. . . a Wkfiflvw. 35...?“ 5.,1 . .....J a... n t f}. .. v 3.1.99... ... . ,. 1.». a t kw: . atti‘. 3.2!..- rilwt‘l .. 1;. A .1 VHF-uwhhummmfigwwwuw , . . ‘ . . . . I— O? V In . t ‘ . ‘ . ‘ _ _ H ‘ , , , .. :§.fl?.mu m...v.‘........_zr:mp5.. V 2 ‘ 3.... ...CH ‘ . ... ‘I ‘ . . .. \| I (I LS 3003 LIBRARY Michigan State University This is to certify that the thesis entitled A State-Wide Assessment of Water Clarity Trends in Michigan Lakes: 1974-2001 presented by Laura Christine Bruhn has been accepted towards fulfillment of the requirements for M.S. Resource Development degree in / Major professor Date /Z//l//JL 0-7639 MS U i: an Affirmative Action/Equal Opportunity Institution PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/01 c:/ClRC/DataDue.p65~p.15 A STATE-WIDE ASSESSMENT OF WATER CLARITY TRENDS IN MICHIGAN LAKES: 1974-2001 By Laura Christine Bruhn A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Resource Development 2002 ABSTRACT A STATE-WIDE ASSESSMENT OF WATER CLARITY TRENDS IN MICHIGAN LAKES: 1974-2001 By Laura Christine Bruhn The approximately 11,000 inland lakes in the state of Michigan are valued ecosystems yet are susceptible to lake degradation due to anthropogenic stresses. Most data on water quality trends in Michigan have focused on the Great Lakes. There is little information as to how Michigan’s inland lakes have changed over time. My first objective was to assess historical water quality trends of 71 inland lakes using volunteer-collected Secchi depth data from Michigan’s Cooperative Lakes Monitoring Program (CLMP) with a data span from 1974 to 2001. State-wide, lake clarity in Michigan has been increasing since the 1970s. Of the individual lake trends, 31% are increasing in clarity, 6% are decreasing in clarity, and 63% have no trend. The influence of factors driving the trends such as ecoregions, land use, and non-indigenous species were examined. Ecoregions showed more influence on the clarity of lakes than did land use and the mean Secchi depth was lower for the southern ecoregion section. These analyses did not detect a strong effect of land use on water clarity in lakes across the state. Volunteer monitoring programs an invaluable contribution to water quality information. Results of this study have helped elucidate anthropogenic impacts on inland lakes in Michigan and can assist in setting priorities for statewide lake monitoring. ACKNOWLEDGEMENTS I extend my gratitude to my committee, who helped me through my studies at Michigan State. My major professor, Dr. Jon Bartholic, (professor in Resource Development and Director, Institute of Water Research), and Dr. Scott Witter (Acting chair, Resource Development), guided and inspired me and provided helpful comments and suggestions on my research. Dr. Patricia Soranno, associate professor in the department of Fisheries and Wildlife provided many hours of guidance and valuable advice as I worked through the research and writing process. The Institute of Water Research (IWR) at Michigan State University supported me during my studies and research. This research was funded by a grant with the United States Geological Survey, in cooperation with IWR. I would also like to thank the Michigan Department of Environmental Quality and Ralph Bednarz for helping me access these Cooperative Lakes Monitoring Program (CLMP) data, and to the many volunteers in the CLMP who have collected these data over the years. Michael Belligan also helped me access Geographic Information Systems data for this research. Last but not least, I thank my family for their endless support and encouragement during my studies. iii PREFACE Chapter 1 of this thesis provides a background and literature review to the topic. Chapter 2 is written in manuscript form that will be submitted to the journal of the North American Lake Management Society: Lake and Reservoir Management. It is written in the style and format consistent with the journal. The authors are L.C. Bruhn, P.A. Soranno, and J.F. Bartholic. Chapter three includes the future research directions, conclusions, and management implications of the research. Appendix A contains the tables and Appendix 8 contains the figures referenced in Chapter 2. Appendix C contains results from the sampling frequency analysis. Appendix D contains the metadata reports for the CLMP database that I compiled, which describes in detail the procedures for compiling the data for my analyses. It also lists the description of Excel files and the worksheets within the files. Appendix E contains the summer Secchi depth average (Ave) for each year, for each lake. Appendix E also lists the new key code (NKC), number of samples per summer (n), standard deviation (St.Dev) and standard error (St.Err) for each year. iv TABLE OF CONTENTS LIST OF TABLES ................................................ vi LIST OF FIGURES ............................................... vii CHAPTER 1: INTRODUCTION Introduction ................................................. 1 Literature Review Water Quality Defined ................................... 1 Point and Non-Point Source Pollution ....................... 2 Eutrophication and Sedimentation of Lakes .................. 4 Land Use and Water Quality of Lakes ....................... 6 Anthropogenic Land Use and Water Quality of Lakes ........... 6 Natural Land Use and Water Quality of Lakes ................ 11 CLMP Programs ...................................... 13 The Secchi Disk ...................................... 15 Thesis Objective ............................................ 16 Research Benefits .......................................... 17 CHAPTER 2: MANUSCRIPT Introduction ............................................... 20 Methods .................................................. 23 Results ................................................... 31 Discussion ................................................ 33 Conclusions ............................................... 43 CHAPTER 3: CONCLUSIONS Future Research ................................... I ........ 46 Conclusions ............................................... 48 Management Implications .................................... 49 APPENDIX A: TABLES ............................................ 52 APPENDIX B: FIGURES .......................................... 55 APPENDIX C: SAMPLING FREQUENCY RESULTS .................... 69 APPENDIX D: METADATA ......................................... 71 APPENDIX E: LAKE SECCHI AVERAGE BY YEAR ..................... 76 LITERATURE CITED ............................................. 98 LIST OF TABLES Table 1. Characteristics of the 71 selected lakes ..................... 52 The 71 lakes selected from the CLMP for analyses. The new key code is a standard identifying system for all Michigan lakes, used to link this database to others. The surface areas are in hectares and maximum and average depths are meters. All surface area and depth data available is from STORET or CLMP files. Average depth is water volume divided by surface area. Trophic classification is based on the average of the 1996-2001 Secchi depths using Forsberg and Ryding’s (1980) criteria. Ecoregion is based on Albert’s (1995) ecoregion classification system. Table 2. Sampling frequencies .................................... 69 The data span available for each lake and the sampling frequency; lists the number of years with one missing month, the number of years using all sampling frequencies (one missed month, one sample per month, two samples per month, or three or more samples per month), and number of years with the minimum of two samples per month. Table 3. Lakes with significant trends .............................. 54 Lists the lakes with significant (P _<. 0.1) trends, the direction of the trend, and the P-value and R2 from the regression. Table 4. Percent land use types in ecoregion sections ................ 54 Compares the average land use for ecoregion Section 6 (southern Michigan) versus section 7 (northern Michigan), for both buffer distances (100 m and 500 m), using a t-test. vi LIST OF FIGURES Figure 1. Ecoregions with selected lakes ........................... Illustrates the 71 lakes used from the CLMP and ecoregion subsections. No long term data was available for any lakes in the upper peninsula or for ecoregions 6.5 and 6.5. Ecoregions 7.1, 7.5 and 7.6 were dropped in the ecoregion subsection analyses due to low sample size. Ecoregion is based on Albert’s (1995) delineation. Figure 2. State-wide Secchi depth trend ........................... The state-wide Secchi depth clarity trend for the 31 lakes that were sampled in each time period. The figure is plotted with standard error bars. Figure 3. State-wide 1996-2001 trophic classification ................ The state-wide trophic state of all 71 lakes, using Forsberg and Ryding’s (1980) trophic criteria and 1996-2001 average Secchi depths. Figure 4. Ecoregion section Secchi depth average 1996-2001 ......... Compares the 1996-2001 average Secchi depth for Section 6 (southern Michigan) versus section 7 (northern Michigan). The figure is plotted with standard error bars and a t-test was used. Figure 5. Ecoregion subsection Secchi depth average 1996-2001 ...... The 1996-2001 average Secchi depth for ecoregion subsections. Ecoregions 7.1, 7.5 and 7.6 were dropped in the ecoregion subsection analyses due to low sample size. The figure is plotted with standard error bars. Figure 6a-c. 1996-2001 trophic classification by ecoregion subsection. The trophic state of ecoregion subsections, using Forsberg and Ryding’s (1980) trophic criteria and 1996-2001 average Secchi depths. Ecoregions 7.1, 7.5 and 7.6 were dropped in the ecoregion subsection analyses due to low sample size Figure 7. Significant lake clarity trends by ecoregion ................ Lakes with significant (P s 0.1) increasing or decreasing trends sorted by ecoregion section. .55 . .56 .' .56 . .57 .57 .' .58 .59 Figure 8a-e. Land use versus Secchi depth: 100 m buffers ............ 60 Percent (1978) land use in the 100 m buffer versus Secchi depth for lakes with Secchi depth data from 1974-1983. vii Figure 9a-e. Land use versus Secchi depth: 500 m buffers ............ 62 Percent (1978) land use in the 500 m buffer versus Secchi depth for lakes with Secchi depth data from 1974-1983. Figure 10a-d. Examples of individual lake Secchi depth trends ......... 64 2a shows Sherwood lake in Oakland county, 2b shows Big Platte lake in Benzie county, 2c shows Klinger lake in St. Joseph county and 2d shows Pleasant lake in St. Joseph county. All figures are plotted with standard error bars. Figure 11. The influence of zebra mussels on average Secchi depth 1996- 2001 ........................................................... 66 1996-2001 average Secchi depth of lakes with zebra mussels versus lakes without, using the Sea Grant (2001) database of zebra mussel monitoring. The figure is plotted with standard error bars and a t-test was used. Figure 12. The influence of zebra mussels on average Secchi depth 1974- 1990 ........................................................... 66 1974-1990 average Secchi depth of lakes with zebra mussels versus lakes without, using the Sea Grant (2001) database of zebra mussel monitoring. The figure is plotted with standard error bars and a t-test was used. Figure 13a-c. Land use versus Secchi depth by ecoregions: residential and agricultural ..................................................... 67 Percent (1978) land use in the 100 m buffer versus Secchi depth for lakes with Secchi depth data from 1974-1983, separated into all ecoregion six, versus all ecoregion seven lakes. 13a shows ecoregion six residential land use in the 100 m buffer, 13b shows ecoregion seven residential land use in 500 m buffer, and 13c shows ecoregion six residential + agricultural land use in the 100 m . bufien Figure 14a-b. Land use versus Secchi depth by ecoregions: wetlands. . . 68 Percent (1978) land use in the 100 m buffer versus Secchi depth for lakes with Secchi depth data from 1974-1983, separated into all ecoregion six, versus all ecoregion seven lakes. 14a shows ecoregion seven wetlands in the 100 m buffer, and 14b shows ecoregion seven wetlands in the 500 m buffer. viii CHAPTER 1: INTRODUCTION Introduction We depend on water resources for a multitude of uses including irrigation, drinking supply, energy production, recreation, industry, wildlife habitat, and aesthetics. Even though the availability of adequate water quantity and quality are necessities of life-sustaining importance, approximately 40% of the lakes, rivers and estuaries assessed in the United States do not meet basic water quality standards (USEPA, 2000). Rapid land development has resulted in increased pollution loads to our rivers, lakes, wetlands and reservoirs, threatening the value and use of these essential aquatic resources (NRC, 1992). Water Quality Defined Biologically and chemically, water quality is defined by a number of factors, and these parameters can generally indicate if a water body is degraded. What type of use of the water body is needed, or what type of assessment that is required, may influence which or how many characteristics are used to determine water quality. Common measurements include chlorophyll, clarity, coliforrn bacteria, conductivity, dissolved oxygen, hardness, nitrogen (ammonia nitrogen, nitrate, and nitrite), pH, phosphorus, temperature, and total suspended solids (Brooks et al., 1997). It is important to remember that a chemical constituent only becomes a pollutant when it reaches an undesirable level, and that definitions of water quality usually depend on if the water is acceptable for the stakeholder-defined designated use (Lee et al., 1982). Various water quality standards exist, based on many of these parameters, however they vary based on the use. For example drinking water and irrigation water have different standards. From a regulatory and public awareness perspective, the US Environmental Protection Agency (USEPA) has set two goals that specifically relate to water quality. The first requires safe drinking water: “Every American public water system will provide water that is consistently safe to drink.” (USEPA, 1996A). The second goal requires that “America’s rivers, lakes, and coastal waters will support healthy communities of fish, plans and other aquatic life, and will support uses such as fishing, swimming, and drinking water supply for people. Wetlands will be protected and rehabilitated to protect wildlife habitat, reduce floods, and improve water quality. Ground waters will be cleaner for drinking and other beneficial uses.” (USEPA, 1996A). The USEPA has set five distinct water quality objectives based on these goals and indicators for each, to help determine if these objects are being met. Point and Non-Point Source Pollution While anthropogenic uses of our water resources produce benefits to humans such as recreation, housing, and economic growth, they also generate pollution that necessitates watershed management. In recent years in the United States, much effort has been expended in reducing point sources of pollution. Point sources of pollution can be fairly easily identifiable back to a distinct point of origin, such as an industrial outfall or sewage treatment plant. This nature makes point sources relatively easier to monitor or set up control measures than non-point source pollution. As a result of the effort to reduce and regulate point sources of pollution, non-point source (NPS) pollution has now become the largest threat to water quality (USEPA, 19968). NPS pollution can be difficult to identify and control because it is diffuse and can originate from urban runoff, construction sites, agricultural land, leaking septic tanks, and atmospheric deposition. In Michigan, at least 86% of impaired (not meeting one or more designated uses) lake acres can be traced back to “inconclusive” pollutant sources, including atmospheric deposition, while point sources of pollutants only contribute approximately 4.4% (MDEQ, 2000). NPS pollution is also often influenced by seasonal land uses or climate changes, as well as significant storm events. Because NPS pollution occurs over such a wide area, and can be transported by variable ways, it is difficult to identify, manage, or regulate. Due to the challenges of pinpointing sources, the management options for point and NPS pollution are numerous. The following is a general overview that includes examples of structural, vegetative and managerial options. Structural controls of point sources usually consist of a process of pre-treatment, primary, secondary, tertiary, sludge treatment, and waste disposal (Heathcote, 1998). Treatments can target the outfall pollution and/or focus on prevention strategies. Structural controls for urban NPS pollution can include storm sewer screens, porous surfaces, sediment basins, grassed waterways, and for agricultural NPS pollution, terraces, composting, waste lagoons, grassed waterways, and retention ponds (Heathcote, 1998). Vegetative controls for urban and agricultural NPS pollution can include filter strips, constructed wetlands, riparian buffers, cover crops, critical area planting, and crop rotations (Heathcote, 1998). Managerial controls can include proper timing and amount of pesticide and manure applications, drip irrigation, public educational programs, storm drain stenciling, nutrient trading, and erosion control in construction areas (Heathcote, 1 998). Eutrophication and Sedimentation of Lakes Eutrophication and sedimentation of lakes are two key processes fueled by NPS pollution. Lakes, which have slower turnover and longer retention times than streams, can be more susceptible to degradation. Eutrophication can be a natural successional process, shifting lake waters from higher to lower clarity because increased nutrient levels promote greater plant and algal growth. However, eutrophication due to excessive nutrient levels of phosphorus and nitrogen can be greatly accelerated by human-induced pollution sources, causing harmful ecological effects such as low dissolved oxygen levels, fish kills, widespread algal blooms, and turbid waters (USEPA, 1996A). Algal blooms, because of their visibility, are often what the public first notices and raises in water quality concerns. Nationally, nutrients contribute most to impaired lake acres, 44%; metals are second, and sediments follow as the third most common lake pollutant (USEPA, 2000). In many lakes, increased inputs of phosphorus have been shown to fuel eutrophication (Schindler, 1977). Yet, the algal productivity of lakes can also respond toboth nitrogen and phosphorus inputs. Powers et al. (1972) conducted in-situ field experiments on lakes in Minnesota and Oregon, and all lakes responded positively in algal growth with additional of nitrogen, phosphorus, or the combination of both. Eutrophication has also been shown to decrease biodiversity. Cichlid fish species diversity was lost in Lake Victoria due to eutrophication because the turbid waters interfered with mating, vision, and sexual selection (Seehausen, et al., 1997). Additionally, in a lake in China, macrozoobenthos species diversity decreased from 19605 levels as the lake became more eutrophic (Gong and Xie, 2001) Sedimentation of lakes is also a significant problem. In the US, approximately 108 million acres of cropland are excessively eroding, resulting in 1.3 billion tons of erosion (NRCS 2000). The Natural Resource Conservation Service estimates that 60% of sedimentation originates from agricultural lands (NRCS 1997). Sediment loading increases turbidity in waters, thereby clogging fish gills, burying spawning beds, and interfering with fish foraging behavior (Home and Goldman, 1994). In addition, sediments often transport adsorbed nutrients such as phosphorus, which exacerbate eutrophication when released in the water (Novotny and Olem, 1994). .l. Land Use and Water Quality of Lakes Comparatively, lakes consist of a small portion of the landscape; nevertheless they provide many valuable uses. Recreational, industrial, residential, drinking water supplies, ecological habitat, and water storage are some of the ways humans depend on, and value lakes. However, these multitudes of uses often result in lake water quality impairment for either human or wildlife uses (NRC, 1992). Elevated levels of nutrients running off from a lake’s watershed are often the cause of excess phytoplankton growth, and turbidity often originates from land uses in the watershed that may be causing accelerated erosion (Davies-Colley et al.,1993). Because of this, we often focus on the human impact of land uses on a lake’s water quality, but both anthropogenic land use and natural land types influence water quality. These affects are outlined below. Anthropogenic Land Use and Water Quality of Lakes Urban land use It is well documented that NPS pollution originating from both agricultural and urban land use decreases the quality of water bodies, which receive runoff containing nutrients, sediments, oils, salts, chemicals, pathogens, and other substances harmful to ecosystems (USEPA, 1996A). For example, Blais et al. (2000) determined that two lakes in Alberta became more eutrophic than their pre-196OS eutrophic state, as the land in their watersheds