sustainability Article A multi-Criteria Wetland Suitability Index for Restoration across Ontario’s Mixedwood Plains Sally J. Medland 1, Richard R. Shaker 1,2,3,4,* , K. Wayne Forsythe 1,2,3, Brian R. Mackay 2,3 and Greg Rybarczyk 5,6,7 1 Department of Geography & Environmental Studies, Ryerson University, Toronto, ON M5B 2K3, Canada;
[email protected] (S.J.M.);
[email protected] (K.W.F.) 2 Graduate Programs in Environmental Applied Science & Management, Ryerson University, Toronto, ON M5B 2K3, Canada;
[email protected] 3 Graduate Program in Spatial Analysis, Ryerson University, Toronto, ON M5B 2K3, Canada 4 Department of Geography, University at Buffalo, Buffalo, NY 14261, USA 5 University of Michigan-Flint, Flint, MI 48502, USA;
[email protected] 6 The Michigan Institute for Data Science (MIDAS), Ann Arbor, MI 48108, USA 7 The Centre for Urban Design and Mental Health, London SW9 7QF, UK * Correspondence:
[email protected]; Tel.: +1-416-979-5000 Received: 7 November 2020; Accepted: 24 November 2020; Published: 28 November 2020 Abstract: Significant wetland loss (~72%; 1.4 million hectares) in the Province of Ontario, Canada, has resulted in damage to important ecosystem services that mitigate the effects of global change. In response, major agencies have set goals to halt this loss and work to restore wetlands to varying degrees of function and area. To aid those agencies, this study was guided by four research questions: (i) Which physical and ecological landscape criteria represent high suitability for wetland reconstruction? (ii) Of common wetland suitability metrics, which are most important? (iii) Can a multi-criteria wetland suitability index (WSI) effectively locate high and low wetland suitability across the Ontario Mixedwood Plains Ecozone? (iv) How do best sites from the WSI compare and contrast to both inventories of presettlement wetlands and current existing wetlands? The WSI was created based on seven criteria, normalized from 0 (low suitability) to 10 (high suitability), and illustrated through a weighted composite raster.