Canadian Journal of Forest Research

Developing a two-level fire regime zonation system for Canada

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2019-0191.R1

Manuscript Type: Article

Date Submitted by the 09-Oct-2019 Author:

Complete List of Authors: Erni, Sandy; Natural Resources Canada, Canadian Forest Service Wang, Xianli; Natural Resources Canada, Canadian Forest Service Taylor, Steve; Natural Resources Canada, Canadian Forest Service Boulanger, Yan; Natural Resources Canada, Canadian Forest Service Swystun, Thomas;Draft Natural Resources Canada, Canadian Forest Service Flannigan, Mike; University of Alberta, Renewable Resources Parisien, Marc-André; Natural Resources Canada, Canadian Forest Service

Keyword: Canada, spatial analysis, boreal forest, zonation, wildland fire regime

Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? :

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1 Developing a two-level fire regime zonation system for Canada

2 Sandy Erni1, Xianli Wang1, Steve Taylor2, Yan Boulanger3, Tom Swystun1, Mike Flannigan4, Marc- 3 André Parisien5 4 5 1. Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street 6 East, Sault Ste. Marie, ON P6A 2E5

7 2. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre. 506 West Burnside Road, 8 Victoria, BC V8Z 1M5

9 3. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., P.O. 10 Box 10380, Stn. Sainte-Foy, Quebec, QC G1V 4C7

11 4. Department of Renewable Resources, University of Alberta. 751 General Service Building Edmonton, 12 AB T6G 2H1

13 5. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320-122nd Street, 14 Edmonton, AB T6H 3S5 15 Draft 16 Corresponding authors: [email protected], [email protected]

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17 ABSTRACT

18 Fire regime zonation systems are critical tools for research and management activities. In this study, we 19 develop a hierarchical framework that applies both qualitative and quantitative approaches to create a 20 two-level fire regime zonation system for Canada. The finer-scale level, Fire Regime Units (FRUs), was 21 created through a stepwise synthesis of fire regime metrics based on 1970-2016 fire records, 22 environmental attributes such as topographic features and vegetation, and literature review/expert advice. 23 Each of these 60 Fire Regime Units exhibits an internal homogeneity in fire regime. As non-contiguous 24 units can show similar patterns in fire-related measurements, we performed a clustering analysis on the 25 FRUs to define 15 broad-scale Fire Regime Types (FRTs). Each type is characterized by a unique set of 26 indices related to fire activity, seasonality, and ignition cause. This two-level fire regime zonation system 27 has a large range of applications (e.g., modeling, gradient analysis) and is flexible enough to be updated 28 with new data or when notable shifts in fire dynamics occur.

29

30 Key words: wildland fire regime, Canada, spatial analysis, boreal forest, zonation

31 Acknowledgements Draft

32 The green-up grids were kindly provided by Peter Englefield, of the Canadian Forest Service in 33 Edmonton, and we also thank him for advice on these data. The authors thank Bill de Groot and Joshua 34 Johnston for their help and discussions about fire regime patterns. This research was funded through a 35 Canadian Safety and Security Program grant to X. Wang (CSSP-2016-CP-2286).

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36 1. INTRODUCTION

37 From coastal rain forests to tundra barrens, as well as the vast boreal biome, Canada displays a wide range 38 of fire regimes. While wildland fire is an inherently stochastic process, its meso-scale regional patterns 39 and consequently fire regimes, emerge in response to geographic variation in climate, vegetation, ignition 40 agents (lightning and human), topography, and fire-management activities (Parisien et al. 2011; Stocks et 41 al. 2002). A fire regime is a set of fire-related characteristics of a geographic region, connected to a set of 42 fire events that are relatively homogenous in time and space. Typically, fire regime components include 43 fire frequency, size, intensity, severity, seasonality, type (surface, crown, or ground), and ignition cause 44 (Hanes et al. 2019; Stocks et al. 2002; Weber and Flannigan 1997; Whitman et al. 2015).

45 Zonation system frameworks are normally created through the combination of attributes related to a 46 particular response variable (Cheruvelil et al. 2013), such as vegetation characteristics for ecosystems, or 47 land use and population density for administrative units. However, regional fire regime patterns can be 48 either amplified or masked when using multi-purpose zonation systems that were not designed to be 49 compatible with fire disturbance (Bailey 2010; Marcoux et al. 2013). The process of mapping areas with a 50 homogeneous fire regime is challenging becauseDraft it aims to divide intrinsically stochastic and dynamic 51 disturbances (i.e. wildland fires) into static geographical units. Boundaries between adjacent regions thus 52 represent transition zones created through the spatial partitioning of fire regime measurements and 53 environmental gradients. As a nominal classification system, a fire regime zonation is useful where the 54 discretization of the landscape and continuous processes provides a level of generalization that aids in 55 understanding fire dynamics at large spatial scales, and identifies gaps in knowledge about fire activity 56 over time and space.

