na onal lakes assessment...i na onal lakes assessment analysis of physical habitat condi on indices...
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Na onal Lakes Assessment
Analysis of Physical Habitat Condi on Indices (PHab) for Minnesota Lakes
This is part of a series based on Minnesota’s par cipa on in U.S. EPA’s 2007 and 2012 Na onal Lakes Assessments
April 2015
Minnesota Department of Natural
Resources
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Minnesota National Lakes Assessment Project: Analysis of Physical Habitat Condition Indices (PHab) for Minnesota Lakes
ACKNOWLEDGEMENTS
NLA study coordination: Steve Heiskary, Minnesota Pollution Control Agency (MPCA) & Michael Duval and David Wright, Minnesota Department of Natural Resources (MnDNR)
MPCA sampling team:
2007 - Team leads for the survey: Jesse Anderson, Steve Heiskary, Matt Lindon, and Kelly O’Hara (emphasis on water quality and sediment sample collection). Student interns David Tollefson and Monica Brooks assited with sampling.
2012 - Team leads for the survey, which included responsibility for field reconnaissance, assembling and purchasing needed equipment, office logistics, and sampling of the lakes was led by Pam Ander-son, Jesse Anderson, Kelly O’Hara, Lee Engel, Dereck Richter, and Steve Heiskary. Other staff as-sisting with sampling included: Amy Garcia, Courtney Ahlers-Nelson, Mike Kennedy and Andrew Swanson. Student workers Will Long and Ben Larson also assisted with the sampling.
MnDNR sampling team:
2007 – Paul Eiler, Mark Henry, Andy Levar, Dale Lockwood and Jason Neuman (emphasis on near-shore assessment, plant identification and benthic collection)
Lake IBI sampling: Bobbi Chapman, Paul Eiler (plus another staff member from NE), Chris Foster, Chris Gelner, Melissa Lasch, Ryan Lisson, Jason Neuman, Ryan Ransom, Nissa Rudh, and Kimber-ly Strand
2012 – Andy Levar, Michael Duval
In addition to MPCA and MnDNR staff, U.S. Forest Service and Native American Band natural resources staff were instrumental in support of sampling and reconnaissance for many of the lakes in this survey.
Report author: Michael Duval, MnDNR
Technical contributors: Donna Dustin, MnDNR and David Staples, MnDNR
Report Review: Steve Heiskary (Environmental Analysis and Outcomes), MPCA; and David Wright (Ecological and Water Resources), MnDNR
We would like to thank the Environmental Protection Agency and the Minnesota Pollution Control Agency for providing funding and coordination for this project.
wq-nlap1-15
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Table of Contents
Introduction ............................................................................................................................................. 1
National Lakes Assessment Project Overview ................................................................................ 1
Minnesota’s NLA Overview ............................................................................................................ 1
This Study ......................................................................................................................................... 3
Methods ................................................................................................................................................... 4
Lake Selection ................................................................................................................................... 4
Physical Habitat Assessment ............................................................................................................ 5
Physical Habitat Condition Indices .................................................................................................. 5
Comparative Analyses ...................................................................................................................... 5
Results and Discussion ........................................................................................................................... 6
The Minnesota Sample Draw ........................................................................................................... 6
Comparative Condition of Minnesota Lake Habitats - 2007 NLA ................................................ 7
2007 Habitat Condition Summary ................................................................................................... 7
Relationship of 2007 Habitat Condition to Other NLA Variables ............................................... 12
Comparative Condition of Minnesota Lake Habitats - 2012 NLA .............................................. 14
2012 Habitat Condition Summary ................................................................................................. 20
Relationship of 2012 Habitat Condition to Other NLA Variables ............................................... 20
Habitat Condition Indices Between Survey Years ........................................................................ 22
Comparative Performance of PHab Indices for Describing Minnesota Lake Habitat Condition 22
Summary and Conclusions ................................................................................................................... 26
Issues to Resolve ................................................................................................................................... 28
References .............................................................................................................................................. 29
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List of Tables
Table 1. Weighted regression coefficients for a suite of morphometric and water quality para-
meters analyzed against NLA habitat condition indices from the 2007 survey ............ 13
Table 2. P-values from Tukey pairwise comparison of means for lake physical habitat condition
indices by HUC-2 watershed in the 2007 NLA survey ................................................. 14
Table 3. Weighted regression coefficients for a suite of morphometric and water quality para-
meters analyzed against NLA habitat condition indices from the 2012 survey .......... 21
Table 4. P-values from Tukey pairwise comparison of means for lake physical habitat condition
indices by HUC-2 watershed in the 2012 NLA survey ................................................. 22
List of Figures
Figure 1. Location of Minnesota’s NLA lakes surveyed in 2007 and 2012 by aggregated Omernik
level 3 ecoregion ............................................................................................................ 2
Figure 2. EPA NLA analysis regions representing aggregations of Omernik Level III ecoregions 4
Figure 3. Comparative cumulative distribution functions by size class of lakes sampled in each
NLA survey year and the population of Minnesota lakes, excluding border waters ..... 5
Figure 4. Schematic of typical physical habitat sampling station distribution within a basin and
detail of habitat observation zones used in the NLA ..................................................... 6
Figure 5. Comparative cumulative distribution functions of NLA sampled lakes in Minnesota
and in three aggregated Omernik Level III ecoregions .................................................. 6
