1. Development of a Macrophyte-based IBI for Minnesota Lakes
Marcus Beck
University of Minnesota
Department of Fisheries, Wildlife, and Conservation Biology
Hodson Hall, 1980 Folwell Ave., St. Paul, MN 55108

2. Project Background Identification of a set of indicators
responsive to changes in lake quality
“To develop an ecological assessment method for Minnesota lakes that meets the requirements of the CWA through the vehicle of the SLICE program.”
Literature Review
Data Search
Index Development

3. Today’s Talk Developing the index Methods and analyses Initial results
Project culmination

4. Today’s Talk Literature review and data search suggested one thing…
Development of a
Macrophyte-based
lake IBI

5. Why use aquatic plants? Relation to fish community Immobile
Ease of identification
Available data
Lessons from Wisconsin

6. Sum of Metric Scores = IBI
Collect Data
Analyze Biological Attributes
Species
Richness
Taxa Richness
Number of darter species
Trophic Function
Number of
insectivore species
omnivore species
Abundance/Condition
Number per meter
DELT (deformities, eroded fins, lesions, tumors)
Select, Verify and Score Metrics
Sum of Metric Scores = IBI
Interpretation of IBI Score
The assessment tool that we use is called the IBI
Use of the attributes
Response to stressor gradient
Scoring of metrics
summation
interpretation 6 10
100
Excellent 90 5 80 7
Good 4
Metric
Scores 70 60 5
IBI Score
Fair
Darter Species
Number of 3 50 40 2 2
Poor 30 20 1 10
Very Poor 20 40 60 80
100 20 40 60 80
100
% Watershed Disturbance
%Watershed Disturbance

7. Development Methods DNR Point Intercept surveys (Madsen 1999)
82 lakes, 105 surveys
Lake classes same as fish IBI

8. Distribution of lake classes within dataset
Distribution of lake classes within dataset. Lake classes are defined by size, depth, chemical fertility, and length
of growing season (Schupp 1992).
Location of lakes by ecoregion used for IBI development.

9. AMCI (Weber et al. 1995; Nichols et al. 2000)
“…a multipurpose, multimetric tool to assess the biological quality of aquatic plant communities in lentic systems.” Nichols et al. 2000
Maximum depth of plant growth
Percentage of littoral zone vegetated
Simpson’s Diversity Index
Relative frequency of submersed species
Relative frequency of sensitive species
Relative frequency of exotic species
Taxa number
Regional Adaptation?

10. Development Methods Regional adaptations
Exotic, submersed, sensitive spp. in MN
MPCA wetland FQA, appendix A

11. Index Analysis Correlations to measured levels of disturbance
TSI, watershed land use
Ecoregion differences
Metric sensitivity analysis
Metric redundancy analysis
Effect of variable sampling effort on IBI score

12. Distributions of seven raw metric scores for a sample of MN lakes (n=105).

13. Standardized metric scores for Simpson’s Diversity metric plotted against
raw metric scores.

14. Scaled/standardized raw metrics
MDPG
<4.2 1
>=4.2, <6 2
>=6, <8 3
>=8, <10 4
>=10, <11.3 5
>=11.3, <14.5 6
>=14.5, <16 7
>=16, <19 8
>=19, <21.6 9
>=21.6 10
%LV 1
>0, <15.15 2
>=15.15, <17.33 3
>=17.33, <21.82 4
>=21.82, <23.08 5
>=23.08, <30.92 6
>=30.92, <33.92 7
>=33.92, <40.92 8
>=40.92, <50 9
>=50 10
SDI
<43.69 1
>=43.69, <65.04 2
>=65.04, <73.46 3
>=73.46, <76.58 4
>=76.58, <79.29 5
>=79.29, <83.37 6
>=83.37, <87.98 7
>=87.98, <89.43 8
>=89.43, <91.49 9
>=91.49 10
RFSU
<13.32 1
>=13.32, <44.57 2
>=44.57, <57.57 3
>=57.57, <60.88 4
>=60.88, <67.74 5
>=67.74, <70 6
>=70, <72.59 7
>=72.59, <73.66 8
>=73.66, <75 9
>=75, <85 10
>=85, <88.96
>=88.96, <92.24
>=92.24, <95.96
>=95.95, <98.53
>=98.53
RFSE
<0.47 1
>=0.47, <1.27 3
>=1.27, <2.82 4
>=2.82, <4.49 5
>=4.49, <5.56 6
>=5.56, <6.74 7
>=6.74, <11.31 8
>=11.31, <18.41 9
>=18.41 10
RFEX
<0.12 10
>=0.12, <2.27 6
>=2.27, <8.33 5
>=8.33, <16.11 4
>=16.11, <24.86 3
>=24.86, <37.35 2
>=37.35 1 TN
<4 1
>=4, <6 2
>=6, <8 3
>=8, <9 4
>=9, <12 5
>=12, <16 6
>=16, <19.8 7
>=19.8, <23.2 8
>=23.2, <27.6 9
>=27.6 10

15. Initial Results R² 0.6364 P<0.0001
Least-squares regression of IBI scores against Trophic State Index (Carlson 1977) for a sample of MN lakes (n=105). Results of the regression model are significant.

