a review of secchi transparency trends in minnesota lakes

June 2016

A review of Secchi transparency trends in Minnesota lakes

Minnesota Pollution Control Agency 520 Lafayette Road North | Saint Paul, MN 55155-4194 |

651-296-6300 | 800-657-3864 | Or use your preferred relay service. | [email protected] This report is available in alternative formats upon request, and online at www.pca.state.mn.us.

Document number: wq-s2-08

Authors Steven Heiskary Lindsay Egge Water Quality Monitoring Unit, MPCA

Data management Jordan Donatell Water Quality Monitoring Unit, MPCA

Review and edits Pam Anderson Water Quality Monitoring Unit, MPCA

John Erdman Metro Watershed Unit, MPCA

John Genet South Biological Monitoring Unit, MPCA

Heidi Rantala Section of Fisheries, Minnesota Department of Natural Resources

Jamie Schurbon Anoka Conservation District

Editing and graphic design Jennifer Holstad Sherry Mottonen

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Foreword Minnesota has abundant Secchi transparency data for lakes. As of 2014, 4,509 lakes had Secchi data and of these, 1,578 had eight or more years of data. Data records for some lakes extend to four decades. These data allow for annual assessments of trends for Minnesota lakes. This report examines the trend information and provides insights into factors that affect the transparency and quality of Minnesota’s lakes.

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Contents Foreword ........................................................................................................................................................ i

Contents ....................................................................................................................................................... iii

List of tables ................................................................................................................................................. iii

List of figures ................................................................................................................................................ iv

Introduction .................................................................................................................................................. 1

Methods and study approach ....................................................................................................................... 4

Results and discussion .................................................................................................................................. 5

Is there a difference in trends among recreational use support categories? .......................................... 5

Is there a difference in trends among ecoregions? .................................................................................. 6

Is there a difference in trends between urban versus rural lakes? .......................................................... 8

Is phosphorus the primary driver of trends? ............................................................................................ 8

Are aquatic invasive species drivers of trends? ...................................................................................... 13

Summary and recommendations................................................................................................................ 17

References .................................................................................................................................................. 20

Appendix ..................................................................................................................................................... 21

List of tables Table 1. Trend significance, p values, and narrative description. ................................................................ 4

Table 2. Significant (4 & 5) trend lakes sorted by recreational use support status ..................................... 6

Table 3. Lakes with a significant decreasing trend (-4 and -5). .................................................................... 7

Table 4. Lakes with a significant increasing trend (+4 and +5) .................................................................... 7

Table 5. CHF ecoregion lakes with significant (S=4 or 5) increases or decreases in transparency .............. 8

Table 6. Summary of transparency (trans.) and TP trends by county ....................................................... 13

Table 7. Aquatic invasive species summary for lakes with significant trends (4 or 5) ............................... 17

Table 8. Lakes with significant (S=4 or 5) increases or decreases in transparency by ecoregion .............. 17

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List of figures Figure 1. Lake transparency trends for 1972-2014 (2014 assessment) ....................................................... 2

Figure 2. Lake transparency trend assessments for 2013, 2012, 2010, and 2009. ...................................... 3

Figure 3. TP, Chl-a, and Secchi transparency relationships ......................................................................... 9

Figure 4. Examples of good correspondence between transparency and TP trends ................................ 11

Figure 5. Examples of poor correspondence between transparency and TP trends ................................. 12

Figure 6. Influence of zebra mussels on lake transparency in Lake Carlos Chain of Lakes ........................ 16

Figure 7. Lake Mille Lacs transparency and TP trends ............................................................................... 16

A review of Secchi transparency trends in Minnesota lakes • June 2016 Minnesota Pollution Control Agency

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Introduction A commonly heard statement is that the “Water quality of Minnesota’s lakes continuously gets worse.” If we can equate water transparency with “water quality,” we can test this assumption by using Minnesota’s abundant Secchi transparency data, in concert with standardized statistical analysis.

The Secchi disk has long been a standard tool for measuring the transparency of lakes and is used by citizen-volunteers and professionals alike. In Minnesota, it has been used since the early 1970s as the basis for measuring lake transparency in the Citizen Lake Monitoring Program. Secchi transparency is the depth at which a white disk lowered into the water column can be seen. Since suspended algae are the primary limiter of transparency in Minnesota lakes, measuring transparency provides a basis for estimating the amount of algae in a lake. In turn, the nutrient phosphorus (P) has been shown to be the primary nutrient that limits the growth of algae in Minnesota lakes. Statistical relationships that describe the interrelationship among Secchi transparency, algae (represented by the measure chlorophyll-a [Chl-a]), and total phosphorus (TP) are the three measures used to describe the “trophic status” of lakes.

When Secchi measurements are collected during the open water season, the measurements can be used to describe seasonal changes in transparency and when collected over several years provide a basis for describing trends. For Minnesota, this is particularly true because of the large number of volunteers and professionals that collect transparency data and the large number of lakes with long-term data. The Minnesota Pollution Control Agency (MPCA) has conducted periodic trend assessments since the 1980s. Results from these analyses for individual waterbodies were often reported in the 305 (b) report to Congress but there was seldom any detailed statewide review of observed trends. However, in the 1990s there was a concerted effort to look at both statewide and lake-specific trends (e.g. Heiskary et al. 1993 and Heiskary and Lindbloom 1993) and discern what factors may have contributed to the trends.

A more systematic approach to trend analysis was undertaken in 2000 and from 2009 forward, a consistent methodology was used. In this approach, all Secchi data were extracted from EQuIS, the MPCA water quality database. The Seasonal Kendall statistical test was conducted using the software program R to determine whether the data for each lake exhibited increasing or decreasing trends and the strength of the trend. The most recent assessment was for the period 1972 through 2014 (Figure 1). The percent with an increasing (22%) trend was similar to previous assessments (Figure 2) and in all cases; the percent with increasing trends is greater than the percent with decreasing trends. This pattern prompted several questions regarding regional patterns and drivers of the observed trends. The 2014 trend assessment was the focus of this report and was used to answer several questions on transparency trends in Minnesota lakes, describe potential causes for the trends, and discern whether Minnesota lake-water quality is getting “worse” or “better” over time.