Figure 6. Comparative cumulative distribution functions of NLA sampled lakes in Minnesota
and other select states surveyed in 2007 and 2012 ......................................................................... 7
Figure 7. Riparian human disturbance index values of lakes sampled in the 2007 NLA ............. 8
Figure 8. Comparison of riparian human disturbance index values for Minnesota and
Michigan lakes sampled n the 2007 NLA ...................................................................... 8
Figure 9. Riparian vegetation cover complexity indices for lakes sampled in the 2007 NLA ..... 9
Figure 10. Comparison of riparian vegetation complexity index values for Minnesota and
Michigan lakes sampled in the 2007 NLA ..................................................................... 9
Figure 11. Littoral cover complexity index values of lakes sampled in the 2007 NLA ............. 10
Figure 12. Comparison of littoral cover complexity index values for Minnesota and Michigan
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lakes sampled in the 2007 NLA ................................................................................... 10
Figure 13. Littoral-riparian habitat complexity index values of lakes sampled in the 2007 NLA 11
Figure 14. Comparison of littoral-riparian habitat complexity index values for Minnesota and
Michigan lakes sampled int eh 2007 NLA ................................................................... 11
Figure 15. Cumulative distribution functions of physical habitat index values for Minnesota lakes
surveyed in the 2007 NLA ........................................................................................... 12
Figure 16. Riparian human disturbance index values of lakes sampled in the 2012 NLA ......... 15
Figure 17. Comparison of riparian human disturbance index values for Minnesota, Michigan,
and Wisconsin lakes sampled n the 2012 NLA ............................................................ 15
Figure 18. Riparian vegetation cover complexity indices for lakes sampled in the 2012 NLA .. 16
Figure 19. Comparison of riparian vegetation complexity index values for Minnesota, Michigan,
And Wisconsin lakes sampled in the 2012 NLA ......................................................... 16
Figure 20. Littoral cover complexity index values of lakes sampled in the 2012 NLA .............. 17
Figure 21. Comparison of littoral cover complexity index values for Minnesota, Michigan, and
Wisconsin lakes sampled in the 2012 NLA ................................................................. 17
Figure 22. Littoral-riparian habitat complexity index values of lakes sampled in the 2012 NLA 18
Figure 23. Comparison of littoral-riparian habitat complexity index values for Minnesota, Michigan,
and Wisconsin lakes sampled int eh 2012 NLA ........................................................... 18
Figure 24. Cumulative distribution functions of physical habitat index values for Minnesota lakes
surveyed in the 2012 NLA ........................................................................................... 19
Figure 25. Comparison of Minnesota habitat condition indices by survey year of the National
Lake Assessment ......................................................................................................... 23
Figure 26. Relationship of riparian disturbance index (RDIS_IX) and dock density as a surrogate
measure of riparian development intensity plotted by aggregated Omernik Level III
ecoregions (a) and lake depth class (b) ....................................................................... 24
Figure 27. Relationship of three composite habitat quality indices and dock density as a surrogate
measure of riparian development intensity ................................................................. 25
Figure 28. Nearshore fish IBI score as a function of dock density along shorelines for a suite of
28 headwater lakes in Minnesota ................................................................................ 26
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INTRODUCTION
National Lakes Assessment Project (NLA) Overview
Paulson (2006) describes U.S. Environmental Protection Agency (EPA) motivation for and survey design considerations leading to a national survey of lakes. Key among the motives was to develop a framework for characterizing the health of all the nation’s water resources. Water quality monitoring programs at the national, regional, state, and tribal level have been criticized for their relative inability to describe an average condition for the overall population of lakes under the respective jurisdiction because of a programmatic fo-cus on impaired systems or systems experiencing a higher degree of stress.
The EPA has a responsibility to assess the health of the Nation’s water resources. One of the methods for assessment is statistically-based surveys. The Survey of the Nation’s Lakes, conducted in 2007 and repeated in 2012, is one of a series of water surveys being conducted by states, tribes, the U.S. EPA, and other part-ners. In addition to lakes, partners will also study coastal waters, wadable streams, rivers, and wetlands in a revolving sequence. The purpose of these surveys is to generate statistically-valid and environmentally rele-vant reports on the condition of the Nation’s water resources of streams, lakes, wetlands and estuaries at na-tion-wide and regional scales.
The goal of the National Lake Assessment (NLA) is to address two key questions about the quality of the Nation’s lakes, ponds, and reservoirs:
• What percent of the Nation’s lakes are in good, fair, and poor condition for key indicators of trophic state, ecological health, and recreation?
• What is the relative importance of key stressors such as nutrients and pathogens?
The sampling design for this survey is a probability-based network which will provide statistically-valid esti-mates of the condition of all lakes with known confidence. It is designed using modern survey techniques. Sample sites are selected at random to represent the condition of all lakes across the nation and each region. The sample set comprises natural and man-made freshwater lakes, ponds, and reservoirs located in the con-terminous United States that are at least one meter in depth and greater than 10 acres (2007 survey) or 2.5 acres (2012 survey). A total of 909 discrete lakes were included in the 2007 national survey and 904 in the 2012 survey.
The typical sampling effort at each site includes a variety of samples (measurements) collected at a mid-lake index site (often over the deepest point in the lake) including: a two meter integrated sample for water chem-istry, chlorophyll-a, microcystin and algal identification; oxygen and temperature profiles; zooplankton tow; and sediment core sample for diatom reconstruction of total phosphorus (based on top and bottom slices from the core) and surface sediment sample for mercury. In addition, 10 random near-shore sites are qualita-tively assessed for various littoral and riparian habitat-related measures and a sample for a bacterial indicator was collected. Further details on the survey including methods, parameters measured, and statistical design may be found on the USEPA NLA web page at: http://www.epa.gov/owow/lakes/lakessurvey/.
