Objectives and Results
This project sought to develop an optimized macroinvertebrate IBI for perennial, wadeable streams of the NWGP ecoregion of South Dakota (Chapter 2). In addition, component metrics of the NGP and NWGP IBIs were compared and differences in IBI scores were evaluated among level IV ecoregions within each level III ecoregion. The NWGP and NGP IBIs were expected to significantly differ in over half the component metrics, and all metrics comprising each IBI differed. This indicated that the two ecoregions differ biologically, as identical methods were used to sample, process, and analyze the macroinvertebrate assemblages in each ecoregion. The two IBIs displayed a similar range of scores and successfully differentiated between impaired and non-
impaired sites. No significant differences in IBI scores could be detected among level IV ecoregions for either the NWGP or NGP.
This project also sought to identify the most influential statewide in-stream drivers of biotic integrity and evaluate the importance of regions in predicting in-stream driver values (Chapter 3). Drivers associated with cultivation were expected to be most influential in the NGP, while drivers related to rangeland and soil erosion were expected to be most influential in the NWGP. Regional variables were predicted to be important for determining in-stream habitat. Cluster analysis and NMDS results indicated distinct separation between NGP and NWGP macroinvertebrate assemblages. Nitrogen loading, human use, and pasture/hay were more positively correlated with NGP sites and riparian and watershed herbaceous cover and turbidity more positively correlated with NWGP sites. These results aligned with the hypothesized major drivers for each level III
ecoregion. Random forest modeling indicated that IBI scores were related to salinity, specific conductance, total riffle length, gravel and fine substrates, and stream discharge at the time of sampling. However, variation in each of the component IBI metrics was explained by a unique number and combination of drivers. Identified drivers of component metrics included measures of substrates, stream discharge, salinity, total length of riffles, specific conductance, nutrient concentrations, dissolved oxygen, turbidity, suspended solids, channel morphology, and bank vegetation. This knowledge may assist in causal analysis, by allowing managers to evaluate changes in individual metrics as well as overall IBI scores and determine which aspects of biotic integrity are impacted by impairment. Management and mitigation efforts can then be tailored more specifically to expected causes of impairment. Important drivers of in-stream habitat were nitrogen loading, human use, and herbaceous cover in the watershed and riparian zone. This falls in line with the land use gradient identified in the NMDS plot. Regional variables, especially level IV ecoregion and river basin, improved models of in-stream habitat and were also found to be associated with different responses in in-stream habitat.
Implications for Biological Assessments in South Dakota
The United States Environmental Protection Agency (US EPA) has encouraged the development of biological monitoring and assessment tools by state water resource agencies (USEPA 1987a). In addition, states have been encouraged to link the new biological tools to existing physical and chemical assessments (USEPA 1987b). In response, South Dakota developed a narrative statement to protect the biological integrity of waters, and has used beneficial use assessment to meet Clean Water Act requirements
and protect aquatic life uses (SDDENR 2016, USEPA 2017). However, no numeric biological criteria had been defined and the use of biological assemblages in impairment decisions was limited to the NGP ecoregion and the IBI developed by the previous study (Bertrand and Troelstrup 2013, USEPA 2017). As a result of this project, South Dakota state water resource managers have an expanded biological assessment toolbox with regional IBIs for perennial, wadeable streams across 80% of the state. Random forest modeling results also demonstrate that statewide biotic integrity is related to salinity and specific conductance, and that component IBI metrics are also related to total suspended solids, dissolved oxygen, and water temperature. These stream characteristics are
important stressors identified by the South Dakota DENR as part of beneficial use assessment for the state (SDDENR 2016). This project has nearly fulfilled US EPA recommendations by helping develop tools for state biological assessments and linking them to current physical and chemical assessments.