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Figure 1. Lake transparency trends for 1972-2014 (2014 assessment). NLF=Northern Lakes and Forests, CHF=North Central Hardwoods Forests, WCP=Western Corn Belt Plains, and NGP=Northern Glaciated Plains.

NLF

NGP

WCP

CHF


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Figure 2. Lake transparency trend assessments for 2013, 2012, 2010, and 2009.

2013 2012

2010 2009


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Methods and study approach The MPCA’s annual automated trend assessment of Secchi transparency for 2014 provided the “population” of lakes considered in this study. In addition, the study used MPCA’s “TSI-trend year file,” which is a spreadsheet with annual summer-means, standard deviation, standard error of the mean, and number of observations for each Minnesota lake, with TP, Chl-a, or transparency data in EQuIS. The TSI-year file provided a basis for comparison of TP trends and R-based transparency trends to see if and how they co-vary over the data record for lakes where comparisons were made.

The seasonal Kendall test was applied to all June through September transparency data for each lake with a minimum of eight years of data required to run the test. The median transparency was calculated and charted along with the minimum and maximum measurements for each year. The summer-median and a smoothing technique were used to draw the regression line. The trend (Rk) was calculated based on all available data for the period, rather than summer-means, as is the case in the TSI-trend year file. The Rk, number of years, slope, p-value, and significance of the trend were reported for each lake. Significance of the trends was derived from the Rk (± confidence interval) and p values. The p values, significance, and narrative description are summarized in Table 1.

Table 1. Trend significance, p values, and narrative description.

p value significance description >0.10 0 & ±1 no trend 0.10-0.19 ±2 weak evidence of a possible trend 0.05-0.099 ±3 evidence for a possible trend 0.011-0.049 ±4 evidence for a trend ≤0.01 ±5 strong evidence for a trend

The primary focus of this study was on those lakes with “evidence” or “strong evidence” (ratings 4 or 5) of trends, which was intended to minimize the “noise” from lakes where there is greater uncertainty as to the statistical significance of the trend. We answered several questions using this dataset:

1. Is there a difference in transparency trends for lakes that meet eutrophication-related water quality standards as compared to those that do not?

2. Is there a difference in transparency trends among ecoregions? 3. Is there a difference in transparency trends among urban versus non-urban lakes? 4. For lakes with sufficient TP data, is there good correspondence in TP and transparency trends such

that it is likely that changes in TP (and its effect on Chl-a) are a primary reason for observed Secchi trends?

5. Aquatic invasive species may affect primary productivity (algae). Is there a difference in transparency trends for lakes with aquatic invasive species as compared to those without?

We used various sources of data to aid this analysis. The Minnesota Department of Natural Resources (DNR) provided listings of lakes where aquatic invasive species were confirmed. For our study, we focused on zebra mussels (ZM), Eurasian water milfoil, spiny waterflea, and curly-leaf pondweed. For the first three, there was often a year associated with when the species was first confirmed and when the lake was formally listed for that species. For curly-leaf, only presence was recorded. These data were merged with our list of lakes where trend analysis had been conducted. Statistics and descriptions refer to those lakes with significant trends (4 or 5). Thus, any reference to numbers of lakes or percent of lakes with exotics species, refers to these lakes and not the state as a whole.


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Climate can influence nutrient transport (rainfall and runoff) to lakes and processing within the lake e.g., algal production. For example, extended wet periods (e.g. much of the 1990’s) can increase P loading to lakes and extended dry periods (e.g. 1987-1989) may reduce runoff but provide good conditions for algal growth. For our analysis, we were interested in climatological patterns over the range of years in the trend assessments. For this purpose, we used data from the National Oceanic and Atmospheric Administration (NOAA) “Climate at a Glance” page http://www.ncdc.noaa.gov/cag/time-series/us/21/6/phdi/4/9/1975-2015?base_prd=true&firstbaseyear=1970&lastbaseyear=2000.

We downloaded precipitation and Palmers Drought Severity (Hydrologic) Index (PDHI) by Climate Division (~regions) for Minnesota. We used data for June through September and the years 1970 through 2015 as a consistent basis for comparing Secchi and climate trends. PDHI is a measurement of soil dryness based on recent precipitation and temperature and indirectly accounts for factors such as evapotranspiration and soil recharge. It is most often used to measure long-term drought (e.g. several months). Since it incorporates several important factors, which influence both P loading and lake response, we felt it might be a good indicator of climate condition – for a single summer or a series of summers. Charts for precipitation and PDHI are included in the Appendix of the report. These charts were used in support of the county-based trend summaries.

In addition, we assembled county-based summaries for the trend lakes. In these summaries, we included the R-generated transparency trend chart, TP trend chart, and very brief discussions on the observed trends within each lake and possible causes based on MPCA staff insights and when possible, the insights of soil and watershed conservation district, watershed district, or other local staff. DNR’s LakeFinder was a source for background information on the lakes.

Results and discussion Of 4,509 lakes with Secchi disk data in the 2014 analysis, 1,578 had sufficient data for trend analysis (Figure 1). Of this number, 1,035 had no evidence of a trend, while 378 had “evidence” (category 4) or “strong evidence” (category 5) of a trend. Of the 378 with evidence or strong evidence, 93 (25%) exhibited decreasing trends (6% total lakes with sufficient data for trend analysis), while 285 (75%) exhibited increasing trends (18% of total lakes with sufficient data for trend analysis). The 2014 results are similar to those from past assessments in terms of the relative percentages of increasing and decreasing transparency and the regional distribution of these percentages (Figure 2).

In this current study, we sought to discern the underlying reasons for the observed trends on statewide, region-wide, and individual lake basis. To aid our analysis, we included county-based case studies (Appendix) for counties with several trend lakes. These case studies feature lake-specific and countywide summaries. The individual lake-by-lake summaries used an approach similar to that used in Heiskary et al. (1993). In our current study, we offered and tested several hypotheses in an attempt to discern the factors that contribute to increasing or decreasing Secchi transparency. We use the questions, as posed in the Methods section, as a basis for organizing the remainder of the Results and discussion. Our intent was that the various sections should build upon one another.