57 A fire regime zonation system should include the main attributes of fire regimes, as well as the 58 environmental factors that influence fire regimes, and should consider the spatio-temporal scales at which 59 each one operates. The advantages of such a system are to decrease the uncertainty in defining causal 60 associations between fire and environment and to guide the evaluation of expected regional changes in 61 fire regimes over the next century (Flannigan et al. 2005; Flannigan et al. 2009; Oris et al. 2013; Price et 62 al. 2013; Tymstra et al. 2007). The accuracy of projections relies strongly on our ability to quantify the 63 recent fire activity inside discrete units. Fire-dominated zonation systems could also assist fire protection 64 and forest management in identifying region-specific strategies for mitigating wildfire impacts on 65 communities, biodiversity, and forest productivity (Boulanger et al. 2014; Burton and Boulanger 2018).

66 The primary difference in procedures used to create zonation systems is whether they are qualitative or 67 quantitative. By applying expert judgement-based heuristics, qualitative synthesis of biotic/abiotic data 68 has historically been the most commonly used method in developing environmental zonation systems

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69 (Bailey 1983; Fenneman 1917; Holdridge 1967; Olson et al. 2001). Ecoregional units in the United States 70 (Omernik 1987) and in Canada (Ecological Stratification Working Group 1995; Wiken 1986) are two 71 well known examples of qualitative-based products stemming from large-scale ecological land 72 classifications. This approach has the advantage of enabling integration of multiple data sets, often from 73 different resolutions and sources (e.g., remote sensing data, fire regime measurements, climatic and 74 geographical attributes, maps, published studies, and expert knowledge), that better suit the wide range 75 and multi-scale aspect of fire regimes.

76 With the development of computer technology, quantitative methods have been widely used for the 77 delineation of zones, including fire regime zones, largely because they are more explicit and repeatable 78 than their qualitative counterparts. Quantitative methods split the study area into spatially discrete units 79 (e.g. grid cells, ecozones) and compute the values of some selected fire metrics and environmental factors 80 to delimit each unit (Fréjaville and Curt 2015; Lefort et al. 2004; Mansuy et al. 2014; Wu et al. 2015). 81 The units are then grouped into a predefined number of classes, often through a cluster analysis 82 (Archibald et al. 2013; Chuvieco et al. 2008). In Canada, two quantitative fire regime zonation systems 83 have been developed, one national and one Draftprovincial (British Columbia), both using a statistical 84 procedure that considers fire regime parameters, weather conditions, and fuel attributes (Boulanger et al. 85 2014; Boulanger et al. 2012; Burton and Boulanger 2018). Although easier to apply over a large spatial 86 extent, quantitative methods are bound by the short temporal depth of mapped fire ignitions and 87 perimeters, which limits the building of high-resolution zones while maintaining an appropriate 88 representation of each fire regime parameter. Fire regime descriptors also change depending on the spatial 89 scale at which they are measured. That is, the interpretation of fire patterns from the calculations of fire 90 metrics can be valid at a given spatial resolution or extent but not at another (Morgan et al. 2001; Scholtz 91 et al. 2018). This leads to potential misclassifications of fire regimes.

92 The reasons for preferring qualitative or quantitative methods have been extensively debated in the 93 literature, mostly with respect to ecosystem boundaries and ecological zonation (Hargrove and Hoffman 94 2004; Kreft and Jetz 2010; McMahon et al. 2004; Omernik 2004; Omernik and Griffith 2014). We believe 95 that this debate has become trivial as both approaches have proven to be effective for delineating reliable 96 and useful homogeneous zones for research and assessment projects (McKenney et al. 2001; Omernik 97 1987; Wiken 1986). In fact, qualitative and quantitative approaches should be considered together to 98 overcome the shortcomings of individual approaches, given that they are complementary (Holling 1996). 99 Here, we present an approach that combines both qualitative and quantitative methods into a single 100 framework for fire regime zonation, and develop a two-level fire regime zonation system for the forested 101 area of Canada as an update of Boulanger et al. (2014). Specifically, we present a rigorous procedure for

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102 delineating the base-level Fire Regime Units (FRUs), based on a hierarchical discrimination process to 103 integrate fire regime metrics, fire history, and environmental conditions. We then aggregate the FRUs to 104 create a second level of fire regime zonation, the Fire Regime Types (FRTs), which is more suitable for 105 subcontinental-scale analysis.

