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Lake Monitoring

Presenting Monitoring Results

Overview of Data Presentations

One of the basic tenets of successful lake monitoring programs is that sampling data must be properly analyzed and presented. Personnell need to see their sampling data interpreted and presented as findings if they are to maintain their interest in t he program. Organizing agencies and other data users also need to see that the program is generating useful information.

Techniques for presenting data may vary depending on the technical background of the target audience.

The Importance of Credible Data

Lake monitoring programs must ensure that data released to the public are absolutely accurate. Misinformation can occur when data are too hastily or sloppily collected, stored, analyzed, or presented. When this happens, the credibility and hence, the utility of the monitoring program is thrown into question.

To ensure that collected data are credible and defensible, program managers must carefully plan and maintain a quality assurance

program. Approved data collection methods must be established and followed; data must be stored and documented according to specific quality assurance protocols; incoming data must be constantly reviewed; and staff time should be committed in advance to c onduct concise, clear, accurate analyses and presentations of  data.

Presenting the Data

Some citizen monitoring programs issue annual reports at the end of the sampling season. Others rely on regularly-issued newsletters or bulletins. Whatever the format, it is always important to keep in mind the interest, background, and level of technical understanding of the target audience.

Three rules apply when presenting data.

  • The data presentation should not be overly technical or insultingly simple. Graphics are extremely helpful.
  • The data presentation should convey information with a specific purpose in mind (e.g., to show a trend, to illustrate seasonal variations or variations with depth, or to identify problem sites). 
  • The data presentation must be timely and relevant to the lake condition. Data users will lose interest in the program if significant delays occur between the sampling season and the presentation of data results.



    It is not enough to simply list the data when preparing a summary report. Instead, the author of the report should use an appropriate combination of graphs, summary statistics, maps, and narra tive interpretation. Some common options for pr esenting the data are discussed below.


    Choosing a graphic format that will best transfer information about the monitoring data requires careful thought. Three basic types of graphs are often used to present monitoring information:

  • bar graph,
  • pie graph, and
  • line graph.



    The bar graph uses column bars of varying lengths to compare data. This graph places special emphasis on individual values in the data set rather than overall trends.

    The pie graph compares parts to the whole. In a pie graph, each value in the data set is represented by a wedge in a circular pie. The pie as a whole equals 100 percent of the total values in the data set. The size of any individual wedge, therefore, corresponds to the percentage that the value represents to the total.

    The line graph effectively shows changes (or trends) over a period of time or space. Unlike bar graphs, it does not place emphasis on the individual values in the data set.

    Listed below are some basic rules when creating graphs.

  • Prepare the graph with an informational purpose in mind.
  • Limit the number of elements used in the graph. The number of wedges in a pie graph should be five or less. The bars in a bar graph should fit easily. Limit the number of overlaying lines in a line graph to three or fewer.
  • Expand elements to fill the dimensions of the graph. Unless there is a specific reason to emphasize magnitude or scale, trends and patterns can be distorted if the graph is off -balance. Strive to balance the height and width so that informa tion is rep resented accurately.
  • Choose scales that quickly and easily illustrate values.
  • Title the graph to describe clearly what it presents.
  • Label the axes clearly and do not overcrowd points along axis lines.
  • Use a legend (or key) when appropriate.
  • Present information concerning sampling time or conditions when appropriate.



    Summary Statistics

    Summary statistics are useful for conveying information about a data set. These statistics should succinctly, yet efficiently, transfer facts about the measured variable.

    Textbook statistics assume that if a parameter is measured a large number of times under a common universe of circumstances, the measurement values will be distributed at random around an average value. If the relative frequency of these values are plotte d against value magnitude, the result will be the familiar Gaussian (normal or bell-shaped) curve. The specific shape of this curve is defined by two statistics, the mean (or average) of the data set values and the standard deviation.

    The mean is a statistic that describes the central tendency of the data set. Standard deviation describes the variability or spread of the data around the mean. Traditionally, the mean and standard deviation are the statistics used to summarize a set of l ake data.

    In practical application, however, the mean and standard deviation are not always the appropriate summary statistics to use because lake data do not usually follow textbook patterns of normal distribution around an average value. Instead, the data are fre quently skewed in one direction or the other.

    This skewness occurs because there are many important factors that influence lake conditions, including the changing seasons, weather condi tions, and activity in the lake and watershed. As a result, the parameters used to describe lake conditions are con stantly in a state of flux.

    Thus, skewness can usually be expected, especially when measuring the parameters that characterize an algal condition (Secchi disk transpar ency, chlorophyll a, and total phosphorus concentration). Chlorophyll a concentration, for example, m ay go through several cycles each year. It may be low in the spring, high during a mid-summer algae bloom, and low again in the fall. 

    Robust Statistics

    Whenever there is an irregular or uncertain pattern of data values for a lake parameter, robust summary statistics should be used. A robust statistic conveys information under a variety of conditions. It is not overly influenced by data values at the extr emes of the data distribution.

