, Volume 820, Issue 1, pp 115–133 | Cite as

A comparison of the taxonomic and trait structure of macroinvertebrate communities between the riffles and pools of montane headwater streams

  • David B. Herbst
  • Scott D. Cooper
  • R. Bruce Medhurst
  • Sheila W. Wiseman
  • Carolyn T. Hunsaker
Primary Research Paper


Macroinvertebrate community taxonomic and trait structure showed consistent differences between riffles and pools across 12 headwater streams in the Sierra Nevada (California) even as flows varied from wet to dry years and between seasons. Densities of Ephemeroptera, Plecoptera, Trichoptera, Elmidae, Orthocladiinae and Diamesinae midges, and mites were greater in riffles, whereas Tanypodinae, Chironominae, Sialis, and Pisidium were more abundant in pools. Pools had higher densities but estimated biomass was greater in riffles. Collector-gatherer and micropredator abundances were greater in pools whereas grazers, collector-filterers, and macropredators were more abundant in riffles. Stonefly shredders were most abundant in riffles but some caddis shredders were more abundant in pools. Trait state patterns were related to food resource and physical habitat differences between riffles and pools. Of the distinct pool–riffle differences we found among taxa, only about half conformed to expectations from the literature. Pool and riffle assemblages were most dissimilar at intermediate discharge and converged at low and high flows when one or the other habitat dominated. Bioassessment sampling will need to account for these flow-related differences. Benthic invertebrate communities in these mountain streams clearly differ between pools and riffles, but the relative extent of habitats and biological similarity shift with flow regime.


Stream invertebrates Pools Riffles Sierra Nevada Bioassessment Stream geomorphology Headwaters Habitat preference Patch dynamics 



This research was funded through Joint Venture Agreements between the Pacific Southwest Research Station, Forest Service, and the University of California, Santa Barbara (12-JV-11272139-070). We also received support in the early years from California’s State Water Resources Control Board, through Proposition 50 (the Water Security Clean Drinking Water, Coastal, and Beach Protection Act of 2002). We thank Ian Bell, Mike Bogan, Bruce Hammock, Jeff Kane, Sandi Roll, and Matt Wilson for laboratory and field assistance during this study. Helpful reviews from Sherri Johnson and Daren Carlisle improved this paper. We also thank the many Forest Service employees over the years who collected streamflow and stream habitat data for this project.

Supplementary material

10750_2018_3646_MOESM1_ESM.xlsx (56 kb)
Supplementary material 1 (XLSX 56 kb). Appendix 1. List of collected taxa and their tolerance values (TV, scaled 0 = most sensitive to 10 = most tolerant to stress), thermal preference distributions (TempCD75 is the 75th percentile of the cumulative distribution of temperature (°C) observations for field collections of the taxon specified, Yuan, 2006), and assignment to functional feeding groups (FFG) and trait groups associated with voltinism (Volt), development (Devel), body size, temperature preferences (Temp), behavioral characteristics (Habit), and flow affinity (Rheo), based on Barbour et al. (1999), Vieira et al. (2006), Poff et al. (2006), and Merritt et al. (2008). The right side of this Appendix, after the Rheo column, shows pool (P, depositional), riffle (R, erosional), and mixed habitat (M) designations in Poff et al. (2006) and Merritt et al. (2008), as well as the results of Indicator Species Analysis on taxa relative abundances showing significant associations (P < 0.05, Monte Carlo tests) of common taxa (occurring in > 25% of samples) with pools (P) versus riffles (R) (all sites-times) and statistical results (*P < 0.05, **P < 0.01, ***P < 0.001) of mean time-averaged density differences between pools and riffles (n = 12 for each pool v riffle comparison) using paired t-tests with Benjamini–Hochberg corrections for all comparisons. The next column indicates the number of sites (out of 12) where each taxon was collected. Statistical tests were only performed where n> 5. The last four columns show the mean densities and associated standard errors for individual taxa in pools versus riffles. Abbreviations and codes: For FFGs: p predator, mp micro-predator, cg collector-gatherer, cf collector filterer, g grazer, sh shredder, ph piercer-herbivore. For Traits: Volt (voltinism, 1 = semivoltine < 1 generation/year, 2 = univoltine 1 generation/year, 3 = multivoltine > 1 generation/year); Devel (development rate, 1 = fast, 2 = slow, 3 = nonseasonal); Size (1 = small 2–9 mm, 2 = medium 9–16 mm, 3 = large 16–30 mm); Temp (temperature preference, 1 = cold stenotherm, 2 = cool eurytherm, 3 = warm eurytherm); Habit (behavioral habit, 1 = burrower, 2 = climber, 3 = sprawler, 4 = clinger, 5 = swimmer), and Rheo (rheophily flow/habitat preference, 1 = depositional, 2 = mixed, 3 = erosional). Note that there were insufficient data to code traits of some rare taxa in this data set (blanks), and that Chironomidae are grouped by subfamily or tribe
10750_2018_3646_MOESM2_ESM.xlsx (13 kb)
Supplementary material 2 (XLSX 13 kb). Appendix 2. Predictions and statistical results of comparisons between pools (P) and riffles (R) for the relative and absolute abundances of trait states, and for community indices of stress (biotic index) and temperature (thermal index) tolerance. Hypotheses regarding the pool (P) and riffle (R) affinities of different trait state groups were based on the references listed in the Appendix 1 legend and through considerations of trait state relationships to environmental factors that varied between pools (P) and riffles (R) (Table 2, see text). The results of paired t-tests for time averaged mean values across sites (n = 12 P v R pairs for each comparison) with Benjamini–Hochberg corrections (false detection rate = 0.05) for multiple comparisons across all traits are shown. Significant pool-riffle transformed (logit for proportions, log for densities) abundance differences were designated with asterisks: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Pearson’s correlation coefficients (r) and associated P values are shown, to the right of the trait state pool versus riffle results, for hypothesized relationships between transformed abundances of trait states and transformed environmental variables. This part of the appendix lists specific hypotheses and hypothesized correlations between the abundances of organisms belonging to different traits and environmental factors (Pearson’s correlation coefficient r, P value of test, and sample size n) using the full data set


