Skip to main content Skip to main navigation menu Skip to site footer
Type: Article
Published: 2023-07-31
Page range: 107-117
Abstract views: 189
PDF downloaded: 10

Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA

University of Illinois, Department of Entomology, Urbana, IL, 61801, USA
University of Illinois, Illinois Natural History Survey, Champaign, IL, 61801, USA
Plecoptera biogeography conservation distribution modeling aquatic insects


Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: Acroneuria abnormis (Newman, 1838), A. frisoni Stark & Brown, 1991, A. internata (Walker, 1852) and A. lycorias (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for A. abnormis and A. internata. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss.


  1. Bracken, J.T., Davis, A.Y., O’Donnell, K.M., Barichivich, W.J., Walls, S.C. & Jezkova T. (2022) Maximizing species distribution model performance when using historical occurrences and variables of varying persistency. Ecosphere, 13, e3951.
  2. Cao, Y., Cummings, K., Hinz, L., Douglass, S.A., Stodola, A.P. & Holtrop, A.M. (2017) Reconstructing the natural distribution of individual unionid mussel species and species diversity in wadeable streams of Illinois, USA, with reference to stream bioassessment. Freshwater Science, 36, 669–682.
  3. Cao, Y., DeWalt, R.E., Robinson, J.L., Tweddale, T., Hinz, L. & Pessino, M. (2013) Using Maxent to Model the Historic Distributions of Stonefly Species in Illinois streams: The Effects of Regularization and Threshold Selections. Ecological Modelling, 259, 30–39.
  4. DeWalt, R.E., Favret, C. & Webb, D.W. (2005) Just How Imperiled Are Aquatic Insects? A Case Study of Stoneflies (Plecoptera) in Illinois. Annals of the Entomological Society of America, 98, 941–950.[0941:JHIAAI]2.0.CO;2
  5. DeWalt, R.E. & Grubbs, S.A. (2011) Updates to the Stonefly Fauna of Illinois and Indiana. Illiesia, 7, 31–50.
  6. DeWalt, R.E. & Ower, G.D. (2019) Ecosystem Services, Global Diversity, and Rate of Stonefly Species Descriptions (Insecta: Plecoptera). Insects, 10, 99.
  7. DeWalt, R.E, Grubbs, S.A, Armitage, B., Baumann, R., Clark, S. & Bolton, M. (2016) Atlas of Ohio Aquatic Insects: Volume II, Plecoptera. Biodiversity Data Journal, 4, e10723.
  8. Elith, J. & Leathwick, J.R. (2009) Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics, 40, 677–697.
  9. Environmental Systems Research Institute (ESRI) (2022) ArcGIS Pro: Release 3.03. Redlands, California.
  10. Favret, C. & DeWalt, R.E. (2002) Comparing the Ephemeroptera and Plecoptera Specimen Databases at the Illinois Natural History Survey and Using Them to Document Changes in the Illinois Fauna. Annals of the Entomological Society of America, 95, 35–40.[0035:CTEAPS]2.0.CO;2
  11. Grubbs, S.A., Pessino, M. & DeWalt, R.E. (2012) Michigan Plecoptera (Stoneflies): Distribution patterns and an updated species list. Illiesia, 8, 162–173.
  12. Grubbs, S.A., Pessino, M. & DeWalt, R.E. (2013) Distribution patterns of Ohio stoneflies, with an emphasis on rare and uncommon species. Journal of Insect Science, 13 (72), 1–18.
  13. Guisan, A., Broennimann, O., Engler, R., Vust, M., Yoccoz, N.G., Lehmann, A. & Zimmermann, N.E. (2006) Using Niche-Based Models to Improve the Sampling of Rare Species. Conservation Biology, 20, 501–511.
  14. Hill, R.A., Weber, M.H., Leibowitz, S.G., Olsen, A.R. & Thornbrugh, D.J. (2016) The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States. JAWRA Journal of the American Water Resources Association, 52, 120–128.
  15. Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199, 142–152.
  16. Hogan, P. & DeWalt, R.E. (2023) Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA, Dryad, Dataset.
  17. Inoue, K., Stoeckl, K. & Geist, J. (2017) Joint species models reveal the effects of environment on community assemblage of freshwater mussels and fishes in European rivers. Diversity and Distributions, 23, 284–296.
