Development of a Macrophyte-based IBI Development of a Macrophyte-based IBI for Minnesota Lakesfor Minnesota Lakes

Marcus Beck University of Minnesota

Department of Fisheries, Wildlife, and Conservation Biology Hodson Hall, 1980 Folwell Ave., St. Paul, MN 55108

Project Background

• Identification of a set of indicatorsresponsive 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

http://www.mndnr.gov/fisheries/slice/index.html

Today’s Talk

Developing the index

• Methods and analyses• Initial results• Project culmination

Today’s Talk

• Literature review and data search suggested one thing…

• Development of aMacrophyte-basedlake IBI

Why use aquatic plants?

• Relation to fish community• Immobile• Ease of identification• Available data• Lessons from Wisconsin

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%Watershed Disturbance

IBI S

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% Watershed Disturbance

Num

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Collect Data Analyze Biological Attributes

Abundance/Condition

Number per meter

DELT (deformities, eroded fins, lesions, tumors)

Select, Verifyand Score Metrics

Interpretation of IBI Score

Sum of Metric

Scores = IBI

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Fair

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MetricScores

Species Richness

Taxa Richness

Number of darter species

Trophic Function

Number ofinsectivore species

Number ofomnivore species

Very Poor

Development Methods

• DNR Point Intercept surveys (Madsen 1999)

• 82 lakes, 105 surveys• Lake classes same as fish

IBI

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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.

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?

Development Methods

• Regional adaptations– Exotic, submersed, sensitive spp. in MN

– MPCA wetland FQA, appendix Ahttp://www.pca.state.mn.us/publications/wetlandassessment-guide.html

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

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SDI

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

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

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Raw SDI Metric

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Scaled/standardized raw metricsMDPG

<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

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>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 9

>=88.96, <92.24 8

>=92.24, <95.96 7

>=95.95, <98.53 6

>=98.53 5

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

Initial Results

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.

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TSI

IBI

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R² 0.6364P<0.0001

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.

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IBI S

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*NLF R-squared 0.317 P<0.001

*NCHF R-squared 0.656 P<0.0001

NGP, WCP R-squared 0.127 P=0.1925

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IBI S

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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.

R² 0.3827P<0.001

R² 0.1475P<0.01

R² 0.4555 P<0.001

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 TN17.31 10.71 7.83 11.68 13.90 35.22 5.93

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

%LV 0.498 0.312 0.273 0.13 0.432

SDI 0.276 0.251 -0.136 0.702

RFSU -0.208 0.256 0.079

RFSE -0.255 0.32

RFEX -0.173

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

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Points/Acre

IBI

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Christmas

Jane

Square

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.

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

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

Additional Analyses

• Examine each metric• Relationships to determinants of WQ• Effects of seasonal, annual variability• Management questions, e.g. sampling

differences/taxonomic resolution?

Project Culmination• Inclusion of SLICE vegetation surveys• Index modification

– Metric additions/modifications– Metric scoring

• Comparisons to fish IBI

• Future work?

Acknowledgements• 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

ReferencesCarlson, R.E. 1977. Trophic State Index for Lakes. Limnol. Oceanogr. 22: 361-369.

Madsen, J.D. 1999. 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. 1994. 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: 1804-1822.

Nichols, S. 1999. Floristic quality assessment of Wisconsin lake plant communities with example applications. Lake Reserv. Manage. 15: 133-141.

Nichols, S., Weber, S. and Shaw, B. 2000. A proposed aquatic plant community biotic index for Wisconsin lakes. Environ. Manage. 26: 491-502.

Schupp, D.H. 1992. 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. 1995. Aquatic macrophyte communities in eight northern Wisconsin flowages. Final report to Wisconsin Department of Natural Resources, Madison, Wisconsin, U.S.A. pp. 60.

NLF NCHF NGP, WCP

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

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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.

R² 0.1036P<0.001