    Median and interquartile range are robust statistics that describe central tendency and spread around the median, respectively. Both these summary statistics are unaffected by extreme points. Consequently, they are usually more appropriate f or summarizing lake data than the traditional mean and standard devia tions.

    Both the median and interquartile ranges are based on order statistics. They are derived by ordering data values from high to low. The median is simply the middle value of the data set. The interquartile range is the difference between the value at the 75 percent level and the value at the 25 percent level.

    The Box Plot

    The box plot is a convenient method of presenting lake data based on the robust order statistics. In one simple graphic, the box plot can provide information on:

  • the median,
  • data variability around the median,
  • data skew,
  • data range, and
  • the size of the data set.



    A box plot is constructed using the following steps:

    1. Order the data from lowest to highest.

    2. Plot the lowest and highest values on the graph as short horizontal lines. These are the extreme values of the data set and repre sent the data range.

    3. Determine the 75 percent value and 25 percent value of the data set. These values define the interquartile range and are represented by the location of the top and bottom lines of the box.

    4. The horizontal length of the lines that define the top and bottom lines of the box (the box width) can be used as a relative indica tion of the size of the data set. For example, the box width that describes a lake data set of 20 values can be displayed twice as wide as a lake with a data set of 10 values. Alternatively, the width may be set as proportional to the square root of the sample size. Any proportional scheme can be used as long as it is consistently applied.

    5. Close the box by drawing vertical lines that connect to the ends of the horizontal lines.

    6. Plot the median as a dashed line in the box. 

    Algae Results

    The information in this section will be presented by using a data set from a fictitious lake appropriately named  Klettbach Lake.

    Klettbach Lake was sampled on the 1st and 15th of the month from May 15 to October 15. The lake depth at the sampling station was 30 feet. At one sampling site over the deepest part of the lake, personnel:

  • measured Secchi disk transparency,
  • took a chlorophyll a sample at a depth of 3 feet, and
  • took total phosphorus samples at depths of 3 feet and 26 feet. Secchi Disk Transparency



    Secchi disk transparency is a parameter that interests. Data are easily understandable and can be presented by a modified bar graph. The horizontal axis presents the sampling dates and the vertical axis represents the lake's water column. Minia ture Secchi disks extend down from the surface to the actual Secchi disk reading.

    General trends in Secchi disk measurements can be noted in this data presentation, but the graphic emphasis is on the individual reading on each sampling date.

    Chlorophyll a

    Chlorophyll a is usually best presented in a traditional bar graph. By examining this data presentation, personnel can observe when chlorophyll a concentrations were high and low during the sampling season.

    The horizontal axis presents the sampling dates. The vertical axis is a scale of chlorophyll a values. Like the Secchi disk graph, general trends can be noted, but the graphic emphasis is on the chlorophyll a concentration on each sampling d ate.

    Total Phosphorus

    The total phosphorus graph displays the surface and bottom data together. By examining this double bar graph, personnell can observe when phosphorus concentrations were high and low in each zone. In addition, they can compare surface and bottom concentrat ions on each sampling date.

    The horizontal axis presents the sampling dates. The vertical axis is a scale of total phosphorus values. As with the other bar graphs, general trends in measurements can be noted, but the graphic emphasis is on phos phorus concentrations measured on each sampling date.

    Data Interpretation

    In addition to displaying graphs, box plots, and summary statistics, the report author must provide interpretation of what the data presentations mean. The interpretation process begins with a data analysis by an experienced limnologist. The report author then has the critical job of putting technical analysis into terms that can be understood by personnell. Toward this end, data interpretation is often best presented in the context of an explanation of how the lake functions during a seasonal cycle.

    Although time-consuming, a thoughtful explanation by the report author rewards personnell with greater insight and understanding of their lake.

    Examples of observations and reasonable conclusions based on the data from Klettbach Lake may include the following:

  • Secchi disk readings were highest in May and October, and lowest in September.
  • Chlorophyll a concentrations were relatively low from May through July. After July 15, concentrations increased, reach ing a maximum concentration on September 15.
  • Phosphorus concentrations at a depth of 3 feet were generally moderate in May and October



    and relatively low in the summer. Phosphorus concentrations near the lake bottom were generally moderate in May, June, and October. Concentrations increased during the summer, reaching a maximum on September 1.

  • The algal population can affect water clarity during the summer and early fall. This is evidenced by the fact that Secchi disk readings and chlorophyll a concentration followed opposite paths. When one was high, the other was low. Notably, the lo west Secchi disk reading occurred on the same date as the highest chlorophyll a concentration (September 15).



    The reduction of water transparency on June 15 may be due to algae, but it may also be due to increased water turbidity from a spring rain. A check of the field data sheet can often explain sudden variations in data magnitude.

