Phytoplankton species have been widely used as indicators of lake conditions, and they may be useful for detecting changes in overall lake condition. In an attempt to inventory and monitor its natural resources, the National Park Service wants to establish a monitoring program for aquatic resources in the Great Lakes Cluster National Parks. This study sought to establish baseline information on the phytoplankton and water chemistry of selected lakes in five national parks in a preliminary effort toward establishing a long-term monitoring program. Phytoplankton and water chemistry samples were collected from ten lakes in five national parks over a two-year period. A total of 176 taxa were identified during the study. Northern lakes generally had higher Shannon-Wiener diversity and clustered together in similarity. Lakes exhibited a south to north gradient of many water chemistry variables, with northern lakes having lower hardness, sulfate, turbidity, and temperature and higher dissolved oxygen. Chloride and sulfate concentrations were the variables that best explained variation among phytoplankton in the ten lakes. A monitoring plan will have to incorporate the differences among lakes, but by coordinating the effort, comparisons within and among parks and other regions will prove useful for determining environmental change.

Introduction

Significant changes in a natural lake system, whether natural or anthropogenic, can be detected with an effective monitoring program. How a lake responds to changes such as nutrient inputs, introduction of non-native species, overuse, or agricultural discharge, are in part determined by the lake's current conditions, ecological history, and the surrounding watershed. Some monitoring programs have been short-term and narrowly focused (Bricker and Ruggiero, 1997), but long-term monitoring programs can provide the context necessary to distinguish between negative impacts and natural ecological processes (Herrmann and Stottlemeyer, 1991).

The National Park Service, in response to the National Parks Omnibus Act of 1998, has undertaken a plan to inventory natural resources to ‘establish baseline information and provide information on the long-term trends in the condition of National Park System resources’ (National Parks Service, 1998). Some parks face specific and direct threats to their aquatic resources and others may be experiencing degradation very slowly. The boundaries of the Great Lakes Cluster National Parks include numerous lakes and other aquatic resources that are subject to various threats. Despite ecological protection afforded by the national parks, these lakes vary from highly impacted to pristine, as they are subject to different levels of development and use. Most have been periodically studied, but consistent, reliable baseline data are lacking. In addition to having a baseline record of water chemistry and biological communities, the establishment of a monitoring network that maximizes information and resource-sharing, perhaps using biological indicators, would be useful (Palmer et al., 1985; Tinker, 1994).

Phytoplankton communities have been widely used as indicators of overall lake condition (Setaro and Melack, 1984; Cottingham and Carpenter, 1998; Harig and Bain, 1998). Because this group is the first to respond to changes in water quality, lake chemistry conditions can be derived from phytoplankton analyses. Phytoplankton communities have been compared among lakes to establish differences in trophic status, mixing regimes, and predatory influences (Blomqvist, 1996; Makarewicz et al., 1998; Naselli-Flores and Barone, 2000; Weithoff et al., 2000). Ecological factors influencing phytoplankton communities within a geographic range could include lake origin, physical features, and climate, but none of these factors alone likely accounts for community differences.

Lakes bordering the Great Lakes are differentially subject to various microclimatic factors, including thermal buffering, prevailing winds, dune and forest barriers, snow cover, and hydrology, all of which contribute to the lakes' limnological differences. The phytoplankton in these lakes is also influenced by the availability of micronutrients, abundance of dissolved minerals, physical parameters, and biological interactions (Earle et al., 1986; Trifinova, 1998; Nair, 1999). Weighing the importance of individual factors in a complex system can be difficult.

A study of phytoplankton communities and water chemistry in ten lakes located in five national parks was conducted over the course of two years. The objective of this study was to analyze the phytoplankton community and water chemistry characteristics of selected lakes in national parks around the Great Lakes and to highlight differences that should be considered in the establishment of an inter-park coordinated monitoring program.

Methods and materials

Study area description

Lakes selected for this study are located in five national parks around Lakes Michigan and Superior (Figure 1). Extent of use, surrounding development, and lake characteristics are highly variable among lakes (Table 1). Located at the southern end of Lake Michigan and within the Chicago metropolitan area, Indiana Dunes National Lakeshore faces multiple impacts where fragments of natural land are interspersed with heavy industry and residential development. The lakes in this park, including Long Lake, were formed by lake level fluctuations and driving winds (Chrzastowski et al., 1994). At Sleeping Bear Dunes National Lakeshore, direct impacts also include development and the additional threats of agricultural drainage and highway runoff. The lakes, including Loon, North Bar, and Round Lakes, were formed through glacial scouring followed by falling lake levels and post-glacial uplift (Calver, 1942). On Lake Superior, Pictured Rocks National Lakeshore is further removed from highly populated areas, yet water quality is threatened by residential development and building within the lakes' watersheds; Beaver and Grand Sable lakes are typically classified as oligotrophic (Doepke, 1972). Voyageurs National Park, though not directly adjacent to the Great Lakes, is primarily a park of lakes and rivers. Water quality problems are complex and include artificial lake level control, dikes, dams, levees, boat traffic and development within lake watersheds. Locator and Mukooda Lakes are situated in granite-rock dominated bedrock (Winkler and Sanford, 1997) and are typically oligotrophic (Webster et al., 1993). Isle Royale National Park is an island that lies in Lake Superior and is a designated wilderness area; the lakes, including Sargent and Siskiwit Lakes, occupy granite-rock dominated bedrock (Winkler and Sanford, 1997), are typically oligotrophic (Toczydlowski et al., 1980), and are extremely remote. Anthropogenic impact is generally limited to aerial contamination, including dioxins and furans (Czuczwa et al., 1984) and PCBs (Swain, 1978).

Figure 1.

Map of the western Great Lakes with national parks included in study indicated.

Figure 1.

Map of the western Great Lakes with national parks included in study indicated.

Table 1.

Location and descriptions of lakes selected for this study; latitude and longitude refer to lake location. Lakes are listed in order from south to north

Lake Latitude Longitude Park Max. depth Surface area Surrounding area 
Long Lake N 41′ 37″ W 87′ 12″ Indiana Dunes National Lakeshore 1.3 m 34 hec Highly developed, industrial/residential area 
Loon Lake N 44′ 42″ W 86′ 08″ Sleeping Bear Dunes National Lakeshore 21 m 38 hec Recreational Platte River flows through lake  
North Bar Lake N 44′ 50″ W 86′ 03″ Sleeping Bear Dunes National Lakeshore 10 m 14 hec Recreational. Separated from Lake Michigan by narrow sand bar. 
Round Lake N 44′ 41″ W 86′ 11″ Sleeping Bear Dunes National Lakeshore 8 m 6 hec Nearby residential. Connected with large Crystal Lake 
Beaver Lake N 46′ 34″ W 86″ 19″ Pictured Rocks National Lakeshore 13 m 310 hec Recreational. Connected to Lakes Superior, Little Beaver 
Grand Sable Lake N 46′ 38″ W 86′ 02″ Pictured Rocks National Lakeshore 22 m 255 hec Recreational. Connects to Lake Superior 
Sargent Lake N 48′ 05″ W 88′ 40″ Isle Royale National Park 15 m 142 hec Isolated. North side of island 
Siskiwit Lake N 48′ 00″ W 88′ 46″ Isle Royale National Park 49 m 1605 hec Minimal recreation. Creek flows to Lake Superior. 
Locator Lake N 48′ 32″ W 93′ 00″ Voyageurs National Park 16 m 57 hec Recreational use. Within chain of four lakes 
Mukooda Lake N 48′ 20″ W 92′ 30″ Voyageurs National Park 24 m 305 hec Recreational use. Easily accessed. 
Lake Latitude Longitude Park Max. depth Surface area Surrounding area 
Long Lake N 41′ 37″ W 87′ 12″ Indiana Dunes National Lakeshore 1.3 m 34 hec Highly developed, industrial/residential area 
Loon Lake N 44′ 42″ W 86′ 08″ Sleeping Bear Dunes National Lakeshore 21 m 38 hec Recreational Platte River flows through lake  
North Bar Lake N 44′ 50″ W 86′ 03″ Sleeping Bear Dunes National Lakeshore 10 m 14 hec Recreational. Separated from Lake Michigan by narrow sand bar. 
Round Lake N 44′ 41″ W 86′ 11″ Sleeping Bear Dunes National Lakeshore 8 m 6 hec Nearby residential. Connected with large Crystal Lake 
Beaver Lake N 46′ 34″ W 86″ 19″ Pictured Rocks National Lakeshore 13 m 310 hec Recreational. Connected to Lakes Superior, Little Beaver 
Grand Sable Lake N 46′ 38″ W 86′ 02″ Pictured Rocks National Lakeshore 22 m 255 hec Recreational. Connects to Lake Superior 
Sargent Lake N 48′ 05″ W 88′ 40″ Isle Royale National Park 15 m 142 hec Isolated. North side of island 
Siskiwit Lake N 48′ 00″ W 88′ 46″ Isle Royale National Park 49 m 1605 hec Minimal recreation. Creek flows to Lake Superior. 
Locator Lake N 48′ 32″ W 93′ 00″ Voyageurs National Park 16 m 57 hec Recreational use. Within chain of four lakes 
Mukooda Lake N 48′ 20″ W 92′ 30″ Voyageurs National Park 24 m 305 hec Recreational use. Easily accessed. 