Minnesota’s NLA Overview
Minnesota’s NLA effort was led by the Minnesota Pollution Control Agency (MPCA) and Minnesota De-partment of Natural Resources (MnDNR). Various other collaborators were engaged in this study as well including the U.S. Forest Service (USFS), Minnesota Department of Agriculture (MDA), and U.S. Geologi-cal Survey (USGS). MPCA and MnDNR combined on initial planning of the survey and conducted a vast majority of the sampling, which took place in July and August for most of the sampled lakes. USFS staff were instrumental in sampling of remote lakes in the Boundary Waters Canoe Area Wilderness (BWCAW).
Minnesota received 41 lakes as a part of the original draw of lakes for the 2007 national survey – the most of any of the lower 48 states. Minnesota chose to add nine lakes to the survey to yield the 50 lakes needed for statistically-based statewide estimates of condition (Figure 1a). In addition to the 50 lakes several reference
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lakes were later selected and sampled by USEPA as a part of the overall NLA effort. Data from the reference lakes provide an additional basis for assessing lake condition as a part of NLA.
In addition to basic sample metrics collected in the 2007 NLA survey, Minnesota chose to add several value-added measurements to the survey of lakes. Examples of these add-ons are: pesticide samples (in conjunction with the MDA); water mercury (in conjunction with USGS); sediment samples for analysis of metals, trace organics and other parameters; identification of macrophytes and maximum rooting depth of macrophytes at the random near-shore sites; fish-based lake IBI sampling; and samples for microcystin at the index and a ran-dom near-shore site. Each of these add-ons and several of the standard assessments are the subject of a series of reports that draw from the NLA work.
In the 2012 national survey, Minnesota received 42 lakes as a part of the original draw of lakes, approximately half of which were resample lakes from the 2007 NLA. Minnesota added additional sample lakes to the sur-vey to yield the 50 lakes needed for statistically-based statewide estimates of condition (Figure 1b). In addi-tion to the 50 lakes, an additional four reference lakes were later selected and sampled by USEPA as part of the overall 2012 NLA effort.
Figure 1. Loca on of Minnesota’s NLA lakes surveyed in 2007 (a) and 2012 (b) by aggregated Omernik Level III ecoregions.
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This Study
The complexity and extent of near-shore and upland riparian natural vegetation, woody debris and substrates influence the richness of aquatic and terrestrial animal communities (e.g., Sass et al. 2006; Smokorowski and Pratt 2007). These same areas tend to be the focus of residential and sometimes commercial development in Minnesota and elsewhere as humans are drawn by the aesthetic and/or recreational value of lakes.
Vegetation, wood, and bottom substrates (collectively considered physical habitat for fish and wildlife) are di-rectly impacted by human development activity along shorelines (Beck et al. 2013; Borman 2007; Christensen et al. 1996; Marburg et al. 2006; Ness 2006; Radomski 2006). Development activities may cause the removal of physical habitat, for example, by clearing terrestrial vegetation to prepare a building site and lawn or by re-moving rocks, aquatic plants and wood from shallow, nearshore areas to reduce interference with boating and swimming. Secondary activities associated with development further affect habitats, for example, aquatic plant and bottom substrate disturbance related to boating in shallow water.
Physical habitat disturbances consequently have measurable impacts on the biological community around lakes (e.g., Garrison et al. 2005; Lindsay et al. 2002; Radomski and Goeman 2001; Woodford and Meyer 2003). Impacts to biological communities may affect population size structure, species composition or repre-sentativeness, the number of individuals present, or various combinations of these responses.
Near-shore habitat impacts can be extensive along lake shorelines. For example, Radomski et al. (2010) esti-mated an average of 14% of the shoreline of 142 surveyed lakes in north-central Minnesota was impacted by docks and associated in-lake activities. In a study of 16 popular lakes in west-central Minnesota, Schmidt (2010) found an average of 37% of lake shorelines exhibited some form of human disturbance (e.g., vegetation clearing, shoreline armoring, sand blankets). In that same study, shoreline alterations on one study lake cov-ered nearly 75% of the entire lake perimeter. It is not uncommon to visually conclude similar extensive shore-line alteration has occurred along other lakes throughout urban and rural Minnesota.
The potential for nearshore habitat disturbances to impact biological community diversity is a concern for re-source managers and has resulted in efforts at local, state, regional, and national levels to try to quantify the extent of disturbed habitats around lakes (e.g., Schmidt 2010; Radomski et al. 2010; Francis 2009; Merrell et al. 2009; Kaufmann et al. 2014a).
The objectives for this report are to:
1. Characterize habitat condition of Minnesota lakes based on the NLA survey results from 2007 and 2012.
2. Compare NLA habitat condition indices for Minnesota lakes against three Omernik aggregated Level III ecoregions and selected states.
3. Analyze the relationship between habitat condition indices and a subset of water quality metrics meas-ured during the NLA survey.
4. Interpret the NLA habitat condition indices based on what is known from other measures and profes-sional knowledge of lake habitats in Minnesota.
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METHODS
Lake Selection
The National Lake Assessment (NLA) used a probability-based study design to sample over 900 lakes within the lower 48 contiguous states in each survey year. Sample lakes were randomly drawn from the National Hydrography Dataset with minimum lake area thresholds for inclusion in the survey set at 4 ha (2007 survey) and 1 ha (2012 survey). The survey design used weighting to direct the lake selections so as to provide sta-tistically valid conclusions for the nation and aggregated ecoregions (Figure 2). Attributes that were weighted in the sample draw included: lake size, ecoregion, state, and occurrence in prior national survey initiatives. Additional lake selections were available for states interested in generating statistically valid wa-ter quality and habitat condition estimates.
