I. INTRODUCTION: CSLAP DATA AND YOUR LAKE

Lakes are dynamic and complex . They contain a variety of aquatic plants and animals that interact with each other and the environment. As quality changes, so too will the plants and animals that live there, and these changes in the food web also may additionally affect water quality. Water quality monitoring provides a window into the numerous and complex interactions of lakes. Even the most extensive and expensive monitoring program cannot completely assess a lake’s water quality. However, by looking at some basic chemical, physical, and biological indicators, it is possible to gain a greater understanding of the general condition of lakes. Such information is critical for managing lakes, assessing short- and long-term water quality conditions and trends, and for comparing lakes sharing common geographic settings and lake uses.

The Citizens Statewide Lake Assessment Program (CSLAP) is a volunteer lake monitoring program conducted by the NYS Department of Environmental Conservation and the NYS Federation of Lake Associations. Founded in 1986 with 25 pilot lakes, the program now involves more than 150 lakes, , and reservoirs and 1000 volunteers from eastern Long Island to the Northern Adirondacks to the western-most lake in New York, including several Finger Lakes, Lake Ontario, and lakes within state parks. In this program, lay volunteers trained by the NYSDEC collect water samples, observations, and perception data every other week in a fifteen-week interval between May and October. Generally, water samples are collected from the lake surface at the deepest part of the lake, using standard limnological equipment and sampling procedures. Water samples are analyzed by the NYS Department of Health. Analytical results are interpreted by the NYSDEC and utilized for a variety of purposes by the State of New York, local governments, researchers, and, most importantly, participating lake associations

CSLAP collects some of the most important water quality indicators in lakes. Some of these indicators, particularly those related to lake eutrophication (literally lake nourishment), are collected to assess the aesthetic and ecological “health” of the lake, while others are used for characterizing lakes. Eutrophication indicators are most closely monitored because eutrophication represents the most common water quality problem in NYS lakes. CSLAP also collects information about the perception of the lake, to link one of the objectives of water quality monitoring (to assess lake use impairment) to the data collected in these monitoring programs. Through vegetation and zebra mussel surveys, CSLAP also gathers information about exotic invasive organisms and macrophyte communities in each lake. These indicators collectively serve to provide a “snapshot” of conditions at each program lake, and, when collected over a longer period, serve to provide a contemporary assessment of each lake.

II. CSLAP SAMPLING PARAMETERS: WHAT AND WHY

CSLAP monitors several parameters related to the trophic (extent of eutrophication) state of a lake. Three parameters are the most important measures of eutrophication in most New York lakes: total phosphorus, chlorophyll a (measuring algal standing crop), and Secchi disk transparency. Because these parameters are closely linked to the growth of weeds and algae, they provide insight into “how the lake looks” and its suitability for recreation and aesthetics. Additional CSLAP parameters are chosen to optimize the need to characterize lakes while balancing fiscal and logistic necessities (i.e. “the biggest bang for the buck…”). In addition, CSLAP also uses Field Observation Forms to gauge perceptions of lake water quality. Most water quality “problems” arise from impairment of accepted or desired lake uses, or the perception that such uses are somehow degraded. As such, any water quality monitoring program should attempt to understand the link between perception and measurable quality.

The parameters analyzed in CSLAP (Figure 1) provide valuable information for characterizing lakes. By adhering to a consistent sampling protocol provided in the CSLAP Sampling Protocol, volunteers Page 2 collect and use data to assess both seasonal and yearly fluctuations in these parameters, and to evaluate the water quality in their lake. By comparing a specific year's data to historical water quality information, lake managers can pinpoint trends and determine if water quality is improving, degrading or are remaining stable. Such a determination answers a first critical question posed in the lake management process.

Figure 1. CSLAP Sampling Parameters PARAMETER SIGNIFICANCE Water Temperature (°C) Water temperature affects many lake activities, including the rate of biological growth and the amount of dissolved oxygen. It also affects the length of the recreational season Secchi Disk Transparency (m) Determined by measuring the depth at which a black and white disk disappears from sight, the Secchi disk transparency estimates the clarity of the water. In lakes with low color and rooted macrophyte ("weed") levels, it is related to algal productivity Conductivity (µmho/cm) Specific conductance measures the electrical that passes through water, and is used to estimate the number of ions (charged particles). It is somewhat related to both the hardness and alkalinity (acid-buffering capacity) of the water, and may influence the degree to which nutrients remain in the water. Generally, lakes with conductivity less than 100 µmho/cm are considered softwater, while conductivity readings above 300 µmho/cm are found in hardwater lakes. pH pH is a measure of the (free) hydrogen ion concentration in solution. Most clearwater lakes must maintain a pH between 6 and 9 to support most types of plant and animal life. Low pH (<7) are acidic, while high pH waters (>7) are basic Color (true) (platinum color The color of dissolved materials in water usually consists of organic matter, such as decaying units) macrophytes or other vegetation. It is not necessarily indicative of water quality, but may significantly influence water transparency or algae growth. Color in excess of 30 ptu indicates sufficient quantities of dissolved organic matter to affect clarity by imparting a tannic color to the water. Phosphorus (total, mg/l) Phosphorus is one of the major nutrients needed for plant growth. It is often considered the "limiting" nutrient in NYS lakes, for biological productivity is often limited if phosphorus inputs are limited. Many lake management plans are centered on phosphorus controls. Nitrogen (nitrate, mg/l) Nitrogen is another nutrient necessary for plant growth, and can act as a limiting nutrient in some lakes, particularly in the and early summer. For much of the sampling season, many CSLAP lakes have very low or undetectable (<0.02 mg/l) levels. Chlorophyll a (µg/l) The measurement of chlorophyll a, the primary photosynthetic pigment found in green plants, provides an estimate of phytoplankton (algal) productivity, which may be strongly influenced by phosphorus. In most lake monitoring programs, this is a better indicator of planktonic (floating or suspended cellular) phytoplankton than of filamentous (thread-like fixed) phytoplankton or other algae

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Understanding Trophic States All lakes and ponds undergo eutrophication, an aging process, which involves stages of succession in biological productivity and water quality (see Figure 2). Limnologists (scientists who study fresh water systems) divide these stages into trophic states. Each trophic state can represent a wide range of biological, physical, and chemical characteristics and any lake may “naturally” be categorized within any of these trophic states. In general, the increase in productivity and decrease in clarity corresponds with an enrichment of nutrients, plant and animal life. Lakes with low biological productivity and high clarity are considered oligotrophic. Highly productive lakes with low clarity are considered eutrophic. Lakes that are mesotrophic have intermediate or moderate productivity and clarity.

Eutrophication is a natural process, and is not necessarily indicative of man-made pollution. In fact, some lakes are thought to be “naturally” productive. It is important to understand that trophic classifications are not interchangeable with assessments of water quality. One person's opinion of degradation may be viewed by others as harmless or even beneficial. For example, a eutrophic lake may support an excellent warm- water fishery because it is nutrient rich, but a swimmer may describe that same lake as polluted. However, a lake’s trophic state is still Figure 2. Trophic States important because it provides lake managers with a reference point to view changes in a lake’s water quality and begin to understand how these changes may cause use impairments (threaten the use of a lake or swimming, drinking water or fishing), since changes in trophic status, particularly over a short period, are probably ecologically stressful and represent progression toward water quality degradation.