Is there a difference in trends among recreational use support categories? Minnesota promulgated ecoregion-based lake eutrophication standards (LES) in 2008 (Heiskary and Wilson 2008). Determination of use support for lakes is based on the LES, which requires the causative variable: TP and the response variables: Chl-a and Secchi. Assessments are based on average TP, Chl-a,

http://www.ncdc.noaa.gov/cag/time-series/us/21/6/phdi/4/9/1975-2015?base_prd=true&firstbaseyear=1970&lastbaseyear=2000

http://www.ncdc.noaa.gov/cag/time-series/us/21/6/phdi/4/9/1975-2015?base_prd=true&firstbaseyear=1970&lastbaseyear=2000


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and Secchi over the most recent ten years. These averages are compared to the LES and are used to determine if the lake meets (fully supports; FS) or does not meet (nonsupport; NS) the water quality standards. Lakes that do not have sufficient data or where the cause and response variables do not agree are termed insufficient information (IF). Since Secchi is typically the largest data set for most lakes, we wanted to see if there were differences in trends among use support categories and how trend information may help inform the assessment and total maximum daily load (TMDL) process.

We sorted the 1,578 trend lakes into the three categories: FS, NS, and IF (Table 2). Of the 767, fully supporting lakes, 238 (31%) had significant trends and of these, 82% were increasing and 18% decreasing. Of the 390 nonsupporting lakes, 70 (18%) had significant trends and of these, 66% were increasing and 34% decreasing. For the lakes with IF, more exhibited significant increasing trends (63%) as compared to decreasing trends (37%).

Lake protection has become a new priority within the Watershed Restoration and Protection Strategies (WRAPS) and transparency trends are a consideration in prioritization efforts. These trend data help identify lakes “at risk.” In the case of this data set (Table 2), the 43 FS lakes with significant decreasing trends should be among those prioritized for action (protection).

For the NS lakes, more exhibited increasing trends as compared to decreasing trends. An important question here is – of those lakes exhibiting increasing trends, which ones are related to TMDL implementation, watershed best management practices, or other lake management activities? Those lakes exhibiting significant improvements in transparency (46 lakes) would be good candidates for follow-up and many are included in the county-based summaries in the Appendix.

During the assessment process, lakes are deemed to have IF for various reasons. Most commonly, there were insufficient number of samples, a lack of Chl-a, lack of Secchi data, or the cause and response variables do not concur on the appropriate assessment. A routine decision that follows the IF designation is which lakes should be prioritized for further monitoring so valid assessments can be made. The lakes in the trend data set provide an objective way to make this decision. In this case, the 26 lakes with significant decreasing trends might be considered a priority for additional monitoring.

Table 2. Significant (4 & 5) trend lakes sorted by recreational use support status: fully support (FS), nonsupport (NS), and insufficient information (IF). Relative number and percentages in increasing and decreasing trend categories.

Support (strength) Sum

Significant increase

Significant decrease

# % # % FS (#) 238 195 82% 43 18% NS (#) 70 46 66% 24 34% IF (#) 70 44 63% 26 37% Sum 378 285 75% 93 25%

Is there a difference in trends among ecoregions? There are distinct regional patterns in lake morphometry, land use, and water quality across Minnesota. This was a primary reason for adopting the ecoregion framework for characterizing lake condition and developing ecoregion-based LES in Minnesota (Heiskary and Wilson 2008). In general, lakes of northern (Northern Lakes and Forests ecoregion [NLF]; Northern Minnesota Wetlands [NMW]) Minnesota are oligotrophic to mesotrophic, whereas southern (Western Corn Belt Plains and Northern Glaciated Plains


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[WCP & NGP]) Minnesota are eutrophic to hypereutrophic, and central (North Central Hardwoods Forest [CHF]; Driftless Area [DA]; and Red River Valley [RRV]) Minnesota are intermediate between these two.

The CHF and NLF ecoregions were well represented in the trend assessment, accounting for 50% and 45% of the lakes with sufficient data for trend assessment (remaining 5% in WCP and NGP; Figure 1). Lakes with decreasing trends (93 lakes) were almost equally divided between the NLF and CHF ecoregions at 45% and 43 % respectively (Table 3). However, the percentage of decreasing trend in the NLF was higher than the CHF, when expressed as a percent of the total for the ecoregion (Table 3). Somewhat surprising, a majority of the lakes that exhibited significant decreasing trends (45%) were lakes that meet the LES and assumed to be of good quality. This supports the idea that lakes with a decreasing trend should be priorities for protection in Watershed Restoration and Protection Strategies (WRAPS). It also raises the question as to whether other factors, beyond P loading, may be responsible for decreasing trends in lakes that meet the LES. The remainder of the lakes with decreasing trends was divided between those that do not meet LES (NS) or had insufficient information (IF) to assess condition.

The highest percentage (61%) of lakes with increasing trends (285 lakes) was in the CHF ecoregion (Table 4). The WCP ecoregion had an equal number of lakes with increasing and decreasing trends (Table 3 and Table 5); however, the overall number of lakes with sufficient data for trend assessment was small. Of the lakes with increasing trends, 68% were in fully supporting lakes, while 16% were in nonsupporting lakes.

Table 3. Lakes with a significant decreasing trend (-4 and -5). Summary of recreational use support status by ecoregion.

Total # of lakes

Total # and % of lakes Number of such lakes with:

Percentage of such lakes with:

Ecoregion significant trend

decreasing trend FS NS IF FS NS IF

NLF 152 45 (30%) 26 4 15 58% 9% 33% CHF 216 43 (20%) 16 18 9 37% 42% 21% WCP/NGP 10 5 (50%) 1 2 2 20% 40% 40% Sum 378 93 (25%) 43 24 26 45% 26% 29%

Table 4. Lakes with a significant increasing trend (+4 and +5). Summary recreational use support status by ecoregion.