  18. Kass, J., Muscarella, R., Galante, P., Bohl, C., Pinilla-Buitrago, G., Boria, R., Soley-Gaurdia, M. & Anderson, R. (2021) ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution, 12, 1602–1608.
  19. Kirk, M.A. & Wissinger, S.A. (2020) Assessment of Long-Term Trends in Fish Distributions at Multiple Scales Decreases Uncertainty Associated with Historical Datasets. Environmental Management, 66, 136–148.
  20. Labay, B., Cohen, A.E., Sissel, B., Hendrickson, D.A., Martin, F.D. & Sarkar, S. (2011) Assessing Historical Fish Community Composition Using Surveys, Historical Collection Data, and Species Distribution Models. PLOS ONE, 6, e25145.
  21. Lobo, J.M., Jiménez-Valverde, A. & Real, R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145–151.
  22. Liu, C., Berry P.M., Dawson, T.P. & Pearson, R.G. (2005) Selecting thresholds of occurrence in the predictions of species distributions. Ecography, 28, 385–393.
  23. Liu C., Newell, G. & White M. (2016) On the selection of thresholds for predicting species occurrence with presence-only data. Ecology and Evolution, 6, 337–348.
  24. Martínez-Freiría, F., Tarroso, P., Rebelo, H. & Brito, J.C. (2016) Contemporary niche contraction affects climate change predictions for elephants and giraffes. Diversity and Distributions, 22, 432–444.
  25. Mattingly, R.L., Herricks, E.E. & Johnston D.M. (1993) Channelization and levee construction in Illinois: review and implications for management. Environmetal Management, 17, 781–795.
  26. Mehrabi, Z. & Naidoo, R. (2022) Shifting baselines and biodiversity success stories. Nature, 601, 17–18.
  27. Merow, C., Smith, M.J. & Silander Jr., J.A. (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058–1069.
  28. Monsarrat, S., Novellie, P., Rushworth, I. & Kerley, G. (2019) Shifted distribution baselines: neglecting long-term biodiversity records risks overlooking potentially suitable habitat for conservation management. Philosophical Transactions of the Royal Society B: Biological Sciences, 374, 20190215.
  29. Pauly, D. (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends in Ecology & Evolution, 10, 430.
  30. Pessino M., Chabot, E. T., Giordano, R. & DeWalt, R.E. (2014) Refugia and postglacial expansion of Acroneuria frisoni Stark & Brown (Plecoptera: Perlidae) in North America. Freshwater Science, 33, 232–249.
  31. Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E. & Blair, M.E. (2017) Opening the black box: an open-source release of Maxent. Ecography, 40, 887–893.
  32. Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.
  33. R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Austria.
  34. Soga, M. & Gaston, K.J. (2018) Shifting baseline syndrome: causes, consequences, and implications. Frontiers in Ecology and the Environment, 16, 222–230.
  35. Stewart, K.W. & Stark, B.P. (2002) Nymphs of the North American Stonefly Genera. 2nd Edition. The Caddis Press, Columbus, Ohio, 510 pp.
  36. Tingley, M.W. & Beissinger, S.R. (2009) Detecting range shifts from historical species occurrences: new perspectives on old data. Trends in Ecology & Evolution, 24, 625–633.
  37. Urban M.A. & Rhoads, B.L. (2003) Catastrophic human-induced change in stream-channel planform and geometry in an agricultural watershed, Illinois, USA. Annals of the Association of American Geographers, 93, 783–796.
  38. Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J. & Elith, J. (2022) Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92, e1486, 1–27.
  39. von Takach B., Steele, B.C., Moore, H., Murphy, B.P. & Banks, S.C. (2020) Patterns of niche contraction identify vital refuge areas for declining mammals. Diversity and Distributions, 26, 1467–1482.
  40. Worth, J.R.P., Williamson, G.J., Sakaguchi, S., Nevill, P.G. & Jordan, G.J. (2014) Environmental niche modelling fails to predict Last Glacial Maximum refugia: niche shifts, microrefugia or incorrect paleoclimate estimates? Global Ecology and Biogeography, 23, 1186–1197.