Sampling and data collection

Eight of the ten study lakes were sampled once a month between June and September in 1997 and May and September in 1998. Additionally, Long Lake was sampled in 1997, and Round Lake was sampled in 1998. In a given month, personnel stationed at each of the parks sampled the lakes on a designated day, so that typically, all ten lakes were sampled within a two-day period. Sampling sites were located at the deepest point of each lake.

Physical measurements were recorded using a YSI 6820 sonde attached to a 610-DM readout (Yellow Springs, Inc.). Measurements were taken one meter below the surface, including dissolved oxygen, pH, specific conductivity, temperature, and total dissolved solids. Water clarity was estimated using a standard 20-cm Secchi disk.

A Kemmerer sampler was used to collect water for water chemistry and phytoplankton analysis. Water chemistry samples were collected one meter below the surface and were preserved according to requirements for tests to be conducted (APHA, 1998). Water for analyzing ammonia, nitrate plus nitrite, and total phosphorus was preserved with sulfuric acid; water for analyzing silica, calcium, and magnesium was preserved with nitric acid. No preservative was added to samples collected for chloride, alkalinity, turbidity, and sulfate analysis. Samples were placed in a cooler immediately after collection.

Three whole water phytoplankton samples were also collected from one meter below lake surface and poured into three 1-liter bottles. Approximately 3 ml of Lugol's solution was added to each, and the sample was mixed gently. Samples were placed out of the sunlight to prevent degradation (APHA, 1998).

Water samples were analyzed according to EPA-approved methods (US EPA, 1983). Variables analyzed and methods used included ammonia (US EPA method 350.1), nitrate plus nitrite (353.2), total phosphorus (365.3), silica (6010), chloride (300.0), calcium and magnesium hardness (6010B), alkalinity (310.1), and sulfate (300.0).

Glutaraldehyde was added to composited phytoplankton samples containing 100 ml from each of the three samples. Following Standard Methods, permanent mounts were made by filtering subsamples through a gridded 0.45 μm filter, rinsing the filters with ethanol, and adding clove oil under a coverslip (APHA, 1998). This method is effective for including smaller plankton in the counts, but certain forms can become misshapen during filtering, and others may be obscured by particles in the sample water (APHA, 1998). After the slides cleared, phytoplankton were identified and counted using natural unit counts (Ingram and Palmer, 1952). Natural unit counts refer to each natural grouping of algae, whether individual filament, colony, or isolated cell; using this technique prevents certain forms from dominating a count. Phytoplankton species were identified using standard taxonomic texts (Patrick and Reimer, 1966, 1975; Whitford and Schumacher, 1969; Prescott, 1982).

Statistical analysis

Phytoplankton data for both years were combined for most analyses because there was no significant difference between total counts or Shannon-Wiener diversity between years. Using Primer software (version 5.0), phytoplankton data were analyzed for community characteristics, including species diversity, richness, and evenness (Pielou, 1975). Analysis with Bray-Curtis similarity followed by multi-dimensional scaling (MDS) was used for individual sampling dates to determine the similarity at a site over the course of a season relative to the other lakes sampled. Multi-dimensional scaling configures relationships set by the rank similarity matrix (Clarke and Warwick, 1994). Stress indicates the goodness-of-fit measure of the regression; a stress value < 0.05 is an excellent representation, < 0.1 is an ordination with no real prospect of misinterpretation, and < 0.2 provides a potentially useful picture (Clarke and Warwick, 1994). Major taxonomic groups were used to describe communities: Chlorophyta (green algae) Bacillariophyceae (diatoms), Chrysophyceae (yellow-green algae), Dinophyceae (dinoflagellates), Cryptophyta (Cryptomonads), Cyanophyta (blue-greens), and Euglenophyta (Euglenoids).

Data for water chemistry and physical characteristics were tested for normality using a Kolmogorov-Smirnov test (SPSS software (SPSS, 1999)). Data that were not normally distributed were log10 transformed to achieve normality; these data were compared among lakes using one-way ANOVA followed by a Duncan post-hoc test. Water chemistry and physical data were examined for collinearity using regression analysis, which produced a collinearity tolerance value. Due to the high collinearity among hardness variables (Cl, Ca, Mg, total hardness, alkalinity, and specific conductivity), only Cl was retained for further analyses with phytoplankton communities. Ammonia was eliminated because results were below detection limits for most water samples.

In order to link phytoplankton communities with water chemistry, the BIOENV procedure of Primer software was used (Clarke and Gorley, 2001b). The procedure involves a comparison of the ranks of environmental and biological data through a rank correlation coefficient. The measure of agreement is calculated between the fixed phytoplankton Bray-Curtis matrix and each of the possible water chemistry Euclidean distance matrices (Clarke and Gorley, 2001a). In this analysis, a weighted Spearman rank correlation was used, which places more emphasis on the small distances.

Results

Phytoplankton assemblages

A total of 176 phytoplankton taxa were collected from the ten lakes included in this study (Table 2). The composition of the collected phytoplankton by group included 119 diatoms (Chrysophyta: Bacillariophyceae), 31 chlorophytes (Chlorophyta), 10 cyanophytes (Cyanophyta), 6 chrysophytes (Chrysophyta: Chrysophyceae), 4 euglenoids (Euglenophyta), 3 dinoflagellates (Pyrrophyta: Dinophyceae), 2 cryptomonads (Cryptophyta), and 1 xanthophyte (Chrysophyta: Xanthophyceae). Only 32 taxa comprised greater than 1% of the phytoplankton collected from a single lake (Table 3).

Table 2.