Additional details about the national study design can be found on the EPA website.
The accumulated distribution of NLA lakes sampled in Minnesota by size class differed from the overall Minnesota population of lakes greater than 1 hectare (Figure 3) and demonstrates the effect of the selective survey design weighting toward larger lakes, which are numerically less abundant than smaller lakes both in Minnesota and across the nation as a whole. An adjustment to the 2012 survey design to include smaller lakes is also apparent in the cumulative distribution function.
Figure 2. EPA NLA analysis regions represen ng aggrega ons of Omernik Level III ecoregions.
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Figure 3. Compara ve cumula ve distribu on func ons by size class of lakes sampled in each
NLA survey year and the popula on of Minnesota lakes, excluding border waters.
Physical Habitat Assessment
Physical habitat metrics were assessed at 10 randomly selected stations evenly distributed around the perimeter of each study lake (Figure 4). Measurements were constrained to an observation window representing three zones: riparian zone, shoreline zone, and littoral zone (Figure 4, inset).
Within an observation window, a suite of habitat measurements related to littoral bottom substrate, aquatic macrophytes, fish cover, and riparian canopy, understory, and ground cover were recorded to categorically characterize habitat features. The presence and type of human disturbance were qualitatively described within and adjacent to each sampling station.
Field operations manuals describing the field data collection methods more completely for each survey year are available from the EPA NLA website.
Physical Habitat Condition Indices
Four indices were developed by EPA to describe physical habitat condition and human disturbance for each study lake; riparian human disturbance (RDis), riparian vegetation cover complexity (RVeg), littoral cover complexity (LitCvr), and littoral-riparian habitat complexity (LitRipCvr). The indices were calculated using individual station-level measurements as described in the EPA 2007 NLA Technical Appendix and by Kauf-mann et al. (2014a). EPA has since made some minor recalculations to the reference condition thresholds (Good, Fair, Poor) to account for low sample size in some ecoregions during the 2007 survey (Kaufmann, pers. comm.). We use these revised reference condition thresholds for this report.
Comparative Analyses
We elected to use data from three aggregated ecoregions (Upper Midwest, Temperate Plains, and Northern Ap-palachia) and data from selected state surveys with adequate sample size (n = 50) in each survey year to make comparisons of Minnesota habitat condition to NLA data from other parts of the country having similar natural lake origins and land use types.
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RESULTS AND DISCUSSION
The Minnesota Sample Draw
The sample draw of Minnesota lakes in the NLA was comparable by lake size class to other ecoregions of the national assessment for both survey years (Figure 5). When compared to other Midwest states having abun-dant natural lakes and sufficient sample size (n = 50 lakes) in the national database, Minnesota sample lakes generally shared similar distribution across size classes (Figure 6), although Minnesota had larger sample lakes than the Michigan sample in the 2007 survey.
These differences are mainly illustrative of the various comparative sample pools - any large departures would warrant cautious interpretation of results. We do not see sufficient differences between the comparative sam-
Figure 4. Schema c of typical physical habitat sampling sta on distribu on within a basin and detail of habitat observa on
zones used in the NLA. Figure excerpted from EPA‘s 2007 Field Opera ons Manual.
Figure 5. Compara ve cumula ve distribu on func ons of NLA sampled lakes in Minnesota and in three aggre-
gated Omernik Level III ecoregions (NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Mid-
west) surveyed in 2007 and 2012.
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ple pools. Furthermore, when conducting regional and state-to-state comparisons, the overall habitat condi-tion and human disturbance indices are used because they account for the weighting influence in calculating the condition or disturbance score.
Comparative Condition of Minnesota Lake Habitats - 2007 NLA
Human Disturbance: Approximately two-thirds of Minnesota lakes were subjected to low human develop-ment stress (= Good condition) while approximately a quarter of lakes experienced intermediate stress (= Fair condition), and overall human disturbance was similar to the Upper Midwest ecoregion of the NLA study (Figure 7). Minnesota lakes were most similar to the Upper Midwest ecoregion with very low propor-tion of lakes in the high disturbance category (= Poor) and intermediate proportion in the moderate disturb-ance category (= Fair). Minnesota had significantly more lakes in the low disturbance category and signifi-cantly fewer lakes in the high disturbance category than the Northern Appalachian and the Temperate Plains ecoregions.
Human disturbance index for Minnesota lakes was significantly different from Michigan lakes in all catego-ries (Figure 8). Michigan had a higher proportion of lakes in the high and intermediate disturbance catego-ries and a lower proportion of lakes in the low disturbance category compared to Minnesota.
Riparian Vegetation Cover Complexity: A majority of Minnesota lakes (53.4%) had Good riparian vege-tative habitat condition (Figure 9). Minnesota riparian vegetation cover complexity was not significantly different from other ecoregions.
Michigan had significantly more lakes with Poor riparian vegetative cover complexity than Minnesota (Figure 10).
Littoral Cover Complexity: Over half of Minnesota lakes had Good nearshore habitat complexity while another quarter were categorized as Fair (Figure 11). These results do not differ significantly from other ecoregions, nor does Minnesota’s littoral habitat complexity differ from Michigan’s (Figure 12).
Littoral-Riparian Habitat Complexity: Overall lakeshore habitat condition (the combined riparian and nearshore habitat index) for Minnesota lakes was generally Good (40%) with the remainder of lakes nearly evenly split among Fair and Poor condition categories and was not different from the other comparative ecoregions (Figure 13). There were no significant differences between Minnesota and Michigan for this combined habitat measure (Figure 14).