16. Least-squares regression of IBI scores against TSI separated by ecoregion (n=105). Results of the regression models are significant for the NLF and NCHF ecoregions.

17. R²
P<0.001 R²
P<0.01 R²
P<0.001
IBI scores plotted against the proportion of land use within a lake’s watershed (N=65). Land use proportions were arcsine square root transformed to better approximate normality.

18. Sensitivity Analysis Methods in Minns et al. (1994)
Remove metric, recalculate score
Difference of original and recalculated
Variance of difference indicates sensitivity
MDPG
% LV
SDI
RFSU
RFSE
RFEX TN
17.31
10.71
7.83
11.68
13.90
35.22
5.93

19. Redundancy Analysis
Stepwise comparison between raw metrics using Pearson Correlation Coefficients (ρ)
No correlations exceed 0.8, -0.8
%LV
SDI
RFSU
RFSE
RFEX TN
MDPG
0.279
0.512
0.334
0.058
0.095
0.687
0.498
0.312
0.273
0.13
0.432
0.276
0.251
-0.136
0.702
-0.208
0.256
0.079
-0.255
0.32
-0.173

20. IBI at reduced sampling effort
Lakes oversampled at point density ~3.3 pts/acre
Scores calculated for 10% to 90% at 10% intervals for three lakes
Points randomly selected from surveys at specified level of effort
Scores calculated from means of 100 iterations for each level of effort

21. IBI scores and 95% confidence intervals for three lakes (Jane, Square, and Christmas) plotted against varying levels of sampling intensity. Sampling intensity is shown for 10% intervals from 10% to 100% effort. Mean IBI scores were obtained using 100 estimates of IBI scores for each level of sampling intensity.
Fig. 5 IBI scores and 95% confidence intervals for three lakes (Jane, Square, and Christmas) plotted against varying levels of sampling intensity. Sampling intensity is shown for 10% intervals from 10% to 100% effort. Mean IBI scores were obtained using 100 estimates of IBI scores for each level of sampling intensity.

22. Conclusions
IBI shows predictable responses to changes in water quality for a variety of lake classes that differed by ecoregion
Sensitivity analysis suggests index is most influenced by presence of exotic species and least influenced by species richness

23. Conclusions
Metrics provide unique information about ecosystem health (not redundant)
The IBI is not heavily influenced by sampling effort and any effects should be considered negligible dependant upon desired management goals

24. Additional Analyses Examine each metric
Relationships to determinants of WQ
Effects of seasonal, annual variability
Management questions, e.g. sampling differences/taxonomic resolution?

25. Project Culmination Inclusion of SLICE vegetation surveys
Index modification
Metric additions/modifications
Metric scoring
Comparisons to fish IBI
Future work?

26. Acknowledgements References Minnesota Department of Natural Resources
DNR:Dave Wright, Ray Valley, Melissa Drake, Cindy Tomcko, Donna Perleberg, Nicole Hansel-Welch, Nick Proulx
PCA: Steve Heiskary, Joe Magner
U of M: Ray Newman, James Johnson, Susan Galatowitsch, Christy Dolph, Statistics Counseling/Statistics Department
Data sources
Field personnel
References
Carlson, R.E Trophic State Index for Lakes. Limnol. Oceanogr. 22:
Madsen, J.D Point intercept and line intercept methods for aquatic plant management. APCRP Technical Notes Collection (TN APCRP-M1-02). U.S. Army Enginee Center, Vicksburg, MS, U.S.A.
Minns, C.K., Cairns, V., Randall, R. and Moore, J An index of biotic integrity (IBI) for fish assemblages in the littoral zone of Great Lakes' Areas of Concern. Can. J. Fish. Aquat. Sci. 51:
Nichols, S Floristic quality assessment of Wisconsin lake plant communities with example applications. Lake Reserv. Manage. 15:
Nichols, S., Weber, S. and Shaw, B A proposed aquatic plant community biotic index for Wisconsin lakes. Environ. Manage. 26:
Schupp, D.H An ecological classification of Minnesota lakes with associated fish communities. Investigational Report 41, Section of Fisheries, Minnesota Department of Natural Resources.
Weber, S., Nichols, S.A. and Shaw, B Aquatic macrophyte communities in eight northern Wisconsin flowages. Final report to Wisconsin Department of Natural Resources, Madison, Wisconsin, U.S.A. pp. 60.

27. Five number summary boxplots of IBI scores separated by ecoregion (n=105).

28. R²
P<0.001
Least-squares regression of IBI scores against Shoreline Development Factor for a sample of MN lakes (n=105). Results of the regression model are significant.