Another major implication of this study for water resource managers of South Dakota is the importance of regional stratification in monitoring and assessments, as shown by the analyses conducted in this study. Macroinvertebrate assemblages appear to differ by level III ecoregion as a result of the land use gradient that distinguishes the NGP from the NWGP. Level IV ecoregion and river basins were important correlates of in- stream habitat. For State water resource managers, this indicates that biological
assessments in South Dakota should be conducted separately between level III ecoregions and take into account level IV ecoregion and river basin differences in abiotic
disturbed condition should be developed in each level III ecoregion and used to re- evaluate differences between level IV ecoregions (Stoddard 2005, MPCA 2014).
This study also provides managers with baseline information and visualizations of expected interactions between measures of biotic integrity and in-stream habitat, and between in-stream habitat and watershed characteristics and regions in perennial,
wadeable streams of South Dakota. Managers now have a means of evaluating the current condition of these streams and detecting impairment, and a method to evaluate
differences in condition. Overall, this study supplies water resource managers in South Dakota with new tools to evaluate biotic integrity of stream systems and identify drivers and pathways of biological impairment. The NGP IBI is already in use by SD DENR for assessments and undergoing continual calibration (SDDENR 2016); the NWGP IBI developed here will likewise be adopted.
Implications for the North American Great Plains
The results of this study fit within the broader patterns and drivers of biotic integrity in the Great Plains. The finding of different component IBI metrics in individual ecoregions is consistent with work done in Minnesota and Wyoming (Hargett 2011, MPCA 2014). Individual IBI metrics were generally consistent with those found elsewhere in the central United States (Griffith et al. 2005, Weigel and Dimick 2011, Larsen 2013, MPCA 2014), although the combination of metrics remains unique to each ecoregion. This study also highlighted a major land use gradient driven by changes in herbaceous cover, human use, and nitrogen loading, which corresponded to a shift from rangeland/grassland to cultivated agriculture. These landscape drivers coincide with the
major types of land use/land cover in the Great Plains (Taylor, J.L. et al. 2015), which are in turn known to affect water quality and aquatic and terrestrial biodiversity (Allan 2004, Foley et al. 2005, Lenhart et al. 2011, Newbold et al. 2015, Tanaka et al. 2016).
Results of this effort contribute to assessment and management elsewhere in the Great Plains. Data collected in this study improve general knowledge of prairie stream macroinvertebrate assemblages and the response of biotic and abiotic aspects stream components to natural and anthropogenic pressures, which are areas of concern for water resource managers (Matthews 1988, Mullen et al. 2011, Roberts et al. 2016). A total of 222 taxa representing 201 genera and 80 families were identified in this study. Voucher specimens have been submitted to the South Dakota Aquatic Invertebrate Collection housed at South Dakota State University to supplement sparse records west of the Missouri River. The importance of ecoregions and river basins as drivers of biotic integrity and in-stream habitat may reflect the natural gradients in geology, precipitation, and temperature that define the Great Plains (Trimble 1980, Ojima et al. 2012, Basara et al. 2013), which in turn result in patterns of hydrology, turbidity, substrates, land use, and other habitat characteristics that determine the structure and composition of biotic
assemblages (Matthews 1988, Dodds et al. 2004, Phillips et al. 2016). The present study indicates the need for regional stratification of biological assessment efforts in light of these patterns; this should be evaluated in other Great Plains states where the importance of stratification may not have been tested. The methods used in this study are capable of identifying major drivers of biotic integrity and their relationships to in-stream habitat in environmentally harsh prairie streams and may be applied by other water resource managers in the Great Plains.
Additional Considerations for Future Work
Additional work is needed to ensure that the full range of stream conditions in South Dakota are being evaluated. This includes extending IBI development to other ecoregions in the State, such as the Northwestern Glaciated Plains and the Middle
Rockies of the Black Hills. Additionally, other abiotic variables and possible interactions should be evaluated. Natural gradients like precipitation, elevation, or geology might be incorporated into analyses of landscape drivers to more thoroughly evaluate underlying drivers of in-stream habitat. For instance, direct measurements of geology may improve substrate models. Additional anthropogenic variables should also be measured, such as pesticides or other toxins, as these are known to have effects on freshwater invertebrate biodiversity (Beketov et al. 2013).