2007 Habitat Condition Summary
Nearly 40% of Minnesota lakes exhibited very low lakeshore human disturbance (index value = 0) and the
Figure 6. Compara ve cumula ve distribu on func ons of NLA sampled lakes in Minnesota and other select
states surveyed in 2007 and 2012.
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Figure 7. Riparian human disturbance index values of lakes sampled in the 2007 NLA. Bars are 85% confidence interval.
NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2). Star indicates significant
difference from Minnesota value.
Figure 8. Comparison of riparian human disturbance index values for Minnesota and Michigan lakes
sampled in the 2007 NLA. Bars are 85% confidence interval. Star indicates significant difference from
Minnesota value.
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Figure 9. Riparian vegeta on cover complexity index values of lakes sampled in the 2007 NLA. Bars are 85% confidence
interval. NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2).
Figure 10. Comparison of riparian vegeta on cover complexity index values for Minnesota and Michigan
lakes sampled in the 2007 NLA. Bars are 85% confidence interval. Star indicates significant difference
from Minnesota value.
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Figure 12. Comparison of li oral cover complexity index values for Minnesota and Michigan lakes sam-
pled in the 2007 NLA. Bars are 85% confidence interval.
Figure 11. Li oral cover complexity index values of lakes sampled in the 2007 NLA. Bars are 85% confidence interval.
NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2).
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Figure 14. Comparison of li oral-riparian habitat complexity index values for Minnesota and Michigan
lakes sampled in the 2007 NLA. Bars are 85% confidence interval.
Figure 13. Li oral-riparian habitat complexity index values of lakes sampled in the 2007 NLA. Bars are 85% confidence
interval. NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2).
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Figure 15. Cumula ve distribu on func ons of physical habitat index values for Minnesota lakes surveyed in the 2007 NLA. a -
Riparian human disturbance, b - Riparian vegeta on cover complexity, c - Li oral cover complexity, d - Li oral/Riparian habitat
complexity.
a. b.
c. d.
overall distribution was highly skewed toward low disturbance (Figure 15a). Other habitat condition measures were more evenly distributed across the stressor gradient (Figure 15b-d).
Relationship of 2007 Habitat Condition to Other NLA Variables
Minnesota human disturbance index values were significantly related to lake morphometric features of lake size, maximum depth, and shoreline complexity but not to water quality variables such as chlorophyll-a, to-tal phosphorus, and water clarity (Table 1). These results are consistent with observed development patterns in Minnesota whereby large, deep lakes historically have experienced more residential and resort develop-ment along their shorelines, and lakes with more complex shorelines provide more linear frontage for devel-opment as opposed to round lakes. Riparian vegetation cover complexity (RVeg_OE) was negatively relat-ed to lake depth (Table 1) indicating that deeper lakes had less riparian vegetation complexity.
Pairwise comparison of the various NLA habitat indices by HUC-2 watersheds of the state found no signifi-
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Table 1. Weighted regression coefficients for a suite of morphometric and water quality parameters analyzed
against NLA habitat condi on indices from the 2007 survey. Habitat index variables are: RDis_IX -Riparian human
disturbance; RVeg_OE - Riparian vegeta ve cover complexity; LitCvr_OE - Li oral cover complexity; LitRipCvr_OE
- Li oral-riparian habitat complexity.
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Table 2. P-values from Tukey pairwise comparison of means for lake physical habitat condi on
indices by HUC-2 watershed in the 2007 NLA survey.
cant differences between the Red River drainage lakes and the Great Lakes drainage lakes (Table 2). The Red River drainage covers a broad range of lake types in northern Minnesota, including Canadian Shield lakes and glacial outwash lakes. This variability in lake types within the drainage may overwhelm any dif-ferences with the Great Lakes drainage, particularly with the small sample size (n = 4) for the latter drainage.
The Upper Mississippi drainage lakes and Great Lakes drainage lakes were significantly different for the ri-parian vegetation cover complexity index but not for other NLA habitat condition indices (Table 2). Similar to the Red River drainage, the Upper Mississippi drainage comprises a diversity of landscape types ranging from coniferous forests to agricultural prairie and also includes the heart of lake cabin country. Significant differences in riparian cover complexity between these two drainages may simply reflect the singular forest-ed character of the Great Lakes drainage in a region of the state with less intense shoreline development.
The NLA habitat indices were all significantly different between the Red River and the Upper Mississippi drainages (Table 2).
Comparative Condition of Minnesota Lake Habitats - 2012 NLA
Human Disturbance: Sixty percent of Minnesota lakes exhibited low shoreline human disturbance (= Good) in the 2012 NLA and another third experienced intermediate (= Fair) development stress (Figure 16). Human disturbance condition of Minnesota lakes did not differ significantly from the Northern Appalachian and the Upper Midwest comparative ecoregions (Figure 16). Minnesota had significantly more lakes with low human disturbance condition and significantly few lakes with high human disturbance condition than the Temperate Plains ecoregion (Figure 16).
Minnesota lakes were in significantly better shape with regard to the degree of human disturbance than Michigan and Wisconsin lakes (Figure 17). Michigan had significantly fewer lakes in Good condition and significantly more lakes in Poor condition relative to Minnesota, while Wisconsin had significantly fewer Good condition lakes and significantly more Fair condition lakes than Minnesota.