When human activities accelerate lake eutrophication, it is referred to as cultural eutrophication. Cultural eutrophication, caused by shoreline , agricultural and , wastewater discharges or septic seepage, and other nonpoint source pollution sources are examples of activities that greatly accelerate the natural aging process of lakes, and significantly impair the water quality and value of a lake. These changes can cause succession changes in the plant and animal life within the lake, along the shoreline and in the surrounding watershed. They may ultimately extend aquatic plants and emergent vegetation throughout the lake, resulting in the transformation of the lake into a marsh, prairie, and forest. The extent of cultural eutrophication, and the corresponding pollution problems, can be signaled by significant changes in the trophic state over a short period of time.

Expected Ranges in Trophic Indicators The relationship between phosphorus, chlorophyll a, and Secchi disk transparency has been explored by many researchers, in hopes of assessing the trophic status (the degree of eutrophication) of lakes. Figure 3 shows ranges for phosphorus, chlorophyll a, and Secchi disk transparency (summer averages) that are representative for each of the major trophic classifications:

These classifications Figure 3. Trophic Status Indicators are valid for clear- water lakes only Parameter Eutrophic Mesotrophic Oligotrophic Honeoye Lake (waters with less than Phosphorus > 0.020 0.010 - 0.020 < 0.010 0.027 (mg/l) 30 platinum color Chlorophyll a > 8 2- 8 < 2 19.3 units). Some humic or (µg/l) “tea color” lakes, for Secchi Disk 2 2- 5 > 5 3.0 example, naturally Clarity (m) have dissolved organic material with greater than 30 color units. This will cause the water transparency to be unexpectedly poor relative to low phosphorus and chlorophyll a levels. Water transparency can also be surprisingly lower than expected in shallow lakes, due to influences from the bottom. Even shallow lakes with high water clarity, low nutrient concentrations, and little algal growth may also have significant weed growth due to shallow water conditions. While such a lake may be considered unproductive by most standards, that same lake may experience severe aesthetic problems and recreational impairment related to weeds, not trophic state. Generally, however, the trophic relationships described above can be used as an accurate "first" gauge of productivity and overall water quality.

By the trophic standards described above, Honeoye Lake would be considered to be a eutrophic lake (although water clarity readings are more typical of moderately productive lakes).

Aquatic Vegetation Although the greatest portion of aquatic Figure 4. Types of Algae vegetation consists of the microscopic algae referred to as phytoplankton, and the other Phytoplankton Free-floating algae algal types listed in Figure 4, “aquatic Periphyton Algae attached to surfaces vegetation” usually refers to the larger rooted Charaphytes Larger branched alga plants called macrophytes (although Charaphytes such as Chara or Nitella are common mistaken for macrophytes).

Aquatic plants should be recognized for their contributions to lake beauty as as providing food and shelter for other life in the lake. Emergent and floating plants such as water lilies floating on the lake surface may provide aesthetic appeal with their colorful flowers; sedges and cattails help to prevent shoreline erosion, and may provide food and cover for birds. Submergent plants like pondweeds and leafy waterweed harbor insects, provide nurseries for amphibians and fish, and provide food for birds and other animals. Macrophytes can be found throughout the littoral zone, the near-shore areas in which sufficient light reaches the lake bottom to promote photosynthesis. Plant growth in any particular part of the lake is a function of available light, nutrition and space, bottom substrate, wave action, and other factors.

However, of particular concern to many lakefront residents and recreational users are the exotic, or non- native macrophytes that can frequently dominate a native aquatic plant community and crowd out more beneficial species. These plants may be introduced to a lake by waterfowl, but in most cases they are introduced by fragments or seedlings that remain on watercraft transported from already-infested lakes. Page 5

Once introduced, these species have tenacious survival skills, frequently crowding out, dominating and eventually aggressively overtaking the indigenous (native) plant communities, interfering with recreational activities such as fishing, swimming or water-skiing. Some species can reduce water flow in lakes and . Eurasian watermilfoil (Myriophyllum spicatum) is the most common non-native species found in New York State Other non-native species found in NYS lakes are Curly-leaf pondweed (Potamogeton crispus), Eurasian water chestnut (Trapa natans), and Fanwort (Cabomba caroliniana). These species need to be properly identified for lake associations to effectively manage their lake. If these plants are not present, efforts should be made to continue protecting the lake from the introduction of these species.

Whether the role of the lake manager is to better understand the lake or better manage the aquatic plant community, knowledge of the macrophyte species distribution is paramount to the management process. There are many procedures available for assessing and monitoring aquatic vegetation. The CSLAP Sampling Protocol contains procedures for a “semi-quantitative” plant monitoring program. Volunteers collect plant specimen and provide field information and qualitative abundance estimates for an assessment of the macrophyte communities within critical areas of the lake. While these techniques are no substitute for professional plant surveys, they can help provide better information for lake managers. Lake associations planning to devote significant time and expenditures toward a plant management program are advised to pursue more extensive plant surveying activities.

The following aquatic plant species have been identified through CSLAP at Honeoye Lake:

Non-Native Species Year First IDd Perceived Abundance Myriophyllum spicatum (Eurasian watermilfoil) 1997* not reported Potamogeton crispus (curly-leafed pondweed) 1997* moderate? Native Species Perceived Abundance Graminea spp. (unidentified grass) scarce Potamogeton gramineus (variable pondweed) abundant Potamogeton hillii (Hills pondweed) not reported Potamogeton illinoensis (Illinois pondweed) moderate Potamogeton perfoliatus (redhead pondweed) scarce-moderate Potamogeton richardsonii (clasping-leaf pondweed) not reported Unidentified moss not reported Vallisneria americanum (eelgrass, tapegrass, wild celery) moderate Zanichellia palustris (horned pondweed) not reported

*- these plants (and perhaps some or all of the others) were also identified in previous aquatic plant surveys of the lake, such as those conducted by Bruce Gilman of the CCFL.

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III. UNDERSTANDING YOUR LAKE DATA

CSLAP is intended to help lake associations understand their lake’s conditions and foster sound lake protection and pollution prevention decisions supported by a strong database. This individual lake summary for 1997 contains two forms of information. These raw data and graphs present a snapshot or glimpse of water quality conditions at each lake. They are based on (at most) eight sampling events during the summer. As lakes are sampled through CSLAP for a number of years, the database for each lake will expand, and assessments of lake conditions and water quality data become more accurate. For this reason, lakes participating in CSLAP for only one year will not have information about annual trends.

Raw Data Two “data sets” are provided in Table 1 and Appendix A. The data presented in Table 1 show the entire CSLAP sampling history of your lake, including the minimum, maximum, average, and number of samples for each sampling year and parameter. These data may be useful for comparing a certain data point for the current sampling year with historical information. This table also includes data from other sources for which sufficient quality assurance/quality control documentation is available for assessing the validity of the results. Appendix A contains the “raw” data collected during all sampling seasons and years in which the lake was sampled as part of CSLAP. Page 7