was developed into urban and agriculture. Urbanization creates impervious surfaces that increase the risk of contamination to surface waters due to. increased water runoff volume and pollutant loadings. Michigan has a history of losing forested and agricultural land to urban uses. Since the 19503, Michigan has lost 605,000 forested acres and is projected to lose an additional 1.4 million acres by.2050; urban land use - has increased by 895,000 acres since 1950 and is projected to increase by 300,000 acres by the year 2050 (Mauldin et al., 1999). lmpervious surfaces like pavement, roads, and rooftops prevent pollutants such as sediments and nutrients from infiltrating and being absorbed by vegetation and soil; instead, they move directly to receiving water bodies. For example, Amell (1983) measured rainfall and runoff in five urban test watersheds and found greater runoff with increasing imperviousness. Water quality problems from urbanization can even be observed even in areas that are not intensely built up. Suburban residential areas are about 20% impervious, a relatively low level compared to commercial areas at about 85%, but water quality degradation can be detected starting at about 10 to 20% imperviousness (USEPA, 1996A). Various studies have revealed the effect of urbanization on water quality in lakes. Hasler (1947) surveyed 37 lakes from Austria, England, Finland, Germany, Italy, Sweden, Switzerland, and the United States and found that urban drainage and runoff caused marked changes in the biology of the lakes. Most of these lakes changed from oligotrophic to eutrophic and experienced changes in plankton communities and algal growth resulting from cultural eutrophication (Hasler, 1947). Stemberger and Lazorchak (1994) specifically showed how zooplankton species composition could change in response to water quality conditions. The presence of certain species correlated to ‘ disturbance in the lake (fish stocking, agricultural, residential land use, and . f silvicultural) and could be used as an indicator of disturbance (Stemberger and Lazorchak, 1994). The intensity or distance of development can dictate the extent of water quality changes. Over the 100-year time span of a paleolimnological study, lakes in watersheds that had changed more than 25% in residential land use increased in specific conductivity, pH, and trophic state, but lakes with less than 20% increase in residential land use did not show water quality changes (Siver et al., 1999). Dillon and Kirchner (1975) found that the export of phosphorus from agricultural and urban land use was at a minimum four times greater than forested land export, and increased depending on the intensity of land use. Hall et al. (1999) found that in a naturally eutrophic lake, land uses were a greater predictor of water quality than climate change, as determined by diatoms and chironomids, but the distance from point sources of pollution impacted the water quality changes. Improvements in urban management practices can have an impact on water quality. In a paleolimnology study, a reservoir’s watershed shifted from primarily agricultural to urban land use, starting in the 19503, and the sediment core indicated decreased turbidity in the water, probably due to decreased erosion from agricultural activities and improved urban storm water management (Bradbury and Van Betre, 1997). Land development can also be a major source of sedimentation and turbidity to lakes; although ‘sites are relatively small in size, then can have high erosion rates if not managed properly. Byron and Goldman (1989) showed that the yield of nutrients and sediments from nonpoint sources increases with more erodable soils and disturbance, such as land development, and results in decreased water quality. Residential land use has also been shown to impact lake bacteriological water quality. Hendry and Toth (1982) found high, but not exceeding contaminant levels of fecal coliforrn densities along stretches of the shoreline of a lake in Ontario. While the lake as a whole was not effected, the pollution distribution was important because of the swimming uses of the lake along the shoreline (Hendry and Toth, 1982). A study of Higgins Lake, Michigan had a similar spatial distribution of pollution from residential development. Minnerick (2001) found that rapid Iakeshore residential development of up to 246% between 1970-1990 did degrade water quality in the shallow shoreline areas, but had not yet affected the whole lake, or the deeper basins. Agricultural land use The agriculture industry in the United States is a billion-dollar industry and produces food and fiber produces for the world. However, agricultural land use, be it either cropland, orchards, or animal operations can also transport pollutants to lakes and other water bodies, especially if not managed appropriately. The primary pollutants from agricultural activities are sediments, pesticides, nutrients, and pathogens. Nitrogen fertilizer use in the United States increased twenty fold and use of phosphorus fertilizer more than tripled in the period 1945-93 (Puckett, 1995). This increased use of nitrogen and phosphorus fertilizers in the last half of the 20th century led to an incredible increase in agricultural productivity but also a substantial increase in potential for contamination of both ground and surface waters by nutrients. Phosphorus has been shown to primarily travel through surface water runoff from the land, as opposed to nitrogen, which is usually lost through groundwater. Therefore, phosphorus is often the-nutrient of greater concern for lakes fed by surface waters. In a study of nutrient dynamics through different land uses, Peterjohn and Correll (1984) found that cropland lost 64% of nitrogen though the harvested crop, 9.2% in surface runoff, and 26% in groundwaler flow, compared to phosphorus losses of 84% in harvested crop, 16% in surface runoff and less than 1% in groundwater flow. Sharpley et al. (1994) discusses how commercial fertilizers and manure leaving agricultural land via runoff contributes to the accelerated eutrophication of freshwaters by phosphorus inputs. For instance, Lake Okeechobee, Florida, transformed into a hypereutrophic state as runoff from cattle operations and vegetable crops transported excess levels of phosphorus and nitrogen (Havens et al., 1995). Agricultural land use can also be a source of sediment NPS pollution. Bradbury and Van Betre (1997) conducted a pollen, diatom, and grain size paleolimnology study to relate the water quality of a reservoir to changing land use in its watershed. From 1912 to 1950, the watershed was primarily 10 A-- agricultural and the analysis indicated significant sedimentation in the lake (Bradbury and Van Betre, 1997). Natural Land Use and Water Quality of Lakes Forested land Generally, both forested land and wetlands are known to have filtering properties that help remove pollutants before they enter water bodies. The vegetation of forested land can uptake nutrients through trees, ground shrubs and other vegetation. Little research exists on the effects of forested land and lakes. Most research involves the effectiveness of riparian buffer in filtering nutrient and sediment runoff into streams As an example, Peterjohn and Correll (1984) investigated the transformations of the nutrients from riparian land to surface runoff. Nitrogen retention by the riparian forest was 89%, and retention of phosphorus was 80% (Peterjohn and Correll, 1984). In comparison, nitrogen retention by cropland was only 8%, and of phosphorus, 40% (Peterjohn and Correll, 1984). Lowrance et al. (1984) found similar results when studying a riparian forest’s filtering capacity in Georgia. This research concluded that the riparian forest can store nutrients in the soil and vegetation over long periods of time and can prevent nutrients in agricultural runoff from reaching stream channels (Lowrance et al., 1984). In a paleolimnological study, first looking at the year 1990, lakes in Connecticut had lower pH, specific conductivity and tropic state with increased forest cover (Siver et al., 1999). Over the 100-year time span of the analysis, ll lakes that had approximately 80% or more forested land in the watershed did not change in water chemistry (Siver et al., 1999). Not all forested land necessarily means undisturbed conditions, however. Watersheds with a history of logging and related soil disturbance can lead to accelerated runoff and nutrient loadings to streams and the lakes that they feed. (Leonard, et al., 1979). Wetlands Wetlands are often transition zones between terrestrial and aquatic landscapes, and because of their position on the land, they are often a collection point for runoff. Wetlands are a natural filtering system for runoff, both settling out sediments and up-taking nutrients through vegetation. Therefore, Secchi disk depths can be positively correlated with the extent of wetlands in the watershed (Detenbeck, 1993). However, the ability of wetlands to trap and process nutrients or sediments can depend on retention time. Jansson et al. (1994) found that water retention time was needed to remove nitrogen from runoff waters. Wetlands in regions or times of year where water flow is great may not have enough retention time to remove nutrients and sediments, compared to other regions or times of year. Wetland waters are often colored with tannins because of low decomposition rates and accumulation of organic matter. When the waters from wetlands reach lakes, this can affect the lake color. Detenbeck et al. (1993) found that the color of lakes increased as the extent of wetlands and seasonally flooded wetlands increased in the lake’s watershed. While algal productivity may 12 be low, due to low nutrients and sediments, Secchi depth clarity values may still be low due to the colored waters. Secchi depth data and land use Despite evidence of site-specific impacts of land use on lake water quality, on a state-wide scale, or with use of Secchi data, correlations may not be as evident. Even monitoring land use changes over time with Secchi data may not pick up land use influences. Smeltzer et al. (1989) used data from volunteer monitoring to examine water clarity over 11 years in Vermont. They concluded that while Secchi depth had less temporal variability than phosphorus and chlorophyll, monitoring conducted over short time spans may have too much variability to provide early detection from land use change and non point source pollution impacts. Terrell et al. (2000) analyzed Secchi data from 127 Florida lakes over 30 years and despite excluding 13 lakes with known management changes such as point source removal or artificial fertilization, the analysis found no significant change in water clarity. The authors questioned whether even 30 years was enough time to detect land use influences, especially nonpoint source pollution with Secchi data. CLMP Programs Management is needed to identify, prevent, and control the sources of cultural eutrophication. Monitoring water conditions, including clarity, is an integral element of management strategies assessing baseline conditions and 13 tracking changes in lakes. Resources at state agencies often limited and agencies and staff cannot create long-term sampling programs for large numbers of lakes. Therefore, programs that take advantage of citizen volunteers are relatively inexpensive and consequently can be maintained for long periods of time at numerous locations. The USEPA supports lake volunteer mcnitoring programs and echoes these statements. From the Volunteer Lake Monitoring Handbook Methods Manual, “Volunteer programs have been found to be of enormous value to states, which can gain a baseline of useful information on lakes that might otherwise have gone unmonitored. States also benefit from new partnerships with educated and involved citizens who actively work to protect their lake resources.” (USEPA, 2002B). These volunteer programs can generate volumes of useful data. For example, Florida’s LAKEWATCH program began in 1986, and by 2000, the Florida Department of Environmental Protection estimated that over the past five years, of all the individuals and agencies, only the Department had provided more data on lakes than had the LAKEATCH program (Canfield et al., 2002). Volunteer lake monitoring programs exist in a number of other states. The state of Illinois has a volunteer lake monitoring program in cooperation with the Illinois EPA and local planning and development commissions. The data from lllinois’s volunteer monitoring program has resulted in a number of lakes being identified for restoration or protection activities (Serton et al., 1983). Wisconsin implemented a self-help lake monitoring program in 1986, in cooperation with the University of Wisconsin Extension and the Department of Natural Resources; 14 Minnesota has a volunteer-based Cooperative Lakes Monitoring Program (CLMP) in cooperation with the Minnesota Pollution Control Agency. Vermont’s Lay Monitoring Program , Missouri’s Lakes of Missouri Volunteer Program, and Rhode Island’s Watershed watch are also examples of volunteer-based lake monitoring programs. A volunteer program to monitor inland lake water clarity began in Michigan in 1974 as a citizen self-help program. This Cooperative Lakes Monitoring Program (CLMP) works with lake property owners, who measure water clarity levels using Secchi disk depth. More recently, the program also began monitoring phosphorus and chlorophyll. In 1992 this program partnered with the Michigan Lake and Stream Associations, lnc. (ML&SA), and is now a cooperative effort that includes the Michigan Department of Environmental Quality (MDEQ) and Michigan State University’s Department of Fisheries and Wildlife. The Secchi Disk The Italian scientist P.A. Secchi invented a method to measure water clarity in 1865 when he lowered a bi-colored disk into the ocean and noted the depth at which it disappeared (Tyler, 1968). This method, using what is now called a Secchi disk, is an inexpensive and fairly simple tool to use for collecting lake data. The Secchi depth measures light attenuating particles in the water, such as phytoplankton or inorganic suspended solids, and the Secchi depth is inversely proportional to their concentration in the water column. 15 The Secchi disk is not a direct measure of water quality parameters such as chlorophyll, colifonn bacteria, pH, nitrogen, phosphorus, dissolved oxygen, or temperature. Secchi disk readings are also influenced by natural organic color of the waters (Brezonik, 1978). However, various studies have shown Secchi depth decreases as lakes become eutrophic (Beeton, 1965; Edmondson, 1970) or with nutrient additions (Cruikshank, 1988). When used appropriately, and when aware of its limitations. the Secchi disk is a robust, easily conducted measurements of a lake’s clarity, that over time can provide a useful record of seasonal plankton cycles and sediment influxes (Preisendorfer, 1986). Secchi disk measurements can also be used to determine lake trophic state indices (Carlson, 1977). Thesis Objective The approximately 11,000 inland lakes in the state of Michigan are ecosystems susceptible to lake degradation due to anthropogenic stresses. Extensive data are available through Michigan’s CLMP, however, these data have not been analyzed comprehensively. Therefore there is very little understanding of how Michigan’s inland lakes have changed through time. In addition, published literature contains essentially no data or reviews about clarity trends of inland lakes in Michigan. This study will analyze these data to quantify trends and to identify factors contributing to the observed changes in lake water clarity. Specifically, I will examine how water clarity has changed through time since 1974 to 2001. I will also examine whether there are differences in water 16 clarity in lakes in different ecoregions and in lakes with different land use. I will address three main questions in this study: 1. How has the water clarity of Michigan’s inland lakes changed from 1974 to present? 2. Do lakes in different ecoregions have different water clarity? Have lakes in different ecoregions exhibited different trends in water clarity through time? 3. Can land use around lakes explain patterns in water clarity across the state? Research Benefits Organizing and compiling historical water quality data into a database can prove very useful for uses such as public education and information dissemination programs, identifying sources of pollution, and supporting research. Maas, et al (1991) discusses the challenges of determining water quality within watersheds, and the need for quality, long-term data that can be available to watershed managers. This research will analyze the only long-term monitoring data of inland lakes available in the state of Michigan. Without knowledge-based, effective policy and Best Management Practices (BMPs) in place, lakes are at greater risk to cultural eutrophication and I7 sedimentation, resulting in degraded ecosystems, reduction of property values and loss of recreational spending revenue. This study will provide baseline data illustrating water clarity trends for the inland lakes of Michigan in the last ten to thirty years. This analysis will expand our knowledge of water quality trends in Michigan, as well as identify key biophysical factors correlated with increases or decrease in water quality in inland lakes. Results of this study will have three important benefits: 1. To improve our understanding of how lakes respond to changing anthropogenic stresses, which will be widely applicable to other regions, and will be disseminated through publications in the primary literature. 2. To help lake management agencies in Michigan set priorities for statewide lake monitoring. 3. To aid individual communities in Michigan to create watershed management plans for their lake or watershed. This latter effort will be facilitated through integrating our data and results into MSU’s Institute of Water Research’s interactive, web-based Geographic Information Systems (GIS) tool. This program uses an Internet based GIS system 18 to compile biophysical information on a sub-watershed level for educational and watershed planning purposes. a The following chapter is the manuscript for is written in manuscript form and will be submitted to the journal of the North American Lake Management Society: Lake and Reservoir Management. It is written in the style and format consistent for the journal. 19 CHAPTER 2: MANUSCRIPT Introduction The approximately 11,000 inland lakes in the state of Michigan are valued ecosystems yet are susceptible to lake degradation due to human-induced stresses such as point and nonpoint source pollution, exotic species invasions, water draw-downs, and shoreline erosion. The total sum of anthropogenic stressors can increase or decrease over time, and water quality will also be expected to change over time. However, most data on water quality trends in Michigan have focused on the Great Lakes. Less information is available on how Michigan’s inland lakes have changed over time, or their response to anthropogenic stresses. Monitoring water conditions such as water clarity is an integral element of management strategies assessing baseline conditions and tracking changes in lakes. Secchi disk depth is an inexpensive and fairly simple tool to use for measuring water clarity data. Although Secchi disk is a rough measure of both water clarity and water quality, when used appropriately, the Secchi disk is a robust, easily collected measure of a lake’s clarity that can be effectively used to monitor trends in individual lake water clarity through time. However, resources at state agencies are often limited and agencies and staff cannot create long- tem'r sampling programs for the large numbers of lakes that states like Michigan have. On the other hand, programs that take advantage of citizen volunteers are relatively inexpensive and consequently can be maintained for long periods of 20 time for a potentially large number of lakes. One program to monitor inland lake water clarity in Michigan began in 1974 as a citizen self-help program (current name: Cooperative Lakes Monitoring Program (CLMP), Michigan Department of Environmental Quality) works with lake property owners, who measure water clarity levels using Secchi disk depth. In 1992 this program partnered with the Michigan Lake and Stream Associations, Inc. (ML&SA), and is now a cooperative - effort that includes the Michigan Department of Environmental Quality and Michigan State University’s Department of Fisheries and Wildlife. Citizen volunteer programs like Michigan’sCLMP have been used in many states to collect lake data, and several studies have confirmed their validity and accuracy. For example, the Missouri Department of Natural Resources began a citizen lake-monitoring program in 1992, and part of the program included an evaluation of the reliability of volunteer-collected samples. During the 1992-1994 seasons, samples at 19 lakes were collected by citizen volunteers and personnel from the University of Missouri on approximately the same-dates (Obrecht et al., 1998). No statistical differences were found between the volunteer data and the University-collected data (Obrecht et al., 1998). In another study, the quality of the volunteer-collected data through the Watershed Watch Program was also tested by staff from University of Rhode Island Cooperative Extension (Herron et al., 1994). The Secchi data collected by volunteers at 21 lakes was as representative of lake water quality as were the Extension staff’s measurements (Herron et al., 1994). Finally, at Florida’s LAKEWATCH program, Secchi depth samples taken by volunteers at 125 lakes were comparable to those taken by 21 professionals and the mean values were strongly correlated (r > 0.99) (Canfield etaL,2002) One potential source of error with the volunteer’s measurements may be change in volunteers over through time. Cruikshank (1988) compared the variability over 6 weeks, between two Secchi depth volunteers, one with eight months experience and one with six years. Of the 20 measurements, there was no significant difference between the Secchi means for the two observers. Few sources of data on water quality in Michigan lakes that have been consistently collected, except in the CLMP program. Thus, a vast amount of data on Michigan lakes has remained unanalyzed and warrants further study. I address three main questions in this study: 1. How has the water clarity of Michigan’s inland lakes changed from 1974 to present? 