Riparian Vegetation Cover Complexity: About half of Minnesota’s lakes were rated Fair for riparian veg-etation cover complexity and only 29% were in the Good condition category (Figure 18). Minnesota lakes were not significantly different from other lakes in the Upper Midwest and the Temperate Plains ecoregions but had significantly more lakes in Fair riparian vegetative habitat condition than the Northern Appalachian ecoregion (Figure 18).
Compared to neighboring states, Minnesota had significantly more Fair condition lakes and significantly fewer Poor condition lakes than Michigan, and significantly fewer Good condition lakes than Wisconsin (Figure 19).
Littoral Cover Complexity: Over half of Minnesota lakes had Good nearshore habitat condition and 30% were in Fair condition, which was not significantly different from other comparative ecoregions in the NLA
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Figure 17. Comparison of riparian human disturbance index values for Minnesota, Michigan, and Wisconsin lakes sampled
in the 2012 NLA. Bars are 85% confidence interval. Star indicates significant difference from Minnesota value.
Figure 16. Riparian human disturbance index values of lakes sampled in the 2012 NLA. Bars are 85% confidence interval.
NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2). Star indicates significant
difference from Minnesota value.
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Figure 18. Riparian vegeta on cover complexity index values of lakes sampled in the 2012 NLA. Bars are 85% confidence
interval. NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2). Star indicates sig-
nificant difference from Minnesota value.
Figure 19. Comparison of riparian vegeta on cover complexity index values for Minnesota, Michigan, and Wisconsin lakes
sampled in the 2012 NLA. Bars are 85% confidence interval. Star indicates significant difference from Minnesota value.
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Figure 20. Li oral cover complexity index values of lakes sampled in the 2012 NLA. Bars are 85% confidence interval.
NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2).
Figure 21. Comparison of li oral cover complexity index values for Minnesota, Michigan, and Wisconsin lakes sampled in
the 2012 NLA. Bars are 85% confidence interval.
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Figure 22. Li oral-riparian habitat complexity index values of lakes sampled in the 2012 NLA. Bars are 85% confidence
interval. NAP - Northern Appalachians, TPL - Temperate Plains, UMW - Upper Midwest (see Figure 2). Star indicates sig-
nificant difference from Minnesota value.
Figure 23. Comparison of li oral-riparian habitat complexity index values for Minnesota, Michigan, and Wisconsin lakes
sampled in the 2012 NLA. Bars are 85% confidence interval. Star indicates significant difference from Minnesota value.
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Figure 24. Cumula ve distribu on func ons of physical habitat index values for Minnesota lakes surveyed in the 2012 NLA. a -
Riparian human disturbance, b - Riparian vegeta on cover complexity, c - Li oral cover complexity, d - Li oral/Riparian habitat
complexity.
a. b.
c. d.
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(Figure 20). Minnesota lake condition was most similar to the Upper Midwest ecoregion.
Minnesota nearshore lake habitat condition was not significantly different from Michigan, however, Wiscon-sin had significantly fewer Poor condition lakes than Minnesota (Figure 21).
Littoral-Riparian Habitat Complexity: The combined riparian and nearshore habitat condition index for Minnesota indicated nearly half of the lakes were in Poor condition while only 28% were in Good condition (Figure 22). This pattern was marginally similar to the Upper Midwest ecoregion but differed significantly from the Northern Appalachian ecoregion, which had significantly fewer lakes in Poor condition relative to Minnesota (Figure 22).
In comparison to neighboring states, Minnesota was not significantly different from Michigan but had signif-icantly fewer lakes in Good condition and significantly more lakes in Poor condition than Wisconsin (Figure 23).
2012 Habitat Condition Summary
Nearly 45% of Minnesota lakes exhibited very low lakeshore human disturbance (index value = 0) and the overall distribution was highly skewed toward low disturbance (Figure 24a). Other habitat condition measures were more evenly distributed across the stressor gradient (Figure 24b-d).
Relationship of 2012 Habitat Condition to Other NLA Variables
Minnesota lakeshore human disturbance index values were significantly related to lake depth but not to wa-ter quality variables such as chlorophyll-a, total phosphorus, and water clarity (Table 3). Habitat condition indices for riparian vegetative cover complexity (RVeg_OE) and nearshore habitat complexity (LitCvr_OE) were both significantly though weakly related to lake size (Table 3). Riparian vegetative cover complexity was higher for larger lakes. Littoral cover complexity was lower for larger lakes and probably is influenced by physical factors such as wind fetch in addition to human disturbance factors.
There were no significant relationships between pairwise comparisons of HUC-2 drainages and NLA habitat condition indices except that riparian human disturbance was significantly different between the Red River drainage and the Upper Mississippi drainage (Table 4).
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Table 3. Weighted regression coefficients for a suite of morphometric and water quality parameters analyzed
against NLA habitat condi on indices from the 2012 survey. Habitat index variables are: RDis_IX -Riparian human
disturbance; RVeg_OE - Riparian vegeta ve cover complexity; LitCvr_OE - Li oral cover complexity; LitRipCvr_OE
- Li oral-riparian habitat complexity.
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Table 4. P-values from Tukey pairwise comparison of means for lake physical habitat condi on
indices by HUC-2 watershed in the 2012 NLA survey.
Habitat Condition Indices Between Survey Years
Comparison of the NLA habitat condition indices between the two survey years, 2007 and 2012, revealed no significant differences for any index although the pattern of habitat condition was visually quite different for some of the habitat complexity indices (Figure 25).