TABLE 1: CSLAP Data Summary for Honeoye L. DATA SOURCE KEY CSLAP New York Citizens Statewide Lake Assessment Year Min Avg Max NParameter Program LCI the NYSDEC Lake Classification and Inventory 1996-97 1.30 3.01 6.30 16 CSLAP Zsd Survey conducted during the 1980s and again 1997 2.15 4.06 6.30 8 CSLAP Zsd beginning in 1996 on select sets of lakes, typically 1 to 4x per year 1997 1.68 3.80 6.71 13 Fisheries Zsd DEC other water quality data collected by the 1996 1.30 1.96 2.75 8 CSLAP Zsd NYSDEC Divisions of Water and Fish and Wildlife, typically 1 to 2x in any give year 1996 1.70 2.74 4.25 4 DEC FL Zsd ALSC the NYSDEC (and other partners) Adirondack 1996 1.22 2.29 4.57 7 Fisheries Zsd Lake Survey Corporation study of more than 1500 Adirondack and Catskill lakes during the 1985 1.50 2.68 4.00 5 LCI mid 1980s, typically 1 to 2x 1973 3.00 3.00 3.00 1 Cornell ELS USEPA’s Eastern Lakes Survey, conducted in the fall of 1982, 1x NES USEPA’s National Eutrophication Survey, Year Min Avg Max N Parameter conducted in 1972, 2 to 10x 1996-97 0.009 0.027 0.048 16 CSLAP Tot.P EMAP USEPA and US Dept. of Interior’s Environmental Monitoring and Assessment 1997 0.009 0.018 0.029 8 CSLAP Tot.P Program conducted from 1990 to present, 1 to 1997 0.001 0.004 0.007 6 Fisheries Tot.P 2x in four year cycles CL USEPA Clean Lakes Program administered by 1996 0.025 0.036 0.048 8 CSLAP Tot.P the NYSDEC- regular sampling through the 1996 0.008 0.025 0.048 4 DEC FL Tot.P 1980’s 1996 0.009 0.011 0.014 5 Fisheries Tot.P 1985 0.016 0.022 0.032 5 LCI CSLAP DATA KEY: The following key defines column headings and parameter results 1973 0.016 0.018 0.022 3 Cornell for each sampling season:

Year Min Avg Max N Parameter L Name Lake name Date Date of sampling 1996-97 0.01 0.01 0.02 16 CSLAP NO3 Zbot Depth of the lake at the sampling site, 1997 0.01 0.01 0.01 8 CSLAP NO3 meters Zsd Secchi disk transparency, meters 1996 0.01 0.01 0.02 8 CSLAP NO3 Zsp Depth of the sample, meters 1996 0.01 0.07 0.14 4 DEC FL NO3 TAir Temp of Air, °C 1985 0.01 0.01 0.01 4 LCI TH2O Temp of Water Sample, °C TotP Total Phosphorus, in mg/l 1973 0.01 0.01 0.01 3 Cornell NO3 Nitrate nitrogen as N, in mg/l TColor True color, as platinum color units pH (negative logarithm of hydrogen ion Year Min Avg Max N Parameter concentration), standard pH 1996-97 5 9 20 16CSLAP TColor Cond25 Specific conductance corrected to 1997 8 10 10 8CSLAP TColor 25°C, in µmho/cm Chl.a Chlorophyll a, in µg/l 1996 5 8 20 8CSLAP TColor QA Survey question re: physical condition 1985 1 8 15 5LCI of lake: (1) crystal clear, (2) not quite crystal clear, (3) definite algae greenness, (4) high algae levels, Year Min Avg Max N Parameter and.(5) severely high algae levels QB Survey question re: aquatic plant 1996-97 7.47 7.90 8.28 16 CSLAP pH populations of lake: (1) none visible, (2) 1997 7.47 7.84 8.19 8 CSLAP pH visible underwater, (3) visible at lake surface, (4) dense growth at lake 1997 6.12 6.59 7.21 13 Fisheries pH surface.(5) dense growth completely 1996 7.62 7.96 8.28 8 CSLAP pH covering the nearshore lake surface 1996 7.55 8.22 9.17 7 Fisheries pH QC Survey question re: recreational suitability of lake: (1) couldn’t be nicer, 1985 7.25 7.44 7.65 5 LCI (2) very minor aesthetic problems but excellent for overall use, (3) slightly impaired, (4) substantially impaired, Year Min Avg Max N Parameter although lake can be used, (5) 1996-97 195 205 213 16CSLAP Cond25 recreation impossible QD Survey question re: factors affecting 1997 195 207 213 8 CSLAP Cond25 answer QC: (1) poor water clarity; (2) 1997 187 203 211 13Fisheries Cond25 excessive weeds; (3) too much algae/odor; (4) lake looks bad; (5) poor 1996 198 203 206 8 CSLAP Cond25 weather; (6) other 1996 187 194 211 7 Fisheries Cond25 1985 160 182 200 5 LCI Page 8

Table 1 (cont) Year Min Avg Max N Parameter 1996-97 1.40 19.27 55.00 15 CSLAP Chl.a 1997 1.40 6.03 18.50 7 CSLAP Chl.a 1997 2.94 5.53 11.75 7 Fisheries Chl.a 1996 11.10 30.85 55.00 8 CSLAP Chl.a 1996 14.50 14.82 15.13 2 DEC FL Chl.a 1996 2.14 11.41 18.42 7 Fisheries Chl.a 1985 6.70 10.38 15.20 5 LCI 1973 4.50 12.33 19.50 3 Cornell

Year Min Avg Max N Parameter 1996-97 1.0 2.4 4.0 9 QA 1997 1.0 1.0 1.0 1 QA 1996 2.0 2.6 4.0 8 QA

Year Min Avg Max N Parameter 1996-97 1.0 1.4 2.0 9 QB 1997 1.0 1.0 1.0 1 QB 1996 1.0 1.5 2.0 8 QB

Year Min Avg Max N Parameter 1996-97 1.0 2.0 3.0 9 QC 1997 1.0 1.0 1.0 1 QC 1996 1.0 2.1 3.0 8 QC

Graphs The second form of data analysis for your lake is presented in the form of graphs. These graphs are based on the raw data sets to represent a snapshot of water quality conditions at your lake. The more sampling that has been done on a particular lake, the more information that can be presented on the graph, and the more information you have to identify annual trends for your lake. Therefore, it is important to consider the number of sampling years of information in addition to where the data points fall on a graph while trying to draw conclusions about annual trends.

There are certain factors not accounted for in this report that lake managers should consider. These include:  Local weather conditions (high or low temperatures, rainfall, droughts or hurricanes). Weather data summaries from the nearest NOAA station are provided below for 1997 and previous years to provide some context for understanding measured water quality conditions in the lake; however, for many lakes, the closest NOAA station, or the closest station with a consistent dataset, is too far away for assessing truly local conditions. The 1997 report does include, where appropriate, a more detailed discussion of the effect of weather conditions on the results at each program lake.

 Sampling season and parameter limitations. Because sampling is generally confined to June- October, this report does not look at CSLAP parameters during the winter and other seasons. Winter and spring conditions can impact the usability and water quality of a lake, but for logistic reasons cannot be monitored through CSLAP. Each lake is monitored on a schedule compatible with volunteers’ availability, weather conditions, and other factors, and this schedule often varies slightly from year to year, making annual comparisons somewhat problematic. In addition, there are other Page 9

non-CSLAP sampling parameters (fecal coliform, dissolved oxygen, etc.) that may be responsible for chemical and biological processes and changes in physical measurements (such as water clarity) and the perceived conditions in the lake.

 Other data. While this report attempts to summarize all available historical data, some data may be available to some lake managers that are not summarized here. For example, this report does not generally include discussions of contemporary and historical non-CSLAP parameters, such as total nitrogen, alkalinity, and chloride, even though the monitoring programs summarized in this report may have collected this information.

 Statistical analyses. True assessments of water quality trends and comparison to other lakes involve rigid statistical analyses. Such analyses are generally beyond the scope of this program. Where appropriate, some statistical summaries have been provided within the report and are documented in Appendix B of this report.

