soil Pollution, 76, 383–396.

    CAS  Google Scholar 

  • Paerl, H. W., & Otten, T. G. (2013). Harmful cyanobacterial blooms: causes, consequences, and control. Microbial Ecology, 65, 995–1010.

    CAS  Google Scholar 

  • Paerl, H. W., Xu, H., McCarthy, M. J., Zhu, G., Qin, B., Li, Y., & Gardner, W. S. (2011). Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy. Water Research, 45(5), 1973–1983.

    CAS  Google Scholar 

  • Patidar, S. K., Chokshi, K., George, B., Bhattacharya, S., & Mishra, S. (2015). Dominance of cyanobacterial and cryptophytic assemblage correlated to CDOM at heavy metal contamination sites of Gujarat, India. Environmental Monitoring and Assessment, 187(1), 1–9.

    CAS  Google Scholar 

  • Perona, E., Bonilla, I., & Mateo, P. (1998). Epilithic cyanobacterial communities and water quality: an alternative tool for monitoring eutrophication in the Alberche River (Spain). Journal of Applied Phycology, 10(2), 183–191.

    Google Scholar 

  • Pyo, J., Ha, S., Pachepsky, Y. A., Lee, H., Ha, R., Nam, G., Kim, M. S., Im, J., & Cho, K. H. (2016). Chlorophyll-a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea. Remote Sensing Letters, 7, 407–416.

    Google Scholar 

  • Quetglas, A., Ordines, F., & Guijarro, B. (2011). The use of artificial neural networks (ANNs) in aquatic ecology. In C. L. P. Hui (Ed.), Artificial neural networks — application (pp. 576–586). InTech Publishers.

  • Randolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D. L., & Soyeux, E. (2008). Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112(11), 4009–4019.

    Google Scholar 

  • Rengefors, K., Gustafsson, S., & Ståhl-Delbanco, A. (2004). Factors regulating the recruitment of cyanobacterial and eukaryotic phytoplankton from littoral and profundal sediments. Aquatic Microbial Ecology, 36, 213–226.

    Google Scholar 

  • Saqrane, S., & Oudra, B. (2009). CyanoHAB occurrence and water irrigation cyanotoxin contamination: ecological impacts and potential health risks. Toxins, 1(2), 113–122.

    CAS  Google Scholar 

  • Sarada, R., Pillai, M. G., & Ravishankar, G. A. (1999). Phycocyanin from Spirulina sp.: influence of processing of biomass on phycocyanin yield, analysis of efficacy of extraction methods and stability studies on phycocyanin. Process Biochemistry, 34(8), 795–801.

    CAS  Google Scholar 

  • Sayers, M., Fahnenstiel, G. L., Shuchman, R. A., & Whitley, M. (2016). Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing. International Journal of Remote Sensing, 37, 4148–4171.

    Google Scholar 

  • Schalles, J. F. (2006). Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal waters with varying suspended matter and CDOM concentrations. Remote Sensing and Digital Image Processing, 9, 27–79.

    Google Scholar 

  • Scheffer, M., Rinaldi, S., Gragnani, A., Mur, L. R., & van Nes, E. (1997). On the dominance of filamentous cyanobacteria in shallow, turbid lakes. Ecology, 78(1), 272–282.

    Google Scholar 

  • Schrader, K., & Kingsbury, K. (2000). Evaluation of limnocoral for studying the effects of phytotoxic compounds on plankton and water chemistry in aquaculture ponds. Journal of the World Aquaculture Society, 31(3), 403–415.

    Google Scholar 

  • Sengpiel, E. (2007). Using airborne hyperspectral imagery to estimate chlorophyll a and phycocyanin in three central Indiana mesotrophic to eutrophic reservoirs. Master’s Thesis Indiana University – Purdue University, Indianapolis, USA. 163p.

  • Shanmugam, P., Varunan, T., Nagendra Jaiganesh, S. N., Sahay, A., & Chauhan, P. (2016). Optical assessment of colored dissolved organic matter and its related parameters in dynamic coastal water systems. Estuarine, Coastal and Shelf Science, 175, 126–145.

    CAS  Google Scholar 

  • Silveira, S. T., Burkert, J. F. M., Costa, J. A. V., Burkert, C. A. V., & Kalil, S. J. (2007). Optimization of phycocyanin extraction from Spirulina platensis using factorial design. Bioresource Technology, 98(8), 1629–1634.

    CAS  Google Scholar 

  • Simis, S. G. H., Peters, S. W. M., & Gons, H. J. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland waters. Limnology and Oceanography, 50(1), 237–245.

    CAS  Google Scholar 

  • Song, L., Marsh, T. L., Voice, T. C., & Long, D. T. (2011). Loss of seasonal variability in a lake resulting from copper sulfate algaecide treatment. Physics and Chemistry of the Earth, 36(9–11), 430–435.

    Google Scholar 

  • Song, K., Li, L., Tedesco, L., Clercin, N., Hall, B., Li, S., Shi, K., Liu, D., & Sun, Y. (2013). Remote estimation of phycocyanin (PC) for inland waters coupled with YSI PC fluorescence probe. Environmental Science and Pollution Research, 20(8), 5330–5340.

    CAS  Google Scholar 

  • Steinberg, C. E. W., & Hartmann, H. M. (1988). Planktonic bloom-forming cyanobacteria and the eutrophication of lakes and rivers. Freshwater Biology, 20(2), 279–287.

