Wetlands Ecology and Management

, Volume 27, Issue 2–3, pp 207–221 | Cite as

Hydrogeomorphic modeling of low-marsh habitat in coastal Georgian Bay, Lake Huron

  • J. Daniel WellerEmail author
  • Patricia Chow-Fraser
Original Paper


Potential impacts of global climate change on the amount of low-marsh habitat in coastal wetlands of the Great Lakes are unknown, which may have important implications for the Great Lakes fish community that use such habitat. We developed a generalized linear model that uses only hydrogeomorphic (HGM) features and lake elevations to predict the extent of low marsh in coastal wetlands of eastern and northern Georgian Bay. The McMaster Coastal Wetland Inventory was used as a reference dataset to train the model, while best available data were assembled to create a digital elevation model that was used to derive all HGM features at a lake elevation of 176.17 m (International Great Lakes Datum 1985). The best predictive model included depth, slope, and exposure as HGM variables, yielding an area under the curve (AUC) score of 0.83. We classified the model output into low-marsh and open-water habitat using a threshold value identified by maximizing the true skill statistic. The classified model output had sensitivity and specificity scores of 0.80 and 0.75, respectively, and correctly identified 81% of the low-marsh units present in the reference dataset with an average 60% areal overlap between the model prediction and reference dataset. We applied the model to two external datasets to check model performance, and found the lowest AUC to be 0.79, with associated sensitivity and specificity scores of 0.65 and 0.77, respectively. Applying this model with future water-level scenarios should provide a cost-effective alternative for forecasting changes in the amount of low marsh-habitat in Georgian Bay.


Coastal wetlands Georgian Bay Water levels Low marsh Hydrogeomorphology Modeling 



With regards to the DEM developed for this project, it was produced by McMaster University based on Canadian Hydrographic Service charts and/or data, pursuant to CHS Direct User Licence No. 2016-1121-1260-M. The incorporation of data sourced from CHS in this product shall not be construed as constituting an endorsement by CHS of this project. This product does not meet the requirements of the Charts and Nautical Publications Regulations, 1995 under the Canadian Shipping Act, 2001. Official charts and publications, corrected and up-to-date, must be used to meet the requirements of those regulations. This work was supported in part through an Ontario Graduate Scholarship for Dan Weller and a research contract from Environment and Climate Change Canada. We appreciate the comments from two anonymous reviewers whose input improved the quality of this manuscript.