Total # of lakes

Total # and % of lakes Number of such lakes with:

Percentage of such lakes with:

Ecoregion significant trend

increasing trend FS NS IF FS NS IF

NLF 152 107 (70%) 76 4 27 71% 4% 25% CHF 216 173 (80%) 118 39 16 68% 23% 9% WCP/NGP 10 5 (50%) 1 3 1 20% 60% 20% Sum 378 285 (75%) 195 46 44 68% 16% 15%


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Is there a difference in trends between urban versus rural lakes? The best opportunity to test this question was to compare the Seven County Metro area with all other CHF ecoregion lakes outside of the metro area (includes five in DA and RRV ecoregions). The “hypothesis” in this case is that there are abundant monitoring efforts coordinated by the municipalities, watershed districts, and watershed management organizations across the Metro area. In addition, there have been substantial efforts to reduce urban stormwater inputs over past 10 to 15 years and this has reduced nutrient loading, leading to increased transparency in Metro lakes. In contrast, non-metro lakes may have long transparency records; however, there has been no comparable large-scale effort to address nutrient loading in rural watersheds and few lakes exhibit increased transparency as a result.

The Metro lakes comprised 41% of the total number of CHF lakes with adequate data for trend assessment, with the non-Metro accounting for the remainder. The number of lakes with significant trends was almost equally divided between the Metro and non-Metro lakes (Table 5). When viewed as a percentage of the total lakes in the trend assessment, the percent of Metro lakes exhibiting trends was higher than the non-Metro. However, when compared to just the lakes that exhibited trends, the percentage in the non-Metro was higher (Table 5). The relative percentages of lakes with significant increasing trends were similar between the Metro and non-Metro lakes, with 25% and 20% respectively, of the total lakes included in the assessment. Lakes with decreasing trends were much lower in terms of numbers and percentages for both of these groups in comparison. Based on these comparisons, there did not appear to be substantial differences between the Metro or non-Metro lakes relative to the number of lakes with trends, trend direction, nor percent of the population (total or trend) for either group.

Table 5. CHF ecoregion lakes with significant (S=4 or 5) increases or decreases in transparency. Comparison of lakes in seven-county Metro to all other CHF counties. Percentages for total # in assessment and # with trends.

Total # (%) in assess.

# w/ trends

% of total

Increase # lakes

Increase % total

Increase % trend

Decrease #

Decrease % total

Decrease % trend

Metro 324 (41%) 105 32% 81 25% 77% 24 7% 23% Non-Metro 470 (59%) 109 23% 92 20% 83% 19 4% 17% Total 794 216 27% 173 22% 80% 43 5% 20%

Is phosphorus the primary driver of trends? There are numerous reasons for water transparency to change in a lake. On a seasonal basis, changes in algal biomass (estimated by Chl-a) is a primary reason for change in transparency. The amount of Chl-a is often driven by the amount of TP, with sunlight and temperature being two important factors that moderate the production of algae. The interrelationship among these three variables is demonstrated using data from typical Minnesota lakes (Figure 3). The TP*Chl-a relationship is strong and is typically represented by a log-log relationship. Curvilinear relationships between Chl-a*Secchi and TP*Secchi demonstrated the best fit of the data and identified where the most dramatic changes in transparency occur. For example, as Chl-a increases above 40 ppb the change in transparency is minimal. In contrast, changes in Chl-a over the range of 1-20 ppb result in large changes in transparency. For TP, the largest change in Secchi occurs as TP increases or decreases (above or below) the 30-50 ppb range. This means that small changes in TP at high (hypereutrophic) concentrations are unlikely to yield a measurable change in transparency, whereas the same magnitude of change in oligotrophic or mesotrophic lakes yields a very perceptible change in transparency.


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Rainfall and watershed runoff transport TP to lakes via overland runoff, streamflow, and precipitation on the surface of lakes. In summers of high rainfall and runoff, this often represents the majority of TP loaded to lakes. In summers of low rainfall and runoff, internal recycling of TP from lake sediments may be a significant source. However, given all these factors, it has been firmly established that TP, Chl-a, and transparency (Figure 3) are closely interrelated and are used to describe the “trophic status” of a lake (Carlson 1977). Thus, transparency measurement is a well-accepted means for estimating lake trophic status and tracking changes in trophic status (and hence TP and Chl-a). Thus, we typically presume that a significant decline in transparency is a reflection of increased TP loading and subsequent algal growth and increased transparency is the inverse. While this is often true, we are less certain as to the actual source of excess TP loading or the particular practices or factors that contribute to a decline in TP loading.

Figure 3. TP, Chl-a, and Secchi transparency relationships. Based on summer-mean data from ecoregion reference lakes (N=105).

To explore the question of “phosphorus as a driver of trends” we used a semi-quantitative approach, since there was no automated Kendall-tau trend analysis for the TP data (as was the case for Secchi). In our approach, summer-mean TP was plotted for lakes exhibiting significant transparency trends (“TSI-year trend” spreadsheet). Simple linear regression and examination of the data provided the basis for discerning the “significance” and direction of the TP trend. We affirmed TP as a potential driver if the direction of the TP trend was opposite that of transparency and there were four or more years of TP data available for the analysis (Table 8). Figure 4 provides an example of strong correspondence between transparency and TP trends. In this example, Lake George exhibited a significant decrease in transparency over time, with the steepest decrease from 2000-2014. Over this same period, a significant linear increase in TP was evident. While the changes in TP were small – they occurred over a range in TP where lakes are most responsive to additional TP loading – transition from mesotrophic to eutrophic conditions. Two additional examples, Fox (Becker) and Calhoun (Hennepin) Lakes, also exhibited good correspondence between transparency and TP trends and in each of these cases, the changes in TP (and transparency) can be traced back to watershed or in-lake management practices.