List of taxa collected and lakes in which they were found. Lake codes: L = Long, O = Loon, N = North Bar, R = Round, B = Beaver, G = Grand Sable, S = Sargent, K = Siskiwit, C = Locator, and M = Mukooda

Taxon Lakes 
Division: Chlorophyta  
   Actinastrum spp. O, N, R, B, G, S, K, C, M 
   Ankistrodesmus/ L, O, N, B, G, S, K, C, M 
Quadrigula  
   Arthrodesmus spp. L, C, M 
   Carteria spp. L, O, G, S, M 
   Chlamydomonas spp. L, G, S, C, M 
   Cosmarium spp. L, O, N, R, G, S, K, C, M 
   Crucigeniaquadrata L, B, S, K, C, M 
   Crucigeniarectangularis L, O, R, G, S, M 
   Crucigeniatetrapedia B, S, C, M 
   Docidium spp. 
   Euastrum spp. 
   Golenkinia paucispina 
   Hormidium/Spondylosium 
   Hormidium/Stichococcus L, S, C, M 
   Kirchneriella spp. L, O, B, S, C, M 
   Oocystis spp. L, O, N, R, B, G, S, K, C, M 
   Pediastrum boryanum L, R, B 
   Pediastrum duplex L, B 
   Pediastrum integrum 
   Pediastrum spp. L, S 
   Pediastrum tetras L, M 
   Scenedesmus acuminatus 
   Scenedesmus dimorphus 
   Scenedesmus spp. L, O, N, R, B, G, S, K, C, M 
   Sorastrum spp. 
   Sphaerocystis schroeteri L, O, N, R, B, G, S, K, C, M 
   Spondylosium spp. S, K, L, M 
   Staurastrum spp. L, B, G, S, K, C, M 
   Tetraedron spp. L, S 
   Tetrastrum glabrum L, G, S, K, C, M 
Division: Chrysophyta, Class: Bacillariophyceae 
   Achnanthes clevei 
   Achnanthes exigua O, N, S 
   Achnanthes flexella O, N, B 
   Achnanthes hauckiana 
   Achnanthes lanceolata L, O, M 
   Achnanthes lapponica 
   Achnanthes linearis O, G 
   Achnanthes microcephala O, N 
   Achnanthes minutissima L, O, N, R, B, G, S, K, C, M 
   Achnanthes pinnata O, B, K 
   Achnanthes spp. L, O, B, G, S, C 
   Achnanthes sublaevis 
   Amphora ovalis O, N, B, G, S 
   Amphora perpusilla O, N, G, S, K 
   Amphora spp. O, N, B, G, S, K, C 
   Anomoeoneis vitrea S, C 
   Anomoeoneis zellensis 
   Asterionella formosa O, N, B, G, S, K, C, M 
   Coloneis spp. 
   Cocconeis placentula L, O, N, B, G, S, M 
   Cocconeis spp. 
   Cyclotella comta O, N, R, B, G, S, K, C, M 
   Cyclotella spp. L, O, N, R, B, G, S, K, 
    C, M 
   Cyclotella stelligera B, S, K, C, M 
   Cymatopleura solea 
   Cymbella amphicephala 
   Cymbella cesati O, N, G 
   Cymbella cymbiformis N, B 
   Cymbella delicatula L, N, R, B, G, K 
   Cymbella diluviana 
   Cymbella hybrida 
   Cymbella lunata O, N 
   Cymbella microcephala O, N, B 
   Cymbella minuta N, B, G, S, K, C 
   Cymbella muelleri 
   Cymbella norvegica 
   Cymbella spp. O, N, R, B, G, S, K, C 
   Cymbella subaequalis 
   Denticula elegans N, R, S, K 
   Denticula spp. B, K 
   Denticula tenuis 
   Diatoma hiemale L, N 
   Diatoma spp. 
   Diatoma tenue L, S 
   Diploneis oculata L, N, B, G, S 
   Diploneis pseudovalis B, S 
   Diploneis spp. 
   Epithemia adnata 
   Epithemia turgida 
   Eunotia incisa 
   Eunotia spp. 
   Fragilaria brevistriata L, O, N, S, K 
   Fragilaria construens L, O, N, B, S 
   Fragilaria crotonensis L, O, N, B, G, S, K, M 
   Fragilaria pinnata L, O, N, B, G, S 
   Fragilaria spp. G, S, M 
   Fragilaria vaucheriae 
   Frustulia rhomboides 
   Gomphonema acuminatum N, S 
   Gomphonema affine 
   Gomphonema angustatum 
   Gomphonema gracile 
   Gomphonema olivaceum 
   Gomphonema simus 
   Gomphonema spp. N, B, G 
   Gomphonema subtile 
   Gomphonema tenellum 
   Gomphonema truncatum 
   Gyrosigma spp. B, S, M 
   Mastogloia spp. 
   Melosira ambigua L, O, N, B, G, S, K 
   Melosira granulata 
   Melosira spp. L, O, N, B, G, S, K, M 
   Navicula bacillum 
   Navicula capitata G, S, M 
   Navicula cincta 
   Navicula costulata 
   Navicula cryptocephala O, N, S, K, M 
   Navicula exigua 
   Navicula gastrum 
   Navicula gottlandica 
   Navicula graciloides O, N 
   Navicula gregaria N, B 
   Navicula halophila 
   Navicula hustedtii 
   Navicula luzonensis 
   Navicula menisculus N, G, S 
   Navicula minima O, B, G, S, K 
   Navicula minuscula 
   Navicula mutica O, S 
   Navicula oblonga 
   Navicula pseudoscutiformis 
   Navicula pupula O, N, G, S, K 
   Navicula radiosa O, N, R, B, G, S, K 
   Navicula rhynchocephala O, N, G, S 
   Navicula seminulum 
   Navicula spp. L, O, N, R, B, G, 
    S, K, C 
   Navicula subtilissima G, K 
   Navicula vulpina L, N 
   Neidium affine 
   Neidium spp. O, N, B, S, K, M 
   Nitzschia spp. O, N, B, S, K, M 
   Nitzschia tryblionella 
   Opephora spp. 
   Pinnularia brebissonii 
   Pinnularia spp. N, G 
   Rhizosolenia eriensis L, O, N, B, G, S, K, C, M 
   Rhizosolenia longiseta 
   Rhizosolenia spp. B, G, K, C 
   Rhopalodia gibba L, B, S 
   Stauroneis anceps 
   Stauroneis smithii 
   Stephanodiscus spp. O, N, B, G, S 
   Surirella spp. N, K 
   Synedra longiceps 
   Synedra spp. L, O, N, R, B, G, S, K, C, M 
   Synedra ulna L, O, N, R, B, G, S, K, M 
   Tabellaria fenestrata L, O, N, R, B, G, S, K, C, M 
   Tabellaria flocculosa L, O, S 
Division: Chrysophyta, Class: Chrysophyceae 
   Chrysosphaerella longspina N, C 
   Dinobryon bavaricum L, N, B, S, K, C, M 
   Dinobryon spp. L, O, N, R, B, G, S, K, C, M 
   Mallomonas pseudocomata R, C, M 
   Mallomonas spp. 
   Ochromonas spp. L, O, N, R, B, G, S, K, C, M 
Division: Chrysophyta, Class: Xanthophyceae 
   Ophiocytium capitatum 
Division: Pyrrophyta, Class: Dinophyceae 
   Ceratium hirundinella L, O, N, R, B, G, S, K 
   Glenodinium/Peridinium L, O, N, R, B, G, S, K, C, M 
   Peridinium cinctum 
Division: Cryptophyta 
   Cryptomonas ovata N, S 
   Cryptomonas spp. L, O, N, R, B, G, S, K, C, M 
Class: Cyanophyta 
   Anabaena spp. L, O, N, R, B, G, S, K, C, M 
   Aphanizomenon flos-aquae G, S, C, M 
   Aphanocapsa/Aphanothece N, R, G, S, K 
   Chroococcus spp. O, N, R, B, G, S, K, C, M 
   Dactylococcopsis spp. G, S, M 
   Gomphosphaeria spp. 
   Merismopedia spp. B, K, C, M 
   Merismopedia tenuissima G, S, C 
   Microcystis spp. N, R, B, G, S, K, C, M 
   Oscillatoria spp. G, S, K, C, M 
   Spirulina spp. S, M 
Division: Euglenophyta 
   Euglena spp. L, G, M 
