Presentation on theme: "Development of a Macrophyte-based IBI for Minnesota Lakes"— Presentation transcript:
1 Development of a Macrophyte-based IBI for Minnesota Lakes Marcus BeckUniversity of MinnesotaDepartment of Fisheries, Wildlife, and Conservation BiologyHodson 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 ReviewData SearchIndex 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 aMacrophyte-basedlake IBI
5 Why use aquatic plants? Relation to fish community Immobile Ease of identificationAvailable dataLessons from Wisconsin
6 Sum of Metric Scores = IBI Collect DataAnalyze Biological AttributesSpeciesRichnessTaxa RichnessNumber of darter speciesTrophic FunctionNumber ofinsectivore speciesomnivore speciesAbundance/ConditionNumber per meterDELT (deformities, eroded fins, lesions, tumors)Select, Verify and Score MetricsSum of Metric Scores = IBIInterpretation of IBI ScoreThe assessment tool that we use is called the IBIUse of the attributesResponse to stressor gradientScoring of metricssummationinterpretation610100Excellent905807Good4MetricScores70605IBI ScoreFairDarter SpeciesNumber of3504022Poor3020110Very Poor2040608010020406080100% Watershed Disturbance%Watershed Disturbance
7 Development Methods DNR Point Intercept surveys (Madsen 1999) 82 lakes, 105 surveysLake 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 lengthof 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. 2000Maximum depth of plant growthPercentage of littoral zone vegetatedSimpson’s Diversity IndexRelative frequency of submersed speciesRelative frequency of sensitive speciesRelative frequency of exotic speciesTaxa numberRegional Adaptation?
10 Development Methods Regional adaptations Exotic, submersed, sensitive spp. in MNMPCA wetland FQA, appendix A
11 Index Analysis Correlations to measured levels of disturbance TSI, watershed land useEcoregion differencesMetric sensitivity analysisMetric redundancy analysisEffect 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.
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 R²P<0.001R²P<0.01R²P<0.001IBI 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 scoreDifference of original and recalculatedVariance of difference indicates sensitivityMDPG% LVSDIRFSURFSERFEXTN17.3110.717.8311.6813.9035.225.93
19 Redundancy AnalysisStepwise comparison between raw metrics using Pearson Correlation Coefficients (ρ)No correlations exceed 0.8, -0.8%LVSDIRFSURFSERFEXTNMDPG0.2790.5120.3340.0580.0950.6870.4980.3120.2730.130.4320.2760.251-0.1360.702-0.2080.2560.079-0.2550.32-0.173
20 IBI at reduced sampling effort Lakes oversampled at point density ~3.3 pts/acreScores calculated for 10% to 90% at 10% intervals for three lakesPoints randomly selected from surveys at specified level of effortScores 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 ConclusionsIBI shows predictable responses to changes in water quality for a variety of lake classes that differed by ecoregionSensitivity analysis suggests index is most influenced by presence of exotic species and least influenced by species richness
23 ConclusionsMetrics 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 WQEffects of seasonal, annual variabilityManagement questions, e.g. sampling differences/taxonomic resolution?
25 Project Culmination Inclusion of SLICE vegetation surveys Index modificationMetric additions/modificationsMetric scoringComparisons to fish IBIFuture work?
26 Acknowledgements References Minnesota Department of Natural Resources DNR:Dave Wright, Ray Valley, Melissa Drake, Cindy Tomcko, Donna Perleberg, Nicole Hansel-Welch, Nick ProulxPCA: Steve Heiskary, Joe MagnerU of M: Ray Newman, James Johnson, Susan Galatowitsch, Christy Dolph, Statistics Counseling/Statistics DepartmentData sourcesField personnelReferencesCarlson, 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 R²P<0.001Least-squares regression of IBI scores against Shoreline Development Factor for a sample of MN lakes (n=105). Results of the regression model are significant.