Finally, the tools developed here for macroinvertebrate assemblages should be applicable to other biological assemblages. Fish IBIs have been developed for the NGP (Krause et al. 2013) and the NWGP (Kaiser 2017), and statewide driver analysis can be conducted using the same methodology as for the macroinvertebrates. Several studies in stream and river systems across Iowa (Quist and Schultz 2014), Michigan (Lammert and Allan 1999), Colorado (Griffith et al. 2005), New York (Johnson and Ringler 2014), and the Southeast United States (Paller et al. 2017) have found that separate biological assemblages are influenced by different environmental drivers and respond differently to disturbance. These findings have also been reported globally in Korea (Bae et al. 2014), New Zealand (Clapcott et al. 2012), Germany and Austria (Dahm et al. 2013), France (Marzin et al. 2013), and Brazil (Tanaka et al. 2016). Taken together, multiple
assemblages may provide a more holistic evaluation of biotic integrity than can be achieved by examining only one assemblage.
BIBLIOGRAPHY
Administrative Rules of South Dakota 74:51:01:12. 1993. Biological integrity of waters. South Dakota Legislature.
Aho, K., T. Weaver, and S. Regele. 2011. Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods. Applied Vegetation Science 14:277-290.
Allan, J.D. 2004. Landscapes and riverscapes: the influence of land use on stream
ecosystems. Annual Review of Ecology, Evolution, and Systematics 35:257-284.
Allan, J.D., L.L. Yuan, P. Black, T. Stockton, P.E. Davies, R.H. Magierowski, and S.M. Read. 2011. Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks. Freshwater Biology 57:58-73.
Andersen, T., P.S. Cranston, and J.H. Epler (Sci. Eds.). 2013. The larvae of
Chironomidae (Diptera) of the Holarctic Region – Keys and diagnoses. Insect Systematics & Evolution, Supplement No. 66:1-571.
Bae, M., F. Li, Y. Kwon, N. Chung, H. Choi, S. Hwang, and Y. Park. 2014. Concordance of diatom, macroinvertebrate and fish assemblages in streams at nested spatial scales: implications for ecological integrity. Ecological Indicators 47:89-101.
Baltensperger, A.P. and F. Huettmann. 2015. Predictive spatial niche and biodiversity hotspot models for small mammal communities in Alaska: applying machine- learning to conservation planning. Landscape Ecology 30:681-697.
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic
macroinvertebrates, and fish, second edition. United States Environmental Protection Agency. EPA 841-B-99-002. United States Environmental Protection Agency, Office of Water, Washington, D.C, USA.
Barbour, M.T. and C.O. Yoder. 2000. The multimetric approach to bioassessment, as used in the United States of America. Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making (eds. W.S. Davis and T.P. Simon), pp. 263-286. CRC Press, Inc., Boca Raton, Florida, USA.
Basara, J.B., J.N Maybourn, C.M. Peirano, J.E. Tate, P.J. Brown, J.D. Hoey, and B.R. Smith. 2013. Drought and associated impacts in the Great Plains of the United States – a review. International Journal of Geosciences 4:72-81.
Bazata, K. 2011. Nebraska stream biological monitoring program 2004-2008: technical report. Nebraska Department of Environmental Quality, Water Quality Division, Surface Water Section, Lincoln, Nebraska, USA.
Bazata, K. 2013. Nebraska stream collections for the 2010-2011 stream biological monitoring program with revisions to the macroinvertebrate and fish metrics classifications. Nebraska Department of Environmental Quality, Water Quality Division, Surface Water Section, Lincoln, Nebraska, USA.