IV. SAMPLING RESULTS FOR HONEOYE LAKE

1997- …What a Crazy Year…! While each sampling season could be rightfully called “unique”, it can also be said that some years are more unique than others. More volunteers and other laymen reported their lakes clearer in 1997 than in any other years since CSLAP began in 1986. Most volunteers reported this belief with a smile, not a frown or even furrowed brow, though many requested an explanation for the good fate of their lakes. And while the change in NYS lakes in 1997 was mostly (sometimes even correctly) attributed to the weather (the convenience for this explanation is that it works both ways, explaining favorable and unfavorable conditions equally well), the frequency of this observation prompted a close analysis of the 1997 data.

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First, the numbers for 1997:

Prior to May May-June July-Aug Sept-Oct Water Clarity: % Lakes Higher NA 68 49 43 % Lakes Unchanged NA 28 32 30 % Lakes Lower NA 2 19 28

Algae Levels % Lakes Higher NA 11 21 17 % Lakes Unchanged NA 33 25 24 % Lakes Lower NA 56 55 59

Phosphorus Readings % Lakes Higher NA 11 13 21 % Lakes Unchanged NA 35 45 28 % Lakes Lower NA 54 42 51

Precipitation % Lakes Higher 23 25 17 NA % Lakes Unchanged 63 31 30 NA % Lakes Lower 13 44 53 NA

Water Temperature % Lakes Higher NA 22 19 19 % Lakes Unchanged NA 58 67 62 % Lakes Lower NA 20 15 19

From this table, it appears that, for most CSLAP lakes, 1997 water quality data, particularly in the early part of the sampling season, showed clearer water, lower levels of algae growth, and lower nutrient concentrations. This appears to be consistent with both the observations of the sampling volunteers and antidotal remarks from other (non-CSLAP) NYS lake residents and visitors.

In general, the winter of 1997 was colder than in a “typical” year, although February was much warmer than usual. May of 1997 was significantly colder than usual, while June had either normal or above normal temperatures. However, the winter throughout NYS was only slightly wetter than usual, while the spring was somewhat drier. This variability may account for the observation that water temperatures generally remained unchanged from previous years. However, although lake-specific daily temperature and precipitation data are not available, data from the closest NOAA meteorological stations to each lake can provide some indication about the relationship between weather and water quality in CSLAP lakes. Page 11

Number of Water Clarity Higher Water Clarity Water Clarity Lower 1997 CSLAP Lakes Unchanged Precipitation Higher 7 1 2 Precipitation Unchanged 12 1 2 Precipitation Lower 10 9 0

This table suggests that, although water clarity was higher in both “wetter” and “drier” CSLAP lakes, water clarity was more likely to increase in lakes where precipitation levels increased or remained unchanged, not lakes with lower precipitation. As such, it appears that higher early season runoff appeared to influence water clarity, although it also appears that other factors were important. It should also be noted that even lakes experiencing lower spring and early summer precipitation demonstrated an increase or stability in water clarity, further suggesting that non-weather factors may have also been important.

One of these factors appears to be hydraulic retention time, inversely related to the amount of time required for water to flush through the lake (also referred to as flushing time). If 0.5 years is defined as the cutoff between “low” (retention > 0.5 years) and “high” (retention <= 0.5 years) flushing rate, then the table below suggests that lakes with a low flushing rate were more likely than lakes with high flushing rate to be influenced by the weather conditions in 1997:

% of Water Clarity Higher Water Clarity Water Clarity Lower 1997 CSLAP Lakes Unchanged Low Flushing Rate 76 16 8 High Flushing Rate 44 44 13

An explanation for this phenomenon may be that, for lakes with low flushing rates, direct precipitation is more likely to constitute a greater percentage of the hydraulic (water) loading, relative to watershed runoff, than in high flushing rate lakes. This is because lakes with a low flushing rate frequently possess a small watershed relative to the size (area and volume) of the lake. Since the early season precipitation was higher in these lakes, and since this direct rainfall loading is likely to be more dilute than runoff waters, this increased precipitation may have resulted in a lower loading of nutrients to the lake. As a result, many lakes were clearer in 1997. It is equally clear that this explanation does not apply to all CSLAP lakes, for each lake is influenced by a complex interaction of factors, but it does appear to be phenomenologically plausible and consistent with the observations reported above.

The connection between weather and water clarity was also apparent in other years. Unfortunately, as alluded to above, precipitation may not be a consistent indicator of hydrologic loading to lakes, although precipitation data are consistently available from many sites near CSLAP lakes via the National Climatic Data Center at the National Oceanic and Atmospheric Administration (NOAA), while runoff data from the US Geological Survey (USGS) are neither as ubiquitous nor as temporally available. To assess the relationship between weather and water clarity, one can look at both NOAA and USGS sites near a cluster of CSLAP lakes. One such location is Madison County, where about a dozen CSLAP lakes have been actively monitored since 1988, and where both NOAA (at Morrisville) and USGS (at Oneida Creek) sites close to these lakes possess data back to 1988. From the precipitation data, it appears that the 1997 weather conditions (monthly patterns and seasonal changes) most closely resembles those from 1990, but the water quality data from those two years cannot be closely correlated. However, the gaging data show that 1997 was most like 1988, and the water quality data in those years Page 12

were more closely related (and the 1988 data were more closely related to 1997 data than were the data from any other years since 1988):

1997 1990 1988 % Lakes with Higher Clarity 67 27 40 % Lakes with Unchanged Clarity 22 45 60 % Lakes with Lower Clarity 11 27 0

Similar runoff patterns were also seen in 1996 and 1990 (where spring and particularly early summer runoff were extremely high), and in 1986 and 1994. The water clarity data for these paired years suggest that the similar runoff characteristics result in similar water quality data for these years.

1996 1990 1994 1986* % Lakes with Higher Clarity 30 27 0 0 % Lakes with Unchanged Clarity 50 45 36 0 % Lakes with Lower Clarity 20 27 64 100

*- only two CSLAP lakes sampled in 1986- both lakes had below-average clarity in 1986 and 1994

Unfortunately, for this set of lakes, and for many lakes in NYS, the precipitation data from the closest NOAA station and the gaging data from the closest USGS station, are not always closely aligned, although extreme peaks in flow (high or low) seem to correspond pretty well with extreme precipitation peaks ( or drought). As noted above, it is clear that other factors are also influencing changing water quality conditions at each CSLAP lake, and that given the lack of runoff data for most CSLAP lakes, each lake cannot be evaluated in the same manner. However, where appropriate, the individual lake summaries below will assess the connection between precipitation, runoff, and water quality. Page 13

Are there any seasonal trends in the data? Seasonal Comparison of Eutrophication Parameters–1997 and in the typical CSLAP Sampling Season Figure 5 and Figure 6 compare data for the measured eutrophication parameters for Honeoye Lake. Figure 5 plots the data points for the current summer season. Figure 6 plots the monthly average of the data points for all the CSLAP sampling seasons at the lake. The second may give a more complete illustration of the seasonal conditions at your lake.

35 0.00

30 1.00

25 2.00

20 3.00 TotalP Chl.a

15 4.00 m Zsd, Zsd

TP and Chl.a, µg/l Chl.a, TP and 10 5.00

5 6.00

0 7.00 5/10/97 5/30/97 6/19/97 7/9/97 7/29/97 8/18/97 9/7/97 9/27/97

Figure 5. 1997 Eutrophication Data for Honeoye Lake This graph illustrates the most recent condition of the lake.