  1. Barbour, M.T., J. Gerritsen, B.D. Snyder & J.B. Stribling, 1999. Appendix B, of Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, and fish. 2nd edition. EPA 841-B-99-002, US Environmental Protection Agency, Office of Water, Washington, D.C.Google Scholar
  2. Benjamini, Y. & Y. Hochberg, 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289–300.Google Scholar
  3. Benke, A. C., A. D. Huryn, L. A. Smock & J. B. Wallace, 1999. Length-mass relationships for freshwater macroinvertebrates in North America with particular reference to the southeastern United States. Journal of the North American Benthological Society 18: 308–343.CrossRefGoogle Scholar
  4. Boersma, K. S., L. E. Dee, S. J. Miller, M. T. Bogan, D. A. Lytle & A. I. Gitelman, 2016. Linking multidimensional functional diversity to quantitative methods: a graphical hypothesis-evaluation framework. Ecology 97: 583–593.CrossRefPubMedGoogle Scholar
  5. Bogan, M. T. & D. A. Lytle, 2007. Seasonal flow variation allows ‘time-sharing’ by disparate aquatic invertebrate communities in montane desert streams. Freshwater Biology 52: 290–304.CrossRefGoogle Scholar
  6. Bonada, N., M. Rieradevall, N. Prat & 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.CrossRefGoogle Scholar
  7. Brown, A. V. & P. P. Brussock, 1991. Comparisons of benthic invertebrates between riffles and pools. Hydrobiologia 220: 99–108.CrossRefGoogle Scholar
  8. Brussock, P. P. & A. V. Brown, 1991. Riffle-pool geomorphology disrupts longitudinal patterns of stream benthos. Hydrobiologia 220: 109–117.CrossRefGoogle Scholar
  9. Brussock, P. P., A. V. Brown & J. C. Dixon, 1985. Channel form and stream ecosystem models. Water Resources Bulletin 21: 859–866.CrossRefGoogle Scholar
  10. Carter, J. L. & S. V. Fend, 2001. Inter-annual changes in the benthic community structure of riffles and pools in reaches of contrasting gradient. Hydrobiologia 459: 187–200.CrossRefGoogle Scholar
  11. Carter, J. L. & V. H. Resh, 2001. After site selection and before data analysis: sampling, sorting and laboratory procedures used in benthic macroinvertebrate monitoring programs by USA state agencies. Journal of the North American Benthological Society 20: 658–682.CrossRefGoogle Scholar
  12. Chevenet, F., S. Dolédec & D. Chessel, 1994. A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology 31: 295–309.CrossRefGoogle Scholar
  13. Cooper, S. D. & T. L. Dudley, 1988. The interpretation of “controlled” vs “natural” experiments in streams. Oikos 52: 357–361.CrossRefGoogle Scholar
  14. Cooper, S. D., H. M. Page, S. W. Wiseman, K. Klose, D. Bennett, T. Even, S. Sadro, C. E. Nelson & T. L. Dudley, 2015. Physicochemical and biological responses of streams to wildfire severity in riparian zones. Freshwater Biology 60: 2600–2619.CrossRefGoogle Scholar
  15. Cummins, K. W., R. W. Merritt & M. B. Berg, 2008. Ecology and distribution of aquatic insects. In Merritt, R. W., K. W. Cummins & M. B. Berg (eds), An Introduction to the Aquatic Insects of North America, 4th ed. Kendall/Hunt Publishing Company, Dubuque: 105–122.Google Scholar
  16. da Silva, M. V. D., B. F. J. V. Rosa & R. G. Alves, 2015. Effect of mesohabitats on responses of invertebrate community structure in streams under different land uses. Environmental Monitoring and Assessment 187: 714.CrossRefPubMedGoogle Scholar
  17. Finlay, J. C., S. Khandwala & M. E. Power, 2002. Spatial scales of carbon flow in a river food web. Ecology 83: 1845–1859.CrossRefGoogle Scholar
  18. Frissell, C. A., W. J. Liss, C. E. Warren & M. D. Hurley, 1986. A hierarchical framework for stream habitat classification: viewing streams in watershed context. Environmental Management 10: 199–214.CrossRefGoogle Scholar