2. Do lakes in different ecoregions have different water clarity? Have lakes in different ecoregions exhibited different trends in water clarity through time? 3. Can land use around lakes explain patterns in water clarity across the state? Based on patterns of anthropogenic stresses in Michigan, I expect the following results. I expect to see a number of lakes with decreasing clarity, 22 especially in urbanized areas due to increasing housing development pressures around lakes. However, due to new local zoning regulations that may limit or strictly regulate shoreline development, or national laws such as the Clean Water - ' Act, a number of lakes may show significant increasing clarity. One system of organizing the landscape into distinct units for management, comparisons, or conservation is by ecoregions. The ecosystem classification system of ecoregions categorizes the landscape into regions based on biotic and aboitic differences, including geology, soil, climate, vegetation, and animals (Albert, 1995). Other studies, for example, Heiskary et al. (1987), have found significant relationships between water quality and ecoregions, and I expect that these analyses for Michigan’s ecoregions will show similar results. I also anticipate finding significant correlations between Secchi depth values and two of the more important land uses for water quality; residential/urban and agricultural land use. To answer the above questions, I compiled CLMP data from 1974-2001 for 71 lakes. Methods CLMP Volunteer Secchi Depth Collection Although there is not complete control over the volunteers’ sampling procedures, the MDEQ has developed standard procedures for Secchi depth measurements and volunteers are trained by MDEQ or ML&SA staff yearly. Volunteers collect Secchi depth readings weekly or bi-weekly (every other week) from mid-May through mid-September, although not all volunteers sample this 23 . 7. , -. ... . . . .‘ , I » . .I . a y . ‘ . . A . ‘ ‘ ‘ ‘ . l I - j. . I v . . ,. .. . , . . ." r ° I ' , ~7 - . a _ - ,’ . .I I I . . u ' . . _ . . ., .1: . . . _ ’ l v .. .0 A. - . I . I . . L .C ‘ 0 x ' O - . - . I I I 7 ' '_ . . , .. , . . . a 1 . , I .. ,. . - . . . V . ‘ . . ._ _ . ' ' . . . .' I , ‘ . I < . I « . . . . u r . . .' ' u ‘ ' ‘ , I . 5 n . . , . . '. . . , . o v n u often. The Secchi depth is taken in the deepest basin of the lake and sometimes at additional stations. Readings are taken on the same day of each week and between 10:00am and 3:00pm to minimize sun angle differences between readings. Secchi data are recorded from an anchored boat and with some distance from the anchor to minimize turbidity created by dropping the anchor. The Secchi disk is lowered on the shaded side of the boat and volunteers are instructed not to wear sunglasses or tinted glasses in order to record more accurate and uniform readings. The Secchi disk is lowered until it disappears, raised until it appears again, and the reading is the average between these two depths. Lake Selection for Analvsis From 1974 to 1994, most of the CLMP data are in hard copy form on original data sheets or summary sheets. The data have been stored in electronic form since 1995. The data from 1974 to 1995 were transferred into Microsoft Excel. All data are reported in feet, and volunteers are instructed to round readings to the nearest half-foot. Occasionally on.the hard' copy data, the Secchi depths were recorded to the inch, and for transfer into electronic form, values were rounded to the nearest quarter-foot. Because not all volunteers collect data on the same day of the week, data were recorded in a “week-of” format, using either Saturday or Sunday as the start of the week. On the occasional occurrence that lakes had more than one Secchi depth reading for the week, either the first date or the date closest to the day of week that other samples 24 4 Or. I l I. c . .91. L r . I . v . V. l. L .. v .. a . t. . n t . . l. o. were taken was used. The accuracy of data entry was checked twice, at the time of first entry on-screen, and later comparing hard copy to a printout of the entered data. For these analyses, 71 lakes were selected from those participating in the CLMP program. Criteria for selecting these lakes were as follows: 1) Secchi depth data must have been available for a minimum of nine years 2) One of the nine years must have included the most recent year available, either 2000 or. 2001. Nine years was chosen because it was a good cut-off point in the availability of the CLMP data, and it is an effective length of time to measure water quality trends. More years of data are required to detect more subtle shifts in water quality of 10 to 20% (Heiskary et al, 1994). For example, after 10 years of summer Secchi data collection, there is a 78% (weekly sampling) and 75% (biweekly sampling) chance of detecting a minimum'of a 20% change in Secchi clarity (Heiskary et al, 1994). The location and characteristics of the 71 selected lakes are listed in Table 1. The selected lakes represent a good geographic dispersion across the lower peninsula of Michigan, (Figure 1). Analysis Averaging When lakes had multiple sampling stations, only measurements from the deepest basin were used for analysis. To capture the summer stratified season, only Secchi data from July, August, and September were used in all analyses. This time frame has been used in other studies (Heiskary et al., 1987) and 25 . Stadelmann et al. (2001) found mid-July to mid-September was the best time to measure Secchi depths because lakes behave in the same way and in-lake variability is minimized. Kloiber et al. (2000) also found that Secchi depth transparency. variability is relatively small during late summer (July 15- Septerrnber 15) and varied only about 20% from the mean. Because the objective of my analyses was to examine annual changes in water clarity, l averaged all summer Secchi depths to calculate one Secchi value per summer. I included data for all years where there was a minimum of three samples over the three month summer period, allowing one missing month of sampling. Stadelmann et al. (2001) found that two measurements during the summer period could estimate the summer Secchi mean clarity with a relative error of 30%. Sixty-eight of 1,183 (5.8%) lake years of CLMP data had a skipped month of sampling, and the majority missed September. Only three lake years had the minimum of three samples and the average number of summer samples per lake year was 11. The data were converted from feet to meters, then for each lake, an annual mean Secchi depth was determined by averaging the summer data points. Statistics The data were normally distributed, and there was no seasonality because of theannual averaging. I used linear regressions and t-tests to analyze the data. Other studies have used regressions to examine water quality trends. For example, Francis et al. (1994) used regressions for their 30-year study on the 26 4“. clarity trends of lake Pontchartrain. Also, Schindler et al. (1996) used regressions of 20-year datasets to determine the effect of climate change on lakes in northwestern Ontario. Finally, Byron and Goldman (1989) used regressions to evaluate the relationship between nutrient and sediment concentrations in runoff water and land disturbance. For all analyses, a p-value of 0.1 or less was considered significant. The objective of these analyses was to examine general trends, not test hypotheses, therefore the 0.1 level was used. Even if the stricter 0.05 level was used, my basic conclusions remain the same since the majority of the results are still significant at the 0.05 level. The 0.1 level has also been used in similar trend analyses using volunteer-collected Secchi depth data (Heiskary and Lindbloom, 1993) Time trends To determine the presence of water clarity time trends for each of the 71 lakes, l regressed the annual Secchi depth means against year using Systat 9.0 (SPSS, 1998). I calculated trends for different datasets: 1) Data with all frequencies of sampling: one sample per month, two samples per month, and three or more samples per month 2) Only data from years that had a minimum of two samples or more per month. Table 2 (Appendix C) shows the data span of each lake and the number of years of data with each scenario. After the initial screening, one lake dropped below the minimum number of 9 years (Painter lake), and was only included in the ecoregion analysis. 27 . I calculated a state-wide Secchi depth average of all lakes in approximately five year intervals (1974-1980, 1981-1985, 1.986-1990, 1991-1995, 1996-2001). To not bias the trend with the larger number of lakes sampled in more recent years, only the lakes (n=31) with Secchidata from each time interval were used (Figure 2). Because of the relatively short data span, it was more informative to look at change through time across 5-year intervals instead of decades. Also, only two lakes consistently had data every year, and only nine started sampling in 1974. The 5-year intervals allowed me to capture more of the lakes in a state-wide analysis. These lakes were fairly evenly distributed between ecoregion section six and seven, 55% and 45%, respectively, and there was at least one lake in each subsection. These lakes all had fairly high residential land use in the 100 m buffer, but otherwise land use was varied. For each lake, the average of the annual means for each time period was calculated. Then the average across all lakes for each time period was calculated. I then regressed the means from both scenarios against time. The present-day state-wide Secchi depth average was calculated from the 1996-2001 Secchi depth means from all 71 lakes. To determine the trophic status of lakes, I used the following criteria: a Secchi depth of less than 2.5 meters is eutrophic, from 2.5 to 4.0 mesotrophic, and greater than 4.0 oligotrophic (Forsberg and Ryding, 1980). 28 Ecoregions A commonly used ecoregion delineation is OmemikI's (1987) ecoregions. Omemik defines ecoregions by land surface characteristics, soils, potential natural vegetation, and land use. In my analyses I sought to first detect the presence of water quality trends, and secondly, the effects of land use and ecoregion on them. Because Omemik’s ecoregions includes land use, the two analyses would be confounded. Therefore, I used Albert’s (1995) ecoregion delineations at the section and subsection level (Figure 1). Albert uses eSsentially the same criteria (vegetation, physiography, climate and bedrock geology), but omits land use in the delineation. The current Secchi depth means for the state’s ecoregion sections and subsections were calculated from the 1996-2001 Secchi depth means from all 71 lakes. This time interval was used to minimize the effect of time trends on the calculations. For the subsection analysis, ecoregions 7.1, 7.5, and 7.6 were dropped due to low sample size. I ran an analysis of variance on the subsection data, using Fisher's Least-Significant-Difference test, and a t-test on the section data. These means were also translated to trophic state based on Forsberg and Ryding’s (1980) criteria. Using the results from the individual lake trend analysis, I analyzed lakes with increasing or decreasing trends by ecoregion section. Land Use The land use information was obtained from Michigan Resource lnforrnation System (MIRIS) data (MDNR, 1999). Land use information for MIRIS 29 was obtained from aerial photos from 1978-1979, and a compilation of data from regional planning commissions. The land use/cover data were classified using. level I classes in the Anderson Classification scheme (Anderson et al. 1976), which includes: urban, agriculture, non-forested vegetation (i.e., grasses and shrubs), forest, “water, and wetlands. Because the urban category is primarily made up of residential land use/cover near lakes, I used the term ‘residential’ for ' this land use/cover. The minimum resolution of the MIRIS land use/cover data is approximately 1 ha. Because land use information for MIRIS was from 19781 only included annual Secchi means within a ten-year span of these data (1974-1983). For each lake that had data during this time period, I calculated the average of the annual means for the time period. To determine the effect of land' use on Secchi depths, I plotted these means against the percent land use within both a 100 and 500 m buffer around each lake. The land use categories selected for analyses were: residential, agriculture, residential combined with agriculture, forest and wetlands. l regressed Secchi means against all land use categories. To determine if there were differences in land use between the ecoregion sections, I calculated the average percent land use types for the two ecoregion sections (six and seven) for each of the land use classifications and buffer distances. I ran a t-test on these data to compare the two sections. 30 Resufls’ Time trends .For the individual lake trend analysis using all sampling frequencies, I found 26 lakes with significant (P .<. 0.1) trends in water clarity. Of the 26 significant trends, 22 were increasing in clarity and 4 decreasing. Table 3 lists the lakes with significant trends. The results of the analyses using the datasets of alternate sampling frequencies is discussed in Appendix C. For the state-wide Secchi depth trend, the 31 lakes showed a significant (P = 0.056) increasing clarity trend (Figure 2). The current state-wide trophic status calculated from the 1996-2001 Secchi depth means from all 71 lakes shows the majority (52%) are mesotrophic; 28% are oligotrophic and 20% are eutrophic (Figure 3). The current trophic state of the individual lakes calculated from the 1996-2001 Secchi depth means are shown in Table 1. Ecoregions The current Secchi depth mean for ecoregion section six (southern Michigan) is 3.1 m, and for ecoregion section seven (northern Michigan), 4.1m (Figure 4). The t-test showed a significant difference between these means (P = 0.003). For the ecoregion subsection means, Fisher’s Least-Significant- Difference test showed a significant difference (P < 0.1) between the means of 31 7.2 and 6.2, 6.3; between 7.3 and 6.1, 6.2, 6.3; between 7.4 and 6.2, 6.3 (Figure 5). The number of eutrophic, mesotrophic, and oligotrophic lakes were grouped-by ecoregion subsection and are illustrated in Figure 6a-c. Ecoregion section six has more (11) eutrophic lakes than section seven (2). Ecoregion section six has slightly more (18) mesotrophic lakes than section seven (16), and ecoregion section six has less (7) oligotrophic lakes than section seven (12). Using results from the individual lake trend analysis, lakes with increasing or-decreasing trends were grouped by ecoregion section six or seven (Figure 7). For the significant trends (P s 0.1), both ecoregions had more lakes with increasing water clarity than decreasing. In ecoregion 6, 21% of lakes are decreasing in clarity and 79% of lakes are increasing in clarity. In ecoregion 7, 8% of lakes are decreasing in clarity and 92% of lakes are increasing in clarity. Land use Comparing land use differences between the ecoregion sections, the significant differences (P S 0.1) were: agricultural land 100 m buffer (P = 0.003) and 500 m buffer (P = 0.023), and forest land 100 m buffer (P = 0.055) and 500 m buffer (P = 0.091) (Table 4). Agricultural land use area was higher in section six compared to seven, 2.7% versus 0.3% in the 100 m buffer and 18.8% versus 9.0% in the 500 m buffer. In contrast, forest land was higher in section seven compared to six, 26.0% versus 16.4% in the 100 m buffer and 47.3% versus 32 37.4% in the 500 m buffer. All other differences between ecoregion sections were not significant. When individual land use categories were plotted against Secchi depths. ' only two categories were significant (Figures 8a and 9e). Residential land use in the 100 m buffer showed a positive relationship with Secchi depth (P = 0.07) (Figure 8a) and wetlands in the 500 m buffer showed a negative relationship (P = . 0.004) (Figure 9e). When the two outliers were removed in the Residential land use in the 100 m buffer regression, the positive relationship was even stronger (P = 0.03, R2 = 0.091). Residential land use in the 500 m buffer, the combined residential and agriculture land use for both buffer sizes, and forest Ianduse in the 500 m buffer showed positive relationships with Secchi depth, but none were significant. Agricultural land use in both the 100 and 500 m buffers, forest in 100 m buffer, and wetlands in the 100 m buffer all showed negative relationships With Secchi depth, but none were significant. Discussion Changes in the water clarity of Michigan’s inland lakes from 1974 to present. It is apparent from the individual lake trends, as well as the state-wide analysis that in general, the clarity of Michigan’s lakes in the lower peninsula has been increasing since 1974. State-wide, there is a significant increasing trend, and the majority of the significant individual lake trends were increasing in clarity. Trend analyses in Minnesota have shown similar patterns in water clarity. Heiskary and Lindbloom (1993) studied volunteer-collected Secchi depth data 33 , , , . , . . 9 1 I I , . ' , . . , . . . . - n ‘. ' n . from 152 lakes with 8 or more years of data, ending in 1992. Twenty nine percent of lakes showed significant increase in clarity, 8% significant decrease, and 63% had no trend. Terrell et al. (2000) analyzed volunteer and agency- collected Secchi data from 127 Florida lakes over 30 years and found no significant change in clarity. However, the analysis excluded 13 lakes with known management changes such as point source removal or artificial fertilization. The reasons for increasing clarity in Michigan may be many, including improved management practices around lakes to control polluted runoff, influences of laws and- regulations such as the Clean Water Act, removal of phosphorus from soap products, switches from septic to sewer systems in residences surrounding lakes, improved urban storm water management, changes in fish communities, and the presence of zebra mussels (Dreissena polymorpha). For example, the Federal Water Pollution Control Act was amended in 1977 and became known as the Clean Water Act. The Act gave the Environmental Protection Agency the power to reduce pollutant discharges into waterways through regulatory and non-regulatory tools. The Act also financed construction of wastewater treatment facilities and management of polluted runoff. In addition to the Clean Water Act, various cost-sharing programs with landowners that started emerging in the late 1980 and 19905 may have helped reduced polluted runoff and improved the clarity of waters. Examples of these include the National Resources Conservation Service’s Environmental Quality 34 Incentives Program (EQIP) and the Farm Service’s Conservation Reserve Program (CRP). Most of the individual lakes water clarity trends are slight, with lakes remaining within their trophic state. The average significant trend magnitude was approximately V2 to 1 m. However, a few lakes had more dramatic shifts of approximately 2 meters. St. Joseph county’s Klinger and Pleasant lakes have shifted from eutrophic in the late 19703 and early'19803 to almost oligotrophic currently. A similar situation exists for Benzie county’s Big Platte lake, shifting from almost eutrophic in the mid-19703 to almost oligotrophic currently. In contrast, Oakland county’s lake Sherwood has shifted from almost oligotrophic in the early 19803, to eutrophic currently (Figure 10a). I investigated some of the lakes with the more dramatic clarity shifts to help determine what may be some influencing factors on the change. For Lake Sherwood, the land along the shoreline of Oakland county’s Lake ShenNood has been highly developed. By the late 19703, about 80% of the homes had already been built along the shoreline, but there has been an increase in subdivision growth and a number of development activities around the lake in recent years (Klemmer, pers. comm.) Three to four years ago a school construction project approximately Mi mile from the lake resulted in sediment runoff to the lake, the developer was sued and the lake needed to be dredged (Till, pers. comm). Many of the homeowners use fertilizers on their lawns and the lake is also treated for weeds, but they are not harvested (Till, pers. comm). All the original homes were on septic systems, but new subdivisions now must be hooked up to 35 a sewer system (Klemmer, pers. comm.) In addition, older homes must be hooked up to a sewer systems if there is a septic tank failure or an addition built to the home (Klemmer, pers. comm.) Currently there is no comprehensive testing for septic tank failures of homes around the lake, but Oakland county will. be starting a program to test all tanks over the next five years (Till, pers. comm.) In contrast, Benzie county’s Big Platte lake has increased in water clarity ' substantially (Figure 10b). Upstream from Platte lake, along the Platte River, is the Michigan Department of Natural Resource’s (DNR) Platte river fish hatchery. In 1986 the Platte Lake Improvement Association filed a‘lawsuit against the DNR to reduce water and phosphorus discharges. In March of 2000, a consent agreement was finally signed that provides a phosphorus concentration limit in Platte lake, discharge limitations, and a six to seven year phase-down plan for phosphorus (GLAO, 2000). Even before the consent agreementwas signed, the hatchery began reducing its phosphorus discharges to less than 10% of its early peak levels by modified sewage procedures and changes in fish food (BFC, 2001). It is likely that these reductions in phosphorus loads to the lake helped reduce phytoplankton populations and therefore increased clarity levels in the lake. A 1972-1996 Secchi depth trend analysis on Narragansett Bay in Rhode Island showed a significant increase in clarity (Borkman and Smayda, 1998). The Bay also responded to management changes; the greatest increase in clarity occurred during a 10-year period when discharges of total suspended solids from wastewatertreatment plants decreased 75% (Borkman and Smayda, 1998). 