    Google Scholar 

  • Steinberg, D. K., Nelson, N. B., Carlson, C. A., & Prusak, A. C. (2004). Production of chromophoric dissolved organic matter (CDOM) in the open ocean by zooplankton and the colonial cyanobacterium Trichodesmium spp. Marine Ecology Progress Series, 267, 45–56.

    CAS  Google Scholar 

  • Stocker, M. D., Pachepsky, Y. A., Hill, R. L., Sellner, K. G., Macarisin, D., & Staver, K. W. (2019). Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA. Science of the Total Environment, 670, 732–740.

    CAS  Google Scholar 

  • Svirčev, Z., Drobac, D., Tokodi, N., Mijovic, B., Codd, G., & Meriluoto, J. (2017). Toxicology of microcystins with reference to cases of human intoxications and epidemiological investigations of exposures to cyanobacteria and cyanotoxins. Archives of Toxicology, 91, 621–650.

    Google Scholar 

  • Teta, R., Romano, V., Della Sala, G., Picchio, S., De Sterlich, C., Mangoni, A., Di Tullio, G., Costantino, V., & Lega, M. (2017). Cyanobacteria as indicators of water quality in Campania coasts, Italy: a monitoring strategy combining remote/proximal sensing and in situ data. Environmental Research Letters, 12(2), 024001.

    Google Scholar 

  • Tourville-Poirier, A. M., Cattaneo, A., & Hudon, C. (2010). Benthic cyanobacteria and filamentous chlorophytes affect macroinvertebrate assemblages in a large fluvial lake. Journal of the North American Benthological Society, 29, 737–749.

    Google Scholar 

  • Vähätalo, A. V., Aarnos, H., Hoikkala, L., & Lignell, R. (2011). Photochemical transformation of terrestrial dissolved organic matter supports hetero- and autotrophic production in coastal waters. Marine Ecology Progress Series, 423, 1–14.

    Google Scholar 

  • Winter, J. M., Huang, H., Osterberg, E. O., & Mankin, J. S. (2020). Anthropogenic impacts on the exceptional precipitation of 2018 in the Mid-Atlantic United States. Bulletin of the American Meteorological Society, 101, S5–S10.

    Google Scholar 

  • Witte, W. G., Whitlock, C. H., Harriss, R. C., Usry, J. W., Poole, L. R., Houghton, W. M., Morris, W. D., & Gurganus, E. A. (1982). Influence of dissolved organic materials on turbid water optical properties and remote-sensing reflectance. Journal of Geophysical Research, 87(C1), 441–446.

    Google Scholar 

  • Wood, R. (2016). Acute animal and human poisoning from cyanotoxin exposure - a review of the literature. Environmental International, 91, 276–282.

    CAS  Google Scholar 

  • Wood, J. D., Franklin, R. B., Garman, G., McIninch, S., Porter, A. J., & Bukaveckas, P. A. (2014). Exposures to the cyanotoxin microcystin arising from interspecific differences in feeding habits among fish and shellfish in the James River Estuary, Virginia. Environmental Science & Technology, 48, 5194–5202.

    CAS  Google Scholar 

  • Wynne, T. T., & Stumpf, R. P. (2015). Spatial and temporal patterns in the seasonal distribution of toxic cyanobacteria in Western Lake Erie from 2002–2014. Toxins, 7, 1649–1663.

    CAS  Google Scholar 

  • Xie, H., Bélanger, S., Song, G., Benner, R., Taalba, A., Blais, M., Tremblay, J.-É., & Babin, M. (2012). Photoproduction of ammonium in the southeastern Beaufort Sea and its biogeochemical implications. Biogeosciences, 9, 3047–3061.

    CAS  Google Scholar 

  • Zamyadi, A., Choo, F., Newcomb, G., Stuetz, R., & Henderson, R. K. (2016). A review of monitoring technologies for real-time management of cyanobacteria: recent advances and future direction. Trends in Analytical Chemistry, 85(Part A), 83–96.

    CAS  Google Scholar 

  • Zanchett, G., & Oliveira-Filho, E. C. (2013). Cyanobacteria and cyanotoxins: from impacts on aquatic ecosystems and human health to anticarcinogenic effects. Toxins, 5(10), 1896–1917.

    Google Scholar 

  • Zhang, F., Harir, M., Moritz, F., Zhang, J., Witting, M., Wu, Y., Schmitt-Kopplin, P., Fekete, A., & Hertkorn, N. (2014). Molecular and structural characterization of dissolved organic matter during and post cyanobacterial bloom in Taihu by combination of NMR spectroscopy and FTICR mass spectrometry. Water Research, 57, 280–294.

    CAS  Google Scholar 

  • Zhou, S., Shao, Y., Naiyun, G., Deng, Y., Qiao, J., Ou, H., & Deng, J. (2013). Effects of different algaecides on the photosynthetic capacity, cell integrity and microcystin-LR release of Microcystis aeruginosa. Science of the Total Environment, 463-464, 111–119.

    CAS  Google Scholar 

  • Zilliges, Y., Kehr, J., Meissner, S., Ishida, K., Mikkat, S., Hagemann, M., Kaplan, A., Börner, T., & Dittmann, E. (2011). The cyanobacterial hepatotoxin microcystin binds to proteins and increases the fitness of Microcystis under oxidative stress conditions. PLoS One, 6, e17615.

    CAS  Google Scholar 

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Smith, J.E., Stocker, M.D., Wolny, J.L. et al. Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA. Environ Monit Assess 192, 706 (2020). https://doi.org/10.1007/s10661-020-08664-w

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