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There are also examples where there is poor correspondence. Pickerel Lake, for example, had no trend in TP but a significant increase in transparency (Figure 5). Cotton Lake (Becker County) had a significant decrease in transparency but little or no change in TP, based on an eight-year record. Eunice Lake (Becker) was quite data-rich and exhibited a significant decline in transparency over time. The TP record varied somewhat over time and exhibited no trend over the entire record. However, a decrease in TP was evident from 2004-2014. In these examples, there may be another driver of the observed transparency trend.

We summarized results from the county-by-county trend summaries in Table 6. This is not statistically representative of all Minnesota lakes or even those in the trend study; rather, this reflects a semi-quantitative and qualitative comparison of transparency and TP trends – for lakes with significant trends and having data in EQuIS. In general, we saw good correspondence between transparency and TP trends for lakes in most of the counties. In counties like Anoka, Dakota, and Hennepin, where there was sufficient long-term TP data to make these comparisons, there was good correspondence in trends for transparency and TP in over 80% of the comparisons. In counties like Hubbard, which did not have long-term TP data for many of the trend lakes, we observed a lower percent correspondence (Table 6). This may be a function of the limited data available for comparison, but could also reflect less intensive watershed management. However, case studies that are more detailed would be needed to discern this.

In the county-based summaries, we also provided potential explanations for the observed trends. Whenever possible, we sought local SWCD, local water planner, watershed district, and/or MPCA regional staff observations. In the Metro Area, many of the lakes that exhibited trends had long data records and there was ample documentation of past or ongoing lake and watershed restoration projects, which help substantiate the trend (e.g. Lake Calhoun; Figure 4). However, there was several non-Metro counties with numerous lakes that exhibited increasing transparency trends (e.g. Becker, Otter Tail, and Hubbard; Table 6) where there was not documentation of extensive lake or watershed management. In these and other instances (in particular among the northern Minnesota counties), there was a consistent theme, among those who reviewed the cases studies, that upgrading of septic (onsite or ISTS) systems was a primary contributor to decreased in-lake TP and increased transparency.

The 1989 State Standards for the Management of Shoreland Areas required local units of government to develop and implement programs to identify and upgrade sewage treatment systems that were inconsistent with state agency rules, specifically MPCA Chapter 7080. Many counties (e.g. Otter Tail and Becker Counties), conducted systematic reviews and on-site inspection programs that involved notifying property owners of non-conforming or illegal systems and required that the systems be brought into compliance. In addition, individual non-conforming septic systems were brought into compliance whenever a permit or variance was granted for a property or when a property was sold (noted in Crow Wing County case study). What further makes this a compelling argument (i.e., reason for decreased TP and increased transparency) for northern Minnesota lakes is that most northern Minnesota lakes have relatively low TP and even minor reductions in TP loading can yield measurable reductions in Chl-a and increased transparency (Figure 3).


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Figure 4. Examples of good correspondence between transparency and TP trends.

Lake George, Anoka County

Fox Lake, Becker County

Calhoun, Hennepin


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Figure 5. Examples of poor correspondence between transparency and TP trends.

Pickerel Lake (Anoka)

Cotton Lake (Becker)

Eunice (Becker)


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Table 6. Summary of transparency (trans.) and TP trends by county. Total # of lakes with significant (sig.) transparency trends, # of lakes increasing, # of lakes decreasing, # of lakes with TP data for comparison of trends, and # and percent of lakes with TP & transparency trend correspondence.

County # sig. trend

# sig. trans. increase

# sig. trans. decrease

# w/ TP data

# TP trend correspond

% TP correspond

Anoka 10 8 2 10 8 80% Becker 12 9 3 11 9 82% Beltrami 8 6 2 8 6 75% Carver 10 7 3 10 7 70% Cass 22 18 4 19 7 37% Cook 4 1 3 3 1 33% Crow Wing 33 22 11 25 16 64% Dakota 9 8 1 9 9 100% Douglas 12 10 2 9 9 100% Hennepin 19 14 5 19 18 95% Hubbard 15 7 5 12 6 50% Itasca 21 15 6 7 4 57% Otter Tail 26 23 3 22 17 77% Pope 7 4 3 7 5 71% Ramsey 14 13 1 14 12 86% St. Louis 21 16 5 3 3 100% Stearns 14 12 2 11 9 82% Washington 35 29 6 34 29 85% Wright 10 7 3 9 8 89%

Are aquatic invasive species drivers of trends? While the TP, Chl-a, and transparency connection may be the most important factor driving trends in Minnesota lakes, the biology/ecology of the lake should be considered as well. In this regard, aquatic invasive species including zebra mussels, Eurasian milfoil, spiny waterflea, and curly-leaf pondweed should be considered as potential drivers. Fish can be quite important as well and important species can include crappie, sunfish, and trout (commonly stocked) that consume zooplankton (which feed on algae). Likewise, carp and bullhead have various deleterious impacts on the ecology and transparency of waters through their feeding and related activities. In this study, we will consider the role of aquatic invasives (listed above) but not fish, since we were unaware of a database that could be merged with our trend database for that purpose.

Aquatic invasives can have a significant impact on the ecology of lakes and it is reasonable to assume they can influence water quality as well. The four aquatic invasives we chose to include in our analysis were zebra mussels, Eurasian watermilfoil, spiny waterflea, and curly-leaf pondweed.

Zebra mussels once established in a lake filter large amounts of water on a daily basis and in the process; remove large quantities of algae, which can have a direct effect on transparency as well as the transfer of energy through the food chain. Benson et al. (2016) provide an informative summary and literature review on ZM ecology and impacts in a USGS Fact Sheet. In reference to zebra mussel affects in the Great Lakes, they note, “Large populations of zebra mussels in the Great Lakes and Hudson River


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reduced the biomass of phytoplankton significantly following invasion. Diatom abundance declined 82–91% and transparency as measured by Secchi depth increased by 100% during the first years of the invasion in Lake Erie (Holland 1993). As the invasion spread eastward during 1988 to 1990, successive sampling stations recorded declines in total algae abundance from 90% at the most western station to 62% at the most eastern (Nichols and Hopkins 1993). In Saginaw Bay, sampling stations with high zebra mussel populations experienced a 60–70% drop in Chl-a and doubling of Secchi depth (Fahnenstiel et al. 1993). Phytoplankton biomass declined 85% following mussel invasion in the Hudson River (Caraco et al. 1997).”