  19. Gordon, N. D., T. A. McMahon, B. L. Finlayson, C. J. Gippel & R. J. Nathan, 2004. Stream Hydrology, An Introduction for Ecologists, 2nd ed. Wiley, West Sussex.Google Scholar
  20. Herbst, D. B., E. L. Silldorff & S. D. Cooper, 2009. The influence of introduced trout on the benthic communities of paired headwater streams in the Sierra Nevada of California. Freshwater Biology 54: 1324–1342.CrossRefGoogle Scholar
  21. Hilsenhoff, W. L., 1991. Chapter 17. Diversity and classification of insects and Collembola. In Thorp, J. H. & A. P. Covich (eds), Ecology and Classification of North American Freshwater Invertebrates. Academic Press, San Diego: 593–663.Google Scholar
  22. Hunsaker, C. T., T. W. Whitaker & R. C. Bales, 2012. Snowmelt runoff and water yield along elevation and temperature gradients in California’s southern Sierra Nevada. Journal of the American Water Resources Association 48: 667–678.CrossRefGoogle Scholar
  23. Huryn, A. D. & J. B. Wallace, 1987. Local geomorphology as a determinant of macrofaunal production in a mountain stream. Ecology 68: 1932–1942.CrossRefPubMedGoogle Scholar
  24. Johnson, D. W., C. T. Hunsaker, D. W. Glass, B. M. Rau & B. A. Roath, 2011. Carbon and nutrient contents in soils from the Kings River Experimental Watersheds, Sierra Nevada Mountains, California. Geoderma 160: 490–502.CrossRefGoogle Scholar
  25. Kaufman, P.R., P. Levine, E.G. Robison, C. Seeliger & D.V. Peck, 1999. Quantifying physical habitat in wadeable streams. EPA 620/R-99/003, US Environmental Protection Agency, Office of Water, Washington, D.C.Google Scholar
  26. Keller, E. A., 1971. Areal sorting of bed-load material: the hypothesis of velocity reversal. Geological Society of America Bulletin 82: 753–756.CrossRefGoogle Scholar
  27. Leopold, L. B., M. G. Wolman & J. P. Miller, 1964. Fluvial Processes in Geomorphology. Freeman and Company, San Francisco.Google Scholar
  28. Logan, P. & M. P. Brooker, 1983. The macroinvertebrate faunas of riffles and pools. Water Research 17: 263–270.CrossRefGoogle Scholar
  29. MacWilliams, M. L., J. M. Wheaton, G. B. Pasternack, R. L. Street & P. K. Kitanidis, 2006. Flow convergence routing hypothesis for pool-riffle maintenance in alluvial rivers. Water Resources Research 42: W10427.CrossRefGoogle Scholar
  30. Mazor, R. D., A. C. Rehn, P. R. Ode, M. Engeln, K. C. Schiff, E. D. Stein, D. J. Gillett, D. B. Herbst & C. P. Hawkins, 2016. Bioassessment in complex environments: designing an index for consistent meaning in different settings. Freshwater Science 35: 249–271.CrossRefGoogle Scholar
  31. Merritt, R. W., K. W. Cummins & M. B. Berg (eds), 2008. An Introduction to the Aquatic Insects of North America, 4th ed. Kendall/Hunt Publishing Company, Dubuque.Google Scholar
  32. Montgomery, D. R., 1999. Process domains and the river continuum. Journal of the American Water Resources Association 35: 397–410.CrossRefGoogle Scholar
  33. Montgomery, D. R. & J. M. Buffington, 1997. Channel reach morphology in mountain drainage basins. Geological Society of American Bulletin 109: 596–611.CrossRefGoogle Scholar
  34. O’Dowd, A. P. & A. Chin, 2016. Do bio-physical attributes of steps and pools differ in high-gradient mountain streams? Hydrobiologia 776: 67–83.CrossRefGoogle Scholar
  35. Ode, P. R., A. C. Rehn, R. D. Mazor, K. C. Schiff, E. D. Stein, J. T. May, L. R. Brown, D. B. Herbst, D. Gillett, K. Lunde & C. P. Hawkins, 2016. Evaluating the adequacy of a reference-site pool for ecological assessments in environmentally complex regions. Freshwater Science 35: 237–248.CrossRefGoogle Scholar