36 St. Joseph county’s Klinger and Pleasant lakes have shifted from eutrophic in" the late 19703 and early 19803 to almost oligotrophic currently (Figure 1'0c,d). The water level in Pleasant lake never varies in thelake more than .three inches, and the lake has greater than normal flushing than other lakes nearby, as it is fed by a spring in the west and outflows to a creek on the east side (Kaiser, pers. comm.) Additionally, minimal farmland exists around the lake (0.9% in the 100 m. buffer and 5.4% in the 500 m buffer), so there is probably . little influence from agricultural runoff and manure from a nearby confined hog farm is trucked away from the area (Kaiser, pers. comm.) The homes around thelake are on septic systems, but in the late 19603 and early 19703, a housing developer raised thelake levels 1 1A2 feet, and the low septic systems were .flooded and abandoned (Kaiser, pers. comm.) Since then, new septic systems have been placed farther away from the lake’s shoreline (Kaiser, pers. comm.) The following information about Klinger lake was obtained by speaking with the CLMP volunteer sampler (McBride, pers. comm.) and it may explain the increase in clarity. In 1972, a sewer system was installed for the homes around Klinger lake (McBride, pers. comm.) It is likely that there was sometime lag until the clarity of the lake responded, dependent on the flushing rate of the lake. In 1996 zebra mussels were spotted in the lake (McBride, pers. comm.; Michigan Sea Grant, 2001). The exotic species Eurasian milfoil (Myriophyllum spicatum) and purple Ioosestrife (Lythrum salicaria) are also beginning to invade via the two lakes ahead of Klinger in a chain of three (McBride, pers. comm.) As with Pleasant lake, minimal farmland exists around the lake (2.3% in the 100 m buffer 37 and 0.0% in the 500 m buffer). The lake also has a number of artesian wells flowing into it (McBride, pers. comm.) and it is also fairly deep compared to other lakes in the state, with a maximum depth of 21.9 meters and average depth 6.4 meters. To investigate the influence of zebra mussels on the increasing clarity of Michigan’s inland lakes, the Sea Grant (2001) database of zebra mussel monitoring was compared to the lakes in the CLMP program. This database records which lakes have been monitored for zebra mussels and what year they were first detected. Zebra mussels are an exotic species, originating in Europe and were first discovered in North America, in Lake St. Clair in 1988 (Herbert, et al., 1991). Zebra mussels feed primarily on algae and are capable of filtering about one liter of water per day (Reeders el al., 1989). Of the lakes with significant increases in clarity, 42% contained zebra mussels. Zebra mussels were also present in 4% of the lakes with significant decreases in clarity. The 1996-2001 mean Secchi depth for all lakes with zebra mussels was 4.0 meters, and for those without, 3.4 meters (Figure 11). A t-test showed this difference was significant (P = 0.091 ) Of the lakes with significant trends, there was one spotting in 1992, but presence in other lakes was not detected until 1995 or later. To clarify if there was a difference between these two groups of lakes before zebra mussel infestation, the average Secchi depths were compared for the time period 1974-1990. The mean Secchi depth for all lakes with zebra mussels was 3.7 meters, and for those without, 3.1 meters (Figure 12). A t-test showed this difference was not significant (P = 0.11), 38 although it is only marginally non-significant. Thus, although statistically, the two lake groups historically have ’similar’ Secchi depths before Zebra mussel invasion, there. may be some biological differences in lakes that have been invaded by zebra mussels. Additionally, the difference between the mean Secchi depth of the lakes 20 lakes with zebra mussels, before and after infestation was not significant. Finally, not every lake in the CLMP program‘has been tested for zebra mussel presence. The infestation of zebra "mussels into Michigan’s inland lakes in recent years may have some influence on the increasing clarity trends, mm is difficult to attribute the observed differences solely to zebra mussels. The influence of ecoregions on the water clarity of Michigan’s inland lakes. The significant differences in Secchi depth between ecoregion sections and subsections (Figures 4 and 5) suggests that management strategies should take into account a lake’s ecoregion in the process of setting water quality goals or standards. The highly significant difference was between ecoregion sections, and the fact that the subsection differences were only between 73 and 63 indicates that ecoregion section seems to explain more than subsection. Ecoregions may be a way to guide management strategies, as they illustrate regional differences in lake water quality characteristics. For example, Heiskary et al. (1987) found great variation in total phosphorus concentrations in Minnesota’s lakes when categorized by ecoregions. Additionally, natural resource managers in' Minnesota have created a model in which ecoregions are used to predict runoff, precipitation, evaporation, stream phosphorus 39 concentration and atmospheric phosphorus deposition (Wilson and Walker, 1989). This program allows screening for lakes that may have unnaturally high phosphorus levels due to their ecoregions, morphometry and hydrology (Wilson and Walker, 1989). Restoration or protection goals may vary by ecoregion and on average, the southern portion of Michigan’s lower peninsula has lower water clarity than the northern portion. This lower clarity is further illustrated by the larger number (11) of eutrophic lakes in section six than in section seven (2) and the larger number of oligotrophic lakes in section seven (‘12) than in six (7). This may be due to the physical properties inherent to the ecoregion. For example, the southern zone is primarily composed of silt and clay loams, while the northern lower peninsula is primarily sands (Albert, 1995). Silty soils have greater erosion potential than sandy soils, and lakes could be more susceptible to sedimentation- induced clarity decreases in the southern ecoregion. In addition, differences in land use between the ecoregions may help explain differences in water quality (see below). For example, ecoregion section six further shows its vulnerability to decreasing water clarity in the individual lake trend analysis (Figure 7). Ecoregion six has more lakes with decreasing clarity and fewer lakes with increasing clarity than ecoregion seven. These results suggest that anthropogenic stressors may be greater in section six and land use is likely a causal factor 40 I. Correlations of land use to ecoregions and the influence of land use on the water clarity of Michigan '3 inland lakes. Comparing land use differences between the ecoregion sections, the significant differences were agricultural and forest land in both buffer sizes. Agricultural land use is greater in southern Michigan and forested land is greater in northern Michigan. This may explain the greater mean clarity of lakes in northern Michigan, since agricultural land is known to be an origin of nonpoint source pollution to water bodies (Sharpley et al., 1994; Carpenter et al., 1998).- However, in the relationship between Secchi depth and agricultural land in the 100 m and 500 m buffer distances are not significant (Figures 8b and 9b). It may be that the range and percentage of agricultural area in both buffer distances is too minimal to detect a significant relationship. In the 100 m buffer, agriculture land use comprises 2.7% for ecoregion six and 0.3% for ecoregion seven, and in the 500 m buffer, 18.8% for ecoregion six and 9.0% for ecoregion seven (Table 4). This dataset also may be too coarse or may need to be combined with other water quality measurements to pick up significant correlations with land use. This is further illustrated in that forested land shows significant differences between ecoregion, but no significant trend comparing Secchi depth to forested land, where we might expect to see a positive relationship. Residential land use in the 100 m buffer showed a significant positive relationship with Secchi depth and the 500 m buffer showed a non-significant, but positive relationship. This result is surprising, but it may be that clearer lakes are favored for housing developments and residences. Interestingly, residential land 41 use is very similar around lakes regardless of ecoregion, suggesting development is occurring around lakes throughout the state of Michigan. As was shown by the ecoregion and land use analysis, the higher density of residential use is within the -100 m buffer around the lake (Table 4). Residential land use area in the 100m buffer is 65% for six and 59% for seven, compared to 21% and 23% in the.500 m buffer. High-quality water is important to people, and degraded water bodies can affect property values. Several studies have illustrated the negative economic consequences of cultural eutrophication of lakes and other water bodies. For example, a study in Maine showed the detrimental impact of poor water quality on lakeside property values, as market prices dropped 10-20% with a one-meter reduction in clarity (Bouchard, 1995). Water quality had a significant influence on home values along the shores of Chesapeake Bay (Leggett and Bockstael, 2000), and home values along Lake Champaign, Vermont were lower compared to homes by a less polluted lake (Young, 1984). Smith et al. (1995) found people’s perception of clarity in lakes was strongly related to site suitability, and 90% of. survey respondents reported water suitable for bathing at a Secchi depth of approximately 2.75 m. In an effort to separate the effects of ecoregion from land use on lake clarity, l regressed Secchi means against the percent land use, for both the 100 and 500 m buffers in each of the ecoregion sections. Of the significant trends (P s 0.1), three supported the previous positive relationship of Secchi depth with residential land use: ecoregion six residential land use in the 100 m buffer, ecoregion seven residential land use in 500 m buffer, and ecoregion six 42 residential + agricultural land use in the 100 m buffer (Figures 13a-c) The most significant relationships (P = 0.03) were negative correlations with Secchi depth and wetlands in ecoregion seven’s 100 and 500 m buffer (Figures 14a-b) It also ' supports the significant negative correlation between all lakes and wetlands in the 500 m buffer. The negative relationships of both distances of wetland buffers with Secchi depth may be due to runoff from wetlands that contain tannins and dead organic material that may color the water and lead to decreased clarity (Wetzel, 2001). Forested land in ecoregion six’s 500 m buffer showed a negative relationship with Secchi depth. This seems somewhat unusual, but it could be do to logging activities that could be disturbing the soil. All other tests were not significant. The fact that few land uses versus Secchi depth relationships were significant again may indicate that this dataset also may be too coarse or may need to be combined with other water quality measurements to pick up significant correlations with land use. Even on.the significant relationships, the R2 values were fairly small. It is crucial to note that these land use analyses were very broad-scale and additional future research is needed to help quantify the effect of land use on lakes. Conclusions State-wide, lake clarity in Michigan has been increasing since the 19703. Of the individual lake trends, 31% are increasing in clarity, 6% are decreasing in clarity, and 63% have no trend. Although further monitoring is needed, the 43 r l. .. . r . r . . . . I v . . . . . :9 4 II . .. u . . .— . .. l 1.... observed increase in water clarity may be due to zebra mussels, better watershed management practices, or local and national regulations. I was not able to determine the underlying causes in these analyses. The highly significant difference between ecoregion sections indicates that section seems to explain more than subsection. The mean Secchi depth was lower for southern Michigan (ecoregion section six). It was easier to pick up differences in lake clarity through ecoregion influences than land use directly and ecoregions may be a way to guide management strategies, since they indicate regional differences in lake water quality characteristics. Although land use and changes in management on an individual lake scale may explain the greater trophic shifts in clarity in some of the lakes, these analyses did not detect a strong effect of land use on water clarity in lakes across the state. This dataset may be too coarse or may need to be combined with other water quality measurements to pick up significant correlations with land use. It is also important to remember that Secchi depth is only one component of water quality and that Secchi readings cannot directly indicate other water quality characteristics that would be harmful to humans or the lake ecosystem, such as the presence of harmful chemicals or pathogens. Volunteer monitoring programs provide an invaluable contribution to water quality information, as they provide people-power for agencies that cannot spend the time or money to send staff to numerous lakes to collect data. These data not only help us determine trends, but also set baseline conditions. Smeltzer’s et al. (1989) study used a model to determine that 10 years of Secchi depth data 44 should be able to detect a future 10% degradation in water clarity. This conclusion is important in that 10 years of monitoring data from programs like Michigan’s CLMP can set baseline information, from which to detect future changes. Smeltzer el al. (1989) also concluded that the temporal variability for phosphorus and chlorophyll was too large to detect non point source pollution influences over short times (less than 10 years), and the Secchi depth was a better monitoring tool to use. At the same time, while we use these volunteer- collected Secchi data, it is also essential to continue the training and checking of volunteers to ensure they are being as consistent as possible and are following procedures. It is important to both apply the CLMP data to understand the water quality status and changes of Michigan’s lakes, and to close the feedback loop by disseminating the information to the volunteers and the general public. One possibility of distribution could be to store the data on a website where it could easily be accessed by the public. Also, as identified by these analyses, it would be beneficial to include the lakes exhibiting significant trends in state monitoring programs. More comprehensive monitoring of these lakes, along with additional information through case studies can help natural resource managers determine what factors may be driving trends in water quality. 45 CHAPTER 3: CONCLUSIONS Future Research I No lakes in the upper peninsula of Michigan, nor the northeast tip of the lower peninsula met the selection criteria for the analyses in this study. The Tip of the Mitt monitoring group, in the northeast area of Michigan conducts an active program of lake monitoring, separate from the CLMP and it would be useful to add their data into these analyses. For a more comprehensive water quality analysis, the phosphorus and chlorophyll data could be added into analyses once the sampling has been ongoing for a longer time..- The case studies on individual lakes revealed specific information on activities around the lakes. Additional research could continue to clarify the relationships between Secchi disk clarity and lake characteristics. The investigations could include shape and depth of lakes, lake geological origin, primary source and rate of water recharge, flooding versus drought years, presence of zebra mussels, residential land use density, use of septic tanks versus sewers, residential landscaping, frequency of algae treatments, local lake management or ordinances, and any unique land uses in the lake’s watershed. Although land use and changes in management on an individual lake scale seemed to explain the greater trophic shifts in clarity in some of the lakes, these analyses did not detect a strong effect of land use on water clarity in lakes across the state. It is crucial to note that these land use analyses were very broad-scale and additional future research is needed to help quantify the effect of 46 land use on lakes. Interesting future analyses could investigate additional individual lake land uses on a detailed, smaller scale, as well as compare the _ 1978 land use to changes currently. A more current, comprehensive, state-wide land use dataset is needed for Michigan to assist in both additional regional and local analyses. Opportunities also exist to combine these CLMP Secchi depth data with new technologies for determining water clarity. For example, there are current research efforts in Minnesota, Wisconsin, and Michigan to quantify Secchi depth transparency from remotely sensed data, using Land sat satellite imagery (Olmanson et al., 2000; Kloiber et al., 2000; Nelson et al., under review). The data compiled here can be used to supplement and compare with satellite-based monitoring programs to determine the status and trends of water clarity of the state’s lakes. Ground-based data can also be used to determine the appropriate sampling time frame for satellite data collection. For these analyses, it was easier to pick up differences in lake clarity through ecoregion influences than land use directly. Ecoregions can be a way to guide management strategies, since they indicate regional differences in lake water quality characteristics. The natural landscapes of the United States are quite varied and unique and it is unreasonable to apply the specific results of this Great Lakes region lakes analysis broadly. However, the strength of the ecoregion predicting capabilities has opportunities for further research and applications on a broader national scale. Interesting future research could compare the impacts of ecoregions on varied aquatic ecosystems and further 47 identify key biophysical factors correlated with increases or decreases in water quality. Ecoregion analyses can assist in identifying regionsthat are potentially more susceptible to degradation based on their biophysical characteristics. Conclusions Protecting the water quality of lakes is imperative, since degraded water bodies can restrict human contact with the water, harm fish, invertebrates, vegetation, and alter biodiversity. Secchi disk monitoring can help determine and track certain aspects of water quality. State-wide, lake clarity in Michigan has been increasing since the 19703. Of the individual lake trends, 31% are increasing in clarity, 6% are decreasing in clarity, and 63% have no trend. While much still remains unknown, of the trends, increasing clarity is the dominant direction, and in this aspect, lakes may be improving in water quality. It