   Phacus spp. L, O, R, G, S, K, C, M 
   Phacus suecicus 
   Trachelmonas spp. L, O, N, R, B, G, S, K, C, M 
Taxon Lakes 
Division: Chlorophyta  
   Actinastrum spp. O, N, R, B, G, S, K, C, M 
   Ankistrodesmus/ L, O, N, B, G, S, K, C, M 
Quadrigula  
   Arthrodesmus spp. L, C, M 
   Carteria spp. L, O, G, S, M 
   Chlamydomonas spp. L, G, S, C, M 
   Cosmarium spp. L, O, N, R, G, S, K, C, M 
   Crucigeniaquadrata L, B, S, K, C, M 
   Crucigeniarectangularis L, O, R, G, S, M 
   Crucigeniatetrapedia B, S, C, M 
   Docidium spp. 
   Euastrum spp. 
   Golenkinia paucispina 
   Hormidium/Spondylosium 
   Hormidium/Stichococcus L, S, C, M 
   Kirchneriella spp. L, O, B, S, C, M 
   Oocystis spp. L, O, N, R, B, G, S, K, C, M 
   Pediastrum boryanum L, R, B 
   Pediastrum duplex L, B 
   Pediastrum integrum 
   Pediastrum spp. L, S 
   Pediastrum tetras L, M 
   Scenedesmus acuminatus 
   Scenedesmus dimorphus 
   Scenedesmus spp. L, O, N, R, B, G, S, K, C, M 
   Sorastrum spp. 
   Sphaerocystis schroeteri L, O, N, R, B, G, S, K, C, M 
   Spondylosium spp. S, K, L, M 
   Staurastrum spp. L, B, G, S, K, C, M 
   Tetraedron spp. L, S 
   Tetrastrum glabrum L, G, S, K, C, M 
Division: Chrysophyta, Class: Bacillariophyceae 
   Achnanthes clevei 
   Achnanthes exigua O, N, S 
   Achnanthes flexella O, N, B 
   Achnanthes hauckiana 
   Achnanthes lanceolata L, O, M 
   Achnanthes lapponica 
   Achnanthes linearis O, G 
   Achnanthes microcephala O, N 
   Achnanthes minutissima L, O, N, R, B, G, S, K, C, M 
   Achnanthes pinnata O, B, K 
   Achnanthes spp. L, O, B, G, S, C 
   Achnanthes sublaevis 
   Amphora ovalis O, N, B, G, S 
   Amphora perpusilla O, N, G, S, K 
   Amphora spp. O, N, B, G, S, K, C 
   Anomoeoneis vitrea S, C 
   Anomoeoneis zellensis 
   Asterionella formosa O, N, B, G, S, K, C, M 
   Coloneis spp. 
   Cocconeis placentula L, O, N, B, G, S, M 
   Cocconeis spp. 
   Cyclotella comta O, N, R, B, G, S, K, C, M 
   Cyclotella spp. L, O, N, R, B, G, S, K, 
    C, M 
   Cyclotella stelligera B, S, K, C, M 
   Cymatopleura solea 
   Cymbella amphicephala 
   Cymbella cesati O, N, G 
   Cymbella cymbiformis N, B 
   Cymbella delicatula L, N, R, B, G, K 
   Cymbella diluviana 
   Cymbella hybrida 
   Cymbella lunata O, N 
   Cymbella microcephala O, N, B 
   Cymbella minuta N, B, G, S, K, C 
   Cymbella muelleri 
   Cymbella norvegica 
   Cymbella spp. O, N, R, B, G, S, K, C 
   Cymbella subaequalis 
   Denticula elegans N, R, S, K 
   Denticula spp. B, K 
   Denticula tenuis 
   Diatoma hiemale L, N 
   Diatoma spp. 
   Diatoma tenue L, S 
   Diploneis oculata L, N, B, G, S 
   Diploneis pseudovalis B, S 
   Diploneis spp. 
   Epithemia adnata 
   Epithemia turgida 
   Eunotia incisa 
   Eunotia spp. 
   Fragilaria brevistriata L, O, N, S, K 
   Fragilaria construens L, O, N, B, S 
   Fragilaria crotonensis L, O, N, B, G, S, K, M 
   Fragilaria pinnata L, O, N, B, G, S 
   Fragilaria spp. G, S, M 
   Fragilaria vaucheriae 
   Frustulia rhomboides 
   Gomphonema acuminatum N, S 
   Gomphonema affine 
   Gomphonema angustatum 
   Gomphonema gracile 
   Gomphonema olivaceum 
   Gomphonema simus 
   Gomphonema spp. N, B, G 
   Gomphonema subtile 
   Gomphonema tenellum 
   Gomphonema truncatum 
   Gyrosigma spp. B, S, M 
   Mastogloia spp. 
   Melosira ambigua L, O, N, B, G, S, K 
   Melosira granulata 
   Melosira spp. L, O, N, B, G, S, K, M 
   Navicula bacillum 
   Navicula capitata G, S, M 
   Navicula cincta 
   Navicula costulata 
   Navicula cryptocephala O, N, S, K, M 
   Navicula exigua 
   Navicula gastrum 
   Navicula gottlandica 
   Navicula graciloides O, N 
   Navicula gregaria N, B 
   Navicula halophila 
   Navicula hustedtii 
   Navicula luzonensis 
   Navicula menisculus N, G, S 
   Navicula minima O, B, G, S, K 
   Navicula minuscula 
   Navicula mutica O, S 
   Navicula oblonga 
   Navicula pseudoscutiformis 
   Navicula pupula O, N, G, S, K 
   Navicula radiosa O, N, R, B, G, S, K 
   Navicula rhynchocephala O, N, G, S 
   Navicula seminulum 
   Navicula spp. L, O, N, R, B, G, 
    S, K, C 
   Navicula subtilissima G, K 
   Navicula vulpina L, N 
   Neidium affine 
   Neidium spp. O, N, B, S, K, M 
   Nitzschia spp. O, N, B, S, K, M 
   Nitzschia tryblionella 
   Opephora spp. 
   Pinnularia brebissonii 
   Pinnularia spp. N, G 
   Rhizosolenia eriensis L, O, N, B, G, S, K, C, M 
   Rhizosolenia longiseta 
   Rhizosolenia spp. B, G, K, C 
   Rhopalodia gibba L, B, S 
   Stauroneis anceps 
   Stauroneis smithii 
   Stephanodiscus spp. O, N, B, G, S 
   Surirella spp. N, K 
   Synedra longiceps 
   Synedra spp. L, O, N, R, B, G, S, K, C, M 
   Synedra ulna L, O, N, R, B, G, S, K, M 
   Tabellaria fenestrata L, O, N, R, B, G, S, K, C, M 
   Tabellaria flocculosa L, O, S 
Division: Chrysophyta, Class: Chrysophyceae 
   Chrysosphaerella longspina N, C 
   Dinobryon bavaricum L, N, B, S, K, C, M 
   Dinobryon spp. L, O, N, R, B, G, S, K, C, M 
   Mallomonas pseudocomata R, C, M 
   Mallomonas spp. 
   Ochromonas spp. L, O, N, R, B, G, S, K, C, M 
Division: Chrysophyta, Class: Xanthophyceae 
   Ophiocytium capitatum 
Division: Pyrrophyta, Class: Dinophyceae 
   Ceratium hirundinella L, O, N, R, B, G, S, K 
   Glenodinium/Peridinium L, O, N, R, B, G, S, K, C, M 
   Peridinium cinctum 
Division: Cryptophyta 
   Cryptomonas ovata N, S 
   Cryptomonas spp. L, O, N, R, B, G, S, K, C, M 
Class: Cyanophyta 
   Anabaena spp. L, O, N, R, B, G, S, K, C, M 
   Aphanizomenon flos-aquae G, S, C, M 
   Aphanocapsa/Aphanothece N, R, G, S, K 
   Chroococcus spp. O, N, R, B, G, S, K, C, M 
   Dactylococcopsis spp. G, S, M 
   Gomphosphaeria spp. 
   Merismopedia spp. B, K, C, M 
   Merismopedia tenuissima G, S, C 
   Microcystis spp. N, R, B, G, S, K, C, M 
   Oscillatoria spp. G, S, K, C, M 
   Spirulina spp. S, M 
Division: Euglenophyta 
   Euglena spp. L, G, M 
   Phacus spp. L, O, R, G, S, K, C, M 
   Phacus suecicus 
   Trachelmonas spp. L, O, N, R, B, G, S, K, C, M 
Table 3.