Beck, M.W., L.K. Hatch, B. Vondracek, and R.D. Valley. 2010. Development of a macrophyte-based index of biotic integrity for Minnesota lakes. Ecological Indicators 10:968-979.
Beketov, M.A., B.J. Kefford, R.B. Schafer, and M. Liess. 2013. Pesticides reduce regional biodiversity of stream invertebrates. Proceedings of the National Academy of Science 110:11039-11043.
Bertrand, K.N and N.H. Troelstrup, Jr. 2013. Development and testing of an integrated index of biotic integrity for wadeable streams of the Northern Glaciated Plains ecoregion. Final Completion Report. Department of Natural Resource
Management, South Dakota State University, Brookings, South Dakota, USA.
Boehme, E.A., C.E. Zipper, S.H. Schoenholtz, D.J. Soucek, and A.J. Timpano. 2016. Temporal dynamics of benthic macroinvertebrate communities and their response to elevated specific conductance in Appalachian coalfield headwater streams. Ecological Indicators 64:171-180.
Boets, P. K. Lock, and P.L.M. Goethals. 2013. Modeling habitat preference, abundance and species richness of alien macrocrustaceans in surface waters in Flanders (Belgium) using decision trees. Ecological Informatics 17:73-81.
Bonada, N., M. Rieradevall, N. Prat, and V.H. Resh. 2006. Benthic macroinvertebrate assemblages and macrohabitat connectivity in Mediterranean-climate streams of northern California. Journal of the North American Benthological Society 25:32- 43.
Boulton, A.J. 2003. Parallels and contrasts in the effects of drought on stream macroinvertebrate assemblages. Freshwater Biology 48:1173-1185.
Bramblett, R.G., T.R. Johnson, A.V. Zale, and D.G. Heggem. 2005. Development and evaluation of a fish assemblage index of biotic integrity for Northwestern Great Plains streams. Transactions of the American Fisheries Society 134:624-640.
Braskamp, C.W. 2002. Biogeographic patterns of littoral invertebrate communities and responses to disturbance in prairie lakes. Thesis, South Dakota State
University, Brookings, South Dakota, USA.
Breiman, L., J.H. Friedman, R. Olshen, and C.J. Stone. 1984. Classification and Regression Trees. Wadsworth, Belmont, California, USA.
Brisbois, M.C., R. Jamieson, R. Gordon, G. Stratton, and A. Madani. 2008. Stream ecosystem health in rural mixed land-use watersheds. Journal of Environmental Engineering Science 7:439-452.
Broussard, W. and R.E. Turner. 2009. A century of changing land-use and water-quality relationships in the continental US. Frontiers in Ecology and the Environment 7:302-307.
Brown, L.R., J.T. May, and M. Wulff. 2012. Associations of benthic macroinvertebrate assemblages with environmental variables in the Upper Clear Creek watershed, California. Western North American Naturalist 72:473-494.
Bryce, S.A., G.A. Lomnicky, and P.R. Kaufmann. 2010. Protecting sediment-sensitive aquatic species in mountain streams through the application of biologically based streambed sediment criteria. Journal of the North American Benthological Society 29:657-672.
Carter, J.L. and V.H. Resh. 2013. Analytical approaches used in stream benthic
macroinvertebrate biomonitoring programs of state agencies in the United States. U.S. Geological Survey. Open-File Report 2013-1129. U.S. Geological Survey, Reston, Virginia, USA.
Chirhart, J. 2003. Development of a macroinvertebrate index of biological integrity (MIBI) for rivers and streams of the St. Croix river basin in Minnesota. Biological Monitoring Program, Minnesota Pollution Control Agency, St. Paul, Minnesota, USA.
Clapcott, J.E., K.J. Collier, R.G. Death, E.O. Goodwin, J.S. Harding, D. Kelly, J.R. Leathwick, and R.G. Young. 2012. Quantifying relationships between land-use gradients and structural and functional indicators of stream ecological integrity. Freshwater Biology 57:74-90.