  1. Albert DA, Wilcox DA, Ingram JW, Thompson TA (2005) Hydrogeomorphic classification for Great Lakes coastal wetlands. J Great Lakes Res 31:129–146Google Scholar
  2. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232Google Scholar
  3. Angel JR, Kunkel KE (2010) The response of Great Lakes water levels to future climate scenarios with an emphasis on Lake Michigan-Huron. J Great Lakes Res 36:51–58Google Scholar
  4. Angradi TR, Pearson MS, Bolgrien DW, Bellinger BJ, Starry MA, Reschke C (2013) Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary. J Great Lakes Res 39(4):536–546Google Scholar
  5. Assel RA, Quinn FH, Sellinger CE (2004) Hydroclimatic factors of the recent record drop in Laurentian Great Lakes water levels. Bull Am Meteor Soc 85(8):1143–1152Google Scholar
  6. Baedke SJ, Thompson TA (2000) A 4,700-year record of lake level and isostasy for Lake Michigan. J Great Lakes Res 26(4):416–426Google Scholar
  7. Bourgeau-Chavez L, Endres S, Battaglia M, Miller ME, Banda E, Laubach Z, Higman P, Chow-Fraser P, Marcaccio J (2015) Development of a bi-national Great Lakes coastal wetland and land use map using three-season PALSAR and Landsat imagery. Remote Sensing 7(7):8655–8682Google Scholar
  8. Boyd LM (2017) Monitoring wetlands during water-level transition periods. Master’s Thesis, McMaster University, Hamilton, ONGoogle Scholar
  9. Brazner J, Sierzen ME, Keough JR, Tanner DK (2001) Assessing the ecological importance of coastal wetlands in a large lake context. Verhandlungen des Internationalen Verein Limnologie 26:1950–1961Google Scholar
  10. Burrough PA, McDonell RA (1998) Principles of geographical information systems. Oxford University Press, New York, p 190Google Scholar
  11. Canadian Hydrographic Service (2013) Standards for hydrographic surveys. CHS survey management guidelines, 2nd Edition.
  12. Ciborowski JJH, Niemi GJ, Brady VJ, Doka SE, Johnson LB, Keough JR, Mackey SD, Uzarski DG (2009) Ecosystem responses to regulation-based water level changes in the Upper Great Lakes. White paper for the International Joint Commission, International Upper Great Lakes Study, p 38Google Scholar
  13. Costanza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O’Neill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of the world’s ecosystem services and natural capital. Nature 387(6630):253–260Google Scholar
  14. Cvetkovic M, Chow-Fraser P (2011) Use of ecological indicators to assess the quality of Great Lakes coastal wetlands. Ecol Ind 11(6):1609–1622Google Scholar
  15. Deleo JM (1993) Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account or uncertainty. In: Proceedings of the second international symposium on uncertainty modelling and Analysis. IEEE Computer Society Press, College Park, MD, pp 318–325Google Scholar
  16. Deleo JM, Campbell G (1990) The fuzzy receiver operating characteristic function and medical decisions with uncertainty. In: Proceedings of the first international symposium on uncertainty modelling and analysis, IEEE Computer Society Press, College Park, MD, pp 694–699Google Scholar
  17. Duarte C, Kalff J (1986) Littoral slope as a predictor of the maximum biomass of submerged macrophyte communities. Limnol Oceanogr 31(5):1072–1080Google Scholar
  18. Duarte C, Kalff J (1990) Patterns in the submerged macrophyte biomass of lakes and the importance of the scale of the analysis in interpretation. Can J Fish Aquat Sci 47(2):357–363Google Scholar
  19. Duarte C, Kalff J, Peters R (1986) Patterns in biomass and cover of aquatic macrophytes in lakes. Can J Fish Aquat Sci 43(10):1900–1908Google Scholar
  20. Environment Canada (2002) Where land meets water: understanding wetlands of the Great Lakes. Environment Canada, Toronto, Ontario, CanadaGoogle Scholar
  21. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49Google Scholar
  22. Fonseca M, Whitfield PE, Kelly NM, Bell SS (2002) Modeling seagrass landscape pattern and associated ecological attributes. Ecol Appl 12(1):218–237Google Scholar
  23. Fracz A, Chow-Fraser P (2013) Impacts of declining water levels on the quantity of fish habitat in coastal wetlands of eastern Georgian Bay, Lake Huron. Hydrobiologia 702(1):151–169Google Scholar
  24. Gathman JP, Albert DA, Burton TM (2005) Rapid plant community response to a water level peak in northern Lake Huron coastal wetlands. J Great Lakes Res 31:160–170Google Scholar
  25. Gronewold AD, Clites AH, Smith JP, Hunter TS (2013) A dynamic graphical interface for visualizing projected, measured, and reconstructed surface water elevations on the earth’s largest lakes. Environ Modell Softw 49:34–39Google Scholar
  26. Hanrahan JL, Kravtsov SV, Roebber PJ (2010) Connecting past and present climate variability to the water levels of Lakes Michigan and Huron. Geophys Res Lett. Google Scholar
  27. Hebb AJ, Mortsch LD, Deadman PJ, Cabrera AR (2013) Modeling wetland vegetation community response to water-level change at Long Point, Ontario. J Great Lakes Res 39(2):191–200Google Scholar
  28. Hutchinson MF (1989) A new procedure for gridding elevation and stream line data with automatic removal of pits. J Hydrol 106:211–232Google Scholar
  29. Ingram J, Holmes K, Grabas G, Watton P, Potter B, Gomer T, Stow N (2004) Development of a coastal wetland database for the Great Lakes Canadian Shoreline, Final Report. The Great Lakes CommissionGoogle Scholar
  30. Jude DJ, Pappas J (1992) Fish utilization of Great Lakes coastal wetlands. J Great Lakes Res 18(4):651–672Google Scholar