The human disturbance index was nearly identical between survey years, which is reasonable to expect with only a 5-year interval between sampling events. An apparent shift toward poorer habitat condition from 2007 to 2012 for the riparian vegetation cover complexity index (RVeg) and the combined riparian and nearshore habitat index (LitRipCvr), however, is not easily explained. The spatial distribution of sample lakes in the two surveys are very similar (Figure 1) and the 2012 survey included a number of repeat samples of 2007 NLA lakes, thus one would not expect to see different habitat conditions between the survey years. The results do demonstrate the strong influence of the riparian vegetation cover complexity index on the overall lakeshore habitat condition score.
Comparative Performance of PHab Indices for Describing Minnesota Lake Habitat Condition
Riparian human disturbance index (RDis_IX) was associated with dock density along shorelines, a surrogate measure of shoreline development (Figure 26). The degree of shoreline human disturbance measured in the NLA was highly variable at low levels of dock density indicating that other factors were contributing to dis-turbance scores. But as dock density approached 10 docks/km, variability in the human disturbance index de-clined considerably suggesting that dock density was a strong predictor of riparian human disturbance.
There were differences in this relationship based on ecoregion (Figure 26a) and lake depth class (shallow vs deep; Figure 26b). In the Upper Midwest ecoregion where shoreline residential development is common, ri-parian human disturbance and dock density appear to be strongly related whereas in the Temperate Plains, hu-man disturbance appears to be influenced more by factors other than shoreline residential development (e.g., agriculture or roads). A similar pattern holds for lake depth class where shoreline human disturbance along deep lakes is strongly related to dock density while shallow lakes (≤ 4.5 meters maximum depth) are controlled more by other factors. Historically in Minnesota, residential development of lakes has focused on the deeper lakes with limited development pressure having been directed toward shallow lakes.
The three habitat complexity indices displayed reduced variability and generally less complexity with increas-ing dock density (Figure 27). For all of the habitat complexity indices, variability became notably less around 10 docks/km suggesting that shoreline residential development becomes a strong driver of habitat condition at intervals greater than the most conservative minimum shoreline lot width governed by shoreland development standards in Minnesota Rules (Minn. Rule 6120.3300 Subp 2a). This conclusion is reinforced by independent data collected by the Minnesota Department of Natural Resources that relates biotic integrity of nearshore fish communities with dock density (Figure 28) and shows a similar breakpoint at 10 docks/km where after varia-bility in the data is greatly reduced.
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Figure 25. Comparison of Minnesota habitat condion indices by survey year of the Na
onal Lake Assessm
ent.
24
Figure 26. Rela
onship of riparian disturbance index (RDIS_IX) and dock den
sity as a surrogate m
easure of riparian developmen
t intensity plo
ed by aggregated
Omernik Level III ecoregions (a) and lake dep
th class (b). D
ata points are combined
2007 and 2012 survey years.
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Figure 27. Rela onship of three composite habitat quality indices (a - riparian vegeta on cover complexity; b - li oral cover com-
plexity; c - li oral-riparian habitat complexity) and dock density as a surrogate measure of riparian development intensity. Dots
represent individual lake es mates and are colorized by lake depth class and aggregated Omernik Level III ecoregions. Data points
are combined 2007 and 2012 survey years.
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Fish
IBI S
core
Figure 28. Nearshore fish IBI score as a func on of dock density along shorelines for a suite of 28 head-
water lakes in Minnesota (MN DNR unpublished data).
SUMMARY AND CONCLUSIONS
The National Lake Assessment survey design provides a means of describing whole-lake human impacts to shoreline habitat using a consistent and statistically valid methodology. From these methods, a suite of habitat condition measures are generated that describe the degree of human disturbance in the nearshore area and three measures of habitat complexity. These indices serve the need to characterize habitat stress on a national scale but they could also have value at the regional or state scale.
Measuring habitat condition as a routine part of recurring monitoring programs is desirable to describe and track human development impacts on lakes. Minnesota Pollution Control Agency (PCA) and Minnesota De-partment of Natural Resources (MnDNR) both have established lake monitoring programs that assess various aspects of lakes consistent with their respective natural resource management responsibilities.
The MnDNR assesses shoreline development and nearshore aquatic habitat as part of recurring Section of Fisheries surveys. These surveys do not provide a concise habitat condition status for the whole basin alt-hough these data conceivably could be analyzed in a future project to develop one. Detailed habitat surveys are not conducted on a routine basis, however, and would not provide more than a decadal or longer snapshot of habitat conditions, which may not provide the temporal resolution for taking meaningful action to intercept undesirable trends.
The PCA conducts lake water quality monitoring on a routine basis but does not take directed measurements of aquatic habitat conditions. Adding habitat measures to existing water quality monitoring activities has the po-tential consequence of increasing either individual lake sampling time or the equipment and field staff neces-sary to complete individual lake surveys.
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Do the NLA lake habitat condition scores tell us anything we don’t already know or suspect or that can’t be measured in a more cost-effective way? We were not able to detect any statistical relationship between lake habitat condition and nutrients in the NLA surveys. This is not surprising because land use in the watershed is a significant determinant of nutrient condition as demonstrated by Cross and Jacobson (2013). They used a relatively simple measure of watershed land use disturbance to predict in-lake summer phosphorus levels for a suite of lakes in Minnesota. The contribution of shoreline disturbance to overall lake nutrient levels is difficult to isolate from the overwhelming delivery of nutrients coming from the much larger contributing watershed.