Cormier, S.M. and G.W. Suter. 2013. A method for assessing causation of field exposure- response relationships. Environmental Toxicology and Chemistry 32:272-276.
Cross, A.T., S.R. Turner, D.J. Merritt, A. van Niekerk, M. Renton, K.W. Dixon, and L. Mucina. 2015. Vegetation patterns and hydro-geological drivers of freshwater rock pool communities in the monsoon-tropical Kimberley region, Western Australia. Journal of Vegetation Science 26:1184-1197.
Cummins, K.W. 2016. Combining taxonomy and function in the study of stream macroinvertebrates. Journal of Limnology 75:235-241.
Cutler, D.R., T.C. Edwards, Jr., K.H. Beard, A. Cutler, K.T. Hess, J. Gibson, and J.J. Lawler. 2007. Random forests for classification in ecology. Journal of Ecology 88:2783-2972.
Dahm, V., D. Hering, D. Nemitz, W. Graf, A. Schmidt-Kloiber, P. Leitner, A. Melcher, C.K. Feld. 2013. Effects of physico-chemistry, land use, and hydromorphology on three riverine organism groups: a comparative analysis with monitoring data from Germany and Austria. Hydrobiologia 704:389-415.
De’ath, G. and K.E. Fabricious. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Journal of Ecology 81:3178-3192.
De’ath, G. 2002. Multivariate regression trees: a new technique for modeling species- environment relationships. Journal of Ecology 83:1105-1117.
De’ath, G. 2007. Boosted trees for ecological modeling and prediction. Journal of Ecology 88:243-251.
Death, R.G. and K.J. Collier. 2010. Measuring stream macroinvertebrate responses to gradients of vegetation cover: when is enough enough? Freshwater Biology 55:1447-1464.
Death, R.G. and M.K. Joy. 2004. Invertebrate community structure in streams of the Manawatu-Wanganui region, New Zealand: the roles of catchment versus reach scale influences. Freshwater Biology 49:982-997.
Dodds, W.K. and E.B. Welch. 2000. Establishing nutrient criteria in streams. Journal of the North American Benthological Society 19:186-196.
Dodds, W.K., K. Gido, M.R. Whiles, K.M. Fritz, and W.J. Matthews. 2004. Life on the edge: the ecology of Great Plains prairie streams. BioScience 54:205-216.
Doledec, S. and B. Statzner. 2010. Responses of freshwater biota to human disturbances: contribution of J-NABS to developments in ecological integrity assessments. Journal of the North American Benthological Society 29:286-311.
Doledec, S., N. Phillips, M. Scarsbrook, R.H. Riley, and C.R. Townsend. 2006. Comparison of structural and functional approaches to determining landuse effects on grassland stream invertebrate communities. Journal of the North Benthological Society 25:44-60.
Dudgeon, D., A.H. Arthington, M.O. Gessner, Z. Kawabata, D.J. Knowler, C. Leveque, R.J. Naiman, A. Prieur-Richard, D. Soto, M.L.J. Stiassny, and C.A. Sullivan. 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews 81:163-182.
Elith, J., J.R. Leathwick, and T. Hastie. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77:802-813.
Erba, S., G. Pace, D. Demartini, D. Di Pasquale, G. Dorflinger, and A. Buffagni. 2014. Land use at the reach scale as a major determinant for benthic invertebrate
community in Mediterranean rivers of Cyprus. Ecological Indicators 48:477-491.
Evans, J.S., M.A. Murphy, Z.A. Holden, and S.A. Cushman. 2011. Modeling species distribution and change using random forest. Predictive species and habitat
modeling in landscape ecology (eds. C.A. Drew, F. Huettmann, and Y. Wiersma), pp. 139-159. Springer, New York, USA.
Fabricious, K.E. and G. De’ath. 2004. Identifying ecological change and its causes: a case study on coral reefs. Ecological Applications 14:1448-1465.
