Phytoplankton assemblages, environmental influences and trophic status using canonical correspondence analysis, fuzzy relations, and linguistic translation Janice L. Pappas ⁎ Museum of Paleontology, University of Michigan, 1109 Geddes Ave., Ann Arbor, MI 48109-1079, United States abstract article info Article history: Received 15 May 2009 Received in revised form 26 August 2009 Accepted 27 August 2009 Keywords: Diatoms Environmental indicator Fuzzy relation Linguistic modifiers Great Lakes Seasonal succession In a global assessment, canonical correspondence analysis (CCA) and partial CCA were used to ordinate Lake Huron phytoplankton abundances from June and August 1991 and environmental variables. June taxa were associated with NO 3 and chloride, while August taxa were associated with SiO 2 and temperature, and to some degree, with TSP and NH 3 . Dominant taxa were Asterionella formosa, Fragilaria capucina, Fragilaria crotonensis, Tabellaria fenestrata, and Urosolenia eriensis in June, and Achnanthidium minutissimum, Cyclotella #6, Cyclotella comensis, Cyclotella michiganiana, and Cyclotella pseudostelligera in August reflecting seasonal change. From local analysis using results from CCA and partial CCA in fuzzy relational analysis, A. minutissimum and C. comensis were influential in June, while F. crotonensis was influential in August. From linguistic translation and trophic status assignment, F. capucina and T. fenestrata indicated eutrophy, A. formosa indicated mesotrophy, C. pseudostelligera indicated mesotrophy–eutrophy, F. crotonensis and U. eriensis indicated oligotrophy–eutrophy, Cyclotella #6 indicated oligotrophy–mesotrophy, and C. michiganiana indicated oligotrophy. A linguistic solution with respect to trophic status is useful for policy makers and others interested in understanding water quality and ways to develop decisions about remediation. © 2009 Elsevier B.V. All rights reserved. 1. Introduction Phytoplankton are a major part of the aquatic food web and are important for relational and predictive schema for ecological assessment of the Great Lakes. Phytoplankton assemblages have been used for some time as water quality bioindicators in the Great Lakes (Stoermer, 1978). Of the phytoplankton, diatoms are recognized as bioindicators by govern- mental agencies and used in water quality assessment in North America (e.g., USEPA Environmental Monitoring and Assessment Program; USGS National Water-Quality Assessment Program; Great Lakes Environmental Indicators Project; Great Lakes Water Quality (Canada)) and Europe (e.g., Water Framework Directive; Biological Diatom Index). Their importance as such is evident in many studies on topics including eutrophication (e.g., Hall and Smol, 1999), lake acidification (e.g., Battarbee et al., 1999), hydrologic and climatic change in lakes (e.g., Fritz et al., 1999), and in oceans (e.g., Sancetta, 1999). Diatoms are widely distributed (Harwood and Nikolaev, 1995) and account for a significant portion of the total worldwide primary production (van den Hoek et al., 1995; Mann, 1994, 1999). They are found in seawater, freshwater, brackish water, and soil (Round et al., 1990). Not only are they found in the plankton, but also on a variety of substrates, including rocks, sand, plants, and animals (Round et al., 1990). Individual species are adapted to a wide range of environ- mental conditions, including 83 °C geyser pools and −2 °C polar sea ice in Iceland (Harwood and Nikolaev, 1995), hot springs in Iceland (Villeneuve and Pienitz, 1998), the Arctic (Hamilton et al., 1994), and in the Antarctic (Spaulding and McKnight, 2000). In ecological studies, temporal variability, spatial scaling, qualitative, and quantitative measures are characteristic of data used in assessment. Because of their siliceous frustules, diatoms are preserved in the sediments (e.g., Wolin et al., 1988), and along with availability in water, provide a long temporal record in aquatic systems such as the Great Lakes. Diatoms occur nearshore and offshore, at the surface and throughout the water column, so they also provide a spatial record. To model complexities of ecosystems, multivariate statistics have been used. These methods are widely accepted and proven ways to transform raw data into a biplot picture of variation (Gower and Hand, 1996). For example, the variation may be a gradient (ter Braak, 1986) or separation of groups as a result of using canonical correspondence analysis (CCA) (ter Braak, 1988a; Jongman et al., 1995). If the raw data do not produce an interpretable picture, data transformation or standardization may be used (Noy-Meir, 1973; Noy-Meir et al., 1975). The picture that emerges from using these techniques is relational in interpretation, but geometric as a result of actual numerical analysis. The exploratory or explanatory results reflect the complexity of ecological data, but these results have limited use in ecosystem modeling over temporal and spatial scales, especially for predictive purposes. In general, there are at least three matters that are relevant for consideration in ecological assessment research. First, there is a great Ecological Informatics 5 (2010) 79–88 ⁎ Tel.: +1 734 764 7207; fax: +1 734 936 1380. E-mail address: jlpappas@umich.edu. 1574-9541/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2009.08.005 Contents lists available at ScienceDirect Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf