An Application of the Effective Mesh Size

Assessing the connectivity of natural systems in the Golden Horseshoe: an application of the effective mesh size

Amal Siddiqui

A thesis submitted in conformity with the requirements for the degree of Master of Forest Conservation John H. Daniels' Faculty of Landscape, Architecture, and Design University of Toronto

Assessing the connectivity of natural systems in the Golden Horseshoe: an application of the effective mesh size

Amal Siddiqui

Master of Forest Conservation

John H. Daniels' Faculty of Landscape, Architecture, and Design University of Toronto

2021

Abstract

The Golden Horseshoe (GH) is a densely populated and rapidly developing region located in southern Ontario. It houses valuable agricultural lands and sensitive ecological features, including the

Niagara Escarpment, Oak Ridges Moraine, and Ontario's Greenbelt, a permanently protected band of nearly 2 million acres. Urban development in the GH has accelerated fragmentation of natural cover and degraded its connectivity and quality. As urbanisation continues to pressure natural systems, it is critical to understand impacts on natural cover by monitoring and detecting changes over time. To assess changes in natural cover fragmentation and connectivity, the effective mesh size (meff) methodology was applied to the standard land cover data (SOLRIS) for the GH. We analysed meff across political and ecological boundaries and tested the metric at various spatial scales. The effective mesh size declined across the study area, indicating a general loss of connectivity throughout the GH despite protective policies, notably

2 in the Oak Ridges Moraine, which declined from 3.15 to 1.68 km . Our findings suggest that meff has the potential to be used as an indicator, as it provides a quantitative measure of a baseline condition upon which it is possible to monitor changes and establish management and policy targets. However, to implement the effective mesh size as an environmental indicator, we recommend finer resolution and more inclusive natural cover mapping. In addition, we recommend monitoring to be complemented with field-based data to improve interpretability of the effective mesh size values and better understand the impact of fragmentation on the ecology of natural systems. A. Siddiqui 3

Acknowledgments

Firstly, I would like to extend my thanks to Mitacs, for funding this research and making my project possible. Thank you to my internal supervisor, Dr. Danijela Puric-Mladenovic, whose knowledge and expertise guided this work to completion. She has been inspiring, patient, and always ready to answer my technical questions. I would also like to thank my external supervisor, Jackie Hamilton, who gave me the opportunity me to contribute to the meaningful work of the Greenbelt Foundation. My experience with the Foundation was incredibly fulfilling and enjoyable, despite the COVID-19 pandemic's unpredictability and restrictions. Special thanks to Anna Shortly and Kathy McPherson for their help and feedback! Lastly, I want to thank my cohort, friends, and family for their support. A. Siddiqui 4

Table of Contents

Abstract ______2 Acknowledgments ______3 Table of Contents______4 List of Tables ______5 List of Figures ______6 List of Appendices ______7 Introduction ______8 Background ______8 Objectives ______11 Methodology ______12 Results ______16 Discussion ______26 Conclusion & Recommendations ______29 References ______31 Appendices ______33

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List of Tables

Table 1: Description of the reporting (planning) units analysed in the study. ______12

Table 2: Effective Mesh Size values inside and outside the Greenbelt (GH0) for 2011 and 2016. ______16

Table 3: Effective mesh size and density across the ecoregions of the GH for 2011 and 2016. ______22

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List of Figures

Figure 1: Reporting units in the Golden Horseshoe. ______13

Figure 2: Reporting units for the Greenbelt area (a) and its policy designations (b). ______13

Figure 3: Effective mesh size CBC (a) and CUT (b) across the Greenbelt’s policy designations in 2011 and 2016. ______16

2 Figure 4: Comparison of the effective mesh size in km (meff) and density (Seff) across upper and single-tier municipalities in the Golden Horseshoe, between 2011 and 2016. ______19

2 Figure 5:Effective mesh size in km (meff) CBC across municipalities of the Golden Horseshoe. ______20

2 Figure 6: Effective mesh size CBC (meff) in km across lower and single-tier municipalities of the Golden Horseshoe. ______20

Figure 7: Distribution of effective mesh size values in lower and single-tier municipalities of the GH. ____ 21

Figure 8: Ecoregions of the Golden Horseshoe. ______22

Figure 9: Effective mesh size CUT (a) and CBC (b) across secondary watersheds in the Golden Horseshoe, representing 2011 and 2016. ______23

Figure 10: The relative effective mesh size (km2) distributed across secondary watersheds in the GH. _____ 24

Figure 11: Effective mesh size CBC (km2) for tertiary watersheds in the Golden Horseshoe for 2011 (a) and 2016 (b). ______25

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List of Appendices

Appendix 1:List of sources for all spatial data. ______33

Appendix 2: Reclassification scheme for SOLRIS 2.0 and 3.0. ______34

Appendix 3: Effective mesh size (meff) and density (Seff) across policy designations in the Greenbelt for 2011 and 2016.______35

Appendix 4: Effective mesh size (meff) and density (Seff) in 2011 and 2016 across upper- and single-tier municipalities in the Golden Horseshoe. ______36

Appendix 5: Report of meff and Seff for lower and single-tier municipalities across the GH, in 2011 and 2016. ______37

Appendix 6: Effective mesh size (meff) and density (Seff) for the secondary watersheds within the GH. ____ 44

Appendix 7: Effective mesh size (meff) and density (Seff) across tertiary watersheds in the GH, in 2011 and 2016. ______45

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Introduction

Background

Urbanization is at the forefront of pressures on biodiversity in Canada, resulting in an ever- increasing number of species-at-risk and habitat degradation (Spang et al., 2012). The resulting decline in ecosystem health is significantly amplified in Canada’s most populated and developed region, southern Ontario. Historically, the biodiversity of this region was devastated by land clearing for settlement and agriculture. However, natural cover and biodiversity still decline due to rapid urbanization and natural cover fragmentation. To promote sustainable development and reduce pressure on natural environment, Ontario has introduced several modes of legislation over the past two decades, such as the Growth Plan for the Greater Golden Horseshoe in 2004 and its accompanying Greenbelt Plan in 2005. The Growth Plan specifies the boundary of the Golden Horseshoe (GH). The GH of southern Ontario is a rapidly developing metropolitan region, home to over 65% of Ontario’s population, and is located in the heart of the Great Lakes region (Ali, 2008). The GH region encompasses the Greater Toronto Area, including sensitive, ecologically valuable landforms like the Niagara Escarpment and Oak Ridges Moraine (OMMAH, 2017a). In 2005, the Greenbelt Act established Ontario’s Greenbelt, a permanently protected band spanning nearly 2 million acres of agricultural and natural lands within the GH (OMMAH, 2017a). The establishment of greenbelts around urban areas dates back to the pre-industrial era in the United Kingdom when they served to stop urban growth and protect agricultural lands from urban encroachment (Ali, 2008). Greenbelt roles have since expanded to include the preservation of natural vegetation and sensitive natural areas. Today, greenbelts have been implemented in several countries, including South Korea, New Zealand, the United States, and Canada (Ali, 2008). Specifically, Ontario’s Greenbelt provides an estimated CAD $2.7 billion in ecological goods and services annually to those in and around the Greenbelt (Wilson, 2008). Its natural assets also create opportunities for tourism and recreation, which add over CAD $1 billion of marketable value (Fung & Conway, 2007; Wilson, 2008). However, the Greenbelt’s ecological services are reliant on ecosystem structure and function, making it imperative that the Province of Ontario closely monitor and identify problems within these systems to maintain and improve their value. Therefore, to maintain and improve the Greenbelt’s provision of ecological goods and services, it is imperative that the Province of Ontario monitors changes in natural cover. The Greenbelt Plan includes policies that restrict urban development in ecologically significant areas (OMMAH, 2017a). However, policies within the Greenbelt Plan related to natural systems have been criticized for multiple reasons. The primary critique is weak and inconsistent policy language. The Greenbelt Plan states that the expansion of infrastructure “shall avoid key natural heritage features (…) A. Siddiqui 9 unless need has been demonstrated” (OMMAH, 2017a). The lack of clear and specific definitions in policy can allow industrial activity and development to proceed where it may directly impact sensitive ecosystems (Smith, 2009). The second criticism is that in some regions, the Greenbelt Plan overlaps with the Oak Ridges Moraine Conservation Plan or the Niagara Escarpment Plan, each with distinct objectives and policies. Though the more restrictive plan should apply, the lack of coordination across policies may result in an inconsistent approach to protecting natural systems across the region (Fung & Conway, 2007; Smith, 2009). The final criticism of the Greenbelt Plan’s protective policies is the lack of effective monitoring for natural system health. With respect to infrastructure development and aggregate extraction, the Plan aims to “minimize negative impacts on and disturbance of the (natural) features or their related functions and, where reasonable, maintain or improve connectivity” (OMMAHa, 2017, p. 40). The Greenbelt Plan recognises key natural heritage features to be preserved within its natural heritage system (NHS).

“Key natural heritage features include: habitat of endangered species and threatened species; fish habitat; wetlands; life science areas of natural and scientific interest (ANSIs); significant valleylands; significant woodlands; significant wildlife habitat (including habitat of special concern species); sand barrens, savannahs and tallgrass prairies; alvars.” (OMMAHa, 2017, p. 25).

The NHS was mapped and prepared by the Ministry of Natural Resources and Forestry with SOLRIS as one of the primary base datasets (OMNRF, 2018). To monitor the performance of the Greenbelt Plan’s policies for natural systems, the province introduced two performance indicators for natural cover: percent woodland and wetland cover under the Protected Countryside, Niagara Escarpment Plan, and the Oak Ridges Moraine Conservation Plan (OMMAH, 2015). This approach presents several limitations. Though the two performance indicators are useful for general quantitative measures, they are insufficient for effective long-term monitoring of the Greenbelt’s natural systems. Alone, area-based indicators do not provide enough information to assess the quality or function of natural systems (Burke & Nol, 2000). Details such as ecological integrity, the state of biodiversity, or changes in connectivity and fragmentation cannot be inferred from quantitative measures of woodlands and wetlands. Furthermore, the two indicators are the outcome of available mapping and are thus sensitive to the resolution and detail of mapping (e.g. SOLRIS).

Natural connectivity and fragmentation

Landscape connectivity is an integral characteristic of functioning ecosystems and landscapes. Fragmentation of natural cover is the process that results in reducing its connectivity. Fragmentation occurs when a large patch of the natural area is divided into smaller fragments. Physical barriers among A. Siddiqui 10 the patches, such as highways, agriculture, urban settlements, and others, prevent mobility between the fragments and reduce the amount of functional habitat (Spanowicz & Jaeger, 2019). As patch size decreases, so does the amount of core habitat available within it. Core habitats are relatively undisturbed, interior areas of habitat patches, and host ecologically sensitive species (Jaeger et al., 2016). When core areas are lost, the ecosystem composition changes to favour more tolerant species (Spanowicz & Jaeger, 2019). In Ontario, land development continues to severely fragment natural cover, making it necessary to conserve and restore natural corridors between habitat patches (Koen et al., 2014; Whitelaw & Eagles, 2007). Without connectivity, populations become isolated, species are unable to move and migrate to new or nearby habitat patches or adapt to the changing climate (A. Smith, 2009). Continuous natural cover is critical for the maintenance of functioning, resilient ecosystems, and the provision of ecosystem goods and services (Fung & Conway, 2007). Furthermore, interconnected habitats also enhance genetic diversity, making communities more resilient to stochastic events and abiotic stresses (Pineda & Halffter, 2004; A. Smith, 2009). On southern Ontario’s rapidly changing landscape, it is vital to incorporate natural connectivity as one of the monitoring indicators. An indicator of landscape connectivity can be used to inform planning and decision-making, and thus help to preserve natural linkages, particularly in habitats of at-risk species. Several techniques are available to measure the extent of landscape connectivity and fragmentation of natural cover. A few such measures include the average patch size, landscape dissection index, the effective mesh size, and many others (Raumer et al., 2004). The effective mesh size, meff, is a metric developed by Dr. Jochen Jaeger and has been used to measure landscape connectivity in over 20 countries worldwide. Because of its intuitive interpretation and simplicity, it has been applied to wide to a variety of disciplines, from land-use planning to conservation (Jaeger et al., 2016). The meff approach has been successfully implemented in Switzerland’s Monitoring Sustainable Development project, or MONET (Jaeger et al., 2016). MONET’s recent addition of the effective mesh size to a list of over 150 indicators reveals the metric’s ease of use and applicability (Bertiller & Jaeger, 2007; Jaeger et al., 2016). The European Environment Agency (EEA) also adopted the effective mesh size to monitor changes in landscape fragmentation over time, particularly due to urban expansion (Jaeger et al., 2016). The effective mesh size is a quantitative measure based on the probability that two random points on the landscape will be connected without any obstruction (Raumer et al., 2004). In the context of natural connectivity, it is the probability that two random individuals can locate each other without encountering a barrier (Jaeger, 2000). The meff method translates barriers to movement as a mesh on the landscape. Higher values of meff denote better natural cover connectivity on the landscape (Jaeger, 2000; Jaeger et al., 2016). The effective mesh size decreases as barriers to movement, such as urbanized areas A. Siddiqui 11 or highways, agricultural or other relevant lands increase (Inkoom et al., 2017). The second measure, the effective mesh density Seff, is a derivative of meff. It is a measure of landscape fragmentation and yields the effective number of meshes per unit area (Jaeger et al., 2016). Seff increases with the rate of fragmentation. Essentially, a well-connected landscape will result in a larger mesh or a high meff size with fewer meshes or a low Seff (Jaeger, 2000). The effective mesh size can be analysed in two ways. In the original CUT procedure, the maximum meff value (i.e. completely unfragmented) equals the area of the reporting unit (Moser et al., 2007). The reporting unit's boundary is interpreted as a barrier, and fragmenting element, thereby lowering the overall meff. Moser et al. (2007) modified the CUT equation to introduce connections across boundaries of reporting units, titled the cross-boundary connections (CBC) procedure. The CBC method is “area-proportionately additive”; a reporting unit contributes to a broader landscape composed of multiple reporting units of similar size (Jaeger et al., 2016; Moser et al., 2007). According to Moser et al. (2007), a reporting unit is one part of a whole, and decidedly interacts with systems outside the polygon (e.g. administrative, ecological) boundary. Though fragmentation and activities outside a reporting unit may be difficult to alter, a holistic understanding of connectivity is important for long-term planning. However, both CUT and CBC procedures are valuable fragmentation measures and can be compared to better understand trends within and outside reporting units.

Objectives Using regional land cover data, this study tested the effective mesh size methodology to the Golden Horseshoe of southern Ontario. The aim was to assess the state of connectivity across the GH and test the effective mesh method's potential to future monitoring strategies. The study’s main objectives were:

i. To test whether effective mesh size (meff) is a suitable indicator for landscape connectivity in the Golden Horseshoe;

ii. To test the application of the effective mesh size (meff) and effective mesh density (Seff) to available spatial data and mapping (SOLRIS); and, iii. To understand and quantify the state of connectivity and fragmentation of natural cover across various political and ecological scales in the Greenbelt and Golden Horseshoe.

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Methodology Study area. This study considered the entirety of the Golden Horseshoe (GH) of southern Ontario. The GH is one of Canada's fastest growing regions, housing sensitive agricultural lands, natural, and hydrological systems, many of which are permanently protected by the Greenbelt Plan (OMMAH, 2019). Different reporting units based on ecological characteristics, political boundaries, and boundaries of government policies and plans were selected to conduct the analysis (Table 1). Specifically, this study considered upper- and single-tier municipalities, and lower-tier municipalities for political boundaries. The Greenbelt and its policy designations were also included as reporting units (Table 1; Fig. 2). The ecological analysis considered secondary watersheds, tertiary watersheds, and ecoregions in the Golden Horseshoe (Table 1; Fig. 1). In total, the Golden Horseshoe overlaps with seven secondary watersheds and twenty-nine tertiary watersheds. Watershed boundaries are typically delineated by the shape and area of land drained by a body of water and its associated streams and tributaries (Kayembe & Mitchell, 2018). For this analysis, the area covered by the GH does not necessarily contain entire watersheds or ecoregions. The GH contains three ecoregions. Ecoregion 5E, Georgian Bay, is composed primarily of mixed forest ecosystems. Lake Simcoe-Rideau, 6E, is dominated by agricultural lands, deciduous, and mixed forests, as well as a small percentage of wetlands (OMNRF, 2007). Lastly, Lake Erie-Lake Ontario, or 7E, is known as the Carolinian region and is the most biodiverse ecoregion in Canada (OMNRF, 2007). 7E is also highly developed, with over 85% of its area converted to urban, suburban, and agricultural lands (OMNRF, 2007).

Table 1: Description of the reporting (planning) units analysed in the study.

Reporting unit Description Golden Horseshoe The entirety of the Golden Horseshoe, as defined by the Growth Plan.

Golden Horseshoe (Greenbelt The entirety of the Golden Horseshoe, with the Greenbelt area, removed. removed) Greenbelt The Greenbelt area, as defined by the Greenbelt Plan. Greenbelt designations The policy designations in the Greenbelt area: Oak Ridges Moraine, Niagara Escarpment, Protected Countryside, Urban River Valley. Ecoregions The ecoregions in the Golden Horseshoe (5E, 6E, 7E). Upper and single-tier municipalities The upper- and single-tier municipalities in the GH. Lower and single-tier municipalities The lower- and single-tier municipalities in the GGH, including First Nation reserves. Secondary watersheds The secondary watersheds in the GH, according to the Ontario Watershed Boundary source data. The GH does not cover the entire watersheds. Tertiary watersheds The tertiary watersheds in the GH, according to the Ontario Watershed Boundary source data. The GH does not cover the entire watersheds.

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a. b. c.

d. e. f.

Figure 1: Reporting units in the Golden Horseshoe. Golden Horseshoe with the Greenbelt removed (a), upper and single-tier municipalities (b), lower- and single-tier municipalities (c), ecoregions (d), secondary watersheds (e), and tertiary watersheds (f).

a. b.

Figure 2: Reporting units for the Greenbelt area (a) and its policy designations (b).

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Data collection and preparation. Municipal and ecological boundaries were obtained from Land Information Ontario’s open data portal (see Appendix 1). To classify natural cover, standard land inventory data for Southern Ontario, the Southern Ontario Land Resource Information System (SOLRIS), was used. Two SOLRIS land cover data sets were used: 2.0 and 3.0. SOLRIS 2.0 reflects land cover classes up to 2011, while SOLRIS 3.0 reflects an updated classification up to 2016-2017. The pixel resolution (15 meters) was consistent between the two data sets. All data layers were projected to the spatial reference NAD83 UTM Zone 17N. The extent of the GH was selected according to the municipalities provided in the Growth Plan for the Greater Golden Horseshoe (OMMAH, 2019). All boundary layers were then clipped to the extent of the GH to prepare the reporting units. Analysis. To conduct the effective mesh size analysis, it was necessary to derive a map of the natural cover for two time periods. The land cover classes from SOLRIS 2.0 and 3.0 were reclassified as either natural or non-natural (for classification details see Appendix 2), and then converted to vector format for analysis. To detect changes in connectivity and fragmentation between the two time periods (the approximate five-year period) the effective mesh size was used. The effective mesh size, using both CUT and CBC methods, was calculated in ArcGIS 10.7 with a tool created by Girvetz et al. (2007) based on Dr. 2 Jaeger’s methodology. The change in meff (km ) between time periods was calculated as � −

�, where a positive change indicates a gain in effective mesh size and a negative change indicates a loss in effective mesh size.

Finally, the effective mesh density (Seff) was derived. Effective mesh density is a measure of the number of patches per unit area (Canedoli et al., 2018). Seff can be scaled as required according to the size of reporting units, making it a practical but simple metric of fragmentation (Canedoli et al., 2018; Raumer et al., 2004). All data preparation and analyses were completed in ArcGIS 10.7. From meff, the effective 2 mesh density per 1000 km , Seff, was calculated as: 1,000 � = m Mapping inconsistencies. There were a number of mapping changes between SOLRIS 2.0 and 3.0. Notably, SOLRIS 2.0 does not entirely cover the County of Peterborough, missing large portions of the Municipality of Trent Lakes, Town of North Kawartha, and the Town of Havelock-Belmont-Methuen (Fig. 13). The City of Kawartha Lakes, a single-tier municipality, is also cut off in the north in SOLRIS 2.0. Though these issues were corrected in SOLRIS 3.0, our analysis required the extent of both data sets to be the same, so SOLRIS 3.0 was clipped to the incomplete extent of the previous version. As a result, the effective mesh size and density derived in this study cannot represent the true state of connectivity in the County of Peterborough, and to a lesser extent, the City of Kawartha Lakes. Additionally, SOLRIS 3.0 incorporated a number of class corrections, in which a land cover class in SOLRIS 2.0 was corrected and A. Siddiqui 15 changed to a different class in SOLRIS 3.0. These mapping corrections, as well as the incomplete extent of SOLRIS 2.0, are illustrated below in Fig. 13. Results for the County of Peterborough that were affected by mapping corrections are marked by an asterisk (*) and distinguished in figures. Not all class corrections were necessarily relevant to natural cover. For example, a correction from treed swamp to thicket swamp would still classify as natural. Similarly, a correction from impervious built-up area to transportation would still classify as non-natural. However, corrections between non-natural and natural cover types carried forward to the reclassification and analysis, making it impossible to overlook the effect of mapping changes on the findings for the affected regions. Inconsistent extent between data sets was a problem isolated to the municipalities highlighted in Fig. 13, but mapping differences were not. The remainder of the GH area was similarly affected by the issue of class corrections, though they were sparse and less severe.

Figure 3: Class corrections between SOLRIS 2.0 and 3.0, depicted by the grey aggregates over the municipal boundary polygons. Though scattered across the rest of the GH (pink), the grey class corrections are concentrated in the beige municipalities. A. Siddiqui 16

Results Overall GH Connectivity. 2 The effective mesh size was greater outside the Greenbelt (GH0), with a maximum of 6.38 km in 2 2011. Outside the Greenbelt (GH0), the effective mesh size decreased from 6.37 to 3.47 km , and mesh density per 1000 km2 increased from 156.72 to 287.9 meshes (Table 2). The maximum mesh density of 1030 meshes per 1000 km2 occurred in 2016 within the Greenbelt. The cross-boundary connection (CBC)

method consistently yielded slightly larger meff values and smaller Seff values in comparison to their CUT equivalents (Table 2). Inside the Greenbelt, the effective mesh size decreased from 1.42 to 0.97 km2, 2 while the mesh density per 1000 km increased from 703.51 to 1030.11 meshes. For both meff and Seff, the

magnitude of change over the five-year period was greater in GH0 compared to the Greenbelt (Table 2).

Table 2: Effective Mesh Size values inside and outside the Greenbelt (GH0) for 2011 and 2016. Effective Mesh Size is based on both CUT and CBC. The area outside the Greenbelt extends only to the boundary of the Golden Horseshoe.

m CUT m CBC S CUT S CBC Year Location eff eff eff eff (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 Inside Greenbelt 1.3707 1.4214 729.52 703.51 2016 0.9378 0.9708 1066.27 1030.11

2011 Outside Greenbelt (GH0) 6.3676 6.3807 157.04 156.72 2016 3.4655 3.4734 288.55 287.90

Connectivity within the Greenbelt’s designations. The Greenbelt area falls under four designations: Niagara Escarpment, Oak Ridges Moraine, Protected Countryside, and Urban River Valleys. Urban river valleys were designated in the revised Greenbelt Plan of 2017 (OMMAH, 2017).

a. b.

Figure 4: Effective mesh size CBC (a) and CUT (b) in 2011 and 2016 across the four policy designations areas within the Greenbelt.

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In both 2011 and 2016, meff is highest in the Oak Ridges Moraine and lowest in Urban River

Valleys (Fig. 3). The largest change in meff occurred in the Oak Ridges Moraine, which declined from 3.15 to 1.68 km2 between 2011 and 2016, followed by the Protected Countryside and the Niagara

Escarpment (Fig. 3, 4a). The change in meff in the Protected Countryside and Niagara Escarpment areas were similar, at 0.15 and 0.13 km2 respectively.

Niagara Escarpment Oak Ridges Moraine

Urban River Valleys Protected Countryside

Niagara Escarpment Oak Ridges Moraine

Urban River Valleys Protected Countryside

Figure 4: Change in effective mesh size (meff) and mesh density (Seff) from 2011 to 2016 across policy designations in the Greenbelt. Both meff and Seff values are based on the CBC method. A. Siddiqui 18

The effective mesh size of urban river valleys did not change over the five-year period with the CUT method. With the CBC method, URV showed an improvement in both effective mesh size and density (Fig. 4). In particular, Seff decreased from 6065.49 to 5817.03 meshes (Fig. 5b). However, the Greenbelt is composed primarily of land under ORM, PC, and NE designations, so the portion of the Greenbelt covered by urban river valleys is significantly smaller than the other designations (Fig. 4). There was no improvement in effective mesh size or density in ORM, PC, or NE areas (see Appendix 3 for a full report of the Greenbelt designation analysis).

Connectivity within upper and single-tier municipalities of the Golden Horseshoe. In total, the Golden Horseshoe houses twenty upper and single-tier municipalities. For both time periods, CBC and CUT methods yielded similar results across municipalities (Fig. 6, 7). As such, Fig. 6 and 7 illustrate only results of the CBC method, as it accounts for connectivity across reporting unit boundaries. 2 At the scale of upper and single-tier municipalities, the mean meff was 0.881 km in 2011 and 2 2 0.736 km in 2016. The mean Seff per 1000 km was 6470.5 meshes in 2011 and 6685.5 meshes in 2016.

Overall, the municipalities with the lowest Seff and highest meff in both time periods were the County of Simcoe and the City of Kawartha Lakes (Fig. 6b). In general, the effective mesh size for most 2 2 municipalities were below 5 km (Fig. 6a). In 2011, the maximum meff of 5.89 km and minimum Seff of 172.18 meshes occurred in the County of Simcoe (Fig. 6), disregarding the County of Peterborough due to mapping inconsistencies between time periods. Over the five-year period, the County of Simcoe’s meff 2 marginally decreased to 5.29 km , while Seff increased to 192 meshes (Fig. 6). There were three municipalities with effective mesh densities greater than 10,000 meshes: City of

Toronto, City of Brantford, and Regional Municipality of Waterloo (Fig. 6b). The minimum meff of 0.028 2 km and maximum Seff of approximately 35617 meshes occurred in the City of Toronto. Excepting the

City of Toronto, Brantford, and Guelph, all upper and single-tier municipalities showed a decline in meff and a corresponding increase in Seff (Fig. 6). Particularly, the effective mesh density of the City of Toronto improves from 35617 meshes in 2011 to 33179 meshes, while the City of Brantford improves from 26980 meshes in 2011 to 26044 meshes in 2016. Due to the inverse relationship between the two metrics and different size of planning units, a change in meff did not correspond to an equivalent change in 2 Seff. As such, change in mesh density per 1000 km was easier to visualise on a map (Fig. 6b).

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2 Figure 4: Comparison of the effective mesh size in km (meff) and density (Seff) across upper and single-tier municipalities in the Golden Horseshoe, between 2011 and 2016.

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2 Figure 5: Effective mesh size in km (meff) CBC across municipalities of the Golden Horseshoe. (a) Upper and single-tier municipalities in 2011; (b) Upper and single-tier municipalities in 2016. Ranges were determined by quantiles in ArcGIS 10.7.

a. b.

+ +

2 Figure 6: Effective mesh size CBC (meff) in km across lower and single-tier municipalities of the Golden Horseshoe. (a) Lower and single-tier municipalities in 2011; (b) Lower and single-tier municipalities in 2016. Ranges were determined by natural breaks in ArcGIS 10.7.

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Connectivity within lower and single-tier municipalities of the Golden Horseshoe. The analysis of lower and single-tier municipalities reveals variation that was not captured at the scale of upper municipalities (Fig. 8). Over 60 municipalities of the 96 considered in the analysis had an 2 meff between 0 and 1 km in both 2011 and 2016, indicating low levels of connectivity across the majority of the Golden Horseshoe (Fig. 7).

Figure 7: Distribution of effective mesh size values in lower and single-tier municipalities of the GH.

2 2 The mean meff in 2011 was 1.18 km , which decreased to 1.00 km in 2016. The mean Seff per 1000 km2 was 9824.12 meshes in 2011 and 10129.59 meshes in 2016. The least connected lower municipalities were the City of Mississauga and Markham, with an effective mesh size of approximately 2 2 0.01 km (Fig. 8). Seff per 1000 km was highest in Mississauga at 81079.57 meshes. The maximum meff occurred in the Township of Severn, part of the County of Simcoe, at 21.97 km2 in 2011 and 21.85 km2 in 2016 (Fig. 8).

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Connectivity (and fragmentation) within the GH ecoregions.

The Golden Horseshoe boundary encompasses three ecoregions based on Ecological Land Classification (OMMAH, 2019): Georgian Bay (5E) in the north, Lake Simcoe-Rideau (6E) which covers the greatest area in the GH, and Lake Erie-Lake Ontario (7E) in the south (Fig. 9).

Figure 8: Ecoregions of the Golden Horseshoe.

Ecoregion 5E overlapped with areas with mapping inconsistencies, marked in Table 3 with an asterisk. Ecoregion 6E was significantly more well-connected than 7E; In 2011, the meff of ecoregion 6E 2 2 was 4.14 km compared to the meff of 7E at 0.16 km (Table 3). Over the five-year period, the meff of 2 2 ecoregion 6E decreased from 4.14 km to 2.35 km , while the meff of 7E showed a marginal decline from 0.16 km2 to 0.15 km2. In 2011, effective mesh density per 1000 km2 across ecoregions 6E and 7E ranged from approximately 242 to 6443 meshes. In 2016, Seff ranged from approximately 426 to 6692 meshes (Table 3).

Table 3: Effective mesh size and density (CUT and CBC) across the ecoregions of the GH for 2011 and 2016.

Seff CUT Seff CBC Ecoregion Ecoregion meff CUT meff CBC Year 2 2 (per 1000 (per 1000 Name Code (km ) (km ) km2) km2) 2011 *Georgian Bay *5E *30.85 *40.20 *32.42 *24.88 Lake Simcoe - 6E 2.90 4.14 344.74 241.82 Rideau Lake Erie - Lake 7E 0.16 0.16 6443.35 6375.02 Ontario 2016 *Georgian Bay *5E *17.72 *21.09 *56.43 *47.42 Lake Simcoe - 6E 1.90 2.35 526.18 426.08 Rideau Lake Erie - Lake 7E 0.15 0.15 6692.20 6603.98 Ontario

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Connectivity (and fragmentation) within the watersheds. The Golden Horseshoe overlaps with seven secondary watersheds, some of which extend past the boundary of the GH. As such, this analysis did not capture or reflect the effective mesh size and density of entire watersheds (Fig. 10).

* b.

Figure 9: Effective mesh size CUT (a) and CBC (b) across secondary watersheds in the Golden Horseshoe, representing 2011 and 2016.

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The effective mesh size (based on both CUT and CBC) decreased consistently across all watersheds except Lake Erie. The minimum meff occurred in Lake Erie for both time periods at 2 approximately 0.063 km . The maximum meff occurred in the Northern Lake Ontario and Niagara River (NLONR) watershed, which also showed the most marked decline in connectivity. The effective mesh size in the NLONR watershed dropped from approximately 11.5 to 5.5 km2 (Fig. 11). As the NLONR watershed overlaps with areas of mapping inconsistencies, the second highest meff occurred in the Eastern Georgian Bay watershed, at 3.55 km2 in 2011 and 3.20 km2 in 2016 (Fig. 11, 12).

Figure 10: The relative effective mesh size (km2) distributed across secondary watersheds in the GH. Refer to Appendix 6 for values.

2 Most tertiary watersheds had values of meff well below 25 km . The maximum meff occurred in one sub-watershed, Kawartha Lakes, of the NLONR watershed in both 2011 and 2016 (Fig. 12). Similar to the secondary watershed analysis, sub-watersheds in Lake Erie and Northern Lake Erie regions fell in 2 the lowest range of meff, 0 to 0.5 km . Though the meff of the NLONR was the highest in the secondary watershed analysis (Fig. 11), its contained sub-watersheds revealed a large amount of variation in meff in A. Siddiqui 25

Fig. 12. Similar to the municipal analysis, tertiary watersheds in the southernmost portion of the GH are less connected than those in the northern portion. The minimum meff occurred in Northeast Lake Erie, at 0.002 km2.

Figure 11: Effective mesh size CBC (km2) for tertiary watersheds in the Golden Horseshoe for 2011 (a) and 2016 (b). A. Siddiqui 26

Discussion The Greenbelt. Effective mesh size and density analyses yielded both quantitative measures and a spatial distribution of where fragmentation is occurring (Girvetz et al., 2007). Overall, the trends in meff and Seff across the Greenbelt and the Golden Horseshoe highlighted a region-wide loss in natural connectivity and corresponding increase in fragmentation (Table 2). Decline in connectivity was greater outside the

Greenbelt (GH0), indicating that the Greenbelt Plan’s protective policies for natural features may be causing the intensification of urbanisation and sprawled development outside the protected Greenbelt area, or that policies outside the Greenbelt may be insufficient in protecting natural areas (Fung & Conway, 2007). However, fragmentation also increased within the Greenbelt, despite the protection granted by the Greenbelt Act (Table 2; Fig. 4). This may be a consequence of inconsistencies in classification, as the Greenbelt’s recognised natural heritage system (NHS) is limited by the scale of its source data, SOLRIS (OMNRF, 2018). At its resolution and extent, SOLRIS does not map or include all the features protected under the Greenbelt Plan, such as the habitat of endangered or threatened species. Though the development of the NHS incorporated additional data sources, not all significant natural features were included due to the regional scale of the NHS (OMNRF, 2018). The NHS thus represents an incomplete picture of the true extent of natural systems in the Greenbelt, which may contribute to the loss of natural connectivity seen in Table 2. The analysis of the Greenbelt’s policy designations enabled us to understand where fragmentation is increasing. While the ORM was the most well-connected designation in the Greenbelt in our analysis 2 with an effective mesh size of 3.15 km in 2011 (Fig. 3), it also showed the largest decrease in meff over the five-year period compared to the Urban River Valleys, Niagara Escarpment, and Protected Countryside (Fig. 4). To understand why the natural areas in the Oak Ridges Moraine were particularly impacted, the overseeing policies and area’s history must be considered. The Oak Ridges Moraine Conservation Plan (ORMCP) restricts urban development that may negatively impact natural heritage features, but these restrictions may be counteracted by legacy, or transitional, development. Transitional development in this context refers to approved development decisions that predate the Oak Ridges Moraine Conservation Act of 2001 (Watt, 2016). These developments were consequently never required to adhere to the ORMCP’s policies, despite the recognised ecological significance of the ORM (Watt, 2016). Tackling the issue of transitional development is a challenge, as decisions were scattered across the ORM and never tracked (Watt, 2016). In addition to historical developments, the ORMCP presently does not prohibit aggregate extraction in its designated natural linkage areas, though aggregate policies for extraction in natural core areas are more rigid (OMMAH, 2017b). The plan also allows “low intensity” infrastructure development and agricultural uses in both natural linkage and core areas, which A. Siddiqui 27

2 may contribute to the rapid decline in the ORM’s meff from 3.15 to 1.68 km (OMMAH, 2017b). Policy language and allowances greatly impact the long-term protection and sustainability of the ORM and the Greenbelt as a whole. Large-scale developments are not the only risk to natural connectivity. Scattered, small-scale changes also exacerbate fragmentation when natural corridors and linkages are not preserved between core habitat areas. The Niagara Escarpment designation was the second highest connected region in the Greenbelt (Fig. 3, 4). Connectivity for the Niagara Escarpment decreased slightly over the five years meff from 0.99 to 0.87 km2. A possible reason for the Niagara Escarpment’s relative stability, compared to the ORM and PC, is that the NEP is overseen by an independent agency, the Niagara Escarpment Commission (NEC). The NEC assumes the responsibility of implementing the NEP, and is critical to the consistency of the plan’s application across the Niagara Escarpment (A. Smith, 2009). The ORM and Greenbelt Plan have no such bodies to integrate and properly implement the plans’ policies in their jurisdiction, leaving the responsibility to individual municipalities (Smith, 2009). The NEC thus ensures stronger protection of its natural areas. However, the NEC’s activities may suffer from a lack of funding, which may explain the loss of connectivity in the NE (Whitelaw et al., 2007). According to Fig. 4, the URV’s connectivity increased under the CBC method by 0.007 km2. This may not reflect a true change. Relative to the three major designations, the total area covered by the URVs is significantly smaller (see Fig. 4). The URVs extend in strips below the Greenbelt area, and their narrow geometries may exacerbate differences in mapping between the two SOLRIS data sets used. It was therefore difficult to interpret how connectivity within the URV designation may have been impacted by surrounding activities over the five-year period.

Municipalities of the Golden Horseshoe. In general, the southern portion of the GH was relatively more fragmented than northern regions (e.g. County of Simcoe) (Fig. 7). In both time periods, fragmentation was highest in the City of Toronto, the metropolitan centre of the Golden Horseshoe (Fig. 6). With an effective mesh size of 0.03 km2 in both 2011 and 2016, the City of Toronto is an example of a landscape nearing the maximum degree of natural cover fragmentation (Fig. 6, Appendix 4). As the degree of landscape fragmentation increases, meff approaches zero (Jaeger et al., 2011). The probability of connection, denoted by meff, was almost zero in Toronto, implying that most natural connections were already lost by 2011. This was also true for the City 2 of Brantford, where meff was approximately 0.04 km in both time periods (Fig. 6, Appendix 4). Loss of connectivity over time was observed in the municipalities surrounding the City of Toronto, such as the Halton, York, and Peel regions (Fig. 6, Appendix 4). These findings provide evidence that landscapes undergoing fragmentation correspond to a decrease in meff, as seen in the A. Siddiqui 28

Waterloo and Peel regions (Fig. 6). Meanwhile, regions approaching maximum levels of fragmentation show little to no change in meff, exemplified by Toronto and Brantford (Fig. 6). Over 65% of lower and single-tier municipalities had an effective mesh size between 0 and 1 km2. Some lower and single-tier municipalities even showed marginal improvements in connectivity through an increase in meff (Fig. 8). With smaller reporting units at a constant spatial resolution, the changes in meff over time were marginal. At this scale, minor improvements or losses in effective mesh size could be attributed to class corrections and differences in mapping rather than true change. It was also not feasible to validate all changes. As a result, the effective mesh size applied to lower and single- tier municipalities was less informative and certain as a metric than when it was applied to upper municipalities.

Ecoregions and watersheds. Mapping issues with the source data layer, SOLRIS, carried over to the analysis of ecological reporting units. Ecoregion 5E had both the highest connectivity and greatest loss of connectivity between 2011 and 2016 (Table 3). Since ecoregion 5E overlaps with much of the problematic area between SOLRIS versions, we cannot definitively interpret the state of its natural connectivity from this analysis

(Fig. 9). Ecoregion 7E, which covers the southernmost extent of the GH, was the most fragmented ecoregion in both years. Similar to municipal findings, no significant change in connectivity was observed 2 in 7E over the five-year period due to its initially fragmented state (meff = 0.16 km ). The majority of the GH belongs to ecoregion 6E, Lake Simcoe-Rideau. There was a notable difference between CUT and CBC methods in 6E across both years and methods. In 2011 for example, 2 2 meff CBC was 4.14 km , while meff CUT was 2.90 km (Table 3). A CBC value higher than the CUT value indicates connections with patches across reporting unit boundaries (Moser et al., 2007). In this case, the larger value of meff CBC in ecoregion 6E implies substantial connections across reporting unit boundaries with ecoregions 5E and 7E. The overall trends on both watershed scales were negative, with a loss of connectivity over the five-year period (Fig. 10). The Northern Lake Ontario and Niagara River (NLONR) watershed was the most well-connected watershed in our analysis, containing both northern and central regions of the GH (Fig. 10, 11). Upon finer-scale analysis, the NLONR secondary watershed was composed of mostly 2 fragmented tertiary watersheds, with only one watershed with an meff above 25 km in 2011 and none in 2016 (Fig. 12). When a reporting unit contains regions at two extremes (e.g. high and low connectivity), one unfragmented area with a high meff may compensate for multiple, extensively fragmented ones (Schmeidel & Culmsee, 2016). The influence of highly unfragmented regions on the overall effective mesh size was evident in the NLONR watershed (Fig. 11). The effective mesh size represents a numerical A. Siddiqui 29 probability, and in certain circumstances, the metric may not meaningfully assess the connectivity of larger areas, especially without additional visual analysis.

Limitations of the study. Habitat fragmentation is a process that interacts with other environmental factors, particularly matrix quality and habitat amount, to produce unique responses in different species (Smith et al., 2011).

To understand what meff and Seff represent in terms of ecology and natural system health, this analysis could be further complemented by ecological data, information on habitats and species. The effective mesh size analysis in this study shows the state and change in general connectivity of natural cover, but the explanatory power of these results in reflecting specific ecological processes, habitats, and species requires further investigation (Inkoom et al., 2017). . Future studies should also test the sensitivity of the effective mesh size metric to mapping. Additionally, only one class of barrier was used in this analysis, representing all non-natural cover. This class includes land use for agriculture, transportation and utility lines, and urban infrastructure. However, barrier elements differ by the extent of restriction they place on the movement of species; for example, sustainably managed agricultural fields may act as natural linkages for small mammals and birds, while a completely urbanised patch may not (Jaeger et al., 2016). Our study did not differentiate between barrier elements. In future studies, agricultural and restored lands can be incorporated as non-barrier elements to allow for an in-depth perspective on how different barriers constrain species movement. In this study, mapping inconsistencies affected the reliability of our findings. To detect changes over time, more rigorous and standard mapping is required. The extent of SOLRIS differed between the two versions, leaving part of the study area unaccounted for. Since SOLRIS covers all of southern Ontario, its resolution is adequate for a general, large-scale study. However, SOLRIS does not capture all classes of natural cover; notably, it excludes smaller wetlands and early-successional forests among other cover types that can serve as natural linkages (OMNRF, 2015). SOLRIS’ resolution may thus be inadequate to analyse smaller, finer-scale reporting units, such as lower municipalities and tertiary watersheds. In these units, meff values were possibly under- or over-estimated.

Conclusion & Recommendations The Golden Horseshoe of southern Ontario is one of the most densely populated regions in North America, and its population will continue to grow in the coming decades. In response, demand for housing and infrastructure is expected to increase, increasing pressures on the GH’s ecosystems and biodiversity. Our analysis of the effective mesh size confirmed the impacts of urbanisation and urban A. Siddiqui 30 sprawl on landscape connectivity. Between 2011 and 2016, connectivity of natural cover declined steadily across the entirety of the Golden Horseshoe. The urban centre of the GH, the City of Toronto, was the most fragmented municipality. Our findings also provide evidence that protective policies in the Greenbelt are not completely deterring urban development and subsequent habitat loss and degradation in the GH’s most sensitive and functional ecosystems, particularly the Oak Ridges Moraine. The effective mesh size can be implemented as an indicator of natural cover fragmentation in the Golden Horseshoe to determine regions where fragmentation is increasing or decreasing. Results from effective mesh size analyses can then be linked to land-use changes or developmental activities in areas of interest and inform future planning. To monitor fine-scale changes in natural cover (e.g. lower municipalities or tertiary watersheds), more rigorous mapping is required. With regional data like SOLRIS, we could not distinguish true changes from differences in mapping in smaller reporting units. Based on the successes and challenges of this study, we propose the following:

1. Establish thresholds for natural system connectivity based on current meff values on municipal and watershed scales. Though this task would require extensive research, baseline values for effective mesh size and density are necessary to monitor changes and direct developmental planning (Jaeger et al., 2016). 2. Standardize mapping procedures on the municipal scale across the Golden Horseshoe to improve monitoring effectiveness. In our study, current standardized data (SOLRIS) was not applicable to fine-scale reporting units like lower municipalities. Detecting true changes over time can identify regions of high and low connectivity and help delineate priority areas for conservation. Standard,

finer monitoring across municipalities will also improve the reliability of meff findings. 3. Monitoring should be informed by a combination of spatial data and field-collected data. Field- collected data is critical to assessing habitat quality. Incorporating qualitative data on ecosystem structure and composition will strengthen the effective mesh size methodology and further specify necessary actions for ecosystem protection (Babí Almenar et al., 2019; Jaeger et al., 2016). Before any of the proposed recommendations can be implemented, the lack of coordination across governmental bodies and provincial plans related to natural system health and protection needs to be addressed. Fixing inconsistencies and weaknesses in policy language should be the first step towards long-term monitoring and protection of natural systems in the Golden Horseshoe.

A. Siddiqui 31

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Appendices

Appendix 1:List of sources for all spatial data. Date updated refers to the last major update to data layer.

date layer time name description produced/ source type period updated

Southern Ontario OMNRF. (2015). Southern Ontario Land Resource 2000- Land Resource Regional, ecologically based, land Information System (SOLRIS) Version 2.0: Data 2002; January Information raster cover /land use inventory. 15 m Specifications. 2009- 2015 System Version pixel resolution. https://geohub.lio.gov.on.ca/datasets/southern- 2011 2.0 ontario-land-resource-information-system-solris-2-0

Southern Ontario OMNRF. (2019). Southern Ontario Land Resource Land Resource Regional, ecologically based, land Information System (SOLRIS) Version 3.0: Data 2000- Information raster cover /land use inventory. 15 m April 2019 Specifications. 2015 System Version pixel resolution. https://geohub.lio.gov.on.ca/datasets/southern- 3.0 ontario-land-resource-information-system-solris-3-0

Municipal OMNRF Information Access and Information Boundary - Upper tier and district municipal Management Policy Unit. 2015. vector NA July 2015 Upper Tier and boundaries https://geohub.lio.gov.on.ca/datasets/municipal- District boundary-upper-tier-and-district-1

Municipal OMNRF Information Access and Information Boundary - Lower and single tier municipal Management Policy Unit. 2015. vector NA July 2015 Lower and boundaries https://geohub.lio.gov.on.ca/datasets/municipal- Single Tier boundary-lower-and-single-tier

Ontario OMNRF Mapping and Information Resources Authoritative watershed boundaries Watershed Branch. 2020. vector in Ontario. Secondary and tertiary NA March 2020 Boundaries https://geohub.lio.gov.on.ca/datasets/mnrf::ontario- layers used (OWB) watershed-boundaries-owb

OMNRF Information Access and Information Ecoregion vector Defines ecoregion boundaries NA July 2015 Management Policy Unit. 2015. https://geohub.lio.gov.on.ca/datasets/ecoregion

OMNRF Information Access and Information Defines the boundary of the Greenbelt Outer Management Policy Unit. 2015. vector Greenbelt, as defined by the NA July 2015 Boundary https://geohub.lio.gov.on.ca/datasets/greenbelt- Greenbelt Plan outer-boundary

Defines the following designations OMNRF Information Access and Information in the Greenbelt: Protected Greenbelt Management Policy Unit. 2015. vector Countryside, Oak Ridges Moraine, NA May 2017 Designation https://geohub.lio.gov.on.ca/datasets/greenbelt- Niagara Escarpment, and Urban designation River Valley

date layer time name description produced/ source type period updated A. Siddiqui 34

Appendix 2: Reclassification scheme for SOLRIS 2.0 and 3.0. Reclass value 1 is non-natural cover; 2 is natural cover.

Grid value Class name Reclass value SOLRIS 11 open beach/bar 2 21 open sand dune 2 23 treed sand dune 2 41 open cliff and talus 2 43 treed cliff and talus 2 51 open alvar 2 52 shrub alvar 2 53 treed alvar 2 64 open bedrock 1 65 sparse treed 2 81 open tallgrass prairie 2 82 tallgrass savannah 2 83 tallgrass woodland 2 90 forest 2 91 coniferous forest 2 92 mixed forest 2 93 deciduous forest 2 131 treed swamp 2 135 thicket swamp 2 140 fen 2 150 bog 2 160 marsh 2 170 open water 1 191 plantation 2 192 hedge rows 2 193 tilled 1 201 transportation 1 202 built up area pervious 1 203 built up area impervious 1 204 extraction-aggregate 1 205 extraction-peat/topsoil 1 250 undifferentiated 1

A. Siddiqui 35

Appendix 3:Effective mesh size (meff) and density (Seff) across policy designations in the Greenbelt for 2011 and 2016.

Year Designation meff CUT meff CBC Seff CUT Seff CBC (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 Niagara Escarpment 0.835 0.993 1196.93 1007.32

Oak Ridges Moraine 3.033 3.147 329.69 317.79

Protected Countryside 0.817 0.865 1223.81 1155.50

Urban River Valley 0.078 0.165 12744.64 6065.49 2016 Niagara Escarpment 0.754 0.867 1325.95 1152.77

Oak Ridges Moraine 1.610 1.680 621.19 595.15

Protected Countryside 0.669 0.714 1495.47 1401.30

Urban River Valley 0.081 0.172 12338.50 5817.03

A. Siddiqui 36

Appendix 4: Effective mesh size (meff) and density (Seff) in 2011 and 2016 across upper- and single-tier municipalities in the Golden Horseshoe. Results for both CUT and CBC methods are reported.

Year Upper/Single-Tier Municipality meff CUT meff CBC Seff CUT Seff CBC (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 CITY OF BARRIE 0.151 0.173 6601.32 5766.71 CITY OF BRANTFORD 0.029 0.037 34913.73 26980.18 CITY OF GUELPH 0.096 0.114 10444.87 8735.61 CITY OF HAMILTON 0.311 0.328 3213.56 3045.46 CITY OF KAWARTHA LAKES 2.563 2.807 390.20 356.27 CITY OF ORILLIA 0.090 0.202 11081.99 4948.97 CITY OF TORONTO 0.027 0.028 37553.49 35617.73 COUNTY OF BRANT 0.269 0.272 3712.58 3673.95 COUNTY OF DUFFERIN 0.785 1.034 1274.54 966.83 COUNTY OF NORTHUMBERLAND 1.040 1.426 961.72 701.17 COUNTY OF PETERBOROUGH 38.865 39.099 25.73 25.58 COUNTY OF SIMCOE 5.808 5.891 172.18 169.75 COUNTY OF WELLINGTON 0.391 0.510 2554.57 1962.26 HALDIMAND COUNTY 0.143 0.152 6977.25 6597.03 REGIONAL MUNICIPALITY OF DURHAM 1.323 1.713 755.98 583.90 REGIONAL MUNICIPALITY OF HALTON 0.637 0.648 1570.18 1542.63 REGIONAL MUNICIPALITY OF NIAGARA 0.212 0.217 4724.79 4608.28 REGIONAL MUNICIPALITY OF PEEL 0.273 0.277 3664.09 3612.23 REGIONAL MUNICIPALITY OF WATERLOO 0.079 0.084 12738.14 11865.45 REGIONAL MUNICIPALITY OF YORK 0.829 0.830 1206.36 1204.57 2016 CITY OF BARRIE 0.146 0.157 6850.47 6361.32 CITY OF BRANTFORD 0.030 0.038 33452.04 26044.01 CITY OF GUELPH 0.096 0.115 10445.39 8728.10 CITY OF HAMILTON 0.294 0.311 3399.09 3215.44 CITY OF KAWARTHA LAKES 2.011 2.190 497.38 456.55 CITY OF ORILLIA 0.091 0.119 11042.24 8380.66 CITY OF TORONTO 0.029 0.030 34859.01 33178.84 COUNTY OF BRANT 0.251 0.254 3985.34 3939.05 COUNTY OF DUFFERIN 0.619 0.795 1614.22 1257.53 COUNTY OF NORTHUMBERLAND 1.019 1.215 981.22 822.94 COUNTY OF PETERBOROUGH 17.558 17.603 56.95 56.81 COUNTY OF SIMCOE 5.182 5.288 192.98 189.11 COUNTY OF WELLINGTON 0.282 0.360 3541.34 2774.89 HALDIMAND COUNTY 0.141 0.150 7095.40 6670.79 REGIONAL MUNICIPALITY OF DURHAM 1.174 1.182 852.06 846.06 REGIONAL MUNICIPALITY OF HALTON 0.625 0.636 1600.82 1573.12 REGIONAL MUNICIPALITY OF NIAGARA 0.208 0.213 4812.36 4686.98 REGIONAL MUNICIPALITY OF PEEL 0.255 0.257 3919.36 3890.12 REGIONAL MUNICIPALITY OF WATERLOO 0.075 0.081 13260.05 12324.48 REGIONAL MUNICIPALITY OF YORK 0.591 0.592 1692.64 1689.15

A. Siddiqui 37

Appendix 5:Report of meff and Seff for lower and single-tier municipalities across the GH, in 2011 and 2016. The containing upper municipality is also listed.

Lower/Single-Tier Upper/Single- m CUT m CBC S CUT S CBC Year Type eff eff eff eff Municipality Tier Municipality (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 Alderville First Nation COUNTY OF Mainland 0.24 0.71 4250.45 1417.00 NORTHUMBERLAND CITY OF BARRIE CITY OF BARRIE Mainland 0.15 0.17 6602.24 5767.42 CITY OF BRAMPTON REGIONAL Mainland 0.02 0.02 57792.99 56039.02 MUNICIPALITY OF PEEL CITY OF BRANTFORD CITY OF Mainland 0.03 0.04 35430.70 27118.64 BRANTFORD CITY OF REGIONAL Mainland 0.17 0.22 5875.01 4634.79 BURLINGTON MUNICIPALITY OF HALTON CITY OF CAMBRIDGE REGIONAL Mainland 0.06 0.08 15490.73 12773.92 MUNICIPALITY OF WATERLOO CITY OF GUELPH CITY OF GUELPH Mainland 0.10 0.11 10445.07 8735.32 CITY OF HAMILTON CITY OF HAMILTON Mainland 0.31 0.33 3213.59 3045.48 CITY OF KAWARTHA CITY OF Mainland 2.56 2.81 390.21 356.27 LAKES KAWARTHA LAKES CITY OF KITCHENER REGIONAL Mainland 0.03 0.04 32194.05 28468.71 MUNICIPALITY OF WATERLOO CITY OF MARKHAM REGIONAL Mainland 0.01 0.01 79443.06 77719.94 MUNICIPALITY OF YORK CITY OF REGIONAL Mainland 0.01 0.01 82279.63 81079.57 MISSISSAUGA MUNICIPALITY OF PEEL CITY OF NIAGARA REGIONAL Mainland 0.12 0.12 8173.26 8052.68 FALLS MUNICIPALITY OF NIAGARA CITY OF ORILLIA CITY OF ORILLIA Mainland 0.09 0.20 11079.67 4948.45 CITY OF OSHAWA REGIONAL Mainland 0.04 0.04 23601.59 23350.17 MUNICIPALITY OF DURHAM *CITY OF COUNTY OF Mainland 0.03 0.05 31907.89 21026.82 PETERBOROUGH PETERBOROUGH CITY OF PICKERING REGIONAL Mainland 0.12 0.27 8481.56 3655.57 MUNICIPALITY OF DURHAM CITY OF PORT REGIONAL Mainland 0.18 0.37 5434.24 2671.06 COLBORNE MUNICIPALITY OF NIAGARA CITY OF ST. REGIONAL Mainland 0.02 0.05 41559.63 19848.00 CATHARINES MUNICIPALITY OF NIAGARA CITY OF THOROLD REGIONAL Mainland 0.19 0.25 5380.43 3949.95 MUNICIPALITY OF NIAGARA CITY OF TORONTO CITY OF TORONTO Mainland 0.03 0.03 37551.64 35616.47 CITY OF VAUGHAN REGIONAL Mainland 0.08 0.08 13007.50 12304.04 MUNICIPALITY OF YORK CITY OF WATERLOO REGIONAL Mainland 0.07 0.07 14848.83 14094.27 MUNICIPALITY OF WATERLOO CITY OF WELLAND REGIONAL Mainland 0.02 0.03 43501.35 36404.47 MUNICIPALITY OF NIAGARA COUNTY OF BRANT COUNTY OF BRANT Mainland 0.15 0.15 6704.93 6588.96 *Curve Lake 35A COUNTY OF Mainland 0.31 0.31 3221.53 3181.83 PETERBOROUGH A. Siddiqui 38

*Curve Lake First COUNTY OF Mainland 0.46 0.60 2177.65 1680.43 Nation 35 PETERBOROUGH Glebe Farm 40B COUNTY OF BRANT Mainland 0.04 0.08 27877.71 11976.78 HALDIMAND COUNTY HALDIMAND Mainland 0.14 0.15 6977.51 6597.21 COUNTY *Hiawatha First Nation COUNTY OF Mainland 1.49 3.01 672.05 332.13 36 PETERBOROUGH Mississauga's of REGIONAL Mainland 0.04 0.16 23780.48 6129.28 Scugog Island MUNICIPALITY OF DURHAM Mnjikaning First Nation COUNTY OF Mainland 1.21 7.27 828.36 137.52 32 SIMCOE MUNICIPALITY OF COUNTY OF Mainland 1.81 2.92 551.98 342.00 BRIGHTON NORTHUMBERLAND MUNICIPALITY OF REGIONAL Mainland 1.94 3.64 514.45 274.74 CLARINGTON MUNICIPALITY OF DURHAM MUNICIPALITY OF COUNTY OF Mainland 1.08 2.67 926.71 374.18 PORT HOPE NORTHUMBERLAND MUNICIPALITY OF COUNTY OF Mainland 0.49 0.74 2037.01 1343.62 TRENT HILLS NORTHUMBERLAND *MUNICIPALITY OF COUNTY OF Mainland 109.29 110.04 9.15 9.09 TRENT LAKES PETERBOROUGH New Credit 40A COUNTY OF BRANT Mainland 0.50 0.61 1991.71 1634.62 Six Nations 40 COUNTY OF BRANT Mainland 0.74 0.76 1357.46 1322.40 *Sugar Island 37A COUNTY OF Mainland 0.20 0.21 4964.10 4823.76 PETERBOROUGH TOWN OF AJAX REGIONAL Mainland 0.05 0.06 18370.89 17156.77 MUNICIPALITY OF DURHAM TOWN OF AURORA REGIONAL Mainland 0.05 0.05 19868.12 19477.58 MUNICIPALITY OF YORK TOWN OF BRADFORD COUNTY OF Mainland 0.68 0.86 1477.21 1165.47 WEST GWILLIMBURY SIMCOE TOWN OF CALEDON REGIONAL Mainland 0.48 0.49 2079.15 2049.90 MUNICIPALITY OF PEEL TOWN OF COBOURG COUNTY OF Mainland 0.03 0.04 31564.67 23112.86 NORTHUMBERLAND TOWN OF COUNTY OF Islands 0.00 0.07 NA 14974.86 COLLINGWOOD SIMCOE TOWN OF COUNTY OF Mainland 0.14 0.15 7134.11 6850.75 COLLINGWOOD SIMCOE TOWN OF EAST REGIONAL Mainland 0.98 0.98 1023.90 1022.56 GWILLIMBURY MUNICIPALITY OF YORK TOWN OF ERIN COUNTY OF Mainland 0.31 0.31 3224.53 3224.10 WELLINGTON TOWN OF FORT ERIE REGIONAL Mainland 0.16 0.16 6129.14 6078.27 MUNICIPALITY OF NIAGARA TOWN OF GEORGINA REGIONAL Mainland 2.83 2.83 353.76 353.66 MUNICIPALITY OF YORK TOWN OF GRAND COUNTY OF Mainland 1.07 2.90 933.87 344.26 VALLEY DUFFERIN TOWN OF GRIMSBY REGIONAL Mainland 0.17 0.18 5822.88 5430.95 MUNICIPALITY OF NIAGARA TOWN OF HALTON REGIONAL Mainland 0.61 0.64 1641.64 1569.20 HILLS MUNICIPALITY OF HALTON TOWN OF INNISFIL COUNTY OF Mainland 0.26 0.36 3828.03 2761.58 SIMCOE TOWN OF LINCOLN REGIONAL Mainland 0.10 0.11 10309.14 9291.03 MUNICIPALITY OF NIAGARA TOWN OF MIDLAND COUNTY OF Mainland 0.56 1.12 1798.57 892.44 SIMCOE TOWN OF MILTON REGIONAL Mainland 1.07 1.09 937.65 916.06 MUNICIPALITY OF HALTON A. Siddiqui 39

TOWN OF MINTO COUNTY OF Mainland 0.25 0.26 3957.86 3829.61 WELLINGTON TOWN OF MONO COUNTY OF Mainland 1.26 1.28 790.91 781.19 DUFFERIN TOWN OF NEW COUNTY OF Mainland 0.09 0.09 10921.85 10878.40 TECUMSETH SIMCOE TOWN OF REGIONAL Mainland 0.03 0.05 36775.28 21454.10 NEWMARKET MUNICIPALITY OF YORK TOWN OF NIAGARA- REGIONAL Mainland 0.02 0.02 44417.98 42958.21 ON-THE-LAKE MUNICIPALITY OF NIAGARA TOWN OF OAKVILLE REGIONAL Mainland 0.08 0.09 12674.54 11669.01 MUNICIPALITY OF HALTON TOWN OF COUNTY OF Mainland 0.03 0.03 39267.63 33789.40 ORANGEVILLE DUFFERIN TOWN OF PELHAM REGIONAL Mainland 0.12 0.16 8414.46 6135.59 MUNICIPALITY OF NIAGARA TOWN OF COUNTY OF Mainland 0.58 0.79 1721.29 1273.62 PENETANGUISHENE SIMCOE TOWN OF RICHMOND REGIONAL Mainland 0.07 0.07 14960.57 14925.49 HILL MUNICIPALITY OF YORK TOWN OF COUNTY OF Mainland 0.01 0.02 78750.55 46212.01 SHELBURNE DUFFERIN TOWN OF WASAGA COUNTY OF Mainland 1.66 1.92 603.04 520.04 BEACH SIMCOE TOWN OF WHITBY REGIONAL Mainland 0.11 0.11 9355.20 9306.79 MUNICIPALITY OF DURHAM TOWN OF REGIONAL Mainland 0.72 0.72 1393.25 1392.42 WHITCHURCH- MUNICIPALITY OF STOUFFVILLE YORK TOWNSHIP OF COUNTY OF Mainland 0.53 0.88 1888.42 1135.70 ADJALA- SIMCOE TOSORONTIO TOWNSHIP OF COUNTY OF Mainland 1.51 1.71 660.92 584.88 ALNWICK/HALDIMAND NORTHUMBERLAND TOWNSHIP OF COUNTY OF Mainland 0.25 0.25 4057.34 4053.90 AMARANTH DUFFERIN *TOWNSHIP OF COUNTY OF Mainland 0.30 0.31 3365.13 3258.41 ASPHODEL- PETERBOROUGH NORWOOD TOWNSHIP OF REGIONAL Mainland 0.52 0.58 1937.15 1737.07 BROCK MUNICIPALITY OF DURHAM *TOWNSHIP OF COUNTY OF Mainland 1.49 4.03 672.86 247.89 CAVAN MONAGHAN PETERBOROUGH TOWNSHIP OF COUNTY OF Mainland 0.07 0.08 13434.58 12864.31 CENTRE WELLINGTON WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.76 2.10 1313.60 475.81 CLEARVIEW SIMCOE TOWNSHIP OF COUNTY OF Mainland 0.69 0.78 1458.88 1275.33 CRAMAHE NORTHUMBERLAND *TOWNSHIP OF COUNTY OF Mainland 28.55 50.96 35.02 19.62 DOURO-DUMMER PETERBOROUGH TOWNSHIP OF EAST COUNTY OF Mainland 0.18 0.20 5610.84 5092.34 GARAFRAXA DUFFERIN TOWNSHIP OF ESSA COUNTY OF Mainland 0.89 1.28 1118.13 780.15 SIMCOE TOWNSHIP OF COUNTY OF Mainland 0.19 0.20 5333.92 5013.66 GUELPH/ERAMOSA WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.29 0.50 3480.01 1999.22 HAMILTON NORTHUMBERLAND *TOWNSHIP OF COUNTY OF Mainland 17.08 35.45 58.55 28.21 HAVELOCK- PETERBOROUGH BELMONT-METHUEN A. Siddiqui 40

TOWNSHIP OF KING REGIONAL Mainland 0.44 0.46 2248.48 2195.71 MUNICIPALITY OF YORK TOWNSHIP OF COUNTY OF Mainland 0.06 0.06 16902.59 16888.69 MAPLETON WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.28 0.29 3545.59 3438.77 MELANCTHON DUFFERIN TOWNSHIP OF COUNTY OF Mainland 1.60 1.83 625.89 546.46 MULMUR DUFFERIN TOWNSHIP OF REGIONAL Mainland 0.19 0.23 5246.15 4277.74 NORTH DUMFRIES MUNICIPALITY OF WATERLOO *TOWNSHIP OF COUNTY OF Mainland 11.56 12.89 86.50 77.59 NORTH KAWARTHA PETERBOROUGH TOWNSHIP OF ORO- COUNTY OF Mainland 2.42 2.45 414.01 408.10 MEDONTE SIMCOE *TOWNSHIP OF COUNTY OF Mainland 0.36 0.39 2790.06 2531.93 OTONABEE-SOUTH PETERBOROUGH MONAGHAN TOWNSHIP OF COUNTY OF Mainland 0.57 0.60 1746.07 1662.46 PUSLINCH WELLINGTON TOWNSHIP OF COUNTY OF Mainland 7.73 8.28 129.43 120.75 RAMARA SIMCOE TOWNSHIP OF REGIONAL Mainland 2.01 2.16 497.29 462.70 SCUGOG MUNICIPALITY OF DURHAM *TOWNSHIP OF COUNTY OF Mainland 0.77 0.78 1294.72 1287.92 SELWYN PETERBOROUGH TOWNSHIP OF COUNTY OF Mainland 21.92 21.97 45.62 45.52 SEVERN SIMCOE TOWNSHIP OF COUNTY OF Mainland 6.66 8.09 150.10 123.54 SPRINGWATER SIMCOE TOWNSHIP OF TAY COUNTY OF Islands 0.06 0.14 16744.86 7326.81 SIMCOE TOWNSHIP OF TAY COUNTY OF Mainland 2.35 2.77 425.31 361.33 SIMCOE TOWNSHIP OF TINY COUNTY OF Mainland 8.03 8.19 124.52 122.09 SIMCOE TOWNSHIP OF REGIONAL Mainland 1.60 1.71 626.15 583.70 UXBRIDGE MUNICIPALITY OF DURHAM TOWNSHIP OF REGIONAL Mainland 0.65 0.76 1530.11 1311.03 WAINFLEET MUNICIPALITY OF NIAGARA TOWNSHIP OF REGIONAL Mainland 0.04 0.04 24129.33 22419.21 WELLESLEY MUNICIPALITY OF WATERLOO TOWNSHIP OF COUNTY OF Mainland 1.13 1.69 884.92 592.50 WELLINGTON NORTH WELLINGTON TOWNSHIP OF WEST REGIONAL Mainland 0.12 0.15 8355.59 6863.89 LINCOLN MUNICIPALITY OF NIAGARA TOWNSHIP OF REGIONAL Mainland 0.05 0.06 18303.23 17305.75 WILMOT MUNICIPALITY OF WATERLOO TOWNSHIP OF REGIONAL Mainland 0.07 0.08 13411.26 12777.63 WOOLWICH MUNICIPALITY OF WATERLOO 2016 Alderville First Nation COUNTY OF Mainland 0.24 0.69 4133.10 1441.95 NORTHUMBERLAND CITY OF BARRIE CITY OF BARRIE Mainland 0.15 0.16 6851.41 6362.15 CITY OF BRAMPTON REGIONAL Mainland 0.02 0.02 57834.97 56083.63 MUNICIPALITY OF PEEL CITY OF BRANTFORD CITY OF Mainland 0.03 0.04 33918.79 26168.82 BRANTFORD CITY OF REGIONAL Mainland 0.17 0.22 5818.17 4595.37 BURLINGTON MUNICIPALITY OF HALTON CITY OF CAMBRIDGE REGIONAL Mainland 0.06 0.08 15587.74 13026.74 MUNICIPALITY OF WATERLOO CITY OF GUELPH CITY OF GUELPH Mainland 0.10 0.11 10445.58 8727.85 A. Siddiqui 41

CITY OF HAMILTON CITY OF HAMILTON Mainland 0.29 0.31 3399.12 3215.46 CITY OF KAWARTHA CITY OF Mainland 2.01 2.19 497.39 456.55 LAKES KAWARTHA LAKES CITY OF KITCHENER REGIONAL Mainland 0.03 0.04 32088.46 28377.23 MUNICIPALITY OF WATERLOO CITY OF MARKHAM REGIONAL Mainland 0.01 0.01 85573.81 83444.28 MUNICIPALITY OF YORK CITY OF REGIONAL Mainland 0.01 0.01 82888.15 81612.84 MISSISSAUGA MUNICIPALITY OF PEEL CITY OF NIAGARA REGIONAL Mainland 0.12 0.12 8550.19 8433.58 FALLS MUNICIPALITY OF NIAGARA CITY OF ORILLIA CITY OF ORILLIA Mainland 0.09 0.12 11040.68 8379.12 CITY OF OSHAWA REGIONAL Mainland 0.04 0.04 24072.49 23781.94 MUNICIPALITY OF DURHAM *CITY OF COUNTY OF Mainland 0.03 0.05 31793.11 21517.65 PETERBOROUGH PETERBOROUGH CITY OF PICKERING REGIONAL Mainland 0.12 0.21 8629.39 4682.59 MUNICIPALITY OF DURHAM CITY OF PORT REGIONAL Mainland 0.16 0.35 6378.68 2880.24 COLBORNE MUNICIPALITY OF NIAGARA CITY OF ST. REGIONAL Mainland 0.02 0.05 45361.04 19803.93 CATHARINES MUNICIPALITY OF NIAGARA CITY OF THOROLD REGIONAL Mainland 0.22 0.30 4608.18 3387.32 MUNICIPALITY OF NIAGARA CITY OF TORONTO CITY OF TORONTO Mainland 0.03 0.03 34857.31 33177.70 CITY OF VAUGHAN REGIONAL Mainland 0.08 0.08 12953.70 12292.92 MUNICIPALITY OF YORK CITY OF WATERLOO REGIONAL Mainland 0.07 0.07 14765.03 14022.99 MUNICIPALITY OF WATERLOO CITY OF WELLAND REGIONAL Mainland 0.02 0.03 43241.39 36259.06 MUNICIPALITY OF NIAGARA COUNTY OF BRANT COUNTY OF BRANT Mainland 0.14 0.14 7032.97 6906.50 *Curve Lake 35A COUNTY OF Mainland 0.32 0.33 3087.10 3049.71 PETERBOROUGH *Curve Lake First COUNTY OF Mainland 0.45 0.59 2203.70 1693.65 Nation 35 PETERBOROUGH Glebe Farm 40B COUNTY OF BRANT Mainland 0.04 0.08 27858.43 12000.56 HALDIMAND COUNTY HALDIMAND Mainland 0.14 0.15 7095.66 6670.97 COUNTY *Hiawatha First Nation COUNTY OF Mainland 1.42 2.92 704.89 342.05 36 PETERBOROUGH Mississauga's of REGIONAL Mainland 0.04 0.16 23275.64 6090.99 Scugog Island MUNICIPALITY OF DURHAM Mnjikaning First Nation COUNTY OF Mainland 1.07 5.90 931.46 169.62 32 SIMCOE MUNICIPALITY OF COUNTY OF Mainland 1.78 2.91 560.89 343.88 BRIGHTON NORTHUMBERLAND MUNICIPALITY OF REGIONAL Mainland 1.94 1.95 516.05 512.23 CLARINGTON MUNICIPALITY OF DURHAM MUNICIPALITY OF COUNTY OF Mainland 1.06 1.28 945.77 781.89 PORT HOPE NORTHUMBERLAND MUNICIPALITY OF COUNTY OF Mainland 0.48 0.73 2074.64 1376.68 TRENT HILLS NORTHUMBERLAND *MUNICIPALITY OF COUNTY OF Mainland 45.92 46.68 21.78 21.42 TRENT LAKES PETERBOROUGH New Credit 40A COUNTY OF BRANT Mainland 0.50 0.61 2008.74 1646.60 Six Nations 40 COUNTY OF BRANT Mainland 0.67 0.69 1501.48 1457.33 *Sugar Island 37A COUNTY OF Mainland 0.20 0.21 4969.48 4829.94 PETERBOROUGH A. Siddiqui 42

TOWN OF AJAX REGIONAL Mainland 0.06 0.06 17445.55 16050.17 MUNICIPALITY OF DURHAM TOWN OF AURORA REGIONAL Mainland 0.05 0.05 22082.92 21615.09 MUNICIPALITY OF YORK TOWN OF BRADFORD COUNTY OF Mainland 0.67 0.85 1484.26 1173.47 WEST GWILLIMBURY SIMCOE TOWN OF CALEDON REGIONAL Mainland 0.45 0.45 2227.64 2211.61 MUNICIPALITY OF PEEL TOWN OF COBOURG COUNTY OF Mainland 0.02 0.04 40717.44 27347.42 NORTHUMBERLAND TOWN OF COUNTY OF Islands 0.00 0.07 NA 15065.45 COLLINGWOOD SIMCOE TOWN OF COUNTY OF Mainland 0.10 0.11 9642.13 9148.74 COLLINGWOOD SIMCOE TOWN OF EAST REGIONAL Mainland 0.80 0.80 1254.24 1252.58 GWILLIMBURY MUNICIPALITY OF YORK TOWN OF ERIN COUNTY OF Mainland 0.28 0.28 3631.98 3631.82 WELLINGTON TOWN OF FORT ERIE REGIONAL Mainland 0.15 0.16 6470.77 6426.02 MUNICIPALITY OF NIAGARA TOWN OF GEORGINA REGIONAL Islands 0.24 1.19 4166.41 841.69 MUNICIPALITY OF YORK TOWN OF GEORGINA REGIONAL Mainland 1.57 1.57 638.53 638.21 MUNICIPALITY OF YORK TOWN OF GRAND COUNTY OF Mainland 1.00 2.19 1003.20 456.85 VALLEY DUFFERIN TOWN OF GRIMSBY REGIONAL Mainland 0.12 0.13 8468.62 7996.17 MUNICIPALITY OF NIAGARA TOWN OF HALTON REGIONAL Mainland 0.58 0.60 1709.83 1663.69 HILLS MUNICIPALITY OF HALTON TOWN OF INNISFIL COUNTY OF Mainland 0.23 0.33 4256.97 2995.74 SIMCOE TOWN OF LINCOLN REGIONAL Mainland 0.09 0.10 10848.99 9724.98 MUNICIPALITY OF NIAGARA TOWN OF MIDLAND COUNTY OF Mainland 0.41 0.80 2416.53 1252.82 SIMCOE TOWN OF MILTON REGIONAL Mainland 1.07 1.08 936.39 923.50 MUNICIPALITY OF HALTON TOWN OF MINTO COUNTY OF Mainland 0.25 0.26 3995.41 3864.46 WELLINGTON TOWN OF MONO COUNTY OF Mainland 0.79 0.79 1260.95 1259.69 DUFFERIN TOWN OF NEW COUNTY OF Mainland 0.08 0.08 12587.40 12531.13 TECUMSETH SIMCOE TOWN OF REGIONAL Mainland 0.03 0.05 34358.74 20631.96 NEWMARKET MUNICIPALITY OF YORK TOWN OF NIAGARA- REGIONAL Mainland 0.02 0.02 42161.65 41174.25 ON-THE-LAKE MUNICIPALITY OF NIAGARA TOWN OF OAKVILLE REGIONAL Mainland 0.08 0.09 11864.76 10977.64 MUNICIPALITY OF HALTON TOWN OF COUNTY OF Mainland 0.02 0.03 43448.17 37037.76 ORANGEVILLE DUFFERIN TOWN OF PELHAM REGIONAL Mainland 0.12 0.17 8444.17 5926.93 MUNICIPALITY OF NIAGARA TOWN OF COUNTY OF Mainland 0.68 0.84 1473.13 1193.72 PENETANGUISHENE SIMCOE TOWN OF RICHMOND REGIONAL Mainland 0.07 0.07 15088.21 15062.70 HILL MUNICIPALITY OF YORK A. Siddiqui 43

TOWN OF COUNTY OF Mainland 0.01 0.02 82258.01 47131.90 SHELBURNE DUFFERIN TOWN OF WASAGA COUNTY OF Mainland 1.65 1.89 604.91 528.78 BEACH SIMCOE TOWN OF WHITBY REGIONAL Mainland 0.11 0.11 9362.28 9343.96 MUNICIPALITY OF DURHAM TOWN OF REGIONAL Mainland 0.65 0.65 1540.81 1539.82 WHITCHURCH- MUNICIPALITY OF STOUFFVILLE YORK TOWNSHIP OF COUNTY OF Mainland 0.51 0.84 1972.33 1190.18 ADJALA- SIMCOE TOSORONTIO TOWNSHIP OF COUNTY OF Mainland 1.52 1.71 655.86 583.23 ALNWICK/HALDIMAND NORTHUMBERLAND TOWNSHIP OF COUNTY OF Mainland 0.23 0.23 4280.98 4277.45 AMARANTH DUFFERIN *TOWNSHIP OF COUNTY OF Mainland 0.27 0.28 3651.41 3526.94 ASPHODEL- PETERBOROUGH NORWOOD TOWNSHIP OF REGIONAL Mainland 0.50 0.56 1985.66 1786.85 BROCK MUNICIPALITY OF DURHAM *TOWNSHIP OF COUNTY OF Mainland 1.01 1.19 994.80 843.51 CAVAN MONAGHAN PETERBOROUGH TOWNSHIP OF COUNTY OF Mainland 0.07 0.08 13895.79 13294.33 CENTRE WELLINGTON WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.72 1.90 1382.20 527.44 CLEARVIEW SIMCOE TOWNSHIP OF COUNTY OF Mainland 0.57 0.67 1758.82 1501.00 CRAMAHE NORTHUMBERLAND *TOWNSHIP OF COUNTY OF Mainland 14.05 18.44 71.16 54.24 DOURO-DUMMER PETERBOROUGH TOWNSHIP OF EAST COUNTY OF Mainland 0.18 0.18 5585.02 5484.25 GARAFRAXA DUFFERIN TOWNSHIP OF ESSA COUNTY OF Mainland 0.72 1.08 1389.96 924.19 SIMCOE TOWNSHIP OF COUNTY OF Mainland 0.19 0.20 5341.39 5017.72 GUELPH/ERAMOSA WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.30 0.51 3388.35 1942.62 HAMILTON NORTHUMBERLAND *TOWNSHIP OF COUNTY OF Mainland 9.26 13.76 108.04 72.65 HAVELOCK- PETERBOROUGH BELMONT-METHUEN TOWNSHIP OF KING REGIONAL Mainland 0.45 0.46 2218.73 2168.28 MUNICIPALITY OF YORK TOWNSHIP OF COUNTY OF Mainland 0.06 0.06 16626.15 16613.78 MAPLETON WELLINGTON TOWNSHIP OF COUNTY OF Mainland 0.28 0.29 3579.56 3471.29 MELANCTHON DUFFERIN TOWNSHIP OF COUNTY OF Mainland 1.25 1.49 796.90 671.71 MULMUR DUFFERIN TOWNSHIP OF REGIONAL Mainland 0.17 0.21 5800.43 4659.23 NORTH DUMFRIES MUNICIPALITY OF WATERLOO *TOWNSHIP OF COUNTY OF Mainland 11.78 13.16 84.88 75.99 NORTH KAWARTHA PETERBOROUGH TOWNSHIP OF ORO- COUNTY OF Mainland 1.83 1.86 546.00 536.99 MEDONTE SIMCOE *TOWNSHIP OF COUNTY OF Mainland 0.35 0.39 2819.31 2563.36 OTONABEE-SOUTH PETERBOROUGH MONAGHAN TOWNSHIP OF COUNTY OF Mainland 0.55 0.58 1822.20 1736.55 PUSLINCH WELLINGTON TOWNSHIP OF COUNTY OF Mainland 7.55 8.35 132.37 119.73 RAMARA SIMCOE TOWNSHIP OF REGIONAL Mainland 1.70 1.75 589.96 571.42 SCUGOG MUNICIPALITY OF DURHAM A. Siddiqui 44

*TOWNSHIP OF COUNTY OF Mainland 0.60 0.61 1663.67 1651.30 SELWYN PETERBOROUGH TOWNSHIP OF COUNTY OF Mainland 21.80 21.85 45.87 45.77 SEVERN SIMCOE TOWNSHIP OF COUNTY OF Mainland 5.21 6.46 192.07 154.92 SPRINGWATER SIMCOE TOWNSHIP OF TAY COUNTY OF Mainland 1.72 1.90 581.69 526.93 SIMCOE TOWNSHIP OF TINY COUNTY OF Mainland 4.49 4.55 222.50 219.93 SIMCOE TOWNSHIP OF REGIONAL Mainland 1.40 1.48 713.29 674.67 UXBRIDGE MUNICIPALITY OF DURHAM TOWNSHIP OF REGIONAL Mainland 0.65 0.76 1527.83 1307.66 WAINFLEET MUNICIPALITY OF NIAGARA TOWNSHIP OF REGIONAL Mainland 0.04 0.04 24269.30 22990.78 WELLESLEY MUNICIPALITY OF WATERLOO TOWNSHIP OF COUNTY OF Mainland 0.63 0.99 1588.55 1008.00 WELLINGTON NORTH WELLINGTON TOWNSHIP OF WEST REGIONAL Mainland 0.12 0.14 8650.88 6984.88 LINCOLN MUNICIPALITY OF NIAGARA TOWNSHIP OF REGIONAL Mainland 0.05 0.06 18197.57 17219.73 WILMOT MUNICIPALITY OF WATERLOO TOWNSHIP OF REGIONAL Mainland 0.07 0.08 13454.79 12944.38 WOOLWICH MUNICIPALITY OF WATERLOO

Appendix 6:Effective mesh size (meff) and density (Seff) for the secondary watersheds within the Golden Horseshoe. Results of CUT and CBC methods are reported for 2011 and 2016.

Year Secondary Watershed meff CUT meff CBC Seff CUT Seff CBC (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 Eastern Georgian Bay 3.514 3.551 284.583 281.584

Eastern Lake Huron 0.358 0.465 2796.324 2152.733

Lake Erie 0.059 0.063 17007.493 15899.493

Lake Huron 3.241 3.379 308.503 295.948 Lake Ontario 0.761 1.052 1313.590 950.943 Northern Lake Erie 0.314 0.323 3180.740 3098.091 *Northern Lake Ontario and 11.489 11.571 87.038 86.419 Niagara River

2016 Eastern Georgian Bay 3.179 3.202 314.573 312.261 Eastern Lake Huron 0.315 0.380 3169.819 2630.154 Lake Erie 0.058 0.063 17116.420 15997.622 Lake Huron 2.142 2.238 466.771 446.742 Lake Ontario 0.543 0.621 1840.684 1611.025 Northern Lake Erie 0.247 0.253 4047.723 3947.075 *Northern Lake Ontario and 5.513 5.541 181.381 180.484 Niagara River

A. Siddiqui 45

Appendix 7:Effective mesh size (meff) and density (Seff) across tertiary watersheds in the Golden Horseshoe, in 2011 and 2016. Results for CUT and CBC methods are reported.

Year Tertiary Watershed meff CUT meff CBC Seff CUT Seff CBC (km2) (km2) (per 1000 km2) (per 1000 km2) 2011 Big River 0.10 0.10 10283.23 9850.17 Gull River* 5.82 17.94 171.90 55.74 Humber River - Don River 0.20 0.22 5040.49 4450.81 Kawartha Lakes* 26.89 33.69 37.19 29.68 Lower Grand River 0.21 0.22 4751.34 4459.48 Maitland River 0.08 0.09 12140.51 10813.97 Moira River 0.84 1.05 1194.81 955.84 North Lake Ontario 0.00 0.00 NA NA North Lake Ontario 1.57 2.25 635.31 445.31 Shoreline Northeast Lake Erie 0.002 0.003 628185.11 314582.04 Northeast Lake Erie 0.06 0.07 15388.67 14341.34 Shoreline Northeast Lake Ontario 1.59 4.02 628.15 248.75 Northeast Lake Ontario 0.53 0.69 1895.90 1439.30 Shoreline Nottawasaga River 3.09 3.77 323.21 265.23 Otonabee River* 6.35 13.53 157.54 73.91 Saugeen River 0.37 0.42 2710.37 2402.39 Scugog River 1.45 1.52 687.89 657.54 Severn River - Lake 3.16 3.65 316.16 273.71 Simcoe Severn River - Lake 3.16 3.65 316.16 273.71 Simcoe Sixteen Mile Creek - 0.55 0.56 1821.81 1795.72 Credit River South Georgian Bay 2.28 2.28 438.32 438.04 South Georgian Bay 3.50 3.68 285.33 271.71 Shoreline Southwest Georgian Bay 1.22 2.31 818.65 433.17 Trent River - Crowe River 5.58 10.33 179.30 96.83 Upper Grand River 0.42 0.44 2355.75 2292.47 Upper Thames River 0.10 0.11 9724.72 8976.54 Welland Canal - Niagara 0.26 0.27 3812.44 3649.00 River West Lake Ontario 0.00 0.00 NA NA A. Siddiqui 46

West Lake Ontario 0.06 0.07 18036.95 15235.12 Shoreline 2016 South Georgian Bay 2.28 2.28 439.06 438.77 Humber River - Don River 0.17 0.19 5963.40 5387.88 Big River 0.09 0.09 11056.00 10586.69 Northeast Lake Erie 0.002 0.003 640085.10 316371.00 Lower Grand River 0.20 0.21 4964.04 4681.20 Northeast Lake Ontario 1.59 3.95 628.76 253.28 Upper Grand River 0.31 0.32 3191.67 3114.01 North Lake Ontario 0.00 0.00 NA NA Sixteen Mile Creek - 0.53 0.54 1882.54 1857.26 Credit River Saugeen River 0.34 0.37 2945.15 2676.22 Scugog River 1.23 1.28 809.84 781.86 Gull River* 3.48 6.19 287.52 161.46 Otonabee River* 3.31 4.92 301.77 203.21 Severn River - Lake 2.91 3.39 343.63 294.90 Simcoe Severn River - Lake 2.91 3.39 343.63 294.90 Simcoe Northeast Lake Erie 0.06 0.07 1587.89 14429.89 Shoreline West Lake Ontario 0.00 0.00 NA NA Northeast Lake Ontario 0.52 0.68 1914.50 1462.92 Shoreline Maitland River 0.08 0.09 12346.16 10981.96 Upper Thames River 0.10 0.11 9785.64 9254.52 West Lake Ontario 0.05 0.06 19299.39 16318.11 Shoreline North Lake Ontario 1.07 1.24 937.42 807.86 Shoreline Trent River - Crowe River 3.26 4.45 306.98 224.92 South Georgian Bay 2.10 2.23 475.20 448.98 Shoreline Nottawasaga River 2.59 3.26 385.73 306.38 Nottawasaga River 2.59 3.26 385.73 306.38 Welland Canal - Niagara 0.26 0.27 3824.86 3671.99 River Kawartha Lakes* 16.33 17.98 61.23 55.60 Southwest Georgian Bay 0.91 1.53 1099.95 652.20 Moira River 0.72 0.77 1391.18 1301.92