is interesting to compare Michigan’s 303(d) list of impaired (not meeting one or more designated uses) lakes and reservoirs to the CLMP database. Of the 71 lakes I analyzed, eight are on the list, out of a total of 102 (USEPA, 2002A). The impairments of these eight lakes are metals, fish consumption advisory, and pesticides (USEPA, 2002A). None are listed for phosphorus or turbidity, the water quality parameters that are measured by Secchi readings. State-wide, Michigan may be reducing these sources of pollution. However, it is key to remember that Secchi depth is only one component of water quality and that Secchi readings cannot directly indicate other water quality characteristics that 48 could be harmful to humans or the lake ecosystem, such as the presence of harmful chemicals or pathogens, or the above mentioned. . These increases in clarity may be the result of some real impacts from better watershed management practices and local and national regulations. Point source pollution control from programs like the National Pollutant Discharge Elimination System (NPDES) certainly has had a role in improvements to surface 'water quality. Another program of the Clean Water Act, section 319 grants have also had a significant part in reducing NPS pollution. Established in 1987, states, territories and tribes and apply for grants for many types of NPS pollution reduction programs. Additionally, in 1977 the State of Michigan implemented a 0.5% phosphorus limit in laundry detergents that may have also had an impact on improving clarity (MDEQ, 2002). In addition to these regulatory and management impacts on reduced pollution to lakes, zebra mussels or other invading exotics may have a real impact on the increasing clarity trends. While it seems beneficialto have the water cleared by these invaders, the impact on the native ecosystem is harmful or still unknown, and zebra mussels should not be relied upon to “clean up” pollution problems that are still occurring from land uses. Management Implications Results of this study have helped elucidate anthropogenic impacts on inland lakes and determine baseline conditions and can help communities in Michigan create watershed management plans for their lake. The watershed 49 management approach is growing in popularity as a method to protect and monitor the quality of our water resources. Whereas some approaches pinpoint a specific pollutant source, or do not take into account the entire water ecosystem, watershed management integrates human, community, biological, chemical, physical, legal, and policy considerations of the land and water. The USEPA (20020) defines a watershed management approach as “...a coordinated framework'for environmental management that focuses public and private sector efforts to address the highest priority problems within hydrologicalIy-defined areas, taking into consideration both ground and surface water flow.” The USEPA began encouraging a watershedapproach to management during the mid-19903. The holistic and ecosystem-based nature, of the watershed approach, is aimed to be more effective than the political boundary-based management. From monitoring programs such as the CLMP, or other programs, watershed or lake organizations can have better information to aid in identification and prioritization of significant contributing sources of pollution or baseline water quality status. With knowledge of current and changing water quality conditions, management choices or studies can be better tailored to the needs of the lake. For example, if the lake is showing increasing clarity, the presence of zebra mussels, or other water quality parameters could be tested to see what other pollution problems may exist. If a lake is showing decreasing clarity, management options could include encouraging sewer installation, testing for septic failures, soil erosion control, timing and reduction of fertilizer use, or 50 use of vegetative buffer strips or native plant landscaping. Even without the presence of any trends, it is always crucial to promote best management practices for residential and agricultural land use. Monitoring program like the CLMP can provide better advice on the baseline or changing conditions of these lakes to help sustain their value to state and local communities. Volunteer monitoring programs an invaluable contribution to water quality information, as they provide people-power for agencies that cannot spend the time or money to send staff to numerous lakes to collect data. Using volunteers to monitor lakes is not only an efficient method, but also is an opportunity to educate the public about the resource. When citizens are involved in taking care of their resource, it certainly brings greater awareness, cooperation, and buy-in to management activities. Even in states where lakes are not a major component of the landscape, with proper training and administrative support, volunteer programs can be used to monitor other biological resources such as rivers, wildlife communities, beaches, and wetlands. Volunteer monitoring programs promote self-reliance and empowerment for lake management decision-making. Lake associations or watershed groups can not only work on their own goals for the lake, but can also help identify targets for protection and restoration for inclusion in state-run programs. Monitoring programs like Michigan’s CLMP can integrate existing water quality information with the tools and plans needed to protect, maintain, and restore water resources. 51 APPENDIX A: TABLES Table 1. Characteristics of the 71 selected lakes. County Lake Name New Surface Max Ave. Trophic Eco— Key Area Depth Depth Classification region Code (haL (m) (m) (1996-2001) Alcona Vaughn 1-29 45 19.8 6.5 Mesotrophic 7.2 Allegan Hutchins 3-178 154 10.4 3.2 Eutrophic 6.3 Allegan & Eagle 360 83 17.1 6.5 Oligotrophic 6.3 Van Buren Alpena Beaver 4-1 280 23.5 8.5 Mesotrophic 7.6 Barry Barlow 8-586 73 18.6 Mesotrophic 6.2 Barry Payne 8-604 46 13.1 4.9 Mesotrophic 6.2 Benzie Biwatte 10-39 1025 27.4 8.2 Mesotrophic 7.4 Benzie Crystal 10-42 3994 48.8 17.5 Oligotrophic 7.4 Berrien Little Paw 11-2 41 9.1 Eutrophic 6.3 ' Paw Branch Coldwater 12-90 640 28.0 5.6 Mesotrophic 6.2 Branch Long 1286 50 13.7 Eutrophic 6.2 Cass Christiana 14- 72 12.2 6.3 Eutrophic 6.2 400- GRP Cass Dewey 14-88 91 15.2 Eutrophic 6.2 Cass Donnell 14-224 100 19.2 7.6 Mesotrophic 6.2 Cass Indiana 14-394 33 21 Mesotrophic 6.2 Cass Juno 14- 88 11.3 Eutrophic 6.2 400- . GRP Cass Painter 14- 42 8.2 Eutrophic 6.2 400- GRP Cass Twin 14-75 26 16.5 5.2 Mesotrophic 6.2 ' Lakes- Nonh Clare Crooked 18-69 107 22.3 4.9 Mesotrophic 7.2 Clare George 18-125 52 7.6 2.8 Mesotrophic 7.2 Clare Shingle 18-124 107 22.9 Mesotrophic 7.2 Crawford Mmthe 20-44 778 19.8 4.7 Mesotrophic 7.2 Genesee Byram 25-54 54 18.3 Mesotrophic 6.4 Genesee Fenton 25-35 351 27.4 6.2 Oligotrophic 6.4 Grand Arbutus 28-84 153 13.4 3.7 Oligotrophic 7.2 Traverse Grand Duck 28-124 787 27.4 7.3 Mesotrophic 7.3 Traverse Grand Long 28-214 1 178 24.4 7.9 Oligotrophic 7.3 Traverse Grand Spider 28-81 180 9.8 2.8 Oligotrophic 7.2 Traverse losco lgg 35-96 197 18.9 5.0 Mesotrophic 7.1 losco Van Etten 35-219 570 10.1 4.6 Eutrophic 7.1 Jackson Clear 38-32 52 Mesotrophic 6.1 52 County Lake Name New Surface Max Ave. Trophic Eco- Key Area Depth Depth Classification region Code (ha) (m) (m) (1996-2001) Jackson Vineyard 38-503 219 12.8 4.2 Mesotrophic 6.1 Kalkaska Cub 40-106 23 7.0 2.9 Oligotrophic 7.2 Kent Camp 41 616 55 15.2 0.0 Oligotrophic 6.4 Lake Harper 43-254 34 18.0 5.5 Oligotrophic 7.3 Leelanau- Glen 45-9 1969 39.6 21.8 Oligotrophic 7.4 Leelanau Leelanau- 45-3 1194 36.9 12.4 Mesotrophic 7.5 Nonh Leelanau Little Glen 45-9 565 4.0 1.8 Eutrophic 7.4 Lenawee Devils 46-45 531 19.2 4.3 Mesotrophic 6.1 Livingston ‘ Coon 47-204 39 Eutrophic 6.1 Livingston Zukey 47-97 60 10.7 Eutrophic 6.1 Manistee Bear 51-132 758 6.1 3.8 Mesotrophic 7.4 Mason Blue 53-131 27 18.3 Oligotrophic 7.3 Mason Ford 53-135 74 22.9 10.8 Oflotrophic 7.4 Mason Hackert 53-101 49 15.8 . 2.0 Mesotrophic 7.4 Mecosta Blue 54-53 93 15.2 . 0.0 Oligotrophic 7.2 Mecosta Horsehead 54-136 179 12.8 0.0 Mesotrophic 7.2 Mecosta Mecosta 54-54 126 1 1 .3 3.2 Mesotrophic 7.2 Mecosta Round 54-51 64 13.7 4.8 Mesotrophic 7.2 Mecosta School 54-57 49 10.1 3.1 Oligotrophic 7.2 Section Missaukee Sapphire 57-70 100 2.4 1.2 Eutrophic 7.2 Montcalm Baldwin 59-99 25 10.7 1.8 Mesotrophic 6.4 Montmorency Avalon 60-162 156 22.6 10.6 Oligotrophic 7.6 Montmorency West Twin 60-19 528 9.1 2.2 Mesotrophic 7.2 Newaygo Bills 62-75 81 27.4 5.7 Mesotrophic 6.4 Newaygo Emerald 62-82 31 15 Mesotrophic 7.3 Newaygo Sylvan 62-81 41 19.2 Mesotrophic 7.3 Oakland Lakeville 63- 174 20.1 3.0 Mesotrophic 6.1 1650 Oakland Sherwood 63-252 99 6.1 Eutrophic 6.1 Oakland Taylor 63— 15 18.3 Oligotrophic 6.1 1025 Oakland Walled 63-16 261 Oligotrophic 6.1 Oakland White 63-575 210 9.8 3.3 Oligotrophic 6.1 Ottawa Crockery 70-164 42 16.5 7.5 Eutrophic 6.3 Roscommon Higgins 72-117 4122 41.1 15.8 Oligotrophic 7 2 Shiawassee Leisure n/a 94 OliLotrophic 6.4 St. Joseph Clear 78-93 261 9.4 3.6 Mesotrophic 6.2 St. Joseph & Corey 14-159 242 24.4 7.7 Mesotrophic 6.2 Cass St. Joseph Klinger 78-171 338 21.9 6.4 Mesotrophic 6.2 St. Joseph Pleasant 78-94 104 16.2 5.9 Mesotrophic 6.2 Van Buren Lake of the 80-288 122 9.1 4.5 Mesotrophic 6.2 Woods Wexford Stone 83-17 34 6.1 Mesotrophic 7.2 Ledge 53 Table 3. Lakes with significant trends. County Lake Name Yrs. Of Yrs. Data Trend P- R2 . Data Span value Branch Long 25 77-01 - 0.091 0.119 Livingston Coon 18 74-01 - 0.094 0.165 Manistee Bear 24 77-01 - 0.044 0.172 Oakland Sherwood 21 80-00 - 0.001 0.44 - Alcona Vaughn 10 75-01 + ‘ 0.034 0.447 Barry Payne 1 1 9000 + 0.04 0.39 Benzie Big Platte 23 77-01 + 0.001 0.415 Berrien Little Paw Paw 10 92-01 + 0.026 0.482 Cass Dewey 25 74-00 + 0.022 0.207 Cass Twin Lakes- North 10 9201 + 0.024 0.491 Grand Traverse Long 15 79-01 + 0.104 0.19 losco Long 28 7401 + 0.003 0.296 Jackson Vineyard 21 77-01 + 0.039 0.206 Kalkaska Cub 9 93-01 + 0.015 0.592 Leelanau Lake Leelanau- North 25 77-01 + 0.001 0.396 Livingston Zukey 13 8001 + 0.017 0.416 Mason Blue 14 88-01 + 0.006 0.48 Mecosta Blue 19 81 -‘01 + < 0.001 0.543 Mecosta Horsehead 21 81 -01 + - 0.002 0.419 Mecosta Mecosta 18 81 -01 + 0.032 0.257 Mecosta School Section 1 1 90-00 + 0.001 0.729 Montcalm Baldwin 23 77-01 + < 0.001 0.613 Oakland Lakeville 19 76-01 + 0.091 0.159 St. Joseph Klinger 17 82-01 + < 0.001 0.729 St. Joseph Pleasant 22 78-01 + < 0.001 0.823 Van Buren Lake of the Woods 21 81-01 + 0.087 0.146 Table 4. Percent land use types in ecoregion sections - Buffer Land use type Ecoregion 6 Ecoregion 7 P-value size (m) % % 100 Residential 64.8 59.4 0.282 100 Agricultural 2.7 0.3 0.003 100 Residential + Agriculture 67.5 59.6 0.116 100 Forest 16.4 25.9 0.055 100 Wetlands 4.8 7.1 0.244 500 Residential 21 .0 23.3 0.601 500 Agricultural 18.8 8.9 0.023 500 Residential + Agriculture 39.8 32.2 0.140 500 Forest 37.4 47.3 0.091 500 Wetlands 10 7.3 0.216 54 APPENDIX B: FIGURES Figure 1. Ecoregions with selected lakes. 9113 DB 7&\ V \ 7.6 Q . 7.5 ' Legend . - Lakes 0 50 100 150 200 Kilometers Egg: [:1 Ecoregions not used Ecoregions dropped in subection analysis 55 Figure 2. State-wide Secchi depth trend. 4.00 3.50 3.00 - Secchi depth (m) 2.50 ~ 2.00 y = 0.0528x + 3.1715 R2 = 0.7532 P = 0.056 I 1974-1980 1981-1985 1986-1990 1991-1995 1996-2001 Year Span Figure 3. State-wide 1996-2001 trophic classification. 40 35- 30- 25- 20- 15- 10— 5- 0- Number of lakes 20% Eutrophic Mesotrophic 52% 28% Oligotrophic Trophic classification 56 Figure 4. Ecoregion section Secchi depth average 1996-2001. 5.0 2.0 - Secchi depth (m) P = 0.003 Section 6 (southern Michigan) Section 7 (northern Michigan) Ecoregion section 0.0 Figure 5. Ecoregion subsection Secchi depth average 1996-2001. 5.50 5.00 -, P or O I 4.00 «- 3.50 -— Secchi depth (m) 3.00 —- , 2.50 -- 6—1 6-2 6-3 6-4 7-2 7-3 7-4 Ecoregion 57 Figure 6. 1996-2001 trophic classification by ecoregion subsection. Lit—:I Eutrophic lakes by ecoregion .n .0 .a O Number of lakes 0 4 N O) A 01 CD \l on (D 6.1 6.2 6.3 6.4 7.2 7.3 7.4 Mesotrophic lakes by ecoregion _A ..s a O Number of lakes 0 —D h) (.0 # 01 O) \l m (D 6.1 6.2 6.3 6.4 7.2 7.3 7.4 E Oligotrophic lakes by ecoregion 11 1 1 Number of lakes .L 1 O—eNW-bU'ICDVG . it: it. 6.1 6.2 6.3 6.4 7.2 7.3 7.4 Ecoreglon hEcoregions 7.1, 7.5, 7.6 omitted due to low sample size] 58 Figure 7. Significant lake clarity trends by ecoregion. 100 90 4 80 - Percentage of lakes 8 10- Ecoregion 6 significant trends Ecoregion 7 significant trends 1:] Increasing clarity El Decreasing clarityJ 59 Figure 8. Land use versus Secchi depth: 100 m buffers. A 8 y = 0.0188x + 2.08 . A7 . g 6 - R2: 0.0617 ' o . .: P=0.07 35 o 0 14 W 'E 3 o o 3 2’ . O 0‘ a: ..0 ’o O 011— 0 O T l l 10 20 30 40 50 60 70 80 90 100 % Residential land use 0 y = -0.074x + 3.3541 R2 = 0.0269 P = 0.236 Secchi depth (m) 0 -l l\) 00 #- 01 O) \l 00 l l E 20 30 40 50 %Agricultural land use y = 0.0161x + 2.2251 6 j R2 = 0.0468 ' . . 5 _ P = 0.116 . Secchi depth (m) .h 0 l O 10 20 30 40 50 60 70 80 90 100 % Agriculture + residential land use 60 Figure 8 (cont’d.) Land use versus Secchi depth: 100 m buffers. D Secchi depth (m) Secchi depth (m) : 4 y = -0.0005x + 3.2525 6 J R2 = 0.000 5 P = 0.961 4 - 3 8 2 1 0 l l T I T T T 0 10 20 30 40 50 60 70 80 90 100 % Forest land 3 y = -0.0307x + 3.4184 s R2 = 0.0255 P = 0.249 5 4 3 2 1 0 I I 40 50 % Wetlands 6l Figure 9. Land use versus Secchi depth: 500 m buffers. A y = 0.0198x + 2.3043. o #:0M% P=0.106 A Secchi depth (m) 0 -* N 00 4) 01 O) \l (I) I r l I T 50 60 70 80 90 100 % Residential land use y = -0.018x + 3.4975 R2 = 0.0437 P = 0.129 Secchi depth (m) 0 -* N 00 h 01 O) \l 00 50 60 70 80 90 100 % Agricultural land use c A; , y=0.0003x+3.23 és- ' R2=0.000 §5_ . . P=0.974 3 4- c ’ c .. . 'E 3* ' i 0Q?" o d 8 2 9 . ..0‘. . o . $1 ' ' OI T T r T I 0 1 0 20 30 40 50 60 70 80 90 100 %Agriculture + residential land use 62 Figure 9 (cont’d.). Land use versus Secchi depth: 500 m buffers. D Secchi depth (m) Secchi depth (m) 8 7 . ' . o 6 - o 5 _. . y = 0.0051x + 3.0265 2 _ 4 . . . .0 o R 3 0.0065 #4‘-T——. 3 ~ o O O 2 - . 0 ' '2- ' '9- o’ , .' 1 c 0 P = 0.561 0 20 30 40 50 60 70 80 90 100 % Forest land 8 7 A O. y = '0.0669X + 3.8321 6 P.- R2 = 0.15 5 , P = 0.004 4 3 2 1 0 I , , . 1 0 20 30 40 50 % Wetla nds 63 Figure 10. Examples of individual lake Secchi depth trends. Bow 6 Bow 3 W OOON W M . OOON 1 1T m % 82. v.“ M. m 89 +ww 82 moo..82 m 0.0. 82 0.9%.”.32 w __ __ 82 o . 82 0 WP w. _._ 82 . 32 v. 39 . 32 82 . 82 ) .82 ) - mm? mv mu "n. 52 n . 52 w . 82 w . 82 d 82 u - 8...: n 8 r .n. . 82 m or a n m 32 w. & . $2 le\ 89 m . mm? a 82 h . mm? d E2 m . 3% m 32 m . 82 . mam, w «a? .m. ..w 52 B . 52 _ 89 - 82 2.2 . 22 wk: . £2 . Rap E: our . £2 . E: T 22 a 4 a n a a a a a 1 4 VNGP _ _ _ . _ _ . _. . . Em: 6555453525150 6%5”4%3:2~2%1%0 AEV 5am“. 230m :55 on Eooom Year Klinger lake (St. Joseph county) Figure 10 (cont’d.). Examples of individual lake Secchi depth trends. m 58 n 88 MW. 3 . Doom %. 1 T Doom ..I a . mmmp 1 a - mmmp x 7. 0. “A 5 O www.mmmw %wo. -mmmp manning mwnn -32 _. 7 camp ._ - 89 v. . 32 y - m8. . v2: - v8. . 82 - 82 - 82 M - «we - 52 m - 52 - 82 w - 82 T 82 .m. - 82 . 82 m a - 82 $2 w. ...w . $2 . 82 m. . 82 mm? M. - mm? . $2 m - 32 82 .m . 32 f mm? m . «m2 . 52 b r 52 82 P . 82 - 29 r 22 - EB . £2 - R2 . R2 82 . £2 . £2 . m3; swam.m.wzwrwoxe ewaurwmmzwnwoxe :5 5%.. 288 D :5 5%.. 28am year 65 Figure 11. The influence of zebra mussels on average Secchi depth 1996- 2001. 4.5 4.0 « 3.5 - 3.0 — 2.5 - 2.0 - 1.5 - 1.0 — Secchi depth (m) P = 0.091 Lakes with zebra Lakes without mussels zebra mussels Figure 12. The influence of zebra mussels on average Secchi depth 1974- 1990. 4.5 4.0 , , r 3.5 < 3.0 ~ 2.5 2.0- 1.5—w Secchi depth (m) 1.0-rm 0.5— 0.0- P=0.ll Lakes with zebra Lakes without mussels zebra mussels 66 Figure 13a-c. Land use versus Secchi depth by ecoregions: residential and agricultural. A 8 A 7 y = 0.0196x + 1.5554 LE. 6 R2 = 0.1136 n. 9 . 8 4 . o o E 3 w . ‘ o. .0 3 2 ~. . . ' o. a) 1 - o 0 10 20 30 40 50 60 70 80 90 100 "/0 residential land use ecoregion 6: 100 meter buffer B 8 A 7 _ . . o y = 0.0495x + 2.5984 56-. s R2=0.1111 .c :30- 5 a . P = 0.10 1, 4 :5: 3 3 2 (n 1 _. O I T T . l l l i 0 10 20 30 40 50 60 70 80 90 100 % residential land use ecoregion 7: 500 meter buffer C 8 A y = 0.018x + 1.6057 s 7 1 I 5 . R2 = 0.1024 ‘5' 5 . P = 0.09 . O 3 3‘ . a - '- ' z ‘1 8 2 1 o o . . ~ Q . 0 1 g 0 in O l T . l 7 T T T O 10 20 30 4O 50 60 7O 80 90 100 °/o residential + agricultural land use ecoregion 6: 100 meter buffer 67 Figure 14a-b. Land use versus Secchi depth by ecoregions: wetlands A 8 ... 7 y = -0.0896x + 4.3848 E. 5 R2 = 0.183 4':- 5 P = 0.03 O. 3 4 IE 3 8 a, 2 ‘0 1 0 . . 30 40 50 °/o wetlands ecoregion 7: 100 meter buffer B - 8 A 7 4 '0 y = -0.0813x + 4.3414 g 6 J." R2 = 0.2009 .c o P==(103 75‘ 5” '0 E 0 0 0 (I) 0 1 O 20 30 4O 50 °/o wetlands ecoregion 7: 500 meter buffer 68 APPENDIX C: SAMPLING FREQUENCY RESULTS Table 2. Sampliry frequencies. County Lake Name Data Years Years Years Span with 1 sampling all minimum missed frequencies 2 samples month per month Alcona Vaughn 75-01 0 10 7 Allegan Hutchins 75-01 1 26 15 Allegan 8. Van Buren Eagle 76-01 1 16 13 Alpena Beaver 77-01 1 25 20 Barry Barlow 80-01 1 22 13 Barry Payne 90-00 0 1 1 9 Benzie Big Platte 77-01 0 23 23 Benzie Crystal 76-01 3 1 1 4 Berrien Little Paw Paw 92-01 1 10 9 Branch Coldwater 77-01 0 25 21 Branch Long 77-01 1 25 18 Cass Christiana 78-01 1 10 9 Cass Dewey 74-00 0 25 23 Cass Donnell 78-01 1 21 18 Cass Indiana 88-01 0 14 14 Cass Juno 78-01 1 10 9 Cass Painter 93-01 0 7 7 Cass Twin Lakes- North 92-01 1 10 9 Clare Crooked 80-01 0 15 12 Clare Lake George 74-01 0 11 10 Clare Shingle 74-01 0 10 8 Crawford Lake Margrethe 75-01 1 12 7 Genesee Byram 74-01 0 17 15 Genesee Fenton 80-01 0 17 8 Grand Traverse Arbutus 88-01 3 14 10 Grand Traverse Duck 92-01 0 10 9 Grand Traverse Long 79-01 1 15 12 Grand Traverse Spider 80-01 0 11 8 losco Long 74-01 3 28 21 losco Van Etten 86-01 1 16 15 Jackson Clear 81 -01 2 17 14 Jackson Vineyard 77-01 0 21 18 Kalkaska Cub 93-01 0 9 9 Kent Camp 90-00 1 9 3 Lake Harper 8901 2 1 1 9 Leelanau Glen 79-01 4 17 11 Leelanau Leelanau- North 77-01 0 25 25 Leelanau Little Glen 79-01 2 18 13 Lenawee Devils 80-01 1 1 1 5 Livingston Coon 74-01 0 18 18 Livingston Zukey 80-01 0 1 3 8 Manistee Bear 77-01 1 24 19 Mason Blue 88-01 1 14 8 Mason Ford 83-00 3 18 1 3 Mason Hackert 89-01 2 13 8 Mecosta Blue 81 -01 0 19 19 69 County Lake Name Data . Years Years Years Span with 1 sampling all minimum missed frequencies 2 samples month per month Mecosta Horsehead 81 -01 2 21 18 Mecosta Mecosta 81 -01 0 1 8 17 Mecosta Round 77-01 1 1 9 18 Mecosta School Section 90-00 0 11 9 Missaukee Sapphire 79-01 1 21 15 Montcalm Baldwin 77-01 0 23 19 Montmorency Avalon 80-01 1 15 9 Montmorency West Twin 92-01 1 10 7 Newaygo Bills 84-01 1 18 14 Newaygo Emerald 80-01 2 20 17 Newaygo Sylvan 80-01 1 20 18 Oakland Lakeville 76-01 1 1 9 17 Oakland Sherwood 80-00 1 21 19 Oakland Taylor 74-01 1 16 14 Oakland Walled 83-01 2 10 7 Oakland White 76-01 2 13 10 Ottawa Crockery 82-01 1 1 1 8 Roscommon Higgins 74-01 1 26 11 Shiawassee Leisure 81 -01 2 14 11 St. Joseph Clear 76-00 3 20 15 St. Joseph & Cass Corey 74-01 0 28 28 St. Joseph Klinger 82-01 1 17 14 St. Joseph Pleasant 78-01 1 22 20 Van Buren Lake of the Woods 81-01 0 21 19 Wexford Stone Ledge 87-01 1 15 13 For the individual lake trend analysis using all sampling frequencies, I found 26 lakes with significant (P 5 0.1) trends. For the dataset using years with a minimum of two samples or more per month, I found 28 lakes with significant (P s .1) trends. Of the 26 significant trend lakes using all sampling frequencies, six were not significant in the other dataset. These six only had a total of 3 years with a missed month of sampling. There was also a great decrease in years and range of data when using the minimum of 2 samples per month. The literature supports a minimum of 3 samples over the summer growing period. Therefore, for all my final analyses, I used the data set that had a minimum of 3 samples per summer. 70 APPENDIX D: METADATA Data description: Secchi depth data for 71 selected lakes in the Cooperative Lakes Monitoring Program (CLMP). Data spans from 1974 to 2001. Secchi depth data collection methods: The Michigan Department of Environmental Quality (MDEQ). has developed standard procedures for Secchi depth measurements and volunteers are trained by MDEQ or Michigan Lakes & Streams Association staff yearly. Volunteers collect Secchi depth readings weekly or bi-weekly (every other week) from mid- May through mid-September, although not all volunteers sample this often. Although there is not complete control over the volunteers’ sampling procedures, they are trained as follows. The Secchi depth is taken in the deepest basin of the lake and sometimes at additional stations. Readings are taken on the same day of each week and between 10:00am and 3:00pm to minimize sun angle ‘ differences between readings. Secchi data are recorded from an anchored boat and with some distance from the anchor to minimize turbidity created by dropping the anchor. The Secchi disk is lowered on the shaded side of the boat and volunteers are instructed not to wear sunglasses or tinted glasses in order to record more accurate and uniform readings. The Secchi disk is lowered until it disappears, raised until it appears again, and the reading is the average between these two depths. Obtained from: Michigan Department of Environmental Quality, Land and Water Management Division, Ralph Bednarz. bednarzr@michigan.qov Complied by: Laura Bruhn, Michigan State University. bruhnlau@m‘su.edu Data format: 1974-1988: Hard copy summary sheets showing “week of” sample dates. 