106

107 2. STUDY AREA

108 The study area encompasses the predominantly forested landmass of Canada, as defined by the Ecological 109 Stratification Working Group (Ecological Stratification Working Group 1995), and covers a total surface 110 area of 650 Mha. Except for the coastal areas, the climate is mostly continental, characterised by long, 111 cold winters, and short, warm summers. Annual mean temperatures decrease from south to north, whereas 112 moisture broadly decreases from east to west. From a biogeographic standpoint, Canada can be roughly 113 described as three main units: the western mountains, the boreal forest, and the southern temperate 114 regions. The western mountains, straddling parts of Alberta, British Columbia, the Northwest Territories 115 and the Yukon Territory (see the Canadian provinces in Figure 1), encompass a large range of altitudes, 116 from mid-elevation plateaus (400-600 m onDraft average) to high peaks (Mount Logan, YT, 5959 m 117 maximum). Organised as parallel mountain ranges, but perpendicular to the maritime airflow, they cause 118 variation in vegetation patterns by intercepting moisture from the incoming westerly winds. Vegetation in 119 the mountains also varies along climate gradients related to (1) altitude, from grasslands in warm valleys 120 to alpine tundra; (2) longitude, from wet coastal forest to dry interior grassland; (3) and latitude, from 121 southern parklands to northern taiga and tundra. Dominated by a mix of coniferous and deciduous forests 122 and wetlands, the boreal zone extends from the Yukon Territory and north of British Columbia to 123 Newfoundland and Labrador, and from northern tundra to southern temperate forests. With a history of 124 repeated episodic disturbances from fire, insects, and timber harvest (in the southern part), the Canadian 125 boreal forest has been in a complex and continually changing mosaic of conditions and successional 126 trajectories. Shaped by human influence, southern temperate regions consist mainly of high-productivity 127 mixed forests within an arrangement of urban (including the major Canadian cities), agricultural, mining, 128 and forest harvesting areas.

129 The diversity of physical and environmental features across Canada is reflected in its array of fire regimes 130 (Hanes et al. 2019). At the national scale (Figure 2 a), fires larger or equal to 50 ha have burned 102.4 131 Mha between 1970 and 2016, which represents 19.8% of the burnable study area (519.4 Mha; excluding 132 lakes and permanent nonfuel)with an average annual burn rate of 0.42% per year. Of these, 74.8% were 133 lightning ignited and occurred mainly during the summer season (Figure 2 a). Fire activity varies widely

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134 from year to year. The lowest fire activity was recorded in 1978, in terms of fire frequency (0.31 fires per 135 Mha), median fire size (220 ha), and burn rate (0.06% of the burnable area). The year 1978 also shows the 136 highest percentage of human-caused fires (44.1%), while 2004 shows the lowest (8.1%). Characterised by 137 multiple fire ignitions, severe weather conditions, and extreme fire behavior (Hirsch 1991; Stocks et al. 138 2002), 1989 was the most active year recorded between 1970 and 2016, in terms of area burned and fire 139 frequency (Figure 2 a). Fires in much of the southern forests in Canada are actively suppressed, although 140 the intensity of suppression varies through time and space, depending on fire-management agencies (i.e., 141 provincial and territorial).

142

143 3. METHODOLOGY

144 3.1. DATASETS

145 To inform the delineation of the national landscape into homogeneous fire regime units, we collated and 146 synthesized various sources of information, including fire records with attributes of fire regime 147 characteristics (e.g. size, ignition cause, andDraft seasonality), ecosystem maps for fuel distribution 148 (Ecological Stratification Working Group 1995), previous fire regime zonation maps (Boulanger et al. 149 2014; Boulanger et al. 2012), phenology data to determine the fire season, topographic data (e.g. DEM, 150 aspects), literature reviews, and expert advice.

151 Fire records

152 Datasets of fire polygons and ignition point data were obtained from the Canadian National Fire Database 153 (CNFDB), which contains information about the location, final size (ha), cause, and start date of fires 154 (Canadian Forest Service 2018). Fire data across Canada are considered exhaustive only since the 1980s 155 (i.e., the advent of satellites), but acceptable since 1970, although there is a detection bias that smaller 156 fires in areas outside of active suppression are underreported (Hanes et al. 2019; Stocks et al. 2002). To 157 ensure the coherence of our analysis at a national scale, we considered fire data between 1970 and 2016, 158 and only during the fire season (1st April - 30th September). Although fires larger than 200 ha contribute 159 to the vast majority of the annual burned area in Canada (Hanes et al. 2019; Stocks et al. 2002), smaller 160 fires can have a significant impact in areas with lower fire frequency and resilience capacity. To avoid the 161 pitfall of including the less-accurate records for smaller fires, we extended the fire database to fires ≥ 50 162 ha in this study. In total, 20,372 fires, 23.5% of human-caused fires and 76.5% of lightning-caused fires 163 were included in the analysis.

164

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165 Zonation systems

166 Two national zonation systems were considered in the procedure described here: the Canadian Ecological 167 Land Classification (ELC) system (Ecological Stratification Working Group 1995) and the Homogeneous 168 Fire Regime (HFR) zones (Boulanger et al. 2014; Boulanger et al. 2012). The ELC divides the country 169 into 15 terrestrial ecozones, each having a unique collection of climate patterns, human activity, 170 vegetation, physiographic divisions, and soil orders. Regional ecoregions and local ecodistricts subdivide 171 ecozones into progressively finer ecological spatial units. The HFR zones were the first nationwide fire 172 regime-zonation system developed for Canada (Boulanger et al. 2014). These 16 zones were delineated 173 from fire statistics (i.e., mean annual number of fires ≥ 200 ha, and mean annual area burned) and, to a 174 lesser degree, fuel attributes (compiled between 1959 and 1999) by applying a spatially constrained 175 agglomerative clustering and partitioning algorithm on a 60-km resolution grid.

176 Where available, provincial ecosystem classification systems were also considered to address localized 177 variation, especially in the mountain area, including the Natural Regions and Subregions (NRS) system 178 of Alberta (Natural Regions Committee 2006), the Biogeoclimatic Ecosystem Classification (BEC) of 179 British Columbia (Meidinger and Pojar 1991),Draft the circumboreal vegetation mapping (CBVM) developed 180 for the Yukon-Alaska region (Jorgensen and Meidlinger 2015), the specific Natural Disturbance Types 181 (NDT) map in British Columbia (Parminter 1995), and the Vegetation zones and bioclimatic domains in 182 Quebec (Saucier et al. 2003). We added a non-forested area to mountaintops in the CBVM, equivalent to 183 the Alpine tundra zone in British Columbia. The altitude of these areas was defined by using remote 184 sensing via Google Earth ProTM (Images Landsat/Copernicus), the national forest cover maps, and 185 provincial reports (Environment Yukon 2016; Grods et al. 2012; MacKenzie 2012; Viereck et al. 1992).

186 Phenology

187 In northern forests, fire behavior and fire activity vary substantially prior to and following green-up (i.e. 188 leaf flush) of broadleaf trees, with a marked decrease in flammability when trees are leaved. The green-up 189 dates were estimated using binary daily NDVI grids (2013-2016, USGS Earth Resources Observation and 190 Science Center), based on a classification algorithm that considered a cell being greened-up when it 191 reaches 70% of its historical maximum. The averaged Julian date of green-up at each cell was used in the 192 analysis. The green-up date estimates were validated with the volunteer phenology network Plantwatch 193 (https://www.naturewatch.ca/plantwatch/; Beaubien and Hamann 2011), which we restricted to trembling 194 aspen (Populus tremuloides Michx.) green-up dates, when the leaves reached 2/3 of their final size, 195 between 1996 and 2016.

196

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197 3.2. DELINEATION OF FIRE REGIME UNITS (FRUs)

198 Inspired by the qualitative methods developed to regionalize ecosystems in United States (Bailey 1983; 199 Bailey et al. 1985; Omernik 1987, 1995; Omernik and Griffith 2014) and in Canada (Wiken 1986), we 200 delineated Fire Regime Units (FRUs) through pattern analysis, on a weight-of-evidence basis. The 201 assumption is that FRUs gained their identity through the spatial coincidence (i.e. homogeneity) of a set 202 of fire regime metrics associated with environmental characteristics that are substantially different from 203 surrounding areas. The approach recognizes that factors causing or reflecting regional patterns in fire 204 regimes are largely expected to be captured in maps and other materials that represent the geographical 205 nature of each factor (Omernik 1995). The fire regime metrics included fire size (ha), fire frequency 206 (number of fires per year per Mha), burn rates (percentage of area burned per year), ignition cause 207 (number of fires and percentage of area burned per year and per cause), and seasonality (number of fires 208 and percentage of area burned per two week periods during the fire season). Calculations included the 209 average, median, and 95th percentile of each metric. As the factors influencing fire regimes vary 210 substantially in time and space, the following principles were developed to determine the FRUs (Figure 211 3): Draft 212 (1) Fire activity measurements are the major criteria used to determine the boundary of FRUs. 213 (2) The size of FRUs must cover a minimum of twice the area of the largest fire recorded in the 214 region since 1970 (O'Neill et al. 1996). 215 (3) To be considered distinct, one proposed FRU must differ substantially from the surrounding 216 delineated areas by at least one of the main fire regime parameters, with respect to regional 217 variability. For example, an area dominated by human-caused fires will be considered distinct to 218 its neighboring area that is dominated by lightning-caused fires, even if the other fire parameters 219 are in the same range of values. Conversely, areas must concord substantially in terms of their fire 220 metrics to be considered as a single proposed FRU. In addition, environmental factors have to 221 corroborate the proposed FRU boundaries; this information can also be used to delineate 222 boundaries when variation in summary fire statistics are not obvious enough to draw a clear 223 transition between proposed FRUs. 224 (4) Variation in environmental characteristics (e.g., physiography, vegetation, land use, populated 225 area, and fire-weather patterns) that do not conform to a reasonable and concomitant variation in 226 fire regimes measurements will not be retained as a boundary between two proposed FRUs. For 227 this reason, similar dominant fuel types, or physiographic features for example, can be found in 228 two adjacent FRUs.

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229 The delineation of FRUs was an iterative procedure, from general to specific. It included three main steps 230 (Figure 3) that followed the aforementioned principles, and gradually scaled down through the integration 231 of regional/local data and component maps. The first step aimed to provide a national overview of fire 232 regimes: what are the major patterns and characteristics of fire activity over Canada? What is their 233 geographic distribution? We computed the summary statistics of fire regimes for each unit of the large- 234 scale zonation systems (HFR and Ecozones), mostly in the form of maps, and evaluated for each unit, as 235 criteria for spatial discrimination across the country: (1) the degree of departure from the national average 236 (Figure 2a), (2) the degree of departure from the other zonation system when they overlapped, and (3) the 237 degree of departure from adjacent proposed FRUs. Depending on their level of contrast or similarity to 238 other units in the set of fire metrics, units have been merged, individualized, or assigned as undetermined. 239 A first raw zonation of the major (i.e. obvious) fire regions was then manually drawn (Figure 3 National 240 level). The main interest of using a qualitative scheme at this stage of the delineation was to tailor the 241 evaluation of fire metrics to account for regional variability. For example, a difference of 500 ha in 242 average fire size would be negligible when considering two areas that both experience very large fires 243 (e.g., Northwest Territories), even though thisDraft amount is much higher than the national average of fire 244 size (460 ha, Figure 2a). When considering areas that experience rare, small fire events (e.g. Pacific 245 coast), a difference of 500 ha in average fire size would be considered substantial and would elicit the 246 requirement that a fire regime delineation be made.

247 We incorporated into the sketch of the main fire regions the environmental variables that are recognized 248 as primary drivers of fire regimes, such as fuels, topography, and weather, to complement the fire data 249 (National level in Figure 3). Weather maps and climate maps and open-source products were obtained 250 from USGS and Environment Canada, as well as those published in the literature (see Appendix for 251 detailed data sources). We delineated major regions of similarity on the basis of visually perceived breaks 252 and patterns in most environmental components, which had to be concomitant with the aforementioned 253 spatial singularity of homogeneous fire regions (i.e., weight-of-evidence). For example, fire size, 254 frequency and burn rates were all very high in northern Saskatchewan, with burn rates double those in any 255 other region (see Appendix Tables A1 and A2). This area was representative of very large and frequent 256 lightning fires that burned continuous patches of boreal vegetation during dry continental summers, over a 257 relatively flat landscape. Alternatively, the temperate forests of southern Quebec and southern Ontario 258 displayed many small fires that burn during spring, which is indicative of the strong human influence on 259 regional fire activity, either through land use or fire-management activities. While some regions were 260 clearly and easily delineated because of their distinctiveness in all factors relative to adjacent regions, a 261 large part of the country exhibited greater complexity and required further investigation.

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262 The second step thus focused on reducing the proportion of undetermined areas from the first step and 263 refining the transitional zones into boundaries at a regional scale (Figure 3 Regional level). In this step, 264 additional variables were considered, alongside those related to fire; including phenology (i.e., green-up 265 dates), land-use maps, populated areas, regional/provincial zonation systems (e.g., ecodistricts), and 266 regional/provincial reports. We specifically addressed the influence of elevation on fire regimes in 267 western Canada by integrating provincial ecosystem classifications, such as the BEC in British Columbia. 268 Progressively, we refined the outlines of homogeneous zones previously drawn at national scale, and also 269 identified new zones in the undetermined parts of the sketch (Regional level in Figure 3). The location of 270 preliminary FRUs boundaries was not yet accurate at this step; they looked like a simpler form of the 271 spaghetti map of the cartography of the Great Plains by Rossum and Lavin (2000).

272 For some areas, FRU boundaries were still unclear (i.e., distinct fire patterns exist within a given area) 273 even after the extensive spatial analysis of fire statistics and environmental characteristics carried out 274 during the first two steps of delineation. A third step, based on an exhaustive literature review of past 275 disturbances and expert judgements, was thus required to provide stratification at local scale (Figure 3 276 Local level). For example, this step was necessaryDraft in Northern and Western Ontario due to the scarcity of 277 fire data in the CNFDB (Bridge 2001; Li 2000; Ter-Mikaelian et al. 2009). The whole process of 278 delineation was not always straightforward and required returns to previous steps to ensure the coherence 279 of the FRU classification.

280 Accurately drawing the FRUs boundaries ended the qualitative zonation process. In this last step of 281 identifying FRUs, it was fundamental to obtain empirical support through the mapped fire regime 282 parameters for each of the different geographic phenomena that help distinguish homogeneous fire 283 regions. The hierarchical aspect of the method described here guided our decisions regarding the location 284 of FRUs boundaries (Figure 3). The components that exert a general control on fire regimes determined 285 the boundaries at the upper level of the classification (i.e. national), providing the foundations of the 286 zonation. Criteria at finer scales (i.e. regional and local) exert more narrow and specific control, and could 287 be used to refine FRUs boundaries or classify areas with poor or missing fire data. In some cases, portions 288 of FRUs’ boundaries approximated the boundaries of published zonation systems (e.g., HFR, Ecozones, 289 BEC Zones), but in many cases the FRUs’ boundaries had to be drawn anew. The design of the FRUs was 290 reviewed and validated, at each step of the process, through the expert judgement of fire scientists that 291 have studied spatio-temporal patterns of wildfires across Canada (including the co-authors of the present 292 study and the scientists in the acknowledgements). Specialists in physical fire science and computer 293 science also supported the process by providing updated high-quality data from different sources, as well 294 as technical advice regarding their use.

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295 3.3. IDENTIFICATION OF FIRE REGIME TYPES (FRTs)

296 The FRUs reflect the spatial variability of fire environment and patterns, by discriminating individual 297 regions from their surroundings. Despite being derived from differences in variables used for 298 discrimination, some FRUs do share common fire regime characteristics and therefore have similar fire 299 regimes, especially when considered across the broad spatial extent of the study area. To characterise the 300 different Fire Regime Types (FRTs) in Canada, we performed a clustering analysis with five fire regime 301 metrics of the FRUs as inputs: fire frequency, fire size, burn rate, ignition cause, and seasonality. The 302 median of each selected metric was calculated by year, using the fire data between 1970 and 2016, and 303 then averaged to obtain one value for each FRU (Table A1 in the Appendix). The seasonality was retained 304 as fire regime descriptor because it is representative of the dynamic between climate and vegetation 305 within a given region (e.g. timing of snow melt, live fuel moisture, storm season, synoptic weather 306 patterns). The ignition cause was also important to consider from a management perspective, as human- 307 caused fires more often occur near or within the Wildland Urban Interface (WUI), and represent a real 308 threat to communities. Identifying regions with human-driven fire regimes could help fire agencies to take 309 appropriate actions and protection measuresDraft for reducing fire occurrence near inhabited areas. 310 Due to the important influence of topography on fire regimes in Western Canada, as emphasised during 311 the first step of the delineation, we performed two separate clustering analyses, one on the 22 FRUs of the 312 Western mountains (37, 39-57 and 59-60, Figure 1) and one on the 38 FRUs of the rest of the country (1- 313 36, 38, and 58, Figure 1). Ward’s minimum variance method without spatial contiguity constraints was 314 chosen for the clustering analysis using the R function hclust (R Core Team 2011). We used the Elbow 315 method (amount of variance explained by the number of clusters) coupled with a visually guided analysis 316 of the dendrograms to assess the optimal number of clusters. Ultimately, one cluster produced in the 317 analysis also corresponds to one FRT.

318 The characterisation of each FRT (i.e., cluster) was based on three newly derived indices summarizing the 319 fire regime metrics used in the clustering analysis: (1) the fire activity index, (2) the seasonality index, 320 and (3) the ignition cause index. (1) The fire activity (FA) index was created to simplify the nominal 321 description of the different types of fire regime, and resulted from the combination of the fire size, 322 frequency, and burn rate metrics; each metric was classified into two categories (signed as 1 and 2), as 323 defined by the median of the corresponding metric over Canada (1 for below the median value and 2 for 324 above the median value; see Figure 2 a). These three values were summed for each FRT (i.e., FA index = 325 size + frequency + burn rate), and the resulting index score was then assigned to a FA level, ranging from 326 low (3) to high (6). For example, a cluster with a fire frequency of 0.45 (below the national value of 0.9), 327 a fire size of 1,000 ha (above 460 ha), and a burn rate of 0.08% (below 0.45%) obtained a total of 4, i.e. a

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328 FA index defined as Moderate-low. A category “Extreme” was added to depict the FRT with the most 329 intense fire activity, which is much higher than the rest of the “highs”. (2) The seasonality index referred 330 to the seasonal distribution of area burned. Spring-dominated meant that more than 65% of area burned 331 was between April 1st and June 21st, while summer-dominated meant that more than 65% of area burned 332 was between June 22nd and September 30th. If both seasons comprised between 35 and 65% of area 333 burned, the seasonality index was defined as mixed. (3) The ignition cause index referred to the 334 percentage of human- and lightning-caused fires, and followed the same categories as the seasonality 335 index. Lastly, an attribute (m) indicated if the FRT was part of the mountain group.

336 The classification of each FRT was therefore summarised as follows:

337 (1) 2 levels of split across the country (a prefix “m” for mountain area, no prefix for the rest) 338 (2) 5 levels of fire activity (FA 1 to 5, from low to extreme); 339 (3) 3 levels of seasonality (SEsp, SEsu, and SEmx, for spring, summer, and mixed fire seasonality); 340 (4) 3 levels of ignition cause (IChm, IClt, and ICmx, for human, lightning, and mixed ignition cause).

341 We also retained the numbering classification for the FRTs, from 1 to 15. For example, FRT 4 was 342 classified as FA4-SEmx-IClt, which indicatedDraft non-mountainous region with a high fire activity, no 343 dominant fire season, and lightning-dominated ignition cause. FRT 15 was classified as mFA1-SEsu- 344 IChm, which indicated a mountainous region with a low fire activity, summer- and human-dominated 345 season and ignition cause (Figure 4).