45 0

40 1 35 2 30 TP 25 3 Chl.a 20

4 (m) Zsd Zsd 15

TP and Chl.a (µg/l) Chl.a TP and 5 10 6 5

0 7 June July Aug Sept Oct

Figure 6. Typical Monthly Averages for Honeoye Lake This graph shows monthly averages compiled from all sampling seasons at the lake. Page 14

These two graphs provide evidence for the following conclusions about seasonal trends:

a) The following measured eutrophication parameters demonstrate significant1 change over the course of a typical summer: Total phosphorus concentrations increase seasonally at a rate greater than the variability normally found from one sample to the next. Although algae levels increase and water clarity decreases in a manner consistent with the seasonal change in total phosphorus, the changes in these parameters are not statistically significant (water clarity demonstrates a “moderate” statistical association with time. b) There appears to be a strong seasonal correlation1 between nutrients and algae at Honeoye Lake, and it is likely that algae growth is most frequently limited by phosphorus concentrations. c) There appears to be a strong seasonal correlation1 between algae and water clarity at Honeoye Lake, and it is likely that algae levels most frequently control water clarity. d) There does not appear to be a strong seasonal correlation1 between water color and clarity at Honeoye Lake, and it is likely that water color does not significantly influence water transparency in Honeoye Lake.

Discussion:

The strong correlation between water clarity, algae, and nutrient concentrations (Secchi disk transparency, chlorophyll a, and total phosphorus, respectively) suggests that lake management activities implemented to increase water clarity will necessarily need to address reductions of algae growth in and nutrient loading to Honeoye Lake. It is not yet known whether the seasonal increase in nutrient levels in the lake is due, directly or indirectly, to the increase in lake and shorefront property use at that time, to increases in watershed nutrient loading, or other more natural phenomena.

1 the definition of “significant” and “strong seasonal correlation”, as defined here, are found in Appendix B Page 15

How has the lake changed since CSLAP began in 1996? Annual Trends in Eutrophication Parameters and Recreational Assessment Figures 7-10 compare the annual Mean Zsd (1996-97) averages for each of the sampled eutrophication parameters, and 6.00 provide information about the 5.00 variability in each year’s data and the 4.00 best-fit lines for describing annual 3.00 trends. Based on these graphs, the 2.00 following conclusions can be drawn:

1.00 a) The following measured 0.00 1995 1996 1997 1998 eutrophication parameters have demonstrated statistically significant

Figure 7 change since CSLAP sampling began Mean Zsd (Water Clarity), 1996-1997 on the lake in 1996: Water transparency has increased, while Mean Chl.a (1996-97) algae and nutrient levels have decreased, with the annual change 45.00 equal to or larger than the 40.00 intraseasonal variability in the data. 35.00 30.00 With only two years of water quality 25.00 data, however, it is impossible to 20.00 statistically label these observations 15.00 10.00 as “trends”. 5.00 b) The Secchi disk transparency 0.00 increase from 1996 to 1997 was 1995 1996 1997 1998 about the same as the intraseasonal

Figure 8 variability found in these two years, Mean Chl.a, 1996-1997 and is consistent with the decrease in chlorophyll a and total phosphorus Mean TP (1996-97) over the same period. c) The chlorophyll a decrease 0.045 from 1996 to 1997 was slightly 0.040 higher than the intraseasonal 0.035 0.030 variability found in 1996 and 1997, 0.025 and is consistent with the decrease in 0.020 total phosphorus over the same 0.015 0.010 period. 0.005 d) The total phosphorus 0.000 decrease from 1996 to 1997 was 1995 1996 1997 1998 slightly higher than the intraseasonal

Figure 9 variability found in these two years, Mean TP, 1996-1997 but may be inconsistent with the slight increase in conductivity over the same period (not shown here).

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Discussion: Figures 7 through 9 suggest that “conditions” at the lake, at least as related to eutrophication, might be improving, although (1) an decrease in algal productivity might not necessarily be an “improvement” and (2) as noted above, it is too early to identify this as a “trend”. The former may be important since Figure 10 suggests that, despite higher perceived water clarity and lower weed growth, recreational assessments of the lake did not change significantly.

Change in Perception (1996-97) What About 1997 at Honeoye Lake? 1995 1996 1997 1998 1.0 1.5 As reported above, most of 2.0 the CSLAP lakes sampled in 1997 2.5 Clarity showed clearer water, with low algae 3.0 Weeds growth and lower nutrient 3.5 Recreation concentrations. For some lakes, this 4.0 4.5 clearer water resulted in more 5.0 significant weed growth (either more dense growth or in deeper parts of Figure 10 the lake), while for others the “flip Mean Perception (Clarity, Weeds, and Recreation), 1996-1997 side” of clear water never occurred. For some of these lakes, particularly those with a small watershed relative to the size of the lake, these findings could be linked to a cooler and slightly wetter than normal winter, a drier spring, and/or rainier summer. And for some lakes, the “improvement” was due to other factors, such as changing biological communities or active lake management. Yet for other lakes, these general statewide conditions were not replicated. The following section summaries the 1997 results for Honeoye Lake, and, where possible, postulates about the cause and/or source of data discrepancies from 1997 to previous CSLAP sampling seasons.

Honeoye Lake was substantially clearer, with lower algae growth and nutrient concentrations, in 1997 than in 1996 (the only other CSLAP sampling season); as noted above, this was an observation common to many CSLAP lakes. As noted above, there is too little water quality data to evaluate the significance of this change in water quality from 1996 to 1997. 1997 appeared to be a fairly dry year, particularly the winter and spring, but a large part of the difference between the 1997 and 1996 water quality data may have been due to the much earlier start of the sampling season in 1997, since, as noted in Figure 6, the lake appears to be clearer in the early summer than in late summer. The nutrient, algae, and particularly water clarity data collected over the same time period in 1996 and 1997 (August and early September) demonstrated very similar characteristics. However, weather clearly influences the condition of Honeoye Lake, and additional CSLAP sampling may provide more insights as to the connection between weather and water quality in Honeoye Lake.

The recreational assessment of the lake also appeared to be strongly influenced by the higher measured water clarity of the lake, although weed growth was also observed to be less significant in 1997. Page 17

Comparison of Honeoye Lake Water Clarity How does this lake compare to other lakes?

1997 1996-97 Class AA Genesee basin NYS Annual Comparison of Eutrophication 0.00 Parameters and Recreational Assessment 1.00 Eutrophic For Honeoye Lake—1997, the Typical 2.00 ------3.00 CSLAP Sampling Season for this lake, Mesotrophic Zsd (m) 4.00 Neighboring Lakes, Lakes with the Same 5.00 ------Lake Classification, and Other NYS Lakes Oligotrophic 6.00 The graphs to the left illustrate Figure 11. Comparison of 1997 Secchi Disk Transparency to Previous Years at the Lake, Lakes With the Same Water Quality comparisons of each eutrophication Classification, Neighboring Lakes, and Other NYS Lakes parameter and recreational perception at Comparison of Honeoye Lake Chl.a Honeoye Lake-in 1997, relative to Honeoye Lake in previous CSLAP 40.00 sampling seasons, other lakes in the same 35.00 30.00 basin, lakes with the same water 25.00 quality classification (each classification is 20.00 Eutrophic 15.00 summarized in Appendix C), and all of Chl.a (µg/l) 10.00 ------Mesotrophic New York State. Please keep in mind that 5.00 ------0.00 differences in watershed types, activities, 1997 1996-97 Class AA Genesee NYS lake history and other factors may result in basin differing water quality conditions at your