1992: Hard copy summary sheets showing “week of” sample dates. 1989-2001: Hard copy individual lake sheets showing exact sample date (not summary sheets). 1995-2001: Pre-entered Excel format electronic data. Data entry was done by a volunteer from CLMP. Data entry of ‘hard copy’ sheets: 1. Occasionally, lakes had multiple samples per week, so to be consistent with the rest of the data, only one value was desired. To obtain one value per week for the summary sheets, the 1St date was used because no exact dates were available. For other data, I used date closest to the day of week that 71 other samples were taken. Almost always, the two Secchi values were very similar. 2. Heading dates are the ‘week of’ (meaning that date, and all future dates during the next week). Heading dates start on either Saturday or Sunday. 3. All data were reported in feet, and volunteers are instructed to round readings to the nearest half-foot. Occasionally on the hard copy data, the Secchi depths were recorded to the inch, and for transfer into electronic form, values were rounded to the nearest quarter-foot. In some cases with the electronic data, values were already entered to the exact nearest inch, and I left them as entered. Below is the rounding criteria that I used. Inch Rounded Exact 9 ' 1 .0 .08 !7 2 .25 .16 1.." 3 .25 .25 4 .25 .33 5 .5 .41 6 .5 .5 7 .5 .58 8 .75 .67 9 .75 .75 10 .75 .83 11 .0 .91 12 .0 .0 Quality control/quality assurance: .On- -screen double check at time of 1St entry (5/2002 & 6/2002). 2. Hard copy check on 6/20/02, 6/21/02, 6/28/02, 7/11/02. (Included check of 1995-2001 electronic data). Warnings] data limitations: Secchi depth is only one component of water quality and Secchi readings cannot compressively indicate site-specific water quality conditions. fiscriLtion of Excel files and sheets wit_hin t_he files: Secchi 74data.xls 1974 originalraw: Raw data from entry or electronic format, QA/QC. Data are in feet/ inches. 72 1974 summer: July through September data only. Multiple samples per week are excluded, and only primary sampling station is used. Data are in feet/inches. These data files continue for every year (Secchi 75data.xls, Secchi 76data.xls, etc.). Info lakes 8+ years data.xls General info: The 71 selected lakes, including name, county, township, surface area, maximum depth, township, and notes. Sampling freq: Lists the number of samples per three month summer period, (0 samples per month, 1, 2, and 3 or more samples :3" per month) for each lake year. For example, “0-2-3” means I' ‘ 0 samples in July, 2 in August, and 3 or more in September. . “1” means 1 sample in July, 1 in August, and 1 in 5‘._ September. A lake year of data was used in analyses if it only missed one month of sampling (one 0) and had a minimum of three samples over the entire summer period. A lake year of data was not used in analyses if it missed more than one month of sampling (two or more Os). Stats: Lists the regression results for each lake using all sampling frequencies versus using only 2 and 3 or more samples per month. Zebra: Zebra mussel data from Sea Grant’s (2001) database. This database records which lakes have been monitored for zebra mussels and what year they were first detected. The analyses compares the lakes containing zebra mussels to their clarity trends, and 1996-2001 mean Secchi depth for lakes with zebra mussels versus those without. Significance: Lists the regression results for each lake, sorted by P-value, and the magnitude of the significant trends. All_Lakes.xls Dates: Summer weeks were put into a 1st week of the month, 2"“, 3'“, 4‘“, and 5th week of the month format. I did this for every summer month (July, August, September). This sheet shows how the dates matched up with each week of the month. FT 0+1+2+3z Data from all lakes, all years, in feet/inches. Data uses all sampling frequencies (0 samples per month, 1, 2, and 3 or more samples per month). 73 Meters 0+1 +2+3: Meters 2+3: Lake trends: Histograms: Trophic: State-wide ave: Ecoregion ave: Ecoregion trends: Data from all lakes, all years, converted to meters. Data uses all sampling frequencies (0 samples per month, 1, 2, and 3 or more samples per month). Data also includes yearly Secchi depth average, number of samples per year, standard deviation and standard error. Data from all lakes, all years, converted to meters. Data uses sampling frequencies of 2 and 3 or more samples per month. Data also includes yearly Secchi depth average, number of samples per year, standard deviation and standard error. Time-based trends of individual lakes, using data from all sampling frequencies (0,1 ,2,3). The graphs show regression equation, and lakes with significant (P s 0.1)trends are noted. Distribution of yearly averages of individual lakes. State-wide and ecoregion trophic state divisions, based on Forsberg and Ryding’s (1980) criteria and 1996-2001 average Secchi depths. State-wide clarity trend, divided by approximately S-year intervals. Average Secchi depth, by ecoregion sections and subsecfions. Individual lake clarity trends, grouped by ecoregion section. Land Use vs. Secchi: The land use information was obtained from Michigan Resource lnforrnation System (MIRIS) data from 1978. Therefore, I only included lakes with annual Secchi means within a ten-year span of these data (1974-1983). Secchi depths annual means were plotted against the percent land use within both a 100 and 500 meter buffer around each lake. The land use categories used were: residential, agriculture, residential combined with agriculture, forest and wetlands. Ecoregion Land Use: Calculations of the average percent land use types for the two ecoregion sections (six and seven) for each of the land use classifications and buffer distances. 74 Ecoregion vs. Land Use: Ecoregion average Secchi depth versus ecoregion land uses. (These analyses not used in thesis). 75 APPENDIX E: LAKE SECCHI AVERAGE BY YEAR County Alcona Alcona Alcona Alcona Alcona Alcona Alcona Alcona Alcona Alcona Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan Allegan 8. Van Buren Allegan & Van Buren Allegan 8. Van Buren Allegan & Van Buren Allegan & Van Buren Allegan & Van Buren Allegan & Van Buren Allegan 8. Van Buren Allegan 81 Van Buren Allegan & Van Buren Allegan & Van Buren Allegan & Van Buren Allegan 8. Van Buren Lake name Vaughn Vaughn Vaughn Vaughn Vaughn Vaughn Vaughn Vaughn Vaughn Vaughn Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Hutchins Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Eagle Year 1975 1976 1977 1992 1994 1997 1998 1999 2000 2001 1975 1976 1977 1978 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1976 1977 1978 1979 1980 1981 1982 1983 1984 1991 1992 1993 1997 Ave. 2.03 2.08 1.75 4.62 2.72 3.67 2.22 3.53 3.96 3.28 2.11 1.97 2.06 2.18 2.83 2.19 2.67 3.48 2.10 2.51 2.62 2.49 2.09 2.49 2.74 2.88 2.89 2.36 1.89 2.64 2.74 2.82 1.96 2.66 2.38 2.38 3.12 3.37 3.78 4.01 3.24 4.66 4.45 4.55 4.27 3.80 2.66 3.45 3.79 d—A-L—b—L: COWONN C4000 ©V®$Vwmdd03mfl®® St.Dev St.Err 0.25 0.02 0.40 0.03 0.52 0 05 0.24 0.02 1.09 0.08 0.42 0.07 0.41 0.05 0.94 0.13 0.63 0.08 0.95 0.16 0.28 0.02 0.32 0.02 0.18 0.02 0.09 0.03 0.56 0.08 0.34 0.03 0.28 0.04 0.72 0.10 0.20 0.02 0.39 0.04 0.70 0.07 0.38 0.03 0.39 0.04 0.39 0.04 0.48 0.05 0.72 0.07 0.67 0.06 ~ 0.38 0.03 0.17 0.01 0.27 0.03 0.45 0.05 0.44 0.06 0.14 0.02 0.52 0.09 0.20 0.03 0.90 0.13 0.47 0.04 0.62 0.06 0.70 0.06 0.26 0.03 0.42 0.03 0.66 0.07 0.57 0.05 0.87 0.11 1.01 0.08 0.52 0.05 0.18 0.04 0.63 0.05 0.89 0.07 County Lake name NKC Year Ave. 11 St.Dev St.Err Allegan8Van Buren Eagle 3-60 1999 3.97 13 0.80 0.06 Allegan 8 Van Buren Eagle 3-60 2000 3.75 13' 0.74 0.06 Allegan 8 Van Buren Eagle 3-60 2001 4.50 11 0.28 0.03 Alpena Beaver 4-1 1977 2.77 10 0.56 0.06 Alpena Beaver 4-1 1978 3.28 12 0.37 0.03 Alpena Beaver 4-1 1979 3.16 8 0.31 0.04 Alpena Beaver 4-1 1980 3.55 11 0.45 0 04 Alpena Beaver 4-1 1981 3.40 12 0.39 0.03 Alpena Beaver 4-1 1982 3.20 12 0.28 0.02 Alpena Beaver 4-1 1983 3.45 1 1 0.22 0.02 Alpena Beaver 4-1 1984 2.41 10 0.42 0.04 Alpena Beaver 4-1 1985 2.44 11 0.35 0.03 Alpena Beaver 4-1 1986 2.33 11 0.28 0.03 Alpena Beaver 4-1 1987 2.51 12 0.50 0.04 Alpena Beaver 4-1 1988 2.37 9 . 0.52 0.06 Alpena Beaver 4-1 1989 2.24 10 0.27 0.03 Alpena Beaver 4-1 1990 2.62 10 0.20 0.02 Alpena Beaver 4-1 1991 2.26 11 0.40 0.04 Alpena Beaver 4-1 1992 3.47 13 0.52 0.04 Alpena Beaver 4-1 1993 3.18 13 0.58 0.04 Alpena Beaver 4-1 1994 3.73 9 0.23 0.03 Alpena Beaver 4-1 1995 2.74 10 0.44 0.04 Alpena Beaver 4-1 1996 3.63 11 0.54 0.05 Alpena Beaver 4-1 1997 3.96 12 0.80 0.07 Alpena Beaver 4-1 1998 3.80 13 0.42 0.03 Alpena Beaver 4-1 1999 3.03 12 0.29 0.02 Alpena Beaver 4-1 2000 3.32 13 0.59 0.05 Alpena Beaver 4-1 2001 4.01 13 0.30 0.02 Barry Barlow 8-586 1980 2.19 10 0.77 0.08 Barry Barlow 8-586 1981 3.70 11 0.81 0.07 Barry Barlow 8-586 1982 3.53 1 1 0.96 0.09 Barry Barlow 8-586 1983 3.60 10 0.61 0.06 Barry Barlow 8-586 1984 2.96 10 0.93 0.09 Barry Barlow 8-586 1985 2.83 9 0.97 0.1 1 Barry Barlow 8-586 1986 4.18 10 1.30 0.13 Barry Barlow 8-586 1987 2.73 1 1 0.67 0.06 Barry Barlow 8-586 1988 1.93 9 0.68 0.08 Barry Barlow 8-586 1989 3.05 1 1 0.71 0.06 Barry Barlow 8-586 1990 3.35 9 1 .1 1 0.12 Barry Barlow 8-586 1991 2.68 10 0.40 0.04 Barry Barlow 8-586 1992 3.00 12 0.82 0.07 Barry Barlow 8-586 1993 2.81 1 1 0.47 0.04 Barry Barlow 8-586 1994 1 .95 10 1 .00 0.10 Barry Barlow 8-586 1995 2.39 10 0.70 0.07 Barry Barlow 8-586 1996 2.65 8 0.83 0.10 Barry Barlow 8-586 1997 3.51 9 0.30 0.03 Barry Barlow 8-586 1998 2.09 10 0.50 0.05 Barry Barlow 8-586 1999 4.04 8 0.58 0.07 Barry Barlow 8-586 2000 2.93 9 0.84 0.09 Barry Barlow 8-586 2001 3.34 10 0.32 0.03 Barry Payne 8-604 1990 2.30 9 0.27 0.03 Barry Payne 8-604 1991 2.54 1 1 0.27 0.02 Barry Payne 8-604 1992 2.59 1 1 0.47 0.04 Barry Payne 8-604 1993 2.92 12 0.37 0.03 Barry Payne 8-604 1994 2.42 10 0.36 0.04 77 h. min" q; County Barry Barry Barry Barry Barry Barry Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Benzie Berrien Berrien Berrien Berrien Berrien Berrien Berrien Berrien Berrien Berrien Branch Branch Branch Branch Branch Lake name Payne Payne Payne Payne Payne Payne Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Big Platte Crystal Crystal Crystal Crystal Crystal Crystal Crystal Crystal Crystal Crystal Crystal Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Little Paw Paw Coldwater Coldwater Coldwater Coldwater Coldwater NKC 8-604 8-604 8-604 8-604 8-604 8-604 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-39 10-42 10-42 10-42 10-42 10-42 10-42 10-42 10-42 10-42 10-42 10-42 1 1 -2 1 1-2 1 1-2 1 1 -2 1 1 -2 1 1-2 1 1 -2 1 1-2 1 1-2 1 1-2 12-90 12-90 12-90 12-90 12-90 78 Year 1995 1996 1997 1998 1999 2000 1977 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 1976 1977 1978 1979 1987 1988 1989 1991 1998 2000 2001 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1977 1978 1979 1980 1981 Ave. 2.25 2.51 3.05 2.66 3.05 3.03 2.13 2.27 2.16 1.61 1.87 2.55 3.63 3.33 3.21 3.53 4.16 3.20 4.71 4.47 4.57 4.49 3.58 4.04 3.14 3.90 4.27 3.27 3.29 5.82 6.36 5.89 6.52 5.46 6.40 6.81 6.78 5.85 5.49 6.81 1.57 1.35 1.90 1.90 1.81 1.61 2.12 1.88 1.90 2.13 2.57 2.36 3.34 2.86 3.04 St.Dev St.Err 0.45 0.06 1.00 0.17 0.73 0.12 0.45 0.06 0.87 0.14 0.85 0.12 0.32 0.03 0.39 0.04 0.50 0.05 0.47 0.04 0.40 0.03 0.57 0.05 0.50 0.04 0.77 0.06 0.92 0.08 0.61 0.05 0.66 0.06 0.71 0.06 1.10 0.08 0.93 0.07 1.03 0.08 0.68 0.05 1.07 0.08 0.69 0.05 0.62 0.05 0.60 0.05 1.03 0.08 0.93 0.07 1.42 0.1 1 0.83 0.07 0.67 0.10 1.50 0.50 1.09 0.22 0.99 0.10 1.26 0.14 1.47 0.15 0.86 0.07 0.62 0.15 0.43 0.11 1.41 0.47 0.23 0.02 0.12 0.01 0.24 0.05 0.40 0.03 0.39 0.04 0.32 0.03 0.23 0.02 0.24 0.02 0.25 0.02 0.41 0.04 0.39 0.05 0.66 0.06 0.50 0.05 0.71 0.06 0.41 0.03 ..-—I 'W 31m »'-1' County Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Branch Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Lake name Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Coldwater Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Christiana Christiana Christiana Christiana Christiana Christiana Christiana Christiana Christiana Christiana NKC 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-90 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 12-86 14400-an 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14400-an 14-400-GRP 14400-an 14400-an 79 Year 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1978 1979 1992 1993 1995 1997 1998 1999 2000 2001 d—L-L-L-A-L-L-L—b 01150050331010 oo-smAm-s coon ..L-tN \Im-i @001 3.08 2.16 1.68 2.02 1.73 1.34 3.74 2.17 St.Dev St.Err 0.52 0.04 0.33 0.03 0.53 0.04 0.53 0 04 0.43 0.04 0.43 0.04 0.27 0.02 0.16 0.02 0.47 0.04 0.72 0.06 0.63 0.05 0.80 0.09 0.71 0.06 0.76 0.19 0.43 0.05 0.40 0.04 0.39 0.06 0.84 0.08 0.35 0.04 0.83 0.08 0.53 0.07 0.26 0.02 0.33 0.03 0.28 0.02 0.21 0.02 0.35 0.03 0.40 0.03 0.42 0.03 0.36 0.03 0.30 0.03 0.41 0.03 0.40 0.04 0.12 0.01 0.19 0.02 0.39 0.03 0.12 0.01 0.17 0.02 0.31 0.03 0.31 0.08 0.32 0.04 0.22 0.02 0.34 0.06 0.42 0.05 0.33 0.05 0.46 0.05 0.28 0.03 0.28 0.02 0.46 0.09 0.61 0.05 0.28 0.02 0.39 0.03 0.45 0.04 0.15 0.01 5.75 0.44 0.41 0.05 County Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Lake name Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Dewey Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell D0nnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Donnell Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana NKC 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14-88 14—88 14-88 14-88 14-88 14-88 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-224 14-394 14-394 14-394 14-394 14-394 14-394 14-394 14-394 14-394 80 Year 1974 1975 1976 1977 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1995 1996 1997 1998 1999 2000 2001 1988 1989 1990 1991 1992 1993 1994 1995 1996 Ave. 0.84 0.81 1.60 1.27 1.74 0.76 0.76 1.52 0.86 1.30 1.08 1.23 2.41 2.34 2.21 1.04 1.29 1.32 1.37 1.55 1.31 2.11 2.12 1.85 1.44 2.30 2.92 2.24 2.11 3.41 2.08 2.07 1.74 2.12 1.75 1.87 1.91 1.78 1.84 3.35 3.03 3.24 2.85 2.50 2.98 2.31 4.09 2.73 4.06 4.21 3.66 4.91 5.88 4.89 4.71 13 12 12 12 12 11 12 12 12 12 11 11 10 11 13 12 12 10 12 11 13 12 11 12 12 12 12 11 11 13 12 12 12 11.- 11 12 11 12 12 11 12 13 13 13 12 13 13 11 St.Dev St.Err 0.45 0.41 0.32 0.12 0.19 0.64 0.37 0.47 0.14 0.17 0.04 0.26 0.25 0.23 0.35 0.26 0.24 0.12 0.09 0.25 0.22 0.22 0.35 0.16 0.49 0.93 1.19 0.50 0.79 1.59 0.50 ' 0.96 0.41 0.72 0.38 0.51 0.85 0.54 0.55 0.56 1.26 0.77 0.36 0.68 0.61 0.74 1.34 0.45 0.91 1.07 0.86 1.35 1.38 1.21 1.09 0.03 0.07 0.03 0.01 0.02 0.05 0.03 0.04 0.01 0.01 0.00 0.02 0.02 0.02 0.04 0.02 0.02 0.01 0.01 0.02 0.02 0.02 0.03 0.01 0.04 0.08 0.10 0.04 0.07 0.14 0.05 0.07 0.03 0.06 0.03 0.05 0.08 0.06 0.05 0.07 0.11 0.06 0.03 0.09 0.06 0.12 0.11 0.03 0.07 0.08 0.07 0.10 0.11 0.09 0.10 73““? an I...” h County Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Cass Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Lake name Indiana Indiana Indiana Indiana Indiana Juno Juno Juno Juno Juno Juno Juno Juno Juno Juno Painter Painter Painter Painter Painter Painter Painter Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Twin Lakes- North (Big) Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Crooked Lake George Lake George Lake George Lake George Lake George Lake George Lake George Lake George NKC 14-394 14-394 14-394 14-394 14-394 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400°GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GRP 14-400-GFIP 14-400-GRP 14-75 14-75 14-75 14-75 14-75 14-75 14-75 14-75 14-75 14-75 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-69 18-125 18-125 18-125 18-125 18-125 18-125 18-125 18-125 81 Year 1997 1998 1999 2000 2001 1978 1979 1992 1993 1995 1997 1998 1999 2000 2001 1993 1995 1997 1998 1999 2000 2001 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1980 1981 1982 1983 1984 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1974 1992 1993 1994 1995 1996 1997 1998 Ave. 4.01 3.27 4.60 2.81 4.38 1.76 1.91 3.05 2.08 1.70 1.85 1.61 1.31 1.98 2.10 1.85 1.50 1.79 1.42 1.63 1.43 1.76 2.65 2.86 3.83 3.38 3.25 4.24 3.68 4.31 3.61 3.90 2.60 2.67 3.52 4.18 3.46 3.31 3.14 2.43 2.50 2.53 2.96 3.14 3.33 3.78 3.51 2.37 3.26 2.22 2.69 2.24 3.11 3.73 2.91 10 11 11‘ 11 12 11 12 12 12 13 12 13 13 12 12 13 12 13 13 13 13 12 12 12 13 13 13 13 12 11 10 11 12 13 13 10 12 13 10 13 13 13 11 13 13 13 13 St.Dev St.Err 1.14 0.1 1 0.37 0.03 1.39 0.13 0.55 0.05 0.76 0.06 0.34 0.03 0.58 0.05 0.41 0.10 0.43 0.04 0.24 0.02 0.28 0.02 0.39 0.03 0.30 0.02 0.19 0.01 0.38 0.04 0.35 0.03 0.40 0.03 0.36 0.03 0.39 0.03 0.49 0.04 0.22 0.02 0.27 0.03 0.26 0.02 0.54 0.04 0.17 0.02 1.08 0.09 0.32 0.03 0.31 0.03 0.50 0.04 0.39 0.03 0.30 0.02 1.09 0.08 0.34 0.03 0.26 0.02 0.21 0.02 0.39 0.04 0.12 0.01 0.16 0.01 0.16 0.01 0.17 0.01 0.29 0.03 0.14 0.01 0.44 0.03 0.23 0.02 0.13 0.02 0.39 0.06 0.27 0.03 0.21 0.02 0.47 0.04 0.31 0.02 0.19 0.02 0.41 0.03 0.76 0.06 0.49 0.04 0.35 0.03 County Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Clare Crawford Crawford Crawford Crawford Crawford Crawford Crawford Crawford Crawford Crawford Crawford Crawford Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Genesee Lake name Lake George Lake George Lake George Shingle Shingle Shingle Shingle Shingle Shingle Shingle Shingle Shingle Shingle Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Lake Margrethe Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Byram Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton Fenton NKC 1 8-1 25 1 8-1 25 1 8-125 1 8-124 1 8-124 1 8-1 24 1 8-1 24 1 8-1 24 1 8-1 24 1 8-1 24 1 8-1 24 1 8-1 24 1 8—1 24 20-44 20-44 20-44 20-44 20-44 20-44 20-44 20-44 20-44 20-44 20-44 20-44 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-54 25-35 25-35 25-35 25-35 25-35 25-35 25-35 25-35 25-35 - 25-35 25-35 25-35 25-35 82 Year 1999 2000 2001 1974 1992 1993 1994 1995 1997 1998 1999 2000 2001 1975 1976 1977 1978 1979 1980 1981 1991 1997 1999 2000 2001 1974 1975 1976 1977 ' 1978 1979 1980 1981 1992 1993 .1994 1995 1996 1997 1998 1999 2001 1980 1981 1982 1983 1984 1985 1986 1987 1988 1992 1994 1996 1997 Ave. 3.49 3.53 2.60 2.67 3.87 4.66 3.36 3.96 4.06 3.24 3.56 4.08 3.17 4.67 4.19 4.14 4.09 4.19 4.30 4.25 4.91 4.61 3.63 3.77 3.52 4.45 3.29 4.24 3.35 3.15 3.18 2.68 2.92 2.65 2.49 3.18 2.34 3.30 3.56 4.17 3.28 3.38 4.08 3.98 4.93 4.59 4.44 3.19 3.99 4.06 3.60 3.70 3.30 3.87 4.52 COUIODN St.Dev St.Err 0.24 0.03 0.87 0.07 0.24 0.02 0.65 0.05 0.55 0.11 0.85 0.12 0.42 0.04 0.63 0.05 0.40 0.03 0.51 0.04 0.55 0.04 0.44 0.03 0.49 0.04 0.46 0.05 0.54 0.05 0.30 0.03 0.21 0.03 0.28 0.03 0.22 0.02 0.37 0.05 0.28 - 0.02 0.31 0.08 0.46 0.04 0.46 0.04 0.46 0.04 0.80 0.07 0.97 0.10 0.38 0.04 0.53 0.07 1.10 0.18 0.52 0.06 0.72 0.14 0.70 0.12 0.42 0.03 0.30 0.02 1.04 0.08 0.61 0.05 0.73 0.07 1.31 0.11 0.77 0.06 0.80 0.06 0.71 0.05 0.75 0.07 0.66 0.06 0.38 0.04 0.77 0.09 0.59 0.07 0.32 0.03 0.37 0.03 0.61 0. 05 0.66 0.07 0.43 0.06 0.58 0.19 0.43 0.09 0.77 0.26 —. n' 5'” .i-o County Genesee Genesee - Genesee Genesee Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse Grand Traverse losco Lake name Fenton Fenton Fenton Fenton Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus Arbutus NKC 25-35 25-35 . 25-35 25-35 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 28-84 Duck Duck Duck Duck Duck Duck Duck Duck Duck Duck Long Long Long Long Long Long Long Long Long Long Long Long Long Long Long Spider Spider Spider Spwer Spider Spider Spider Spider Spider Spider Spider Long 28-124 28-124 28-124 28-124 28-124 28-124 28-124 28-124 28-124 28-124 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-214 28-81 28-81 28-81 28-81 28-81 28-81 28-81 28-81 28-81 28-81 28-81 35-96 83 Year 1998 1999 2000 2001 1988 1989 1990 1991 1992 1993 1994 1995 1996 . 1997 1998 1999 2000 2001 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1979 1980 1981 1985 1986 1987 1992 1993 1994 1995 1996 1997 1999 2000 2001 1980 1987 1988 1989 1990 1991 1997 1998 1999 2000 2001 1974 Ave. 3.94 4.25 4.83 4.35 5.26 5.26 4.45 4.99 5.98 5.74 5.26 5.25 4.53 5.64 5.38 5.05 4.41 5.81 3.41 2.98 3.05 2.86 3.05 3.10 3.17 3.33 3.18 4.06 5.70 6.84 6.34 6.52 6.57 7.99 6.71 7.58 6.31 7.17 6.54 7.26 6.96 7.27 6.99 5.07 4.58 4.93 4.71 4.16 4.81 4.79 4.46 4.10 4.91 4.79 2.40 xicooooocoj-N‘ooximmoo-twww: St.Dev St.Err 0.12 0.04 0.08 0.03 0.23 0.08 0.13 0.01 0.53 0.07 0.77 0.13 0.46 0.09 0.83 0.12 0.43 0.05 0.38 0.03 0.24 0.02 0.20 0.02 0.57 0.07 0.23 0.03 0.13 0.01 0.24 0.03 0.39 0.03 0.34 0.03 0.24 0.02 0.31 0.02 0.23 0.02 0.23 0.03 0.43 0.04 0.47 0.04 0.38 0.03 0.44 0.04 0.30 0.03 0.72 0.06 0.57 0.1 1 0.46 0.04 0.40 0.08 0.80 0.07 0.81 0.07 0.58 0.05 0.73 0.07 1.51 0.19 0.55 0.04 0.98 0.08 0.90 0.08 1.26 0.10 1.48 0.11 0.87 0.07 0.58 0.04 0.78 0.1 1 0.38 0.03 0.33 0.03 0.42 0.04 0.66 0.06 0.86 0.07 0.93 0.09 0.39 0.04 0.47 0.04 1.04 0.10 0.68 0.06 0.33 0.04 33"“me V . ‘ ..i County Lake name NKC Year Ave. n St.Dev St.Err losco Long 35-96 1975 2.43 12 0.54 0.04 losco Long 35-96 _ 1976 2.75 13 0.20 0.02 losco Long 35-96 1977 2.69 1 1 0.28 0.03 losco Long 3596 1978 2.55 12 0.17 0.01 losco Long 35-96 1979 2.77 12 0.24 0.02 losco Long 3596 1980 2.53 12 0.21 0.02 losco Long 35-96 1981 2.71 12 0.24 0.02 losco Long 3596 1982 2.68 12 0.22 0.02 losco Long 35-96 1983 2.65 1 1 0.24 0.02 losco Long 35-96 1984 2.60 12 0.21 0.02 losco Long 35-96 1985 2.54 12 0.31 0.03 losco Long 3596 1986 2.58 12 0.21 0.02 . losco Long 35-96 1987 2.55 12 0.20 0.02 losco Long 35-96 1988 2.59 11 0.18 0.02 r 1 losco Long 3596 1989 2.58 11 0.17 0.02 .