List of taxa that composed > 1% of the taxa collected from each lake and %. Lake codes: L = Long, O = Loon, N = North Bar, R = Round, B = Beaver, G = Grand Sable, S = Sargent, K = Siskiwit, C = Locator, and M = Mukooda

 
Asterionella formosa     
Cyclotella comta         
Cyclotella spp. 85 89 49 18 12 63 
Cyclotella stelligera         
Fragilaria crotonensis         
Melosira ambigua          
Melosira spp.         
Rhizosolenia eriensis       
Rhizosolenia spp.         
Synedra spp. 27 18  
Tabellaria fenestrata      
Actinastrum spp.        
Ankistrodesmus/Quadrigula      
Crucigenia rectangularis          
Crucigenia tetrapedia          
Oocystis spp.        
Scenedesmus spp.         
Sphaerocystis schroeteri    13 10 
Staurastrum spp.          
Ochromonas spp. 18 32 18 17 11 
Dinobryon bavaricum      
Dinobryon spp. 15  11 
Cryptomonas spp. 12 
Anabaena spp.       
Aphanizomenon flos-aquae          10 
Chroococcus spp.     14  
Merismopedia tenuissima         
Microcystis spp.          
Oscillatoria spp.         
Glenodinium/Peridinium       
Euglena spp.          
Trachelmonas spp. 26    
# species 16 11 14 22 12 17 21 
# individuals 4.3 × 106 5.6 × 107 7.1 × 107 1.2 × 107 1.2 × 107 1.1 × 107 1.2 × 107 1.2 × 107 1.3 × 107 8.8 × 106 
 
Asterionella formosa     
Cyclotella comta         
Cyclotella spp. 85 89 49 18 12 63 
Cyclotella stelligera         
Fragilaria crotonensis         
Melosira ambigua          
Melosira spp.         
Rhizosolenia eriensis       
Rhizosolenia spp.         
Synedra spp. 27 18  
Tabellaria fenestrata      
Actinastrum spp.        
Ankistrodesmus/Quadrigula      
Crucigenia rectangularis          
Crucigenia tetrapedia          
Oocystis spp.        
Scenedesmus spp.         
Sphaerocystis schroeteri    13 10 
Staurastrum spp.          
Ochromonas spp. 18 32 18 17 11 
Dinobryon bavaricum      
Dinobryon spp. 15  11 
Cryptomonas spp. 12 
Anabaena spp.       
Aphanizomenon flos-aquae          10 
Chroococcus spp.     14  
Merismopedia tenuissima         
Microcystis spp.          
Oscillatoria spp.         
Glenodinium/Peridinium       
Euglena spp.          
Trachelmonas spp. 26    
# species 16 11 14 22 12 17 21 
# individuals 4.3 × 106 5.6 × 107 7.1 × 107 1.2 × 107 1.2 × 107 1.1 × 107 1.2 × 107 1.2 × 107 1.3 × 107 8.8 × 106 

At the southern end of the study area, Long Lake was dominated by euglenoids (Figure 2), which typically dominate in eutrophic systems; Trachelomonas spp. comprised 26% of the total phytoplankton collected from Long Lake. Yellow-green algae were also abundant, and cryptomonads, greens, and diatoms were less common.

Figure 2.

Percent community composition of major phytoplankton groups over the sampling season for each lake. Lakes are listed in order from south to north.

Figure 2.

Percent community composition of major phytoplankton groups over the sampling season for each lake. Lakes are listed in order from south to north.

Diatoms dominated Sleeping Bear Dunes lakes throughout the study (Figure 2). In both Loon and North Bar Lakes, Cyclotellaspp. dominated the entire phytoplankton community, comprising 85% of the Loon phytoplankton, 89% of the North Bar phytoplankton, and 49% of the Round Lake phytoplankton. In Round Lake, Synedraspp. was also abundant, at 27% of the community. Yellow-green algae were also present in Loon Lake and North Bar Lake with some regularity, and blue-greens made up a small portion of the phytoplankton in Round Lake. Only five or six phytoplankton taxa comprised greater than 1% of the community in these lakes.

Yellow-greens and diatoms dominated the lakes at Pictured Rocks National Lakeshore. Ochromonas spp. was particularly abundant in both lakes: 18% in Beaver and 32% in Grand Sable. There was an early season diatom bloom in Grand Sable Lake in 1998, and diatom blooms occurred in July of both years in Beaver Lake.

Most of the far northern lakes had more varied phytoplankton communities. At Sargent Lake, phytoplankton groups co-existed throughout the sampling season, with diatoms, yellow-green algae, and green algae competing for dominance and a total of 22 species that comprised greater than 1% of the phytoplankton community. Ochromonas spp. (18%) and Sphaerocystis schroeteri (13%) were the dominant taxa in Sargent Lake. Blue-greens, cryptomonads, and euglenoids were also present in appreciable numbers. Siskiwit Lake had similar variation early in the sampling season, but the community then became dominated by diatoms, with yellow-greens also abundant. Cyclotella spp. was the dominant taxa overall in Siskiwit Lake, comprising 63% of the community.

At the far north extreme, Locator and Mukooda Lakes had variation similar to Sargent Lake. Diatoms, green and yellow-green algae, and blue-greens all competed for community dominance. In 1997, no group composed more than 30% of the community in a single sample, although there was a bloom of green algae in 1998 in Mukooda Lake. Locator Lake had 17 individual taxa and Mukooda had 21 taxa that comprised greater than 1% of the phytoplankton community.

Phytoplankton species diversity and evenness

Shannon-Weiner diversity revealed that Sargent, Mukooda, and Locator Lakes had the most diverse communities and the highest evenness (Pielou's) (Table 4), a pattern that was revealed in the co-dominance of the major phytoplankton groups. Lakes at Sleeping Bear Dunes had the lowest phytoplankton diversity, which was clearly the result of the dominance of a single phytoplankton species. There was a general pattern of high diversity in northern lakes and low diversity in southern lakes, with the exception of Siskiwit Lake. Long Lake also had relatively high species diversity, despite being among the most impacted lakes (Table 4).

Table 4.

Metric results for phytoplankton communities. Numbers are in order: Shannon-Weiner diversity (H′), Margalef species richness (d), Species evenness (J′)

Lake H′ J′ 
Long Lake 2.777 4.195 0.665 
Loon Lake 0.847 4.821 0.190 
North Bar Lake 0.684 5.144 0.151 
Round Lake 1.52 2.276 0.427 
Beaver Lake 2.625 4.724 0.603 
Grand Sable Lake 2.637 5.255 0.592 
Sargent Lake 3.309 6.951 0.699 
Siskiwit Lake 1.872 4.338 0.438 
Locator Lake 3.037 3.405 0.751 
Mukooda Lake 3.203 3.992 0.767 
Lake H′ J′ 
Long Lake 2.777 4.195 0.665 
Loon Lake 0.847 4.821 0.190 
North Bar Lake 0.684 5.144 0.151 
Round Lake 1.52 2.276 0.427 
Beaver Lake 2.625 4.724 0.603 
Grand Sable Lake 2.637 5.255 0.592 
Sargent Lake 3.309 6.951 0.699 
Siskiwit Lake 1.872 4.338 0.438 
Locator Lake 3.037 3.405 0.751 
Mukooda Lake 3.203 3.992 0.767 

Phytoplankton similarity among lakes

Using Bray-Curtis similarity, comparisons were made for total numbers of phytoplankton species among parks throughout the study, and this revealed high similarities among phytoplankton communities in lakes in the three northern parks (Figure 3a). Two lakes at Sleeping Bear Dunes were closely clustered, but Round Lake had a different phytoplankton community; of all lakes in the study, it was most similar to Siskiwit Lake (55%). Long Lake was the most dissimilar from all other lakes in the study, with the highest similarity at only 45%, with Sargent Lake.