  31. Keddy P (1982) Quantifying within-lake gradients of wave energy: interrelationships of wave energy, substrate particle-size and shoreline plants in Axe Lake, Ontario. Aquat Bot 14(1):41–58Google Scholar
  32. Keddy P (1984a) Plant zonation on lakeshores in Nova Scotia: a test of the resource specialization hypothesis. J Ecol 72(3):797–808Google Scholar
  33. Keddy P (1984b) Quantifying a within-lake gradient of wave energy in Gillfillan Lake, Nova-Scotia. Can J Bot Rev Can Bot 62(2):301–309Google Scholar
  34. Keddy PA, Reznicek AA (1986) Great Lakes vegetation dynamics: the role of fluctuating water levels and buried seeds. J Great Lakes Res 12(1):25–36Google Scholar
  35. Keough J, Thompson T, Guntenspergen G, Wilcox D (1999) Hydrogeomorphic factors and ecosystem responses in coastal wetlands of the Great Lakes. Wetlands 19(4):821–834Google Scholar
  36. Leblanc JP, Weller JD, Chow-Fraser P (2014) Thirty-year update: changes in biological characteristics of degraded muskellunge nursery habitat in southern Georgian Bay, Lake Huron, Canada. J Great Lakes Res 40(4):870–878Google Scholar
  37. Leira M, Cantonati M (2008) Effects of water-level fluctuations on lakes: an annotated bibliography. Hydrobiologia 613(1):171–184Google Scholar
  38. Lemein T, Albert DA, Del Giudice Tuttle E (2017) Coastal wetland vegetation community classification and distribution across environmental gradients throughout the Laurentian Great Lakes. J Great Lakes Res 43(4):658–669Google Scholar
  39. Lofgren BM, Quinn FH, Clites AH, Assel RA, Eberhardt AJ, Luukkonen CL (2002) Evaluation of potential impacts on Great Lakes water resources based on climate scenarios of two GCMs. J Great Lakes Res 28(4):537–554Google Scholar
  40. Mayer T, Edsall T, Munawar M (2004) Factors affecting the evolution of coastal wetlands of the Laurentian Great Lakes: an overview. Aquat Ecosyst Health Manage 7(2):171–178Google Scholar
  41. Midwood JD (2012) Assessing change in fish habitat and communities in coastal wetlands of Georgian Bay. PhD Thesis. McMaster University, Hamilton, OntarioGoogle Scholar
  42. Midwood J, Chow-Fraser P (2010) Mapping floating and emergent aquatic vegetation in coastal wetlands of eastern Georgian Bay, Lake Huron, Canada. Wetlands 30(6):1141–1152Google Scholar
  43. Midwood JD, Chow-Fraser P (2012) Changes in aquatic vegetation and fish communities following 5 years of sustained low water levels in coastal marshes of eastern Georgian Bay, Lake Huron. Glob Chang Biol 18(1):93–105Google Scholar
  44. Midwood J, Rokitnicki-Wojcik D, Chow-Fraser P (2012) Development of an inventory of coastal wetlands for eastern Georgian Bay, Lake Huron. ISRN Ecol 2012:1–13Google Scholar
  45. Minc LD (1997) Great Lakes coastal wetlands: an overview of abiotic factors affecting their distribution, form, and species composition. Mich Nat Feature Invent, LansingGoogle Scholar
  46. Mortsch LD (1998) Assessing the impact of climate change on the Great Lakes shoreline wetlands. Clim Chang 40(2):391–416Google Scholar
  47. Mortsch LD, Quinn FH (1996) Climate change scenarios for Great Lakes Basin ecosystem studies. Limnol Oceanogr 41(5):903–911Google Scholar
  48. Ontario Ministry of Natural Resources (2013) Provincial Digital Elevation Model: Tiled Dataset, version 3.0 [computer file]. Peterborough: Ontario Ministry of Natural ResourcesGoogle Scholar
  49. Ontario Ministry of Natural Resources (2014) Ontario wetland evaluation system northern manual. Technical Manual V1.3. Peterborough, ON, pp 40Google Scholar
  50. Ontario Ministry of Natural Resources and Forestry (2016) Provincial digital elevation model technical specifications. Queen’s Printer for Ontario.
  51. Quinlan C, Mulamoottil G (1987) The effects of water level fluctuations on three Lake Ontario shoreline marshes. Can Water Resour J 12(1):64–77Google Scholar
  52. Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, New York, pp 359–360Google Scholar
  53. Quinn FH, Sellinger CE (2006) A reconstruction of Lake Michigan-Huron water levels derived from tree ring chronologies for the Period 1600–1961. J Great Lakes Res 32(1):29–39Google Scholar
  54. Randall RG, Minns CK, Cairns VW, Moore JE (1996) The relationship between an index of fish production and submerged macrophytes and other habitat features at three littoral areas in the Great Lakes. Can J Fish Aquat Sci 53:35–44Google Scholar
  55. Sierszen ME, Morrice JA, Trebitz AS, Hoffman JC (2012) A review of selected ecosystem services provided by coastal wetlands of the Laurentian Great Lakes. Aquat Ecosyst Health Manag 15(1):92–106Google Scholar
  56. Wei A, Chow-Fraser P (2008) Testing the transferability of a marsh-inundation model across two landscapes. Hydrobiologia 600:41–47Google Scholar
  57. Wei A, Chow-Fraser P, Albert D (2004) Influence of shoreline features on fish distribution in the Laurentian Great Lakes. Can J Fish Aquat Sci 61(7):1113–1123Google Scholar
  58. Weller JD, Chow-Fraser P (2018) Simulated changes in extent of Georgian Bay low-marsh habitat under multiple lake levels. Wetl Ecol Manag (in press)Google Scholar
  59. Wilcox D, Meeker J (1991) Disturbance effects on aquatic vegetation in regulated and unregulated lakes in northern Minnesota. Can J Bot 69(7):1542–1551Google Scholar
  60. Wilcox DA, Nichols SJ (2008) The effects of water-level fluctuations on vegetation in a Lake Huron wetland. Wetlands 28(2):487–501Google Scholar
  61. Wilcox DA, Xie Y (2007) Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling. J Great Lakes Res 33(4):751–773Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of BiologyMcMaster UniversityHamiltonCanada

Personalised recommendations