- losco Long 35-96 1990 2.62 3 0.02 0.01 i losco Long 3596 1991 3.10 1 0.00 0.00 l’ losco Long 35-96 1992 4.10 8 0.10 0.01 i“. losco Long 35-96 1993 4.07 9 0.22 0.02 ' losco Long 35-96 1994 2.74 13 0.23 0.02 losco Long 3596 1995 2.88 1 1 0.36 0.03 losco Long 35-96 1996 3.08 1 1 0.24 0.02 losco Long 3596 1997 3.08 1 1 0.41 0.04 losco Long 35-96 1998 2.87 10 0.26 0.03 losco Long 35-96 1999 3.25 12 0.36 0.03 losco Long 35-96 2000 2.92 5 0.39 0.08 losco Long 3596 2001 3.15 6 0.21 0.03 losco Van Etten 35-219 1986 0.84 12 0.26 0.02 losco Van Etten 35-219 1987 1.10 12 0.69 0.06 losco Van Etten 35-219 1988 1.16 1 1 0.65 0.06 losco Van Etten 35-219 1989 1.1 1 11 0.61 0.06 losco Van Etten 35-219 1990 1.15 11 0.41 0.04 losco Van Etten 35-219 1991 0.87 1 1 0.37 0.03 losco Van Etten 35-219 - ' 1992 0.97 13 0.34 0.03 losco Van Etten 35-219 1993 1 .50 13 0.52 0.04 losco Van Etten 35-219 . 1994 0.86 1 1 0.18 0.02 losco Van Etten 35-219 ' 1995 1.23 12 ' 0.63 0.05 losco Van Etten 35-219 1996 1 .51 1 1 0.48 0.04 losco Van Etten 35-219 1997 1 .22 12 0.47 0.04 losco Van Etten 35-219 1998 1 .24 12 0.34 0.03 losco Van Etten 35-219 1999 1.08 1 1 0.25 0.02 losco Van Etten 35-21 9 2000 1 .09 12 0.1 9 0.02 losco Van Etten 35-219 2001 1.16 12 0.41 0.03 Jackson Clear 38-32 1981 3.10 1 1 0.25 0.02 Jackson Clear 38-32 1982 2.83 12 0.29 0.02 Jackson Clear ‘ 38-32 1985 2.90 1 1 0.40 0.04 Jackson Clear 38—32 1986 3.15 10 0.22 0.02 Jackson Clear 38-32 1987 2.90 9 0.20 0.02 Jackson Clear 38-32 1988 2.37 9 0.20 0.02 Jackson Clear 38-32 1989 2.16 9 0.23 0.03 Jackson Clear 38-32 1990 1 .98 5 0.15 0.03 Jackson Clear 38-32 1991 1 .92 7 0.08 0.01 Jackson Clear 38-32 1993 2.34 6 0.1 2 0.02 Jackson Clear 38-32 1995 2.18 12 0.22 0.02 Jackson Clear 38-32 1996 2.48 13 0.30 0.02 84 County Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Jackson Kalkaska Kalkaska Kalkaska Kalkaska Kalkaska Kalkaska Kalkaska Kalkaska Kalkaska Kent Kent Kent Kent Kent Kent Kent Kent Kent Lake Lake Lake Lake Lake Lake Lake Lake Lake Lake Lake Lake name (Hear (Near (Hear (Hear (Near Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard Vineyard (Sub (Sub (Sub (Sub (Sub (Sub (Sub (Sub (Sub Camp Camp Camp Camp Camp Camp Camp Camp Camp Harper Harper Harper Harper Harper Harper Harper Harper Harper Harper Harper NKK: 38-32 38-32 38-32 38-32 38—32 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 38-503 40-1 06 40-1 06 40-1 06 40-1 06 40-1 06 40-1 06 40-1 06 40-1 06 40-1 06 41-516 41-516 41-516 41-516 41-516 41-516 41-516 41-516 41-516 43-254 43-254 43-254 43-254 43-254 43-254 43-254 43-254 43-254 43-254 43-254 85 Year 1997 1998 1999 2000 2001 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1997 1998 1999 2000 2001 1993 1994 1995 1996 1997 1998 1999 2000 2001 1990 1991 1992 1993 1995 1996 1997 1998 2000 1989 1990 1991 1992 1993 1995 1996 1998 1999 2000 2001 .Ave. 3.14 3.07 3.26 2.81 2.88 2.98 2.34 2.87 1.83 2.13 2.37 2.07 2.22 2.46 2.97 2.90 2.74 2.92 3.31 3.42 2.84 3.51 2.88 2.56 2.91 2.65 5.06 4.88 5.26 5.51 5.84 5.21 6.15 6.82 5.81 5.33 2.91 3.93 5.58 3.44 5.19 3.37 4.19 3.83 3.98 3.81 4.97 5.09 4.58 4.50 4.88 4.21 3.47 3.63 4.64 St.Dev St.Err 0.42 0.03 - 0.87 0.07 ‘ 0.42 0.03 0.22 0.02 0.29 0.02 0.29 0.03 0.39 0.04 0.39 0.06 0.39 0.04 0.23 0.02 0.39 0.04 0.26 0.03 0.40 0.03 0.44 0.04 0.81 0.07 0.68 0.06 0.66 0.06 0.36 0.03 0.24 0.02 0.36 0.04 0.58 0.05 0.95 0.16 0.78 0.06 1.05 0.08 1.07 0.08 0.91 0.08 0.27 0.02 0.60 0.05 0.51 0.05 0.66 0.06 0.25 0.02 0.34 0.03 0.50 0.04 0.55 0.04 0.52 0.04 0.34 0.03 0.48 0.04 0.32 0.04 0.86 0.07 0.19 0.02 0.59 0.10 0.33 0.05 0.63 0.08 0.62 0.06 1.63 0.15 0.79 0.06 1.53 0.12 0.71 0.05 0.86 0.07 0.52 0.13 0.68 0.14 0.49 0.06 0.46 0.04 0.76 0.08 0.58 0.04 .l County Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Leelanau Lake name Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Glen (Big) Leelanau- North Leelanau— North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Leelanau- North Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen Little Glen NKC 45-9 45-9 45-9 45-9 ' 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-3 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 45-9 86 Year 1979 :1980 1982 1984 1985 1986 1987 1988 1989 1990 1991 1996 1997 1998 1999 2000 2001 - 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 Ave. 7.51 7.35 4.94 3.79 4.95 5.74 5.06 4.85 4.62 5.15 4.96 5.34 5.00 5.46 5.32 6.55 5.72 2.91 2.54 2.64 2.88 3.10 2.18 4.17 3.37 3.24 3.62 3.63 3.30 3.88 3.83 3.21 3.79 5.33 3.89 3.66 3.44 3.91 3.63 3.41 3.99 4.49 2.37 2.36 3.19 1.58 2.00 3.18 2.41 2.51 2.60 3.11 2.80 2.95 2.06 St.Dev St.Err 5.26 0.66 0.42 0.04 ' 0.50 0.10 0.19 0.03 0.42 0.04 0.44 0.04 0.18 0.02 0.67 0.06 0.21 0.03 1.06 0.12 0.49 0.06 1.33 0.22 0.91 0.10 1.00 0.09 0.90 0.10 1.06 0.13 1.30 . 0.12 0.90. 0.08 0.32 0.03 ‘ 0.60 0.05 0.47 0.04 0.56 0.05 0.51 0.04 0.99 0,09 0.69 0.06 0.47 0.04 0.62 0.05 0.61 0.06 0.58 0.05 0.87 0.08 0.64 0.06 0.73 0.06 0.92 0.07 1.78 0.15 0.56 0.05 0.47 0.04 0.34 0.03 1.08 0.10 0.62 0.06 0.51 0.05 0.70 0.07 1.93 0.18 0.21 0.02 0.21 0.02 0.34 0.03 0.11 0.01 0.43 0.07 0.43 0.06 0.30 0.05 0.23 0.02 0.40 0.03 0.21 0.02 0.44 0.04 0.44 0.06 0.14 0.02 County Leelanau Leelanau Leelanau Leelanau Leelanau Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Lenawee Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Livingston Manistee Manistee Manistee Manistee Manistee Manistee Manistee Manistee Lake name LHfleCNen LfifleCNen Little Glen Lflflecmen Lfiflecmen DevMs Devms Devms DevMs Devms Devms Devms DevMs DevMs DevMs DevMs Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Coon Zukey ZUKey Zukey Zukey Zukey Zukey Zukey Zukey Zukey Zukey Zukey Zukey Zukey Bear Bear Bear Bear Bear Bear Bear Bear NKC 45-9 45-9 45-9 45-9 45-9 46-45 46-45 46-45 46-45 46-45 46-45 46-45 46-45 46-45 46-45 46-45 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-204 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 47-97 51 -1 32 51 -1 32 51 -1 32 51 -132 51 -132 51 -132 51 -132 51 -132 87 Year 1992 1996 1998 2000 2001 1980 1981 1982 1983 1984 1985 1987 1993 1995 2000 2001 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1986 1987 1988 1989 1990 1991 2000 2001 1980 1981 1982 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1977 1978 1979 1980 1981 1982 1984 1985 Ave. 2.15 2.24 2.67 1.83 2.36 2.11 2.03 2.57 2.95 2.44 2.74 2.55 3.11 5.42 2.18 3.20 2.54 2.49 2.46 2.68 2.17 2.68 2.27 2.21 2.21 2.49 2.59 2.15 2.48 1.75 2.66 2.17 2.28 2.09 1.65 1.57 2.23 1.89 1.99 1.95 1.83 1.88 2.13 2.29 2.19 2.87 2.53 3.28 3.17 2.87 3.08 3.02 3.37 2.29 2.88 St.Dev St.Err 0.22 0.02 0.35 0.03 0.25 0.03 ' 0.20 0.02 0.31 0.02 0.51 0.09 0.46 0.08 0.56 0.07 0.84 0.1 1 0.66 0.09 0.22 0.05 0.40 0.06 0.77 0.11 0.94 0.08 0.37 0.03 0.45 0.11 0.36 0.03 0.30 0.05 0.30 0.03 0.27 0.02 0.19 0.02 0.22 0.02 0.45 0.04 0.31 0.03 0.33 0.03 0.71 0.06 0.19 0.02 0.75 0.06 0.48 0.05 0.15 0.01 0.20 0.02 0.39 0.03 0.53 0.04 0.38 0.03 ' 0.35 0.07 0.28 0.03 0.61 0.06 0.61 0.06 0.51 0.04 0.32 0.02 0.27 0.05 0.36 0.06 0.43 0.06 0.66 0.11 0.26 0.05 0.45 0.03 0.48 0.04 0.28 0.03 0.26 0.03 0.29 0.03 0.18 0.02 0.42 0.04 0.46 0.05 0.15 0.02 0.22 0.02 County Lake name NKC Year Ave. n St.Dev St.Err Manistee Bear 51 -1 32 1986 2.67 12 0.10 0.01 Manistee - Bear 51-132 . 1987 3.40 12 0.14 0.01 . Manistee Bear 51-132 ' 1988 3.57 11 0.18 0.02 Manistee Bear 51-132 1989 3.06 11 0.16 0.01 Manistee Bear 51 —1 32 1990 3.84 6 0.24 0.04 Manistee - Bear 51 -132 1991 3.05 12 0.16 0.01 - Manistee Bear 51-132 1992 2.69 11 0.14 0.01 Manistee _ Bear 51-132 1993 3.02 12 0.20 0.02 Manistee Bear 51 ~132 1994 3.23 12 0.28 0.02 Manistee Bear 51-132 1995 2.73 10 0.18 0.02 Manistee Bear 51-132 1996 2.59 9 0.35 0.04 Manistee Bear 51-132 1997 2.79 7 0.40 0.06 Manistee Bear 51-132 1998 2.81 7 0.25 0.04 Manistee Bear 51 -1 32 1999 2.90 7 0.62 0.09 Manistee Bear 51 -132 2000 2.32 10 0.17 0.02 Manistee Bear 51-132 2001 2.22 13 0.19 0.01 Mason Blue 53-131 1988 4.82 10 0.81 0.08 Mason Blue 53-131 1989 4.17 9 0.60 0.07 Mason Blue 53131 1990 2.74 8 0.37 0.05 Mason Blue 53-131 1991 3.94 6 0.88 0.15 Mason Blue 53-131 1992 6.93 11 0.94 0.09 Mason Blue 53-131 1993 2.82 6 0.77 0.13 Mason Blue 53-131 1994 3.81 7 0.68 0.10 Mason Blue 53-131 1995 8.02 8 1.20 0.15 Mason Blue 53-131 1996 8.32 10 1.51 0.15 Mason Blue 53-131 1997 8.08 10 1.47 0.15 Mason Blue 53-131 1998 7.74 9 1.46 0.16 Mason Blue 53-131 ‘ 1999 9.92 9 0.66 0.07 Mason Blue 53-131 2000 7.96 8 0.55 0.07 Mason Blue 53-131 2001 6.13 9 1.26 0.14 Mason ~ Ford 53-135 1983 4.19 12 0.62 0.05 ' Mason Ford 53-135 ‘ 1984 4.32 12 0.76 0.06 Mason Ford 53-135 ‘ 1985 4.22 12 ' 0.71 0.06 Mason Ford 53-135 . 1986 4.09 12 0.78 0.07 Mason Ford 53-135 1987 5.21 12 0.87 0.07 Mason Ford 53-135 1988 5.09' 10 1 .47 0.15 Mason Ford . 53-135 . 1989 3.69 9 0.54 0.06 Mason Ford 53-135 1990 4.49 11 0.82 0.07 Mason Ford 53-135 1991 5.45 9 0.72 0.08 Mason Ford 53-135 1992 4.30 10 0.71 0.07 Mason Ford 53-135 1993 4.91 9 0.18 0.02 Mason Ford 53-135 1994 5.03 6 0.57 0.10 Mason Ford 53-135 1995 3.84 12 0.73 0.06 Mason Ford 53-135 1996 4.57 12 0.50 0.04 Mason Ford 53-135 1997 4.17 13 0.69 0.05 Mason Ford 53-135 1998 3.67 12 0.59 0.05 Mason Ford 53-135 1999 3.51 13 0.71 0.05 Mason Ford 53-135 2000 5.46 13 0.31 0.02 Mason Hackert 53-101 1989 3.73 10 0.82 0.08 Mason Hackert 53-101 1990 3.40 12 0.72 0.06 Mason Hackert 53-101 1991 2.74 12 0.61 0.05 Mason Hackert 53-101 1992 3.76 9 0.57 0.06 Mason Hackert 53-101 1993 3.79 7 0.39 0.06 Mason Hackert 53-101 1994 2.97 8 0.39 0.05 Mason Hackert 53-101 1995 4.33 9 0.62 0.07 88 County Mason Mason Mason Mason Mason Mason Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Lake name Hackert Hackert Hackert Hackert Hackert Hackert Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Horsehead Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta NKC 53-1 01 53-1 01 53-1 01 53-1 01 53-1 01 53-1 01 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-53 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-1 36 54-54 54-54 54-54 54-54 54-54 54-54 54-54 54-54 54-54 89 Year 1996 1997 1998 1999 2000 2001 1981 1982 1983 1984 1985 1986 1987 1988 1989 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1981 1982 1983 1984 1985 1986 1989 1990 1991 Ave. 4.72 4.78 3.92 3.96 2.78 2.70 3.35 3.73 3.08 2.72 2.84 2.59 3.69 3.41 3.37 3.25 3.66 4.01 3.86 4.34 4.52 4.49 4.21 4.01 3.73 2.47 2.81 2.97 2.22 2.17 2.22 2.15 2.32 2.41 3.09 2.51 3.03 3.25 3.33 3.43 3.79 3.83 2.96 3.12 3.19 2.89 2.51 3.19 3.40 3.28 3.60 3.45 3.06 2.93 2.82 12 13 13 10 12 12 11 13 12 11 11 11 12 13 13 12 11 12 13 13 13 12 10 11 10 12 12 12 11 10 11 10 10 11 10 11 12 13 12 11 12 12 10 11 13 12 St.Dev St.Err 0.59 0.05 0.72 0.06 0.43 0.03 0.70 0.07 0.47 0.05 0.27 0.04 0.26 0.02 0.33 0.03 0.16 0.01 0.46 0.04 0.46 0.04 0.27 0.03 0.21 0.02 0.38 0.03 0.29 0.03 0.20 0.02 0.35 0.03 0.48 0.04 0.47 0.04 0.48 0.04 0.55 0.05 0.58 0.04 0.81 0.06 0.80 0.06 0.98 0.08 0.35 0.04 0.39 0.04 0.41 0.04 0.31 0.03 0.26 0.02 0.56 0.05 0.24 0.02 0.39 0.04 0.32 0.03 0.39 0.04 0.14 0.02 0.27 0.03 0.18 0.02 0.23 0.02 0.26 0.03 0.38 0.04 0.58 0.06 0.75 0.07 0.21 0.02 0.63 0.10 0.32 0.02 0.19 0.02 0.25 0.02 0.21 0.02 0.40 0.03 0.53 0.05 0.43 0.04 0.30 0.03 0.42 0.03 0.14 0.01 o . . - . Q ‘ . u . . . . n u . o - . . a e 1 I . 1 I c Id County Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Lake name Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Round Round Round Round Round Round Round Round Round Round Round Round Round Round Round Round Round Round Round Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta Mecosta SchoolSecfion SchoolSecfion SchooISecfion Schoo|Secfion SchoolSecfion SchoolSecfion SchoolSecflon SchoolSecflon SchoolSecflon SchoolSecflon SchoolSecfion Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee Missaukee MissaUkee Missaukee Missaukee Sapphke Sapphke Sapphhe Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphke Sapphhe NKC 54-54 54-54 54-54 54-54 54-54 54-54 54-54 54-54 54-54 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-51 ‘ 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-51 54-57 54-57 54-57 54-57 54-57 54-57 54-57 54-57 54-57 54-57 54-57 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 57-70 90 Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 1977 1981 1982 1983 1984 1985 1986 1987 1988 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 1990 1991 1992 1993 1994 1995 1996 .1997 ‘ 1998 1999 2000 1979 1980 1981 1982 1983 1984 1986 1987 1988 1990 1991 1992 1993 1994 1995 1996 Ave. 3.84 4.08 4.27 3.64 3.70 4.33 3.47 3.08 3.75 3.16 2.70 2.56 3.85 3.28 3.19 3.00 3.05 2.94 2.44 3.56 2.86 3.31 3.32 3.70 3.58 3.54 3.61 2.83 2.46 2.86 2.87 3.26 2.48 3.74 4.13 4.17 4.08 3.93 4.08 2.23 2.06 1.94 2.01 2.27 2.41 2.49 2.23 1.56 2.36 2.29 2.57 2.44 1.81 2.09 1.92 11 13 10 13 10 12 11 11 11 13 11 12 11 11 12 13 13 13 10 13 13 13 12 13 12 12 13 12 10 12 12 11 12 13 12 10 10 12 11 11 10 11 11 mVCD-‘mem St.Dev St.Err 0.43 0.04 0.23 0.02 0.43 0.04 0.26 0.04 0.10 0.01 0.46 0.05 0.61 0.05 0.29 0.03 0.66 0.07 0.27 0.07 0.26 0.02 0.21 0.02 0.60 0.05 0.47 0.04 0.37 0.03 0.29 0.02 0.24 0.02 0.25 0.02 0.18 0.02 1.14 0.09 0.29 0.02 0.45 0.03 0.17 0.02 0.41 0.03 0.37 0.03 0.34 0.03 0.46 0.04 0.47 0.04 0.17 0.01 0.32 0.03 0.28 0.02 0.45 0.04 0.26 0.03 0.29 0.02 0.20 0.02 0.18 0.02 0.30 0.03 0.19 0.01 0.21 0.02 0.16 0.02 0.16 0.02 0.15 0.01 0.15 0.01 0.14 0.01 0.12 0.01 0.14 0.01 0.10 0.01 0.32 0.04 0.23 0.04 0.17 0.03 0.24 0.03 0.16 0.02 ' 0.18 0.02 0.30 0.04 0.41 0.05 ‘ l I County Missaukee Missaukee Missaukee Missaukee Missaukee Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montcalm Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Montmorency Newaygo Newaygo Lake name Sapphhe Sapphire Sapphke Sapphke Sapphhe Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Baldwin Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon Avalon West Twin West Twin West Twin West Twin West Twin West Twin West Twin West Twin West Twin West Twin Bills Bills NKC 57-70 57- 70 57-70 57-70 57-70 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 59-99 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-162 60-19 60-19 60-19 60-19 60-19 60-19 60-19 60-19 60-19 60-19 62-75 62-75 91 Year 1997 1998 1999 2000 2001 1977 1978 1979 1981 1982 1983 1984 1985 ' 1986 1 987 1 988 ..1989 ‘1991 11992 ‘.1993 '1994 1995 1996 1997 1998 1999 2000 2001 1980 1981 1985 ' 1989 1990 1991 1992 1994 . 1995 1996 1997 1998 1999 2000 2001 1992 1993 1994 1995 1996 1997 1998 1999 2000 . 2001 1 984 1 985 Ave. 1.85 1.59 2.58 2.41 2.32 2.24 2.64 2.16 2.98 2.50 2.20 2.20 2.49 2.88 2.55 2.87 2.79 2.61 2.89 3.13 2.70 3.81 3.30 3.92 3.61 3.27 3.27 3.24 7.04 7.13 5.65 7.92 7.26 8.20 6.59 7.18 7.16 6.93 5.52 6.73 9.22 7.01 6.82 3.43 4.15 3.31 3.39 3.24 3.34 3.08 3.40 3.85 3.58 3.34 2.69 St.Dev St.Err 0.21 0.39 0.17 0.08 0 07 0.30 0.34 0.27 0.43 0.34 0.89 0.41 0.51 0.22 0.20 0.28 0.20 0.27 0.29 0.44 0.30 0.30 0.72 0.41 0.47 0.34 0.34 0.36 2.35 1.02 0.92 2.58 1.21 0.75 0.15 0.87 2.22 0.91 1.12 1.37 1.71 2.10 1.60 0.64 0.76 0.25 0.50 0.33 0.42 0.27 0.23 0.37 0.34 0.80 0.30 0.03 0.04 0.01 0.01 0.01 0.03 0.03 0.03 0.05 0.05 0.07 0.03 0.06 0.02 0.02 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.06 0.04 0.04 0.03 0.03 0.03 0.24 0.11 0.08 0.26 0.13 0.08 0.01 0.08 0.17 0.08 0.14 0.12 0.43 0.30 0.20 0.08 0.11 0.02 0.04 0.03 0.04 0.03 0.03 0.03 0.06 0.07 0.04 County Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Newaygo Lake name Bills Bills Bills Bills Bills Bills Bills Bills ~ Bills Bills Bills Bills Bills Bills Bills Bills Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Emerald Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan Sylvan NKC 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-75 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-82 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 62-81 . 62-81 62-81 62-81 62-81 62-81 92 Year 1 986 1 987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1993 1994 1996 1997 1998 1999 2000 2001 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1993 1994 1996 ‘ 1997 .1998 1 999 2000 Ave. 3.10 3.39 3.99 3.29 3.54 2.35 3.14 3.34 3.24 3.12 3.39 3.73 3.26 2.83 2.91 2.58 3.02 2.98 2.55 2.72 2.60 2.62 3.31 2.38 3.18 2.68 3.11 2.41 2.34 1.79 2.34 2.67 2.29 3.35 2.96 3.89 3.05 3.16 3.14 3.12 2.67 2.50 3.15 2.34 3.23 2.90 3.72 3.25 3.09 2.29 2.09 2.48 1.85 2.44 2.87 12 12 10 13 10 13 11 10 10 11 12 12 12 12 12 12 11 10 10 11 13 13 12 12 12 10 11 12 12 10 12 12 12 12 11 10 10 11 13 13 12 12 12 10 St.Dev St.Err 0.24 0.02 0.48 0.04 0.39 0.04 0 37 0.04 0.22 0.02 0.56 0.06 0.19 0.01 0.33 0.04 0.60 0.05 0.25 0.03 0.50 0.06 0.36 0.04 0.70 0.07 0.48 0.05 0.34 0.04 0.42 0.04 0.84 0.07 0.44 0.04 0.36 0.05 0.53 0.04 0.41 0.03 0.42 0.04 0.44 0.04 0.20 0.02 0.51 0.06 0.35 0.03 0.31 0.03 0.57 0.09 0.51 0.05 0.33 0.03 0.34 0.03 0.45 0.04 0.82 0.07 1.07 0.09 0.43 0.04 0.61 0.06 0.91 0.08 0.60 0.05 0.25 0.03 0.74 0.06 0.47 0.04 0.56 0.05 0.30 0.03 0.20 0.02 0.46 0.05 0.52 0.05 0.55 0.06 0.37 0.06 0.33 0.03 0.69 0.05 0.17 0.01 0.50 0.04 0.49 0.04 0.64 0.05 0.50 0.05 . war County Newaygo Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Lake name Sann LakevMe LakevMe LakevMe Lakevme LakevMe LakeVMe Lakevme LakevMe LakevMe Lakevme LakevMe LakevMe Lakevme LakevMe LakevMe LakevMe Lakevme Lakevme LakevMe Shen~ood ShenNood Shen~ood Shen~ood ShenNood Shen~ood Shenwood Shen~ood Shenwood Shen~ood ShenNood Shen~ood Shen~ood Shenwood Shenwood Shenwood Shenwood ShenNood Shen~ood Shen~ood ShenNood Taylor Taylor Tawor Taylor Taylor Taylor Taylor Taylor Taylor Taylor Tawor Taylor Taylor TaWor NKC 62-81 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-1 650 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-252 63-1025 63-1025 63-1 025 63-1 025 63-1025 63-1 025 63-1025 63-1 025 63-1025 63-1025 63-1025 63-1025 63-1025 63-1025 93 Year ‘ 2001 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1992 1993 1994 1996 1997 1998 2000 ’ 2001 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1974 1980 1983 1984 1985 1986 1987 1988 1990 1991 1992 1993 1998 1999 Ave. 3.49 2.92 2.69 2.63 2.46 2.99 3.04 3.89 2.49 2.53 2.38 2.80 3.42 2.61 2.40 3.23 3.89 3.24 2.78 3.85 3.58 3.13 3.74 3.79 1.98 2.58 2.13 2.30 2.07 3.08 2.37 2.01 2.16 1.11 1.37 2.12 2.59 2.63 2.39 1.49 1.28 5.04 4.01 5.75 5.32 5.08 4.67 3.96 5.32 4.07 4.08 3.50 3.89 4.71 4.90 11 13 12 12 12 11 12 12 12 11 11 10 10 11 10 13 13 11 10 12 11 10 10 10 13 12 13 13 13 12 12 12 12 12 13 12 10 10 12 12 12 11 13 13 13 13 St.Dev St.Err 0.97 0.65 0.52 0.43 0.51 0.76 1.00 1.39 0.22 0.30 0.62 0.42 0.43 0.21 0.27 0.33 0.48 2.32 0.66 0.85 0.15 0.48 0.55 0.47 0.22 0.42 0.46 0.93 0.61 0.39 0.26 0.16 0.19 0.07 0.28 0.43 0.86 0.27 0.53 0.36 0.12 0.36 0.41 0.39 0.13 0.23 0.10 0.47 1.09 0.19 0.51 0.49 0.48 0.59 0.52 0.09 0.05 0.04 0.04 0.04 0.07 0.08 0.12 0.02 0.06 0.06 0.04 0.04 0.02 0.02 0.05 0.05 0.29 0.05 0.07 0.04 0.05 0.05 0.05 0.02 0.04 0.05 0.09 0.07 0.04 0.02 0.01 0.01 0.01 0.02 0.04 0.07 0.02 0.04 0.03 0.01 0.03 0.07 0.04 0.01 0.02 0.01 0.04 0.10 0.01 0.04 0.05 0.05 0.05 0.04 -T_.___m._.. 7? County Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Ottawa Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Roscommon Lake name Taylor Taylor Walled Walled Walled Walled Walled Walled Walled Walled Walled Walled White White White White White White White White White White White White White Crockery Crockery Crockery Crockery Crockery Crockery Crockery Crockery Crockery Crockery Crockery Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins Higgins NKC 63-1025 63-1025 63-16 63-16 63-16 63-16 63-16 63-16 63-16 63-16 63-16 63-16 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 63-575 70-164 70-164 70-164 70-164 70-164 70-164 70-164 70-164 70-164 70-164 70-164 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 72-117 72-1 17 72-1 17 72-1 17 72-1 17 72-1 17 94 Year 2000 2001 1983 1984 1985 1986 1987 1988 1989 1990 2000 2001 1976 1977 1978 1979 1980 1981 1992 1993 1994 1998 1999 2000 2001 1982 1989 1993 1994 1995 1996 1997 1998 1999 2000 2001 1974 1975 1976 1977 1978 1979 1980 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 Ave. 4.70 5.63 3.72 2.80 2.04 3.51 2.78 2.61 2.37 1.89 3.69 4.38 3.66 3.57 3.83 4.22 4.40 3.87 2.71 3.03 2.90 6.38 3.71 5.15 4.60 2.19 1.32 0.92 1.10 2.65 2.27 1.93 2.21 1.54 1.47 1.62 5.86 7.01 6.35 10.28 7.73 6.19 7.01 7.62 8.26 8.13 8.19 6.34 8.06 8.57 8.32 7.57 9.02 8.02 6.76 11 St.Dev St.Err 0.28 0.60 0.95 0.58 0.46 0.78 0.39 0.23 0.25 0.23 0.84 0.47 0.70 0.21 0.65 0.49 0.54 0.74 0.34 0.1 1 0.45 0.42 0.10 0.57 0.20 0.26 0.15 0.54 0.68 0.84 0.80 0.43 0.81 0.46 0.37 0.58 1.65 1.89 1.02 2.16 1.56 0.91 0.57 0.55 0.65 2.91 2.31 1.08 1.12 0.97 3.02 0.88 0.84 1.17 0.64 0.02 0.05 0.10 0.05 0.04 0.08 0.04 0.03 0.04 0.03 0.12 0.06 0.05 0.02 0.07 0.04 0.08 0.07 0.03 0.03 0.11 0.03 0.01 0.04 0.03 0.03 0.02 0.09 0.07 0.08 0.07 0.04 0.07 0.04 0.03 0.05 0.18 0.24 0.09 0.20 0.14 0.09 0.11 0.05 0.07 0.26 0.21 0.11 0.11 0.11 0.38 0.09 0.08 0.12 0.07 s an.