Figure 12. Comparison of 1997 Chlorophyll a to Previous Years at the lake relative to other nearby lakes. In Lake, Lakes with the Same Water Quality Classification, Neighboring addition, the limited data base for some Lakes, and Other NYS Lakes regions of the state preclude a Comparison of Honeoye Lake TP comprehensive comparison to neighboring lakes. 30

25 Eutrophic Based on these graphs, the following 20 ------conclusions can be made: 15 Mesotrophic TP (µg/l) TP 10 ------a) Using water clarity as an indicator, 5 Oligotrophic Honeoye Lake is more productive than 0 other lakes with the same water quality 1997 1996-97 Class AA Genesee basin NYS classification (Class AA) and other lakes in

Figure 13. Comparison of 1997 Total Phosphorus to Previous Years the Genesee Basin, and less at the Lake, Lakes With the Same Water Quality Classification, productive than other NYS lakes (although Neighboring Lakes, and Other NYS Lakes 1997 readings indicated less productive Comparison of Honeoye Lake Recreational Perception conditions than in other Genesee River

1997 1996-97 Class AA Genesee basin NYS basin lakes, and conditions about as productive as in other Class AA lakes). Perfect b) Using chlorophyll a as an indicator, Excellent Honeoye Lake is more productive than Slightly impaired other NYS and Class AA lakes, and less Greatly Impaired productive than other Genesee River basin

Not Usable lakes (although the chlorophyll a readings in 1997 indicated conditions about as

Figure 14. Comparison of 1997 Recreational Perception productive as in other Class AA lakes, and less productive than in other NYS lakes). Page 18

c) Using total phosphorus concentrations as an indicator, Honeoye Lake is more productive than other Genesee River basin, Class AA, and NYS lakes (although in 1997, phosphorus readings indicated conditions less productive than in other Genesee River basin and NYS lakes). d) Using QC on the field observations form as an indicator, Honeoye Lake is about as suitable for recreation as other NYS lakes, and less suitable for recreation than other Class AA lakes (1997 conditions were assessed as more suitable for recreation than in other NYS and Class AA lakes).

Discussion: The favorable assessment of recreational usability of Honeoye Lake appears to be generally consistent with the relatively high water clarity and low weed growth in the lake. Although some of the water quality “improvements” noted in the 1997 data averaging can be attributed at least in part to sampling artefacts, the favorable spring recreational assessments of the lake, compared to other lakes and to Honeoye Lake in 1996, suggest that the “improved” conditions also reflect changing water quality conditions. Additional CSLAP data, particularly perception data, will help to determine if these changes can be extrapolated beyond the influence of more favorable weather conditions in 1997.

IV. CONSIDERATIONS FOR LAKE MANAGEMENT

CSLAP is intended to be used for a variety of purposes, such as a means for collecting some information required for comprehensive lake management, although it is not capable of collecting all the needed information. The Five Year Summary Report was envisioned to provide an extensive summary and interpretation of all the water quality, survey, perception, and background information available for each CSLAP lake. Also the Reports contained a recommendation section; a summary of the most pressing lake problems as identified by CSLAP, a compendium of strategies often used to address these problems, and identification of those strategies most likely to work at the lake, given various ecological, logistic, and economic considerations.

Staff limitations and the time intensive nature of such an in depth analysis precluded the development of more than a few of these reports. However, the report authors attempt to include in this report a broad summary of the major lake problems and “considerations” for lake management. These include only those lake problems which may have been defined by CSLAP sampling, such as physical condition (algae and water clarity), aquatic plant coverage (type and extent of weed populations), and recreational suitability of the lake, as related to contact recreation. These broad categories may not encompass the most pressing issue at a particular time at any given program lake; for example, local concerns about filamentous algae or site-specific algae blooms may not be addressed in CSLAP sampling. While there is some discretionary latitude for CLSAP trained volunteers to report and assess some of these site specific conditions or concerns on the CSLAP Field Observations Form, such as algae blooms or shoreline vegetation, this section is limited by the confines of the sampling program, and the categories represent the most common, broadest issues within the realm of lake management.

If these summaries look like a compendium of Diet for a Small Lake, then (congratulations!) you have been doing your reading. Each summarized management strategy is more extensively outlined in Diet, and this joint NYSDEC-NYSFLA publication should be consulted for more details and for a broader context of in-lake or watershed management techniques. These “considerations” should not be construed as “recommendations”, since there is insufficient information available through CSLAP to assess if or how a lake should be managed. Issues associated with local environmental sensitivity, permits, and broad community management objectives cannot be addressed here. Rather, the following section should be considered as “tips” should a lake association decide to undertake managing problems Page 19

defined by CSLAP water quality data or articulated by perception data. In 1997, NYSDEC queried each of the CSLAP lake associations for information about management activities, historical and contemporary, on their lakes. When appropriate, this information, and other lake-specific or local “data” (such as the presence of a controllable outlet structure) is reported in bold in this “considerations” section.

Management Focus: Water Clarity/Algae/Physical Condition/Recreational Condition

Issue Through By? Maintain water clarity Maintaining or reducing algae levels Maintaining or reducing nutrient Inputs to the lake

User perception and water quality data indicate a favorable physical condition and water clarity of the lake. This places the focus of water clarity management on maintaining present conditions, an enviable position for many other lake associations. Although some increase in nutrient loading is inevitable, the lake association should devote efforts to minimize the input of nutrients to the lake, or change activities that otherwise influence water clarity. This may include the following:

Monitoring: Continue ambient lake water quality monitoring such as CSLAP to better confirm water quality changes, especially related to eutrophication (clarity) indicators. It is anticipated that Honeoye Lake will continue to be monitored through CSLAP for at least another three years.

Watershed Nutrient controls can take several forms, depending on the original source of the nutrients:  Septic systems can be regularly pumped or upgraded to reduce the stress on the leach fields, which can be replaced (by replacing the soil or moving the from the septic tank to a new field). Pumpout programs are usually quite inexpensive, particularly when lakefront residents negotiate a bulk rate discount with local pumping companies. Upgrading systems (tanks and leach fields) can be expensive, but may be necessary to handle increased loading to the system (through camp expansion or conversion to year-round residency). Replacing leach fields alone can be expensive and limited by local soil or slope conditions, but may be the only way to reduce actual nutrient loading from septic systems to the lake. It should be noted that upgrading or replacing the leach field may do little to change any bacterial loading to the lake, since bacteria are controlled primarily within the septic tank, not the leach field  runoff control plans include street cleaning, artificial marshes, sedimentation basins, runoff conveyance systems, and other strategies aimed at minimizing or intercepting pollutant discharge from impervious surfaces. The NYSDEC has developed a guide called Reducing the Impacts of Stormwater Runoff to provide more detailed information about developing a stormwater management plan. This is a strategy that cannot generally be tackled by an individual homeowner, but rather requires the effort and cooperation of lake residents and municipal officials.  There are nearly an infinite number of agriculture management practices to reduce nutrient export or retain particles lost from agricultural fields, related to fertilizer controls, soil erosion practices, and control of animal wastes. These practices are frequently employed in cooperation with county Soil and Water Conservation District offices, and are described in greater detail in the NYSDEC’s Controlling Agricultural Nonpoint Source Water Pollution in New York State. Like stormwater controls, these require the cooperation of many watershed partners, including farmers.  Streambank erosion can be caused by increased flow due to poorly managed urban areas, agricultural fields, construction sites, and deforested areas, or it may simply come from repetitive flow over disturbed streambanks. Control strategies may involve streambank stabilization, detention basins, revegetation, and water diversion. Page 20

Land use restrictions . Development and zoning tools such as management, development clusters to less environmentally-sensitive areas in the watershed; deeded/ contractual access to the lake, and cutting restrictions can be used to reduce pollutant loading to lakes. This voluntary approach varies greatly from one community to the next and frequently involves balancing lake use protection with land use restrictions. State law gives great latitude to local government in developing land use plans.