346

347 4. RESULTS

348 4.1. FIRE REGIME UNITS (FRUs)

349 In total, 60 FRUs were created (Figure 1), ranging from 40.24 Mha (FRU 26 in southeastern Northwest 350 Territories) to 0.4 Mha (unit 56 in the southern Rocky Mountain Trench, British Columbia). Burn rates 351 are highest in the western part of the country (Alberta, Yukon Territory, Manitoba, Northwest Territories, 352 and Saskatchewan), as well as western Ontario and northwestern Quebec. They range from 0.82 in James 353 Bay (FRU 9) to 2.02 in northern Saskatchewan (FRU 27) (Figure A1 f). The FRUs with fire frequencies 354 higher than the national average (> 0.9 fires / Mha) show a similar spatial pattern to that of burn rates. 355 However, with a maximum of 2.2 fires per Mha (Figure A1 e), the highest fire frequency is found in the 356 western Rockies in BC (FRUs 52 and 56), although burn rates are lower than the national average. 357 Northern regions experience fires larger than southern regions, on average, from 121 ha in coastal BC, to 358 3,347 ha on the eastern side of the Mackenzie Mountains, in NT (Figure A1 d, FRUs 33 and 34). Patterns

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359 of ignition cause largely respond to the population density distribution (Figure A1 a and c). The 360 proportion of human-caused fires is more important in highly populated FRUs (e.g., 100% of human- 361 caused fires in southern QC – FRU 4) and gradually decreases in more sparsely populated areas, where 362 lightning-caused fires dominate (100% of lightning caused fires in the northern Yukon Territory – FRUs 363 35 and 38).

364 Topography has a strong effect on fire activity patterns, and therefore on the delineation of FRUs, 365 especially in western Canada. FRUs over the western mountain area cover 6.14 Mha, about half of the 366 size of the Boreal and southern zones (13.67 Mha; Figure 1 and Table A1). FRUs covering moderate 367 elevation plains and plateaus have the highest burn rates (Figure A1 f), whether in British Columbia 368 (Central Plateau, from mean burn rates 0.112 – mean alt. 1303m in FRU 53 to 0.1686 –958m in FRU 50) 369 or in the Yukon Territory (Yukon River Basin, from mean burn rates 0.4456 % per year – mean alt. 828m 370 in FRU 45 – to 0.6205 % per year – 751m in FRU 41). In contrast, coastal regions and high–altitude 371 FRUs burn the least frequently, with mean burn rates of 0.0323 % per year, or lower. Intermediate- 372 elevation areas, valleys, and slopes range from 0.0644 in the western side of the Rocky Mountains (BC, 373 mean alt. 1467m – FRU 52) to 0.1445 in theDraft Mackenzie Mountains (YT, mean alt. 895m – FRU 42). 374 Annual fire frequency and fire size do not respond to the variation in topography as clearly as the burn 375 rates (Figure A1 d and e). Fire frequency is higher in the Yukon River Basin (1.19 fires per Mha – FRUs 376 40 and 41), in the Thompson Okanagan region (1.63 fires per Mha – FRU 50), and in southern valleys 377 and slopes in BC (from 0.9 to 2.2 fires per Mha - FRUs 48, 52, 53, 55, and 56).

378 Seasonality of fires in Canada is influenced by a latitudinal gradient (Figure A1 b), with most of the area 379 burned during spring being found in the southern regions (94.5% of spring area burned in Parkland region 380 of AB and SK – FRU 20) and a predominance of area burned during summer in the north (93% of 381 summer area burned in Hudson Bay Lowlands, NT – FRU 24). The mountainous regions, however, do 382 not follow this trend. Of the 22 FRUs overlapping with the mountains, 14 of them are summer dominated 383 (i.e. ≥ 65% of area burned between 21st June-30th September) and 8, which are all located between the 384 Yukon River Basin and the Cariboo region in BC, are mixed (i.e. no dominance of spring or summer). 385 Dominated by almost pure conifer forests in the montane and the coastal regions, southern BC comprises 386 the highest percent of summer area burned over Canada, going from 93% in Vancouver Island region 387 (FRU 51) to 82.6% on the coast (FRU 60).

388

389 4.2. FIRE REGIME TYPES (FRTs)