Figure 3.

MDS of Bray-Curtis Similarity for (a) mean water chemistry and individual species counts for each lake; (b) phytoplankton species counts for each sample; (c) water chemistry variables for each sample; and (d) combined phytoplankton species counts and water chemistry variables for each sample. Lake codes: L = Long, O = Loon, N = North Bar, R = Round, B = Beaver, G = Grand Sable, S = Sargent, K = Siskiwit, C = Locator, and M = Mukooda.

Figure 3.

MDS of Bray-Curtis Similarity for (a) mean water chemistry and individual species counts for each lake; (b) phytoplankton species counts for each sample; (c) water chemistry variables for each sample; and (d) combined phytoplankton species counts and water chemistry variables for each sample. Lake codes: L = Long, O = Loon, N = North Bar, R = Round, B = Beaver, G = Grand Sable, S = Sargent, K = Siskiwit, C = Locator, and M = Mukooda.

Cluster analyses were conducted using individual samples to determine if between-lake differences were related to seasonal changes or to true differences among lakes. Within each park, a Bray-Curtis similarity analysis of individual samples generally resulted in the two lakes of each park clustering most closely together, as was also revealed in the MDS ordination (Figure 3b). The highest similarities were at Voyageurs (Locator and Mukooda) where all samples clustered within 36% and Isle Royale (Sargent and Siskiwit), where all samples clustered within 35% similarity. The highest within lake similarity of samples was at Locator Lake (56%), Long Lake (48%) and Siskiwit and Mukooda Lakes (45%). Multi-dimensional scaling of these relationships reveals the similarities between samples within each lake, but also highlights that there is seasonal variation (Figure 3b).

Water chemistry

Among lakes, patterns of water chemistry were apparent along a south to north gradient. The hardness variables (Ca, Cl, Mg, total hardness) were generally higher in the southern lakes with a decrease to north latitudes. Locator Lake had significantly lower hardness, calcium, magnesium, alkalinity, and specific conductivity (p < 0.05) than all other lakes included in the study. The ten lakes were divided into six significantly different groups based on results of total hardness, indicating the wide variation among lakes. Mukooda, Siskiwit, Sargent, and Grand Sable followed Locator with increasing hardness, and North Bar, Loon, and Round lakes had the highest total hardness. This same general pattern was apparent in the other hardness variables except for Long Lake, which had the highest Cl but the lowest total hardness.

Data between years for pH were not combined because means were significantly different. In both years Long Lake had the lowest pH; it was significantly lower than all other lakes in 1998, according to a Duncan post-hoc test, but grouped with Locator in 1997. Long Lake (1997 and 1998) and Locator (1997 only) were the only situations where median pH was below 8.0.

Sulfate, which can originate from organic degradation, weathering, sewage, fertilizer or industrial sources, followed a similar south to north pattern, with significantly higher concentrations in North Bar, Round, and Loon Lakes (p < 0.05). Mukooda had significantly lower concentrations than all other lakes, followed by the groups of Locator and Sargent; Long, Siskiwit, and Grand Sable; and Beaver.

The pattern of silica concentration was quite different from other water chemistry variables. Because silica is required for diatom growth but not for other algal groups, it would be expected that lakes with high available silica would have communities dominated by diatoms. Beaver Lake had significantly higher silica concentration than all other lakes (p < 0.05); it was also dominated by diatoms. Similarly, Sargent and Round Lakes, with high diatom counts, followed in silica concentration and were significantly different from all other lakes. Interestingly North Bar and Loon, which were dominated by Cyclotella spp., were grouped with Grand Sable, followed by Siskiwit. The lowest silica concentrations were in Long, Locator, and Mukooda (p < 0.05).

Both temperature and dissolved oxygen followed somewhat expected patterns. Significantly lower (p < 0.05) water temperatures were measured in Siskiwit, the deepest lake in the study, and Grand Sable Lakes. Highest temperatures were in Round and Long lakes, the two shallowest lakes, which are also at lower latitudes. Dissolved oxygen was significantly lower in Long Lake than all other lakes sampled. Although Long Lake was sampled near the bottom, low oxygen conditions are likely the result of other factors; high mancrophyte coverage and heavy sedimentation likely factor into the low dissolved oxygen to anoxic conditions. Additionally, Siskiwit Lake had the highest dissolved oxygen concentrations. Turbidity was also highest in Long Lake and lowest in Siskiwit Lake. From highest to lowest turbidity, the other lakes were Loon, North Bar, Sargent, Round, Mukooda, Grand Sable, Locator, and Beaver.

Bray-Curtis similarity showed that individual lakes had very similar overall water chemistry throughout the study (Figure 3c). Groups for each lake include all months and both years of sampling, indicating that all of the lakes have stable water chemistry characteristics. These analyses also revealed the relative similarity among all lakes; all single samples were within 80% similarity of each other.

Water chemistry and phytoplankton

Bioenvironmental analysis was used to compare the similarity matrix of individual phytoplankton samples to water chemistry and physical parameters. Accordingly, the combination of variables that best explained the community composition and variation patterns in these lakes and samples was Cl and SO2−4w = 0.463), two variables that showed high variation among the lakes. The combination of Cl, SO4 and turbidity was similarly correlated (ρw = 0.462). The single best variable was Clw = 0.342). When fixed physical lake variables were added to the analysis (i.e., shoreline development, surface area, maximum depth, and latitude), the best correlation was with latitude alone (ρw = 0.533), followed by the combination of latitude, Cl and SO42−w = 0.525). The importance of latitude may be, in part, due to low variation within each lake.

Using MDS, water chemistry and phytoplankton community characteristics were combined to compare the individual lakes (Figure 3d). The relationships were not as tightly correlated as in either the water chemistry or phytoplankton MDS alone. However, lakes still clustered together in obvious groupings, with similar clusters as seen in the separate MDS analyses for phytoplankton and water chemistry. Long Lake was different from all other lakes; Loon, North Bar, and Round were generally clustered together, and the northern lakes were similar.

Discussion

The original purpose of this study was to establish baseline information and to recommend appropriate monitoring protocols. Establishing a coordinated monitoring program requires that differences among individual lakes be taken into account. The five national parks are situated in areas of several different geologic formations, ecotypes, and levels of development. The lakes in this study were selected based on a number of factors, including concerns over potential human impact, lack of information, and importance of the resource, and as such, they represent a wide range of lacustrine ecosystems. Comparisons were made based on chemistry and resident phytoplankton communities, taking into account the wide range of influences.

Geology

Geologic origin can have numerous impacts on limnology, first by determining the lake's morphology, hydrology, and hydrochemistry. Total hardness and associated ions were a major distinguishing factor among lakes included in this study. The northern lakes of Isle Royale and Pictured Rocks are glacial in origin and thus are underlain by hard bedrock. The abundance of rock underlying and surrounding these lakes has led to low hardness, alkalinity, Ca, and Cl due to a lack of ion input. The southern lakes at Indiana Dunes and Sleeping Bear have higher hardness because of the higher solubility of ions in the sediment and sand in these areas previously inundated by Lake Michigan (Chrzastowski et al., 1994).