Lawn fertilizers frequently contain phosphorus, even though nitrogen is more likely to be the limiting nutrient for grasses and other terrestrial plants. By using lawn fertilizers with little or no phosphorus, eliminating lawn fertilizers or using lake water as a “fertilizer” at shoreline properties, fewer nutrients may enter the lake. Planting a buffer strip (trees, bushes, shrubs) along the shoreline can reduce the nutrient load leaving a residential lawn.

Waterfowl introduce nutrients, plant fragments, and bacteria to the lake water through their feces. Feeding the waterfowl encourages congregation which in turn concentrates and increases this nutrient source, and will increase the likelihood that these fragments, particularly plants like Eurasian watermilfoil that easy fragment and reproduce through small fragments, can be introduced to a previously uncolonized lake.

No-wake zones can reduce shoreline erosion and local turbidity. Wave action, which can disturb flocculent bottom and unconsolidated shoreline terrain is lessened.

Management Focus: The Impact of Weeds on Recreational Condition

Issue Effect on Lake Use Low weed growth No use impairments associated with weed growth

Discussion: Weed growth in this lake is not dense enough to have an impact on recreational or aesthetic quality of the lake. For many lake associations this is the ideal situation, even though an ideal condition for swimmers, boaters and lakefront residents may not be ideal for a significant sports fishery. For lakes in this condition, lake management is largely a task of maintaining course, of keeping siltation from the watershed at a very low level, and of keeping nuisance plants out of the lake. The DEC publication, Common Nuisance Aquatic Plants in New York State, contains information about nuisance plants. The following techniques have been useful at minimizing the introduction of nuisance plants to lakes, although by no means foolproof. Longer term watershed protection of the lake from other and nutrient loading which can encourage weed growth, is discussed above in Watershed Controls

-Boat propellers frequently get entangled by weeds and weed fragments. Propellers not cleaned after leaving an “infected” lake or before entering an “uncontaminated” lake may introduce plant fragments to the lake. This is a particular problem for many nuisance plants that propagate through fragmentation.

-Waterfowl may introduce to plant fragments to lakes, particularly nuisance weeds like Eurasian watermilfoil that easily fragment. Encouraging the congregation of waterfowl by feeding will increase the likelihood that these fragments can be introduced to a previously uncolonized lake.

-Weed watcher (“...look out for this plant..”) signs have been successful in reducing the spread of nuisance aquatic plants. They are usually placed near high traffic areas, such as boat launch sites, marinas, and inlets and outlets. Page 21

-Hand harvesting is a very labor-intensive means for controlling weed populations. If only a very small number of nuisance plant stems exist, this may be the best means of control, removing the roots and stems of the entire plant, and disposing properly before they propagate into larger, uncontrollable beds that become the obnoxious neighbors of beneficial native plants.

-Naturally occurring biological controls may include native species of aquatic weevils and moths which eat weeds. These organisms feed on Eurasian watermilfoil, and control nuisance plants in some Finger Lakes and throughout the Northeast. However, they also inhabit other lakes with varied or undocumented effectiveness for the long term. Because these organisms live in the canopy of weed beds and feed primarily on the top of the plants, harvesting may have severe negative impact on the population. Research is on-going about their natural occurrence and their effectiveness both as a natural or deliberately- introduced control mechanism for Eurasian watermilfoil . Page 22

CSLAP DATA KEY: The following key defines column headings and parameter results for each sampling season: L Name Lake name Date Date of sampling Zbot depth of the bottom at the sampling site, meters Zsd average Secchi disk reading, meters Zsp depth of the sample, meters TAir Temp of Air, °C TH2O Temp of Water Sample, °C TotP Total Phosphorus, in mg/l NO3 Nitrate nitrogen as N, in mg/l TColor True color, as platinum color units pH (negative logarithm of hydrogen ion concentration), standard pH Cond25 specific conductance corrected to 25°C, in µmho/cm Chl.a chlorophyll a, in µg/l QA survey question re: physical condition of lake: (1) crystal clear, (2) not quite crystal clear, (3) definite algae greenness, (4) high algae levels, and.(5) severely high algae levels QB survey question re: aquatic plant populations of lake: (1) none visible, (2) visible underwater, (3) visible at lake surface, (4) dense growth at lake surface.(5) dense growth completely covering the nearshore lake surface QC survey question re: recreational suitability of lake: (1) couldn’t be nicer, (2) very minor aesthetic problems but excellent for overall use, (3) slightly impaired, (4) substantially impaired, although lake can be used, (5) recreation impossible QD survey question re: factors affecting answer QC: (1) poor water clarity; (2) excessive weeds; (3) too much algae/odor; (4) lake looks bad; (5) poor weather; (6) other

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Appendix A. CSLAP Data for Honeoye Lake (refer to CSLAP Data Keys on previous page)

PName Date Zbot Zsd Zsamp Tot.P NO3 TColor pH Cond25 Chl.a TAir TH20 QA QB QC QD Honeoye L 7/21/96 8.0 2.10 1.5 0.025 0.01 5 8.02 203 21.00 21 22 3 2 2 12 Honeoye L 8/4/96 1.30 1.5 0.029 0.01 5 8.28 204 47.00 23 25 4 2 3 13 Honeoye L 8/18/96 1.45 1.5 0.033 0.01 20 8.09 204 31.00 24 24 2 2 2 123 Honeoye L 9/1/96 8.4 1.45 1.5 0.038 0.01 10 8.07 205 41.00 22 23 3 1 2 3 Honeoye L 9/15/96 2.75 1.5 0.048 0.02 5 7.86 206 55.00 14 19 2 1 1 15 Honeoye L 9/29/96 8.2 2.00 1.5 0.034 0.01 7 7.77 199 28.50 16 15 3 1 3 1 Honeoye L 10/14/96 2.28 7.3 0.035 0.01 7 8.00 206 11.10 10 12 2 2 2 5 Honeoye L 10/25/96 2.35 8.2 0.043 0.01 5 7.62 198 12.20 13 11 2 1 2 Honeoye L 5/24/97 2.90 2.0 0.017 0.01 10 7.78 195 4.00 22 13 1 1 1 Honeoye L 6/8/97 5.95 2.0 0.014 0.01 10 7.47 203 1.40 20 17 Honeoye L 6/22/97 6.30 1.5 0.009 0.01 10 7.81 206 3.65 25 21 Honeoye L 7/14/97 4.35 1.5 0.020 0.01 10 7.72 207 4.06 20 22 Honeoye L 7/20/97 5.95 0.015 0.01 10 7.81 208 2.41 22 23 Honeoye L 8/3/97 9.4 2.60 1.5 0.015 0.01 9 8.19 210 8.22 25 24 Honeoye L 8/24/97 2.15 1.5 0.029 0.01 8 8.08 212 18.50 20 21 Honeoye L 9/7/97 9.8 2.30 1.5 0.025 0.01 9 7.88 213 19 19 Page 24

Appendix B: Summary of Statistical Methods Used in this Report

A variety of statistical methods have been used to present, analyze, and interpret data collected through CSLAP. Some of these methods are commonly used procedures (and have been used previous in Annual Reports), while others have been modified for use on this dataset. The following is a summary of the methods used, or the terms used to summarize a method:

A brief word about including all data points. Occasionally, a sample result indicates that a laboratory, transport, processing, or collection error has occurred; for example, a pH reading of 2.2 (a not-so-weak acid) or a conductivity reading of 4 (distilled water). These results are not included in the dataset. All other data points are retained unless there is strong independent evidence that the result is erroneous.