  36. Poff, N. L. & J. V. Ward, 1990. Physical habitat template of lotic systems: recovery in the context of historical pattern and spatiotemporal heterogeneity. Environmental Management 14: 629–645.CrossRefGoogle Scholar
  37. Poff, N. L., J. D. Olden, N. K. M. Vieira, D. S. Finn, M. P. Simmons & B. C. Kondratieff, 2006. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. Journal of the North American Benthological Society 25: 730–755.CrossRefGoogle Scholar
  38. Poole, G. C., 2002. Fluvial landscape ecology: addressing uniqueness within the river discontinuum. Freshwater Biology 47: 641–660.CrossRefGoogle Scholar
  39. Rosi-Marshall, E. J., K. L. Vallis, C. V. Baxter & J. M. Davis, 2016. Retesting a prediction of the River Coninuum Concept: authochthonous versus allochthonous resources in the diets of invertebrates. Freshwater Science 35: 534–543.CrossRefGoogle Scholar
  40. Roy, A. H., A. D. Rosemond, D. S. Leigh, M. J. Paul & J. B. Wallace, 2003. Habitat-specific responses of stream insects to land cover disturbance: biological consequences and monitoring implications. Journal of the North American Benthological Society 22: 292–307.CrossRefGoogle Scholar
  41. Silldorff, E., 2003. Stream invertebrate responses to trout introductions: results from large-scale studies in the central Sierra Nevada and Yosemite National Park. Ph.D. Thesis, University of California, Santa Barbara, CA.Google Scholar
  42. Stanley, E. H., S. G. Fisher & N. B. Grimm, 1997. Ecosystem expansion and contraction in streams. BioScience 47: 427–435.CrossRefGoogle Scholar
  43. Statzner, B. & L. Bêche, 2010. Can biological invertebrate traits resolve effects of multiple stressors on running water ecosystems? Freshwater Biology 55(s1): 80–119.CrossRefGoogle Scholar
  44. Statzner, B. & B. Higler, 1986. Stream hydraulics as a major determinant of benthic invertebrate zonation patterns. Freshwater Biology 16: 127–139.CrossRefGoogle Scholar
  45. Townsend, C. R., 1989. The patch dynamics concept of stream community ecology. Journal of the North American Benthological Society 8: 36–50.CrossRefGoogle Scholar
  46. Townsend, C. R. & A. G. Hildrew, 1994. Species traits in relation to a habitat templet for river systems. Freshwater Biology 31: 265–275.CrossRefGoogle Scholar
  47. U.S. Environmental Protection Agency (USEPA), 2013. National Rivers and Streams Assessment 2013-2014: field Operations Manual—Wadeable. EPA-841-B-12-007. U.S. Environmental Protection Agency, Office of Water, Washington, D.C.Google Scholar
  48. Vieira, N.K.M., N.L. Poff, D.M. Carlisle, S.R. Moulton II, M.L. Koski & B.C. Kondratieff, 2006. A database of lotic invertebrate traits for North America. US Geological Survey Data Series 187,
  49. Winemiller, K. O., A. S. Flecker & D. J. Hoeinghaus, 2010. Patch dynamics and environmental heterogeneity in lotic ecosystems. Journal of the North American Benthological Society 29: 84–99.CrossRefGoogle Scholar
  50. Yuan, L.L. 2006. Estimation and application of macroinvertebrate tolerance values. EPA/600/P-04/116-F, US Environmental Protection Agency, National Center for Environmental Assessment, Washington, D.C.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sierra Nevada Aquatic Research LaboratoryUniversity of CaliforniaMammoth LakesUSA
  2. 2.Department of Ecology, Evolution, and Marine BiologyUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Marine Science InstituteUniversity of CaliforniaSanta BarbaraUSA
  4. 4.Pacific Southwest Research Station, USDA, Forest ServiceFresnoUSA

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