Comparisons of phytoplankton communities have often cited hardness or mineral content as the factor that best ‘explains’ differences among lakes (Earle et al., 1986; Earle, 1987; Pinel-Alloul et al., 1990; Trifinova, 1998; Nair, 1999), particularly in total phytoplankton counts. Hardness was the variable in this study that best explained differences in total amounts of phytoplankton: lakes with the hardest water, those at Sleeping Bear Dunes, had the highest total phytoplankton counts. All three of the ions analyzed, calcium, magnesium, and chloride, are required for phytoplankton growth. While Ca and Mg concentrations predominantly originate from rock weathering, chloride can be the result of cultural sources, processes that, in this study, are related to setting. Long Lake, which is closely bordered by US 12 to the south, had the highest Cl concentration, likely derived from road salt use.

Geography

Geographic location can also influence biotic communities and water chemistry when lakes are spread over a limited latitudinal gradient. In this study, geographic location incorporates not only physical differences related to latitude and biome, but also anthropogenic influences. From south to north, the individual lakes included in this study are subject to fewer direct human impacts, and therefore, geographic differences are multifaceted. In terms of physical variables typically associated with a north-south gradient, temperature differences were obvious in these lakes, with higher temperatures in the southern lakes. However, the complication of lake depth directly influenced these results, Long Lake and Siskiwit Lake being the extremes in temperature and depth.

Concentrations of SO42− are possibly related to other geographic factors. Sulfate can originate from many natural sources, but it is also a known aerial pollutant. Like many other air pollutants, SO42− deposition has historically been a problem even in national parks far from urban areas (Bennett and Bannerjee, 1995). In a landmark study in 1978, numerous pollutants were discovered in Siskiwit Lake, including PCBs and organic residues (Swain, 1978). Since then, much research has been devoted to studying the transport of air pollution to otherwise pristine areas (Czuczwa et al., 1984; Gooch and Matsumura, 1987; Thurman and Cromwell, 2000). Generally, there is a concentration gradient of sulfate deposition extending from urban areas outward, meaning Indiana Dunes would experience high amounts of deposition, and Isle Royale, Voyageurs, and Pictured Rocks would experience less, given the locations of high sulfur-producing urban areas (Chicago, Detroit, Minneapolis/St. Paul, Duluth/Superior). According to Bennett and Banerjee (1995), this depiction is accurate for the five parks included in this study, and water results presented here reveal that concentrations in lakes also followed this general pattern.

Other influences of geography can be seen in the phytoplankton diversity of these study lakes. Northern, large lakes, tended to contain diverse phytoplankton communities, with representative species from many groups. The diversity and co-occurrence of numerous phytoplankton groups in northern, oligotrophic lakes has been attributed to slower growth rates and associated niche overlap (Wetzel, 2001). Most of the oligotrophic lakes in this study, Beaver, Grand Sable, Sargent, Locator, and Mukooda, displayed this pattern, which was more pronounced further north. Siskiwit Lake phytoplankton, however, followed an unexpected pattern with low diversity and diatom dominance. It was expected that Siskiwit Lake phytoplankton would be more similar to other oligotrophic, northern lakes, but Siskiwit Lake is extremely large and deep, which sets it apart from other lakes in this study. Although maximum lake depth does not explain differences among lakes, others have shown that depth of sample collection has a significant influence on dominant species (Wall and Briand, 1980). All samples were collected one meter below the surface, and given the high water clarity of Siskiwit Lake, there was likely significant depth partitioning among phytoplankton.

The lowest diversity at Loon and North Bar Lakes can be partly attributed to the extremely high counts of individual organisms in a few specific months, most of which were Cyclotella spp., which has been recognized as a common dominant (Hutchinson, 1967). Long Lake had an unusually high diversity, given its tendency toward eutrophic conditions, but many of the phytoplankton species present were euglenoids or other groups indicative of eutrophic conditions.

The high similarity of within-lake samples at Locator, Long, Siskiwit, and Mukooda Lakes indicates that these lakes have more stable phytoplankton communities and likely fewer drastic seasonal blooms. Seasonal changes increase the within-lake variation, which can influence interpretation of monitoring results. Both Loon and North Bar Lakes experienced wide fluctuations in total phytoplankton counts between months, which were attributable to seasonal diatom blooms. This type of difference in variation suggests that the phytoplankton communities among the wide range of these lakes should not be directly compared.

Monitoring

In these study lakes, the goal for monitoring is to detect or predict changes, according to the National Park Service's monitoring protocols (National Park Service, 1988). The close correlations between water chemistry and phytoplankton indicate that there are few between-year natural changes in these lakes. A monitoring program that follows the protocol set forth in this study could be useful for maintaining a continuous dataset. Attention should be paid to changes between months and typical patterns of seasonal change can be discriminated. Using the comparisons applied here, changes between years, or even between months can be recognized in individual lakes, and any negative impacts on the lakes can be distinguished.

Monitoring within each park might eventually be extended to other lakes. Each of these parks contains dozens of lakes that are equally at risk to pervasive negative impacts. By first gathering baseline information about the other lakes, continuous monitoring would become beneficial for all of the water resources. Regardless, the information collected in this study could be applied to within-park lakes because of the similar geology and geography and associated factors. Further, as also listed in the National Park Service's monitoring protocols, these lakes may ‘provide reference points for comparison with other, more altered environments’ (National Park Service, 1988). Managers of lakes outside of the park boundaries but within the same region could rely on data from these lakes as a starting point for characterizing and assessing nearby lakes.

With a consistent monitoring protocol throughout the Great Lakes cluster, information and assistance could be shared among resource managers. Detecting change within a lake is possible at a single park, but the shared comparison among different regions may also be beneficial. Given the wide range of lake conditions from Indiana Dunes to Voyageurs, lakes outside of a particular region can also be used as reference points for change (e.g., to see if a lake is becoming more eutrophic). Finally, combined management efforts would help maintain a concentrated effort to monitor and protect these valuable lakes.

Acknowledgments

The authors thank Julia Nefczyk and Stephanie Mahoney for their assistance with phytoplankton identifications and Crisley Handly for generating figures. This research was funded in part by the National Park Service, interagency agreement 1443IA603097017. This article is contribution 1257 of the USGS Great Lakes Science Center.