A slightly less brief note about the statistical tools. All of the statistical summaries used here assume a “normal” distribution of data. That means that the data collected constitute a subset of the data that describe the parameter (say total phosphorus readings) that, when graphed, are distributed in a bell- shaped (also called “normal” or “Gaussian”) curve. In such a curve, the majority of the data points are concentrated near the average, and are less abundant near the extreme values. While an individual subset of data, such as the clarity readings for a particular year for a particular lake, may not be distributed normally (there may be too few points to plot a “normal” curve), they are a subset of a larger set of data (describing instantaneous lake water clarity, in this example) that does demonstrate a Gaussian distribution. Thus for all of these statistics, normal distributions are assumed. If no assumptions about the distribution of the data are made, then different and far less powerful, generally non-parametric, statistical tools need to be used. Fortunately, in describing data sets occurring in nature, industry, and research, assumptions of normal distribution are usually valid.

Mean- the statistical “average” of all samples in a particular dataset. Mean is determined by adding all of the data values within the dataset, and dividing by the number of samples in the dataset.

(Mean pH- since pH is not a direct analytical measure, but rather is a mathematical construct from a direct measure (it is the negative logarithm of the hydrogen ion concentration of the water), mean pH is determined by taking the negative logarithm of the mean hydrogen ion concentration)

(Mean NO3- since nitrate is not detectable, an absolute reading for that sample is not obtainable. This becomes problematic when computing an average, or mean, for a set of samples that include undetectable values. For the purposes of calculating means, undetectable nitrate readings (reported as less than 0.02 mg/l) are assumed to be = 0.01 mg/l. Likewise, all other parameters reporting undetectable values are assumed to be 1/2 of the detection limit)

Standard Deviation is a measure of the variability of data points around the calculated mean. A large standard deviation indicates a wide variability in the data (and thus a lower assurance that the mean is representative of the dataset), while a small standard deviation indicates little variability in the data. The standard deviation presented here (the “brackets” on each data point in the How the Lake Has Changed.. section) corresponds to a 95% confidence interval based on a true population standard deviation (), and assumes a normal distribution of data (therefore the number of degrees of freedom approaches infinity)).

Linear Regression is a statistical method for finding a straight line that best fits a set of two or more data points, in the form y = mx + b, with m the slope of the line, and b the value for y when the line crosses the x axis (when x = 0). R2- R is a correlation coefficient used to measure linear association. R shows the strength of the relationship between the regressed parameters—the closer the value of R to 1 Page 25

or -1, the stronger the linear association (R ranges from -1 to +1. When R = 1, the data fall exactly on a straight line with a positive slope, while at R = -1, the data fall exactly on a straight line with a negative slope. This value is squared (R2) in most statistical analyses, in large part so R values < 0 can be compared to R values > 0).

The “significance” of the data reported in linear regressions, standard deviations, and other more rigorous statistical data analyses have been long debated among statisticians. For this report, we hope to provide some rudimentary statistical basis for evaluating the data collected at each lake, and to evaluate larger questions about each dataset, such as water quality trends (“has the lake changed”). In this report, “significant” is defined as the range of the best-fit line exceeding 95% confidence interval of each monthly average, and “strong correlation” is defined as a correlation coefficient (R2) for the best fit line describing the parameters exceeding 0.5. R2 readings between 0.3 and 0.5 suggest a “moderate” correlation, and this terminology is used in this report when appropriate.

This definition of “significant” may appear to be too, well, wordy, but the justification for it is as follows. If the amount that a measure such as water clarity changes over time, as determined by a best- fit line, is less than it changes in any given year, than it is likely that this change is not statistically valid. As an example, if a persons weight fluctuates by 6 pounds (say from 144 to 150) any given day, a reported weight loss of 2 pounds (from 149 to 147) should be considered within the normal range of variability. If you are that person, then you may think you lost weight, and may have according to the scale, but, at least statistically, you didn’t. The justification for “strong correlation” is not as easy to explain, but may be more verifiable- it appears to be a definition consistent with that used to compare other datasets.

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Appendix C. New York State Water Clarity Classifications

Class N: Enjoyment of water in its natural condition and where compatible, as source of water for drinking or culinary purposes, bathing, fishing and fish propagation, recreation and any other usages except for the discharge of , industrial wastes or other wastes or any sewage or waste not having filtration resulting from at least 200 feet of lateral travel through unconsolidated earth. These waters should contain no deleterious substances, hydrocarbons or substances that would contribute to eutrophication, nor shall they receive containing any such substance.

Class AAspecial: Source of water supply for drinking, culinary or food processing purposes; primary and secondary contact recreation; and fishing. These waters shall be suitable for fish propagation and survival, and shall contain no floating solids, settleable solids, oils, sludge deposits, toxic wastes, deleterious substances, colored or other wastes or heated liquids attributable to sewage, industrial wastes or other wastes. There shall be no discharge or disposal of sewage, industrial wastes or other wastes into these waters. These waters shall contain no phosphorus and nitrogen in amounts that will result in growths of algae, weeds and slimes that will impair the waters for their best usages.

Class Aspecial: Source of water supply for drinking, culinary or food processing purposes; primary and secondary contact recreation; and fishing. These waters shall be suitable for fish propagation and survival. These international boundary waters, if subjected to approved treatment equal to coagulation, sedimentation, filtration and disinfection, with additional treatment if necessary to remove naturally present impurities, will meet New York State Department of Health drinking water standards and will be considered safe and satisfactory for drinking water purposes

Class AA: Source of water supply for drinking, culinary or food processing purposes; primary and secondary contact recreation; and fishing. These waters shall be suitable for fish propagation and survival. These waters, if subjected to approved disinfection treatment, with additional treatment if necessary to remove naturally present impurities, will meet New York State Department of Health drinking water standards and will be considered safe and satisfactory for drinking water purposes

Class A: Source of water supply for drinking, culinary or food processing purposes; primary and secondary contact recreation; and fishing. These waters shall be suitable for fish propagation and survival. These waters, if subjected to approved treatment equal to coagulation, sedimentation, filtration and disinfection, with additional treatment if necessary to remove naturally present impurities, will meet New York State Department of Health drinking water standards and will be considered safe and satisfactory for drinking water purposes

Class B Suitable for primary and secondary contact recreation and fishing. These waters shall be suitable for fish propagation and survival

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Class C: Suitable for fishing, and fish propagation and survival. The water quality shall be suitable for primary and secondary contact recreation, although other factors may limit the use for these purposes.

Class D: Suitable for fishing. Due to such natural conditions as intermittency of flow, water conditions not conducive to propagation of game fishery, or bed conditions, the waters will not support fish propagation. These waters shall be suitable for fish survival. The water quality shall be suitable for primary and secondary contact recreation, although other factors may limit the use for these purposes.

Class (T): Designated for trout survival, defined by the Environmental Conservation Law Article 11 (NYS, 1984b) as brook trout, brown trout, red throat trout, rainbow trout, and splake