“ 7... County Lake name NKC Year Ave. n St.Dev St.Err Roscommon Higgins 72-117 4 1995 5.26 9 0.93 0.10 Roscommon Higgins 72-117 4 1996 7.70 6 0.38 0.06 RosCommon Higgins 72-117 , 1997 7.82 3 0.63 0.21 Roscommon Higgins 72-117 1998 7.32 3 2.13 0.71 Roscommon Higgins 72-117 1999 6.71 6 1.47 0.24 Roscommon Higgins 72-117 2000 8.11 5 0.79 0.16 Roscommon Higgins 72-117 2001 7.87 6 2.70 0.45 Shiawassee Leisure n/a 1981 3.73 12 0.66 0.06 Shiawassee Leisure n/a 1982 3.73 12 0.86 0.07 Shiawassee Leisure n/a 1983 1 .95 9 0.36 0.04 Shiawassee Leisure n/a 1984 2.95 8 0.37 0.05 Shiawassee Leisure n/a 1989 3.82 1 1 0.26 0.02 Shiawassee Leisure n/a 1990 3.89 13 0.88 0.07 Shiawassee Leisure n/a 1991 3.17 9 0.79 0.09 Shiawassee Leisure n/a 1994 3.15 13 0.76 0.06 Shiawassee Leisure n/a 1996 5.51 12 1.41 0.12 Shiawassee Leisure n/a 1997 5.65 1 1 0.73 0.07 Shiawassee Leisure n/a 1998 4.47 13 0.85 0.07 Shiawassee Leisure n/a 1999 3.61 13 0.84 0.06 Shiawassee Leisure n/a 2000 2.84 10 0.22 0.02 Shiawassee Leisure n/a 2001 3.34 1 1 0.76 0.07 St. Joseph Clear 78-93 1976 3.70 10 0.29 0.03 St. Joseph Clear 78-93 1977 3.52 12 0.38 0.03 St. Joseph Clear 78-93 1978 3.43 12 0.56 0.05 St. Joseph Clear 78-93 1979 3.58 12 0.33 0.03 St. Joseph Clear 78-93 1981 3.14 11 0.31 0.03 St. Joseph Clear 78-93 1982 2.94 10 0.55 0.06 St. Joseph Clear . 78-93 1983 3.51 10 0.66 0.07 St. Joseph Clear 78-93 1984 2.93 12 0.25 0.02 St. Joseph Clear 78-93 1985 2.87 12 0.51 0.04 St. Joseph Clear 78-93 1986 2.64 12 0.42 0.03 St. Joseph Clear 78-93 1987 2.68 12 0.46 0.04 St. Joseph Clear 78-93 1992 3.21 10 0.46 0.05 St. Joseph Clear 78-93 1993 2.76 7 0.23 0.03 St. Joseph Clear 78-93 ' 1994 3.12 4 . 0.26 0.06 St. Joseph Clear 78-93 1995 3.24 8 0.52 0.06 St. Joseph Clear 78-93 1996 2.88 10 0.39 0.04 St. Joseph Clear 78-93 1997 2.88 12 0.96 0.08 St. Joseph Clear 78-93 1998 3.12 2 0.1 1 0.05 St. Joseph Clear 78-93 1999 4.31 4 0.26 0.07 St. Joseph Clear 78-93 2000 4.27 5 0.15 0.03 St. Joseph & Cass Corey 14-159 1974 2.98 13 0.58 0.04 St. Joseph & Cass Corey 14-159 1975 3.34 10 0.27 0.03 St. Joseph & Cass Corey 14-159 1976 3.19 12 0.71 0.06 St. Joseph & Cass Corey 14-159 1977 2.87 10 0.36 0.04 St. Joseph & Cass Corey 14-159 1978 4.11 10 0.80 0.08 St. Joseph & Cass Corey 14-159 1979 4.35 1 1 0.62 0.06 St. Joseph & Cass Corey 14-159 1980 2.80 10 0.27 0.03 St. Joseph & Cass Corey 14-159 1981 3.15 10 0.64 0.06 St. Joseph & Cass Corey 14-159 1982 2.36 1 1 0.28 0.03 St. Joseph & Cass Corey 14-159 1983 2.52 1 1 0.58 0.05 St. Joseph & Cass Corey 14-159 1984 2.81 12 0.57 0.05 St. Joseph & Cass Corey 14-159 ‘ 1985 2.33 1 1 0.12 0.01 St. Joseph & Cass Corey 14-159 1986 2.95 12 0.41 0.03 St. Joseph & Cass Corey 14-159 1987 3.66 1 1 1.09 0.10 95 _ St. County ’ . Lake name NKC Year Ave. n St.Dev St.Err St. Joseph & Cass Corey 14-159 1988 . 3.08 11 0.32 0.03 St. Joseph & Cass Corey 14-159 1989 2.53 10 0.66 0.07 St. Joseph & Cass Corey 14-159 1990 2.59 10 0.26 0.03 St. Joseph & Cass Corey 14-159 1991 3.44 10 0.61 0.06 St. Joseph & Cass Corey 14-159 1992 3.00 13 0.80 0.06 St. Joseph & Cass Corey 14-159 1993 2.51 10 0.49 0.05 St. Joseph & Cass Corey 14-159 1994 2.67 12 0.36 0.03 St. Joseph & Cass Corey 14-159 1995 2.85 11 0.45 0.04 St. Joseph 8. Cass Corey 14-159 1996 2.67 11 0.88 0.08 St. Joseph & Cass Corey 14-159 1997 3.20 13 0.70 0.05 St. Joseph & Cass Corey 14-159 1998 2.98 1 1 0.51 0.05 St. Joseph & Cass Corey 14-159 1999 3.65 13 0.64 0.05 St. Joseph & Cass Corey 14-159 2000 2.67 13 0.71 0.05 St. Joseph & Cass Corey 14-159 2001 3.31 12 0.75 0.06 St. Joseph Klinger 78-171 1982 1.55 6 0.22 0.04 - St. Joseph Klinger 78-171 1983 2.54 11 0.32 0.03 St. Joseph Klinger 78-171 1985 1.74 5 0.20 0.04 F St. Joseph Klinger 78-171 1986 2.22 12 0.12 0.01 1 St. Joseph Klinger 78-171 1987 2.29 12 0.11 0.01 St. Joseph Klinger 78-171 ‘ 1988 2.45 10 0.17 0.02 St. Joseph Klinger 78-171 1989 2.45 11 0.13 0.01 St. Joseph Klinger 78-171 1990 2.20 13 0.24 0.02 St. Joseph Klinger 78-171 1992 3.11 12 0.20 0.02 St. Joseph Klinger 78-171 1994 3.18 6 054 0.09 St. Joseph Klinger 78-171 1995 3.11 10 0.53 0.05 St. Joseph Klinger 78-171 1996 3.34 11 0.35 0.03 St. Joseph Klinger 78-171 1997 . 3.96 11 0.56 0.05 St. Joseph Klinger 78-171 1998 3.66 11' 0.43 0.04 St. Joseph Klinger 78-171 1999 3.51 12 0.62 0.05 St. Joseph Klinger 78-171 2000 ‘ 3.21 12 0.72 0.06 St. Joseph Klinger 78-171 2001 3.02 13 0.76 0.06 St. Joseph Pleasant 78-94 1978 2.19 1 1 0.47 0.04 St. Joseph Pleasant 78-94 . 1979 2.22 1 1 0.54 0.05 St. Joseph Pleasant 78-94 1980 1.94 11 0.27' 0.02 St. Joseph Pleasant 78-94 .1981 2.12 12 0.46 0.04 St. Joseph Pleasant 78-94 1982 2.07 10 0.39 0.04 St. Joseph Pleasant 78-94 1983 2.70 10 0.80 0.08 St. Joseph Pleasant 78-94 1984 2.60 12 0.71 0.06 St. Joseph Pleasant 78-94 1985 2.04 11 0.52 0.05 St. Joseph Pleasant 78-94 1986 2.16 12 0.55 0.05 Joseph Pleasant 78-94 1989 2.66 11 0.59 0.05 St. Joseph Pleasant 78-94 1990 2.85 1 1 0.80 0.07 St. Joseph Pleasant 78-94 1991 2.79 9 0.67 0.07 St. Joseph Pleasant 78-94 1992 3.46 1 1 0.95 0.09 St. Joseph Pleasant 78-94 1993 3.38 12 1.14 0.10 St. Joseph Pleasant 78-94 1994 4.09 12 0.49 0.04 St. Joseph Pleasant 78-94 1995 3.82 1 1 0.68. 0.06 St. Joseph Pleasant 78-94 1996 3.56 12 0.49 0.04 St. Joseph Pleasant 78-94 1997 4.48 11 0.95 0.09 St. Joseph Pleasant 78-94 1998 3.71 12 0.48 0.04 St. Joseph Pleasant 78-94 - 1999 4.13 13 0.20. 0.02 St. Joseph Pleasant 78-94 2000 3.64 11 0.85 0.08 St. Joseph Pleasant 78-94 2001 3.93 11 ’ 0.81 0.07 Van Buren Lake of the Woods 80-288 1981 2.23 10' 0.32 0.03 Van Buren Lake of the Woods 80-288 1982 1.81 11 0.45 0.04 96 County Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Van Buren Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Wexford Lake name Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Lake of the Woods Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge Stone Ledge NKC 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 80-288 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 83-17 97 ’ Year 1 983 1 984 1 985 1986 1987 1988 1989 1990 1991 1992 1993 1994 ' 1995 1996 1997 1998 1999 2000 2001 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Ave. 2.91 3.08 2.62 2.50 3.03 3.02 3.54 3.78 2.02 2.36 2.30 2.37 3.78 3.15 2.50 3.02 2.98 3.99 3.10 2.04 3.14 2.73 2.58 2.54 2.93 3.23 2.75 3.59 2.77 3.22 2.34 3.14 2.74 3.17 11 13 11 12 11 10 10 11 12 13 13 11 12 13 13 10 13 12 11 10 13 13 12 13 13 13 13 13 13 13 13 St.Dev St.Err 0.46 0.60 0.37 0.17 0.32 0.24 0.39 0.46 0.15 0.57 0.57 0.21 0.08 0.32 0.30 0.34 0.52 1.03 0.23 0.12 0.20 0.62 0.56 0.28 0.31 0.40 0.35 0.19 0.22 0.27 0.42 0.15 0.25 0.29 0.04 0.05 0.03 0.01 0.03 0.02 0.04 0.05 0.01 0.05 0.04 0.02 0.01 0.03 0.03 0.03 0.04 0.10 0.02 0.01 0.02 0.06 0.04 0.02 0.03 0.03 0.03 0.01 0.02 0.02 0.03 0.01 0.02 0.02 LITERATURE CITED Albert,'D.A. (1995) Regional landscape ecosystems of Michigan, Minnesota, and Wisconsin: a working map and classification. Gen. Tech. Rep. NC- 178. St. Paul, MN: US. Department of Agriculture, Forest Service, North Central Forest Experiment Station. Anderson, J.R., E.H. Harvey, J.T. Roach, R.E. Whitman. (1976) A land use sensor and land cover classification system for use with remote sensor data geological survey. Professional Paper 964, US Government Printing Office, Washington, DC. Amell, V.M. (1983) Estimating runoff volumes from urban areas. Water Resources Bulletin. 18(3), p.383-387 l Beeton, AM. (1965) Eutrophication of the St. Lawrence Great Lakes. Limnology and Oceanography. 10(2), p.240-254 Benzie Fishery Coalition (BFC) (2001) Working for cleaner waters. http://www.benziefish.orq/backgroundhtm Blais, J.M., K.E. Duff, D.W. Schindler, J.P. Smol, P.R. Leavitt, M. Agbeti (2000) Recent eutrophication histories in Lac Ste. Anne and Lake Isle, Alberta, Canada, inferred using paleolimnological methods. Lake and Reservoir Management. 16(4), p.292-304 Borkman, D.G., T.J. Smayda (1998) Long-term trends in water clarity revealed by Secchi-disk measurements in lower Narragansett Bay. ICSE Journal of Marine Science. 55, p. 668-679. Bouchard, R. (1995) The relationship between property values and water quality of Maine lakes. Lake and Reservoir Management, 11(2), p.152. Brezonik, PL. (1978) Effect of organic color and turbidity of Secchi disk transparency. Journal of the Fisheries Research Board of Canada. 35, p.1410-1416. Brooks, K.N., P.F. Ffolliott, H.M. Gregersen, L.F. DeBano (1997) Hydrology and the management of watersheds. 2"d edition. Iowa State University Press. Ames, Iowa Byron, E.R., C.R. Goldman. (1989) Land-use and water quality in tributary streams of Lake Tahoe, Califomia-Nevada. Journal of Environmental Quality. 18, p.84-88 98 Canfield, D.E Jr., 00. Brown, R.W. Bachmann, M.V. Hoyer. (2002) Testing the reliability of data collected by the Florida LAKEWATCH program. Lake and Reservoir Management, 18(1), p.1-9. Carlson, RE. (1977) A trophic state index for lakes. Limnology and Oceanography. 22(2), p.361-369 Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth, A.N. Sharpley, V.H. Smith (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications. 8(3), p.559-568 Cruikshank, DR. (1988) Effects of nutrient and acid additions on Secchi depth at the experimental lakes area, 1969-1986. Canadian Technical Report of Fisheries and Aquatic Sciences. 1597 82p. Davies-Colley, R.J., W.N. Vant, D.G. Smith (1993) Colour and clarity of natural waters; science and management of optical water quality. Ellis HonNood Limited. West Sussex. Detenbeck, N.E., C.A. Johnston, G.J. Niemi (1993) Wetland effects on lake water quality in the Minneapolis/St. Paul metropolitan area. Landscape Ecology. 8(1),p.39-61 Dillon, P.J., W.B. Kirchner (1975) The effects of geology and land use on the export of phosphorus from watersheds. Water Research. 9(2), p.135-148 Edmondson, W.T. (1970) Phosphorus, nitrogen and algae in Lake Washington after diversion of sewage. Science. 169(3946), p.690-691 Forsberg, C., 8.0. Ryding. (1980) Eutrophication parameters and trophic state indices in 30 Swedish waste-receiving lakes. Arch. Hydrobiology, 89(1/2), p.189-207. Francis, J.C., M.A. Poirrier, D.E. Barbe, V. Wijesundera, M.M. Mulino (1994) Historic trends in the secchi disk transparency of Lake Pontchartrain. Gulf Research Reports. 9(1), p.1-16. Gong, Z., P. Xie (2001) Impact of eutrophication on biodiversity of the macrozoobenthos community in a Chinese shallow lake. Journal of Freshwater Ecology. 16(2), p.171 -178 Great Lake Angler Online (GLAO) (2000) Consent Agreement reached on Platte river state fish hatchery dispute: MI. Posted March 10, 2000. http://www.glao.com 99 DA.— _ ..- 1 [up ‘5” V I a Hall, R.l., P.R. Leavitt, R. Quinlan, A.S. Dixit, J.P. Smol (1999) Effects of agriculture, urbanization, and climate on water quality in the northern Great Plains. Limnology and Oceanography. 44(3), p.739-756 Havens, K.E., V.J. Bierman, Jr., E.G. Flaig, C. Hanlon, R. T. James, B.L. Jones, V.H. Smith (1995) Historical trends in the Lake Okeechobee ecosystem. Vl. Synthesis. Arch. Hydrobiol., Supplement 107(1) p.101-111 Heathcote, I. W. (1998) Integrated watershed management. John Wiley and Sons, Inc. New York. 414 p. Heiskary, 8., J. Lindbloom (1993) Lake water quality trends in Minnesota. Minnesota Pollution Control Agency. Water Quality Division. May 1993. Heiskary, S., J. Lindbloom, C. B. Wilson (1994) Detecting water quality trends with citizen volunteer data. Lake and Reservoir Management, 9(1) p.4-9. Heiskary, S.A., C.B. Wilson, D.P. Larsen (1987) Analysis of regional lake water quality patterns: implications for lake resource management in Minnesota. EPA/600/D-87/010. Environmental Research Laboratory, office of research and development. US Environmental Protection Agency. Corvallis, OR. Herbert, P.D.N., CC. Wilson, M.H. Murdoch, R. Lazar. (1991) Demography and ecological impacts of the invading mollusc Dreissena polymorpha. Canadian Journal of Zoology. 69, p.405-409 Herron, E.M., L.T. Green, A.J. Gold. (1994) QA/OC Assessment of lay monitoring in Rhode Island. Lake and Reservoir Management, 9(1), p.81 Home, A.J., C. R. Goldman. (1994) Limnology. McGraw-Hill, New York. Kaiser, D. (2002) Pleasant Lake Association member. Personal communication 12/6. Hendry, 6.8., A. Toth (1982) Some effects of land use on bacteriological water quality in a recreational lake. Water Research. 16(1), p.105-122 Klemmer, M. (2002) Lake Shen~ood Association member. Personal communication 10/16, 10/18 and 10/24. Kloiber, S.M., T.H. Anderle, P.L. Brezonik, L. Olmanson, M.E. Bauer, D.A. Brown. (2000) Trophic state assessment of lakes in the Twin Cities (Minnesota, USA) region by satellite imagery. Arch. Hydrobiol. Spec. Issues Advanc. Limmol. 55, p.137-151 100 Lee, G.F., R.A. Jones, B.W. Newbry (1982) Water quality standards and water quality. Journal of the Water Pollution Control Federation. 54(7), p.1131- 1138 Leggett, CG, and NE. Bockstael. (2000) Evidence of the effects of water quality on residential land prices. Joumal of Environmental Economics and Management, 39(2), p.121—144 Leonard, R.L., L.A. Kaplan, J.F. Elder, R.N. Coats, C.R. Goldman (1979) Nutrient transport in surface runoff from a subalpine watershed, Lake Tahoe basin, California. Ecological Monographs. 49(3), p.281-310 Lowrance, R., R. Todd, J. Fail, Jr., 0. Hendrickson, Jr., R. Leonard, L. Asmussen. Riparian forests as nutrient filters in agricultural watersheds. Bioscience. 34(6), p.374-377 Maas, R.P., D.J. Kucken, P.F. Gregutt. (1991) Developing a rigorous water quality database through a volunteer monitoring network. Lake and Reservoir Management. 7(1), p.123-126 Mauldin, T.E., A.J. Plantinga, R.J. Alig. (1999) Land use in the lake states region: an analysis of past trends and projections of future changes. Research Paper PNW-RP-519. US Department of Agriculture, Forest Service, Pacific Northwest Research Station. 24 p. McBride, B. (2002). Klinger Lake CLMP sampler. Personal communication 11/20 Michigan Department of Environmental Quality (2000) Water quality and pollution control in Michigan: 2000 section 305(b) report. Michigan Department of Environmental Quality, surface water quality division. August 2000. Michigan Department of Environmental Quality (2002) Cleaning Agents. Michigan Department of Environmental Quality, surface water quality division. http://www.deq.state.mi.us/documents/deq-swq-Dart31- PART6.doc Michigan Department of Natural Resources (MDNR) (1999) 1978 Michigan Resource lnforrnation System (MIRIS) Land cover/ use. http://www.state.mi.us/webapp/cqi/mng/7action=thm Michigan Sea Grant (2001) Michigan sea grant inland lakes zebra mussel infestation monitoring program record. December 2001 101 Minnerick, R.J. (2001) Effects of residential development on the water quality of Higgins Lake, Michigan 1995-1999. US Geological Survey Water- Resources Investigations Report 01-4055 Natural Resources Conservation Service (NRCS). (1997) Water Quality and Agriculture; Status, Conditions and Trends. Working Paper #16. July 1997. http://www.nhq.nrcs.usda.gov/land/pubs/wq.html Natural Resources Conservation Service. (2000) Excessive Erosion on Cropland, 1997. December 7, 2000. http://www.nhq.nrcs.usda.qov/land/meta/m5083.html National Research Council (NRC) Committee on Restoration of Aquatic Ecosystems- Science, Technology and Public Policy (1992) Restoration of aquatic ecosystems: science, technology, and public policy. National Academy Press. Washington, DC. Nelson, S.A.C., P.A. Soranno, K.S. Cheruvelil, S.A. Batzli, and D.L. Skole. Assessing regional lake water clarity using Landsat and the role of inter- lake variability. Under review at Remote Sensing of Environment. Novotny, V., H. Olem (1994) Water quality: prevention, identification and management of diffuse pollution. Van Nostrand Reinhold. New York, New York. Obrecht, D.V, M. Milanick, B.D. Perkins, D. Ready, J.R. Jones. (1998) Evaluation of data generated from lake samples collected by volunteers. Lake and Reservoir Management 14(1), p.21-27 Olmanson, L.G., P.L. Brezonik, S.M. Kloiber, M.E. Bauer, E.E. Day. (2000) Lake water clarity assessment of Minnesota’s 10,000 lakes: a comprehensive view from space. Virginia Water Research Symposium 2000: Advances in Land and Water Monitoring Technologies and Omemik, J.M. (1987) Ecoregions of the conterrninous United States. Annals of the Association of American Geographers. 77(1), p.118-125 Peterjohn, W.T., D.L. Correll (1984) Nutrient dynamics in an agricultural watershed: observations on the role of a riparian forest. Ecology 65(5), p.1466-1475 Powers, C.F., D.W. Schult, K.W. Malueg, R.M. Brice, M.D. Schuldt (1972) Algal responses to nutrient additions in natural waters. ll. Field Experiments. American Society of Limnology and Oceanography Special Symposia. 1, p.141 -154. 102 Preisendorfer, R.W. (1986) Secchi disk science: visual optics of natural waters. Limnology and Oceanography. 31(5), p.909-926 Puckett, L.J. (1995) Identifying the major sources of nutrient water pollution Environmental Science Technology. 29(9), p.408-414 Reeders, H.H., A. Bij DeVaate, F.H. Slim (1989) The filtration rate of Dreissena polymorpha (bivalvia) in three Dutch lakes with reference to biological water quality management. Freshwater Biology. 22, p.133-141 Schindler, D.W. (1977) Evolution of phosphorus limitation in lakes. Science. 195(4275) p.260-262. Schindler, D.W, S.E. Bayley, B.R. Parker, K.G. Beaty, D.R. Cruikshank, E.J. Fee, E.U. Schindler, M.P. Stainton. (1996) The effects of climatic warming on the properties of boreal lakes and streams at the Experimental Lakes Area, northwestern Ontario. Limnology and Oceanography. 41 (5), p.1004-1017 Seehausen, O., J.J.M. Van Alphen, F. Witte (1997) Cichlid fish diversity threatened by eutrophication that curbs sexual selection. Science. 277(5333), p.1808-181 1 Sefton, D.F., J.R. Little, J.A. Hardin, J.W. Hammel (1983) Volunteer lake monitoring: citizen action to improve lakes. Proceedings of the third annual conference of the North American Lake Management Society. October 18-20, 1983. Knoxville, Tennessee. Sharpley, A.N., S.C. Chapra, R. Wedepohl, J.T. Sims, T.C. Daniel, K.R. Reddy (1994) Managing agricultural phosphorus for protection of surface waters: issues and options. Journal of Environmental Quality.'23, p.437-451. Siver, P.A., A.M. Lott, E. Cash, J. Moss, L.J. Marsicano (1999) Century changes in Connecticut, USA, lakes as inferred from siliceous algal remains and the relationship to land-use change. Limnology and Oceanography. 44(8), p.1928-1935 Smeltzer, E., V. Garrison, W.W. Walker, Jr. (1989) Eleven years of lake eutrophication monitoring in Vermont: a critical evaluation. Enhancing States’ Lake Management Programs. p. 53-62 Smith, D.G., G.F Croker, K. McFarlane. (1995) Human perception of water appearance 1. Clarity and color for bathing and aesthetics. New Zealand Journal of Marine and Freshwater Research. 29, p.29-43 SPSS (1998) Systat standard version 9.0 103 Stadelmann, T.H, P.L. Brezonik, S. Kloiber. (2001) Seasonal patterns of chlorophyll a and Secchi disk transparency in lakes of east-central Minnesota: implications for design of ground- and satellite-based monitoring programs. Lake and Reservoir Management 17(4), p.299- 314. Stemberger, R.S., J.M. Lazorchak (1994) Zooplankton assemblage responses to disturbance gradients. Canadian Journal of Fisheries and Aquatic Sciences. 51, p.2435-2447 Terrell, J.B., D.L. Watson, M.V. Hoyer, M.S. Allen, D.E Canfield, Jr. (2000) Temporal water chemistry trends (1967-1997) for a sample (127) of Florida waterbodies. Lake and Reservoir Management 16(3), p.177-194. Till, M. (2002). Lake Sherwood Association member. Personal communication 10/23 and 12/4 Tyler, J.E (1968) The Secchi disk. Limnology and Oceanography 13(1), p. 1-6. United States Environmental Protection Agency (USEPA). (1996A) Environmental indicators of water quality in the United States. Office of Water Quality. EPA-841-R-96-002 United States Environmental Protection Agency (USEPA). (1996B) Nonpoint source pollution: the nation’s largest water quality problem. EPA-841-F-96- 004A United States Environmental Protection Agency (USEPA). (2000) Water quality condition in the United States: a profile from the 1998 national water quality inventory report to congress. EPA-841-F-00-006. June 2000. http://www.epa.qov/305b/98report/ United States Environmental Protection Agency (USEPA). (2002A) 2000 Section 303(d) List Fact Sheet for Michigan. http://oaspub.epa.qov/waters/state rept.control?p state=Ml United States Environmental Protection Agency (USEPA). (2002B) Volunteer lake monitoring: a methods manual. Office of Water. August 2"", 2002. http://www.epa.cmv/volunteer/lake/lakevolman.pdf United States Environmental Protection Agency (USEPA). (20020) Watershed Approach Framework. Office of Water Quality. August 23'“, 2002. http://www.epa.qov/owow/watershed/framework.html 104 Wetzel, R. G. (2001) Limnology: lake and river ecosystems. Third edition. Academic Press. San Diego, Ca. Wilson, C.B., W.W. Walker, Jr. (1989) Development of lake assessment methods based upon the aquatic ecoregion concept. Lake and Reservoir Management 5(2), p.11-22. Young, GE. (1984) Perceived water quality and the value of seasonal homes. Water Resources Bulletin. (20)2, p.163-166. 105 llllllllllllll O llllllljlill