References

APHA (American Public Health Association)
,
1998
. Standard methods for the examination of water and wastewater, 20th Edition. American Public Health Association, Washington, D.C.
Bennett, J. P. and Bannerjee, M.
1995
.
Air pollution vulnerability of 22 midwestern parks
.
J. Environ. Manage.
,
44
:
339
360
.
Blomqvist, P.
1996
.
Late summer phytoplankton responses to experimental manipulations of nutrients and grazing in unlimed and limed Lake Njupfatet, central Sweden
.
Arch. Hydrobiol.
,
137
:
425
455
.
Bricker, O. P. and Ruggiero, M. A.
1997
.
Toward a national program for monitoring environmental resources
.
Ecol. Appl.
,
8
:
326
329
.
Calver, J. L.,
1942
. The glacial and post-glacial history of the Platte and Crystal Lake depressions, Benzie County, Michigan. University of Michigan, Ph.D. Dissertation, Ann Arbor, MI.
Chrzastowski, M. J., Thompson, T. A. and Trask, C. B.
1994
.
Coastal geomorphology and littoral cell divisions along the Illinois-Indiana coast of Lake Michigan
.
J. Great Lakes Res.
,
20
:
27
43
.
Clarke, K. R., Gorley, R.N.,
2001a
. Primer v5: User Manual/Tutorial. PRIMER-E Ltd., Plymouth, UK.
Clarke, K. R., Gorley, R. N.,
2001b
. Primer, version 5.0. PRIMER-E, Ltd., Plymouth, UK.
Clarke, K. R. and Warwick, R. M.
1994
.
Change in Marine Communities: An Approach to Statistical Analysis and Interpretation
,
Plymouth, UK
:
Plymouth Marine Laboratory
.
Cottingham, K. C. and Carpenter, S. R.
1998
.
Population, community, and ecosystem variates as ecological indicators: Phytoplankton responses to whole-lake enrichment
.
Ecol. Appl.
,
8
:
508
530
.
Czuczwa, J. M., McVeety, B. D. and Hites, R. A.
1984
.
Polychlorinated dibenzo-p-dioxins and dibenzofurans in sediments from Siskiwit Lake, Isle Royale
.
Science
,
226
:
568
569
.
Doepke, P. A.,
1972
. Alger county lake study. Conducted for Central UP Planning and Development District, the Alger County Planning Commission, and the National Park Service, DOI, Marquette, MI.
Earle, J. C.
1987
.
Factors influencing the distribution of phytoplankton in 97 headwater lakes in insular Newfoundland
.
Can. J. Fish. Aquat. Sci.
,
44
:
639
649
.
Earle, J. C., Duthie, H. C. and Scruton, D. A.
1986
.
Analysis of the phytoplankton composition of 95 Labrador lakes, with special reference to natural and anthropogenic acidification
.
Can. J. Fish. Aquat. Sci.
,
43
:
1804
1811
.
Gooch, J. W. and Matsumura, F.
1987
.
Toxicity of chlorinated bornane (toxaphene) residues isolated from Great Lakes lake trout Salvelinus namaycush. Arch
.
Environ. Contam. Toxicol.
,
16
:
349
355
.
Harig, A. L. and Bain, M. B.
1998
.
Defining and restoring biological integrity in wilderness lakes
.
Ecol. Appl.
,
8
:
71
87
.
Herrmann, R. and Stottlemeyer, R.
1991
.
Long-term monitoring for environmental change in US National Parks: A watershed approach
.
Environ. Monit. Assess.
,
17
:
51
65
. http://dx.doi.org/10.1007%2FBF00402461
Hutchinson, G.
1967
.
A Treatise on Limnology. II. Introduction to Lake Biology and the Limnoplankton
,
New York
:
John Wiley and Sons, Inc.
.
Ingram, W. M. and Palmer, C. M.
1952
.
Simplified procedures for collecting, examining, and recording plankton in water
.
J. Am. Water Works Assoc.
,
44
:
617
Makarewicz, J. C., Bertram, P. and Lewis, T. W.
1998
.
Changes in phytoplankton size-class abundance and species composition coinciding with changes in water chemistry and zooplankton community structure of Lake Michigan, 1983 to 1992
.
J. Great Lakes Res.
,
24
:
637
657
.
Nair, M. S. R.
1999
.
Seasonal variations of phytoplankton in relation to physico-chemical factors in a village pond at Imalia (Vidisha), India
.
J. Ecotoxicol. Env. Monitor.
,
9
:
177
182
.
Naselli-Flores, L. and Barone, R.
2000
.
Phytoplankton dynamics and structure: A comparative analysis in natural and man-made water bodies of different trophic state
.
Hydrobiologia
,
438
:
65
74
. http://dx.doi.org/10.1023%2FA%3A1004109912119
National Park Service
,
1998
. Management Policies. Volume 4:4. National Parks Omnibus Act, 1998. 16 U.S.C. 5934. Washington, D.C.
Palmer, R. N., Asce, A. M. and MacKenzie, M. C.
1985
.
Optimization of water quality monitoring networks
.
J. Water Res. Plan. Manage.
,
111
:
478
493
.
Patrick, R. and Reimer, C. W.
1966
.
The Diatoms of the United States
,
Philadelphia
:
The Academy of Natural Sciences
.
Volume 1. In: Monographs of the Academy of Natural Sciences of Philadelphia
Patrick, R. and Reimer, C. W.
1975
.
The Diatoms of the United States
,
Philadelphia
:
The Academy of Natural Sciences
.
Volume 2, Part 1. In: Monographs of the Academy of Natural Sciences of Philadelphia
Pielou, K. C.
1975
.
Ecological Diversity
,
New York
:
John Wiley and Sons
.
Pinel-Alloul, B., Methot, G., Verrault, G. and Vigneault, Y.
1990
.
Phytoplankton in Quebec lakes: Variation with lake morphometry, and with natural and anthropogenic acidification
.
Can. J. Fish. Aquat. Sci.
,
47
:
1047
1057
.
Prescott, G. W.
1982
.
Algae of the Western Great Lakes Area
,
Koenigstein, , Germany
:
Otto Koelz Science Publishers
.
Setaro, F. V. and Melack, J. M.
1984
.
Responses of phytoplankton to experimental nutrient enrichment in an Amazon floodplain lake
.
Limnol. Oceanogr.
,
29
:
972
984
.
SPSS
,
1999
. SPSS, version 10.0. SPSS Inc., Chicago.
Swain, W. R.
1978
.
Chlorinated organic residues in fish, water, and precipitation from the vicinity of Isle Royale, Lake Superior
.
J. Great Lakes Res.
,
4
:
398
407
.
Thurman, E. M. and Cromwell, A. E.
2000
.
Atmospheric transport, deposition, and fate of triazine herbicides and their metabolites in pristine areas at Isle Royale National Park
.
Environ. Sci. Technol.
,
34
:
3079
3085
. http://dx.doi.org/10.1021%2Fes000995l
Tinker, P. B.
1994
. “
Monitoring environmental change through networks
”. In
Long-term Experiments in Agricultural and Ecological Sciences
, Edited by: Leigh, R. and Johnston, A. pp.
407
421
.
Wallingford, UK
:
CAB International
.
Toczydlowski, D. G., Abramson, T., Burdett, R. S.,
1980
. Aquatic baseline on Isle Royale, Michigan. Michigan Technological University, Houghton, Michigan. Report to the National Park Service.
Trifinova, I. S.
1998
.
Phytoplankton composition and biomass structure in relation to trophic gradient in some temperate and subarctic lakes of north-western Russia and Prebaltic
.
Hydrobiologia
,
369–370
:
99
108
. http://dx.doi.org/10.1023%2FA%3A1017074615932
US EPA
.
1983
.
Methods for the Chemical Analysis of Water and Wastes
,
Washington, DC
:
U.S. Environmental Protection Agency
.
Wall, D. and Briand, F.
1980
.
Spatial and temporal overlap in lake phytoplankton communities
.
Arch. Hydrobiol.
,
88
:
45
57
.
Webster, K. E., Brezonik, P. L. and Holdhusen, B. J.
1993
.
Temporal trends in low alkalinity lakes of the Upper Midwest (1983–1989)
.
Water Air Soil Poll.
,
67
:
397
414
. http://dx.doi.org/10.1007%2FBF00478155
Weithoff, G., Lorke, A. and Walz, N.
2000
.
Effects of water-column mixing on bacteria, phytoplankton, and rotifers under different levels of herbivory in a shallow eutrophic lake
.
Oecologia
,
125
:
91
100
.
Wetzel, R. G.
2001
.
Limnology: Lake and River Ecosystems
, 3rd edition,
London
:
Academic Press
.
Whitford, L. A. and Schumacher, G. J.
1969
.
A Manual of the Freshwater Algae in North Carolina
,
Raleigh
:
Agricultural Experiment Station, North Carolina State University
.
Winkler, M. G., Sanford, P. R.,
1997
. Paleolimnological research at Isle Royale, Apostle Islands, and Voyageurs National Parks. USGS, Biological Resources Division, Global Change Research Program, Reston, VA.