There are several examples of the impact of ZM on lake transparency in Minnesota. One recent example is the Lake Carlos (Alexandria) Chain of Lakes (Figure 6). Zebra mussels were first detected in Carlos in 2009 and quickly spread throughout this inter-connected chain of lakes. By 2013, they were well established based on surveys conducted by DNR. Summer-mean transparency increased almost two-fold from levels prior to ZM in Carlos, Darling, and Le Homme Dieu. In contrast, nearby Lake Miltona remained within its typical transparency range (zebra mussels were detected in Miltona in 2013-2014, so it is likely it will exhibit similar increases in future years).

Eurasian milfoil can expand across a lake basin, often limited only by suitable light and substrate. Madsen (1998) notes that E. milfoil has greatest success in lakes with low-moderate TP and relatively good water clarity. Smith and Barko (1990) provide a comprehensive summary on the ecology of E. milfoil and reinforce the importance of water clarity as it influences maximum rooting depth for E. milfoil. As is the case with most macrophytes, E. milfoil provides refuge for zooplankton, which feed on algae and by this mechanism alone, could contribute to increased lake transparency. In addition, Eurasian watermilfoil extract contains hydrolysable polyphenols and inhibits the growth of cyanobacteria, green algae, duckweed, and epiphytes (e.g. Elakovich 1989). Thus, it appears reasonable that lakes with extensive E. milfoil could experience increased transparency over time.

Spiny waterflea directly affects the composition of the zooplankton community by feeding on the small forms, which feed on algae. This could conceivably affect algal abundance. Branstrator et al. (2006) describe habitat requirements for spiny waterflea and its expansion and contraction in northeast Minnesota lakes. Their work suggests spiny waterflea may prosper in lakes with high transparency and low Chl-a if there is adequate refuge from fish predation (e.g. Greenwood Lake in Cook County). They further make the case for low light refuge, which is the case in humic-colored Island Lake Reservoir near Duluth, which has had spiny waterflea since at least 1990. Branstrator (personal communication to Jesse Anderson, MPCA 2/2/16) indicated that the impact of spiny waterflea on algae (Chl-a) was still widely unexplored but did note one study (Lehman 1988) that found no effect on Chl-a in Lake Michigan. Whether this is the case for the much smaller or more eutrophic lakes, in our study, is unclear.

Curly-leaf pondweed serves as a refuge for zooplankton when actively growing in spring and early summer. Lake transparency is often high, most likely a result of dense plant growth and zooplankton grazing on algae (Heiskary and Valley 2012). However, following curly-leaf senescence in early July, algal blooms are common and transparency declines. This seasonal pattern is most pronounced in lakes with few native macrophytes and does not hold for all lakes with curly-leaf. For example, Silver Lake in Ramsey County (62-0001) had both curly-leaf and E. milfoil and exhibited relatively high transparency from 1992-2006; however, following chemical herbicide treatment in 2007 Chl-a increased and Secchi decreased dramatically (Heiskary and Valley 2012). These treatments resulted in a shift from macrophyte dominance (clear water) to algal dominance (turbid water) in this shallow urban lake.

http://jpe.oxfordjournals.org/content/early/2014/01/01/jpe.rtt063.full#ref-11


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Sometimes, multiple factors may contribute to Secchi transparency trends. A good example is Lake Mille Lacs, where reductions in TP over time, Eurasian milfoil, zebra mussel infestation, and spiny waterflea, combine to impact lake ecology and transparency (Figure 7). Previous reductions in TP contributed to early transparency increases over time; however, TP was rather stable from 2005-2013. Eurasian milfoil was first confirmed in 1998. ZM were first observed in 2006 and well established by 2010. A DNR fishery survey report suggests the ZM population stabilized from 2010-2013 but veligers (larval ZM) declined in 2014. Spiny waterflea was first observed in 2009 and zooplankton surveys indicate it has had a big impact on small and large cladocerans (http://www.dnr.state.mn.us/lakefind/showreport.html?downum=480002000), which in turn affects the food web of the lake and may affect algal abundance as well. Transparency trended upward from about 1990-1998 but then reached an asymptote. Since that time (including the period when ZM became established in the lake), transparency is variable with no overall trend. However, we did note that the 2013 transparency was among the highest summer-means recorded for Mille Lacs. A review of aquatic invasive species establishment and water quality data, suggests that for Mille Lacs, there are several factors at work, including chemical (TP) and biological and there is no simple (single) explanation for the observed patterns in transparency.

We sorted the trend data file by the individual aquatic invasive species as a means of reviewing the data for patterns relative to lake transparency trends, as summarized in Table 5. It is important to note that many of the lakes had more than one aquatic invasive species. A summary of findings follows:

Zebra mussels - Of the 1,578 trend study lakes, 123 had confirmed zebra mussel infestations, and of these:

· five had significant (S=4 or 5) decreasing trends and median year of listing was 2013 and confirmation was mid-2013

· 37 had significant increasing trends and the median year of listing was 2013 and confirmation was 2012

Eurasian milfoil - Of the 1,578 trend study lakes, 262 are had confirmed Eurasian milfoil infestations, and of these:

· 12 had significant decreasing trends and all were listed in 2007 and median year of detection was 1996

· 76 exhibited increasing trends and the median year of listing was 2007 and median year of detection was 2002

Spiny waterflea - Of the 1,578 trend study lakes, 24 were confirmed for spiny waterflea, of these

· One had a significant decreasing trend · Six had significant increasing trends

Curly-leaf pondweed - Of the 1,578 trend study lakes, 431 had confirmed curly-leaf infestations, of these

· 57 had significant decreasing trends · 140 had significant increasing trends

We also examined this on an ecoregion basis to see if there were any significant differences among regions. Based on this summary, of the 108 NLF lakes with significant increasing trends, 22% had aquatic invasives and of the 172 CHF lakes, 48% had invasives. Of those with significant declines, a higher percentage of the CHF lakes (30%) had invasives as compared to 13% for the NLF lakes. Overall, this suggests that invasives were prevalent in a higher percentage of CHF lakes exhibiting trends (both increasing and decreasing trends) as compared to NLF lakes exhibiting trends.

http://www.dnr.state.mn.us/lakefind/showreport.html?downum=480002000


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This analysis does not confirm that zebra mussels, E. milfoil, spiny waterflea, or curly-leaf pondweed was a direct “cause” of the observed transparency trend in these lakes. Rather, it indicates that among the lakes exhibiting significant transparency trends, these aquatic invasives were found more commonly in lakes with increasing transparency as compared to those with decreasing transparency. The actual “role” of these invasives is best evaluated on a lake-by-lake basis (Figure 6 and Figure 7) and some examples are mentioned in the county-based trend summaries, developed in support of this report.