  • Algae take up and then remove phosphorus from the surface waters as they die and sink to the lake bottom. This is evidenced by lowered phosphorus concentrations during the summer.
  • The lake probably stratifies into a warmer upper layer (epilimnion) and a colder lower layer (hypolimnion). Also, the lower layer probably is also anoxic in the summer. This theory is supported by the large concentration of phosphorus found in the lower layer during those months. The likely source of this phosphorus is lake b ottom sediments that leach phosphorus to the overlying waters under anoxic conditions.
  • In all likelihood, Klettbbach Lake experiences a spring and fall overturn. This is evidenced by nearly equal shallow and deep total phosphorus concentrations on May 15 (spring overturn), October 1, and again on October 15 (fall overturn). The nearly equal concentrations indicate that the lake is mixing vertically and d istributing phosphorus evenly throughout the water column.
  • There may be a problem with fall algal blooms, which will reduce water clarity. Fall overturn may be stimulating increased growth when it brings phosphorus (released from the sediments during anoxic conditions) to the surface waters. Algae often reproduce rapidly when given this new pulse of nutrients.



    Trophic State

    Secchi disk transparency, chlorophyll a, and total phosphorus are often used to define the degree of eutrophication, or trophic status of a lake. The concept of trophic status is based on the fact that changes in nutrient levels (measured by total phosphorus) causes changes in algal biomass (measured by chlorophyll a) which in turn causes changes in lake clarity (measured by Secchi disk transparency).

    A trophic state index is a convenient way to quantify this relationship. One popular index was developed by Dr. Robert Carlson of Kent State University. His index uses a log transformation of Secchi disk values as a measure of algal biomass on a scale fro m 0 - 110.

    Each increase of ten units on the scale represents a doubling of algal biomass. Because chlorophyll a and total phosphorus are usually closely correlated to Secchi disk measurements, these parameters can also be assigned trophic state index values.

    The Carlson trophic state index is useful for comparing lakes within a region and for assessing changes in trophic status over time. Thus it is often valuable to include an analysis of trophic state index values in summary reports of a  monitoring program. 

    The program manager must be aware, however, that the Carlson trophic state index was developed for use with lakes that have few rooted aquatic plants and little non-algal turbidity. Use of the index with lakes that do not have these characteristics is not appropriate.

    TSI = 60 - 14.41 ln Secchi disk (meters)

    TSI = 9.81 ln Chlorophyll a (µg/L) + 30.6

    TSI = 14.42 ln Total phosphorus (µg/L) + 4.15


    TSI = Carlson trophic state index

    ln = natural logarithm

    The formulas for calculating the Carlson trophic state index values for Secchi disk, chlorophyll a, and total phosphorus are presented below. Also presented is a table that lists the trophic state values and the corre sponding measurements of the t hree parameters. Ranges of trophic state index values are often grouped into trophic state classifications. The range between 40 and 50 is usually associated with mesotrophy (moderate productivity). Index values greater than 50 are associated with eutrophy (high productivity). Values less than 40 are associated with oligotrophy (low productivity).

    Presented below are Carlson trophic state index values for Volun teer Lake. Summer averages (June 15 - September 1) are used in the calcula tions. As seen from the TSI values, Klettbach Lake can be classified somewhere near the border of mesotrophy and eu trophy.

    Secchi Disk

    Average Summer Secchi disk = 5.9 feet = 1.8 meters

    TSI = 60 - 14.41 (ln Secchi disk (meters))

    TSI = 60 - (14.41) (0.59)

    TSI = 51.5

    Total Phosphorus

    Average Summer Surface Total Phosphorus = 19.6 µg/L

    TSI = 14.42 (ln Total phosphorus (µg/L)) + 4.15

    TSI = (14.42) (2.98) = 4.15

    TSI = 47.1

    Chlorophyll a

    Average Summer Chlorophyll a = 17.2 µg/L

    TSI = (9.81) (ln Chlorophyll a (µg/L)) + 30.6

    TSI = (9.81) (2.84) + 30.6

    TSI = 58.5

    Aquatic Plant Results

    Chapter 4 describes three activities that personnell can use to monitor the rooted aquatic plant condition:
  • mapping the distribution of plants at or near the surface;
  • estimating percent composition and relative density of plant types at stations located along a transect line that runs perpendicular from shore; and
  • collecting plant types for professional identification.



    Reporting the results of these activities can be relatively straightfor ward. The rough aquatic plant map drawn by personnell can be cleaned -up and reproduced (see below).

    Estimates of the percent composition of the different plant types at each transect station are best displayed by using a pie graph. Relative density information can also be incorporated into the graph. Identified plants can be listed along with a sketch a nd a short description. 

    Dissolved Oxygen Results

    As in Section above, this information will be presented using fictitous data set from Klettbach Lake.

    Temperature and oxygen profiles were measured at one sampling site located over the deepest part of the lake on April 15 and July 15. Using a temperature/oxygen meter, personnell recorded readings at five-foot intervals from the surface to the lake bottom . A data table of the results is presented below.
    Results of the temperature and dissolved oxygen measurements (profiles) can be presented together on the same line graph (page 117). The horizontal axis displays a range of values that can be read both as dis solved oxygen units (mg/L) and temperature uni ts (°C). The vertical axis represents the water column of the lake with the surface at the graph's top and the lake bottom at the graph's bottom.