One might expect some association between lake habitat condition and fish, amphibian, and songbird abun-dance and diversity (e.g., Kaufmann et al. 2014b). The importance of biotic response variables was not un-recognized by EPA project designers, however, the NLA methods did not include such measures in the standard design. During the 2007 survey, MnDNR conducted enhanced sampling to collect fish data that contributed to the refinement of a lake-based fish index of biotic integrity (Proulx and Drake 2009). Fish IBI responded predictably to various NLA lake habitat condition indices (Figure 29) and showed the best fit with nearshore habitat condition (littoral cover complexity index). High biotic integrity was associated with low human disturbance and high cover complexity in the riparian and nearshore areas. In addition, we found concurrence of NLA lake habitat condition scores in both survey years with a surrogate measure of shoreline disturbance (i.e., dock density) that was correlated with fish biotic integrity. So it appears that the NLA lake habitat condition indices show agreement with other measures being used by the MnDNR to describe biotic stress and habitat condition.
Currently Minnesota does not have an established protocol for describing statewide average or “typical” lakeshore habitat condition, although there are existing programs and products that measure lakeshore habitat at the basin or intra-basin scale (e.g., Fisheries Lake Survey Program for the former and Score Your Shore or Sensitive Lakeshore Identification for the latter). There have been recent efforts to utilize aerial image anal-ysis to characterize the degree of riparian habitat disturbances along waterways in the agricultural region of the state (Environmental Working Group 2014) and nearshore habitat disturbances along lakeshore (Beck et al. 2013; Donna Dustin, unpublished research in progress). These examples, however, are more like an in-tensive census of habitat condition (quantifying all waterways or lakes within the respective study areas) ra-ther than a probabilistic sample extrapolated to the statewide population of lakes.
It’s not clear whether a statewide lake habitat condition “score” would be valuable given the wide range of habitat disturbance across the state from wilderness lakes in the northern portion of the state to highly devel-oped lakes in the central and urbanized regions of the state to lakes embedded in the intensively cultivated southern and western portions of the state. Can meaningful changes in habitat condition be reasonably de-tected within such an extreme gradient of development stress? Confidence intervals for each of the habitat measures in the NLA study were very broad (see Figure 25), suggesting that large changes in habitat condi-tion would be necessary to detect a statistically significant change that would warrant some kind of statewide policy response. More intensive sampling effort, perhaps within a finer spatial scale (e.g., ecoregion or even major watershed), would be necessary to improve change-detection power of the habitat condition indices.
Presumably there are disturbance thresholds that, when crossed, have measurable consequences for aquatic organisms and, to a degree, water quality. We were not able to examine individual NLA lake or site metrics and their relationship to known habitat condition and stressors collected independently by established state sampling programs. An analysis of this type would be valuable if PCA were to consider adopting/adapting the NLA methods into state water quality sampling programs. Work combining remote census techniques and direct field measures is currently in progress by the MnDNR and seems to imply such disturbance thresholds exist, although the exact change point as yet has not been quantitatively determined.
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Figure 29. Rela
onship of NLA
habitat condion indices (a - riparian human
disturbance; b
- riparian vegeta
on cover complexi-
ty; c - li
oral cover complexity; d
- li
oral-riparian habitat complexity) and fish IB
I collected
from corresponding lakes in the 2007
survey.
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ISSUES TO RESOLVE
We encountered the following issues in the conduct of generating this study report and recommend future changes to facilitate local/state analyses of NLA data products:
1. Interagency agreements. There is opportunity to improve the interagency agreement instrument used to formalize the collaboration between PCA and MnDNR. The interagency agreement used in 2012 was cum-bersome to implement. While seemingly efficient for business operations, the 2012 format was not flexible enough to accommodate realities of changing work responsibilities, new analysis directions we wanted to pursue, and the extensive delay in getting 2012 data from EPA. By partitioning the work among MnDNR staff experts as opposed to contracting with MnDNR as an entity for a defined product (as was done with the interagency agreement instrument used in 2007 NLA), we were not able to be nimble in fulfilling the obliga-tions of our interagency agreement. Furthermore, the drafting and processing of the interagency agreement instrument was fraught with unnecessary delays mostly related to routing inefficiencies between the agen-cies. As a result, MnDNR staff donated considerable time to this effort that was not compensated through the Interagency Request for State Employee Services. The agencies should initiate interagency agreement discussions well in advance of future NLA collaborations.
2. Timeliness of data delivery. Planning for future NLA surveys should include a PHab data delivery com-mitment, and EPA cost-share grants should include a grant term sufficient for states to complete state-level PHab analyses. EPA project funding did not adequately cover the period of time currently necessary for EPA to process PHab data and deliver to the states for their use and further analyses. MnDNR was unable to proceed with data analyses for nearly two-thirds of the contract period while EPA completed their initial PHab data analyses and made those data available. There were changes to how some of the habitat index classes of Good/Fair/Poor were calculated following the 2012 survey. These changes are not anticipated to be necessary in future surveys (Phil Kaufmann, personal communication) and may improve turn-around time of analysis products going forward. Despite this potential improvement, the analysis of habitat data is somewhat complex and not yet automated in a way that states could move forward independent of EPA. It seems that individual state data analyses will necessarily lag while EPA conducts their data analysis and quality controls. This is not a criticism of EPA but, rather, an expression of the operating constraints that need to be considered in future interagency collaborations.
3. Consistency of data products between survey years. EPA should strive to standardize their data fields between survey years and PCA should emphasize consistency with prior surveys when making data re-quests. We noted considerable inconsistency in naming of variable fields, formatting of variable names, and organization of data within and between data files. Example fields we struggled with included the index variable names and the aggregated ecoregion variable name. These differences in variable names could have been an artifact of database queries used by EPA staff to generate output files satisfying Minnesota data re-quests in 2012 (2007 data were directly downloaded from EPA’s website).
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