Figure 6. Influence of zebra mussels on lake transparency in Lake Carlos Chain of Lakes.

Figure 7. Lake Mille Lacs transparency and TP trends: a) R-generated transparency trend [median, min, & max), b) Summer-mean ±standard error and c) summer-mean TP.


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Table 7. Aquatic invasive species summary for lakes with significant trends (4 or 5). Eurasian milfoil, zebra mussels, spiny waterflea, or curly-leaf. Note ~ 53 lakes with significant trends have two or more of these exotic species.

Invasive Trend direction # of lakes % of lakes year list

year confirm

Zebra mussel increase 37 88% 2012 2011 decrease 5 12% 2013 2014 E. milfoil increase 76 86% 2007 2002 decrease 12 14% 2007 1996 Spiny waterflea increase 6 86% 2009 2008 decrease 1 14% 2008 2007 Curly-leaf increase 97 77% decrease 29 23%

Table 8. Lakes with significant (S=4 or 5) increases or decreases in transparency by ecoregion. Percent with invasives noted.

Region NLF CHF WCP/NGP Sig increase 107 173 5 % w/invasives 22% 48% 20% Sig decline 45 43 5 % w/invasives 13% 30% 0%

Summary and recommendations Minnesota has a robust water quality database that provides an opportunity for assessing temporal water quality trends in Minnesota lakes. Of the data routinely collected on lakes, Secchi transparency data is the most abundant and perhaps the most useful for trend assessment. Of 4,509 lakes with Secchi disk data in the 2014 analysis, 1,578 had sufficient data for trend analysis. Of this number, 1,035 had no evidence of a trend, while 378 had “evidence” (category 4) or “strong evidence” (category 5) of a trend. Of the 378 with evidence or strong evidence, 93 (25%) exhibited decreasing trends, while 285 (75%) exhibited increasing trends.

The routine and systematic assessment of Secchi transparency trends, which has been conducted for several years, has shown consistent patterns, whereby the majority of lakes do not exhibit significant temporal trends (Figure 1). Of those that do, more exhibit increasing trends, than decreasing trends. This general pattern occurs consistently in the annual trend assessments (Figure 2) and indicates that more lakes in Minnesota are improving in water quality than are declining, assuming that increased transparency equates with improved water quality.

A majority of lakes that exhibited transparency trends were associated with decreases or increases in in-lake phosphorus concentration (Table 6), when there is adequate data to make this comparison. When we reviewed individual lakes with local resource managers, we found many instances where these increases or decreases in in-lake P can be related to improvements in watershed or in-lake management


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practices (or the inverse – anthropogenic impacts). In addition, there was a consistent observation that the local implementation of ISTS rules was an important factor (driver) of decreased TP and increased transparency in many of the lakes, in particular across northern Minnesota where extensive watershed restoration projects were not common. While these county-based summaries were not statistically representative of all lakes in Minnesota, the evidence seemed strong that reductions in phosphorus concentration (or inverse) explained many of the observed trends in these lakes. Nutrient reductions or increases are not the only factor that affects transparency trends in lakes. Aquatic invasives can be an important factor as well with ZM, Eurasian watermilfoil, and curly-leaf being among the most important. Based on this study, these invasives were most common in lakes with increasing transparency, as compared to those with decreasing transparency (Table 7). This does not confirm that they caused the observed trend (absent where detailed case studies have been conducted), however, they may contribute to the trend through a variety of mechanisms. A qualitative review of the lakes with significant trends (county-based summaries), suggest that where the introduction of the aquatic invasive was recent, the aquatic invasive may not yet be having an impact on transparency. However, it is quite likely the invasive will when it is firmly established, as was demonstrated for ZM in Lake Carlos (Figure 6) and Lake Mille Lacs (Figure 7). In the case of Lake Carlos, there was but a four-year lag between initial confirmation of ZM and ZM being firmly established, and resulting in a significant increase in transparency. Future trend assessments should include a focus on the role of aquatic invasives, as an important driver of trends, since the invasives (zebra mussel in particular) will be well-established in lakes where they have been recently confirmed and will likely have a measurable impact on water transparency and various aspects of the ecology of the lakes where they are found.

In closing, let us review some of the questions posed and offer brief answers based on this study:

1. Is there a difference in trends for lakes that meet the LES as compared to those that do not? The 1,578 trend lakes were sorted into the three categories: FS, NS, and IF (Table 2). Of the 767 fully supporting lakes, 238 (31%) had significant trends. Of those with significant trends, 82% were increasing and 18% decreasing. Of the 390 nonsupporting lakes, 70 (18%) had significant trends. Of these, 66% were increasing and 34% decreasing.

2. Are there significant differences in transparency trends among ecoregions? The CHF and NLF ecoregions were well represented in the trend assessment, accounting for 50% and 45% of the lakes with sufficient data for trend assessment (Figure 1). Lakes with decreasing trends (93 lakes) were almost equally divided between the NLF and CHF ecoregions at 45% and 43 % respectively (Table 3). However, the percentage of decreasing trends in the NLF was higher than the CHF, when expressed as a percent of the total for the ecoregion (Table 3). Somewhat surprising, a majority of the lakes that exhibited significant decreasing trends (45%) were lakes that meet the LES and assumed to be of good quality. Of the 285 lakes with significant increasing trends (Table 4), 61% were in the CHF ecoregion as compared to 38% for the NLF ecoregion. Based on this analysis, there are some regional differences in trends between the NLF and CHF lakes with the percent of fully supporting lakes with decreasing trends and the high percentage of lakes with increasing trends in the CHF being the most notable.