Feld, C.K., S. Birk, D. Eme, M. Gerisch, D. Hering, M. Kernan, K. Maileht, U. Mischke, I. Ott, F. Pletterbauer, S. Poikane, J. Salgado, C.D. Sayer, J. van Wichelen, and F. Malard. 2016. Disentangling the effects of land use and geo-climate factors on diversity in European freshwater ecosystems. Ecological Indicators 60:71-83.
Fernandez, I.J., M.B. Adams, M.D. SanClements, and S.A. Norton. 2010. Comparing decadal responses of whole-watershed manipulations at the Bear Brook and Fernow experiments. Environmental Monitoring and Assessment 171:149-161.
Ferreira, W.R., R. Ligeiro, D.R. Macedo, R.M. Hughes, P.R. Kaufman, L.G. Oliveira, and M. Callisto. 2014. Importance of environmental factors for the richness and distribution of benthic macroinvertebrates in tropical headwater streams.
Freshwater Science 33:860-871.
Foley, J.A., R. DeFries, G.P. Asner, C. Barford, G. Bonan, S.R. Carpenter, F.S. Chapin, M.T. Coe, G.C. Daily, H.K. Gibbs, J.H. Helkowski, T. Holloway, E.A. Howard, C.J. Kucharik, C. Monfreda, J.A. Patz, I.C. Prentice, N. Ramankutty, and P.K. Snyder. 2005. Global consequence of land use. Science 309:570-574.
Griffith, M.B. 2012. Classification and regression tree (CART) analysis. United States Environmental Protection Agency. <http://www.epa.gov/caddis/da_basic_4.html>
Griffith, M.B., B.H. Hill, F.H. McCormick, P.R. Kaufmann, A.T. Herlihy, and A.R. Selle. 2005. Comparative application of indices of biotic integrity based on
periphyton, macroinvertebrates, and fish to southern Rocky Mountain streams. Ecological Indicators 5:117-136.
Gronke, A.L. 2004. Development of an integrated index of biotic integrity for prairie pothole lakes of eastern South Dakota. Thesis, South Dakota State University, Brookings, South Dakota, USA.
Grudzinski, B.P. 2014. Influence of watershed grazing management on stream geomorphology in grassland headwater streams. Dissertation, Kansas State University, Manhattan, Kansas, USA.
Hargett, E.G. 2011. The Wyoming stream integrity index (WSII) – multimetric indices for assessment of wadeable streams and large rivers in Wyoming. Wyoming Department of Environmental Quality, Water Quality Division, Cheyenne, Wyoming, USA.
Hawkins, C.P., R.H. Norris, J. Gerritsen, R.M. Hughes, S.K. Jackson, R.K. Johnson, and R.J. Stevenson. 2000. Evaluation of the use of landscape classifications for the prediction of freshwater biota: synthesis and recommendations. Journal of the North American Benthological Society 19:541-556.
Hawkins, C.P., J.R. Olson, and R.A. Hill. 2010. The reference condition: predicting benchmarks for ecological and water-quality assessments. Journal of the North American Benthological Society 29:312-343.
Heino, J., T. Muotka, R. Paavola, H. Hamalainen, and E. Koskenniemi. 2002. Correspondence between regional delineations and spatial patterns in
macroinvertebrate assemblages of boreal headwater streams. Journal of the North American Benthological Society 21:397-413.
Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. The Great Lakes Entomologist 20:31-39.
Holtrop, A.M., L.C. Hinz, Jr., and J. Epifanio. 2006. Ecological classification of rivers for environmental assessment and management: model development and risk assessment. Illinois Department of Natural Resources. Technical Report 2006/12. Illinois Natural History Survey, Champaign, Illinois, USA.
Hughes, R.M. and D.V. Peck 2008. Acquiring data for large aquatic resource surveys: the art of compromise among science, logistics, and reality. Journal of the North