3. Is there a difference in trends among urban vs. non-urban lakes? There was no difference based on a comparison of Seven County Metro lakes and non-Metro lakes in the CHF ecoregion.

4. For lakes with sufficient TP data, is there good correspondence in TP and Secchi trends such that it is likely that changes in TP (Chl-a) are a primary reason for observed Secchi trends? Yes, while not a strictly statistically based analysis, the lake-by-lake pairing of Secchi and TP trends indicated good concurrence when there was adequate data to make the comparison. For most of these lakes, as TP decreased over time, Secchi increased and where TP increased, a decrease in Secchi followed.


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5. Aquatic invasive species may influence nutrient cycling and/or have direct impacts on algae and zooplankton. Is there a significant difference in trend direction for lakes with aquatic invasives, as compared to those without? We did not approach this question as originally stated. Rather, we merged the aquatic invasive species listing with the trend-lake database, sorted by invasive, and trend direction. When we focused only on lakes with significant trends (S=4 or 5), the four aquatic invasives were more commonly found in lakes with increasing trends as compared to decreasing trends (Table 7 and Table 8), which was not necessarily intuitive, in particular as it applied to curly-leaf pondweed.

A few recommendations are offered in closing.

1. It is important to continue the annual statistical assessment of transparency trends. In addition, this R-based approach (Kendall-tau) should be applied to TP as well, which would allow for a straightforward comparison of transparency and TP trends. This might also provide a direct basis to evaluate the impact of watershed and in-lake management on in-lake TP, which may be of value to the TMDL program.

2. It would be good to revisit the questions posed in this study in a future analysis. In particular, there should be continued work to understand the role of aquatic invasives as drivers of trends. It may be worthwhile to expand this work to consider fish, such as carp and bullhead. A more comprehensive look at the role of climate may be of value as well.

3. Lastly, it is important to continue and expand on the case study-approach and whenever possible, the case studies should incorporate observations of local units of governments and volunteer monitors.


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References Benson, A.J., D. Raikow, J. Larson, A. Fusaro, and A.K. Bogdanoff. 2016. Dreissena polymorpha. USGS Nonindigenous Aquatic Species Database, Gainesville, FL. http://nas.er.usgs.gov/queries/FactSheet.aspx?speciesID=5 Revision Date: 6/26/2014.

Branstrator, D.K., M.E. Brown, L.J. Shannon, M. Thabes, and K. Heimgartner. 2006. Range expansion of Bythotrephes longimanus in North America: evaluating habitat characteristics in the spread of an exotic zooplankter. Biological Invasions 8: 1367-1379.

Caraco, N.F., J.J. Cole, P.A. Raymond, D.L. Strayer, M.L. Pace, S.E.G. Findlay, and D.T. Fischer. 1997. Zebra mussel invasion in a large, turbid river: phytoplankton response to increased grazing. Ecology 78:588-602.

Elakovitch, S. 1989. Allelopathic aquatic plants for aquatic weed management. Biologia Plantarum 31(6) 479-486.

Fahnenstiel, G.L., T.B. Bridgeman, G.A. Lang, M.J. McCormik, and T.F. Nalepa. 1993. Phytoplankton productivity in Saginaw Bay, Lake Huron: Effects of zebra mussel (Dreissena polymorpha) colonization. Journal of Great Lakes Research 21:465-475.

Heiskary, S. and J. Lindbloom. 1993. Lake Water Quality Trends in Minnesota: Part of a series on Minnesota Lake Water Quality Assessment. MPCA. St. Paul MN 94 pp

Heiskary, S, J. Lindbloom, and CB Wilson. 1993. Detecting water quality trends with citizen volunteer data. Lake and Reserv. Manage. 9(1) 4-9.

Heiskary, S. and C.B. Wilson. 2008. Minnesota’s approach to lake nutrient criteria development. Lake and Reservoir Management 24(3): 282-296.

Heiskary, S. and R. Valley. 2012. Curly-leaf pondweed trends and interrelationships with water quality. DNR Investigational Report 558. St. Paul MN.

Holland, R.E. 1993. Changes in planktonic diatoms and water transparency in Hatchery Bay, Bass Island Area, Western Lake Erie since the establishment of the zebra mussel. Journal of Great Lakes Research 19:617-624.

Lehman, JT. 1988. Algal biomass unaltered by food-web changes in Lake Michigan. Nature 332, 537 – 538.

Madsen, J.D. 1998. Predicting Invasion Success of Eurasian Watermilfoil. J. Aquat. Plant Manage. 36: 28-32.

R Foundation for Statistical Computing. 2013. Platform: x86_64-w64-mingw32/x64 (64-bit).

Smith, C.S. and J.W. Barko. 1990. Ecology of Eurasian Watermilfoil. J. Aquat. Plant Manage. 28:55-64.

http://nas.er.usgs.gov/queries/FactSheet.aspx?speciesID=5


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Appendix 1. County-based trend summaries available as separate PDF documents at

https://www.pca.state.mn.us/water/citizen-lake-monitoring-program (Note - as more case studies are completed, they will be located at this webpage as well.) Anoka, Becker, Beltrami, Carver, Cass, Cook, Crow Wing, Dakota, Douglas, Hennepin, Hubbard, Itasca, Otter Tail, Pope, Ramsey, St. Louis, Stearns, Washington, Wright

2. Climate summaries by Division (region) in Minnesota. Derived from NOAA

East-central Minnesota

https://www.pca.state.mn.us/water/citizen-lake-monitoring-program


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North-central Minnesota


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Northeastern Minnesota


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Northwestern Minnesota

a review of secchi transparency trends in minnesota lakes

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