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390 The clustering analysis led to the characterization of 15 Fire Regime Types (FRTs) over Canada, each one 391 with a unique combination of topography (m), fire activity (FA), seasonality (SE), and ignition cause (IC) 392 (Figure 4 and Table A2). There are 7 FRTs in the mountains (FRTs 9-15), which are characterized by 393 low-to-moderate fire activity (FA1 or FA2), except for FRT 11 (mFA4-SEmx-IClt) in the Yukon 394 Territory plains, where high fire activity is driven by large fires and high burn rates (Figure 2 d and 2 f). 395 Lightning-caused fires are dominant in FRTs 9-11 in the Yukon Territory and northern British Columbia, 396 whereas the FRTs of southern British Columbia (FRTs 12-15) show the highest proportion of human- 397 caused fires (Figure 2 c). The smallest FRT is found in the Thompson Okanagan and southern Kootenay 398 regions of BC (Figure 4, FRT 14, 5.2 Mha). This FRT has the highest mean fire frequency in the 399 mountain area, and the third highest in Canada (1.67 fires per Mha; Figure 2 e). In contrast, the largest 400 FRT is in the northern subarctic region (FRT 2), with an area of 123.1 Mha. FRT 2 is characterised by a 401 low fire activity in summer time (FA2-SEsu-IClt), although it has a mean fire size greater than the 402 national average (764 ha vs 460 ha, respectively, Figure 2 d). Classified as extreme, the most severe fire 403 regime occurs in the western boreal region, straddling the Northwest Territories, northern Alberta and 404 northern Saskatchewan (FRT 8; FA5-SEsu-IClt).Draft The burn rate there is by far the highest in Canada 405 (1.8% of burned area per year), twice that of the second highest (0.99% in FRT 4, FA4-SEmx-IClt), and 406 is driven by frequent large fires (fire size of 984 ha and frequency of 2 fires per Mha, on average). The 407 southern FRTs showed consistently low-to-moderate fire activity (Figure 4, FA1 to FA3 indexes for FRTs 408 1, 5, 6, and 7), though the fire frequency in FRT 6 is the highest in Canada (mean frequency of 2.04 fires 409 per Mha).

410

411 5. DISCUSSION

412 We devised an updated fire regime zonation system in Canadian forests based on fire regime attributes, 413 environmental factors known to drive fire activities, and published or expert accounts where relevant fire 414 or environmental data was absent or incomplete. The combination of qualitative and quantitative methods 415 for identifying the two levels of fire regime zones (i.e. FRUs and FRTs) highlighted the need for 416 combining other types of information with fire data, as the latter may be limiting in some areas. Region- 417 specific drivers in fire regimes were also implicitly incorporated in the development of the zonation 418 system; for instance, whereas most of the variation in fire regime is climatically driven, topographic or 419 human factors may dominate in some areas at a particular spatio-temporal frame (Parisien et al. 2014). 420 The additional environmental layer of information was particularly important in mountainous areas, 421 where the topographic setting greatly affects fire regimes (Schoennagel et al. 2004). The pertinence of 422 using environmentally derived boundaries was also useful in non-mountainous areas where, for example,

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423 fuel continuity and surficial deposits can substantially affect fire extent, orientation, and shape (Mansuy et 424 al. 2014). In short, creating a new fire regime zonation system emphasized the need for qualitative 425 methods to complement the quantitative ones, especially in the parts of the country that have had sparse 426 or erratic fire activity over the last few decades (i.e., the observational period for the analysis).

427 Creating a new fire regime zonation system emphasized the need for qualitative methods to complement 428 the quantitative ones. Time series of 40 years, or even less in some regions, are relatively short in terms of 429 portraying an exhaustive description of fire regimes. Even if they represent localized information, longer 430 time series of fire data, like those derived from dendrochronology studies or from the assessment of the 431 landscape mosaic (i.e., stand ages) (Bergeron et al. 2004; Erni et al. 2017), can be used to refine the 432 boundaries of a quantitatively assessed fire regime zonation. Similarly, expert advice from ecologists and 433 fire managers familiar with specific geographic areas was also useful in delimiting FRU boundaries. 434 Moreover, the selection of response attributes and environmental factors in quantitative-based zonation 435 systems may be somewhat subjective, as it is often not justified by exploratory analyses but rather solely 436 based on the authors’ conception of fire regimes and their area of expertise (Omernik and Griffith 2014). 437 Proceeding with a first qualitative comparisonDraft among regions, within regions, and over time, when 438 possible, is a powerful way to understand the environmental factors determining and constraining fire 439 patterns. This assists in better designing numeric agglomerative classification procedures (e.g. number of 440 clusters, spatial unit of analysis, metric for measuring similarity of these units).

441 The FRUs and FRTs units exhibit similarities to existing fire regime zonation systems in Canada. The 442 finer-scale FRUs represents a level of mapping that has never been attempted in Canada, though some 443 analysis have been performed, regionally and nationally, using similar-sized ecoregions (Parisien et al. 444 2004; Wotton et al. 2010) and biogeoclimatic zones (Meyn et al. 2010). The broad-scaled FRTs are more 445 similar to the previous two zonation schemes used in fire research in Canada: ecozones, the predefined 446 ecological units that do not consider fires in their delineation, and the Homogeneous Fire Regime (HFR) 447 zones of Boulanger et al. (2014) that, by contrast, were chiefly generated from fire data. Although the 448 FRTs of this study may be viewed as an intermediate between these two schemes, they exhibit substantial 449 regional discrepancies with ecozones and HFR (Figure A2 in Appendix).

450 The FRTs address a common problem when using zones, such as ecozones, that were not created for fire 451 regime characterization: the inclusion of vastly different fire regimes in a single zone. This phenomenon 452 is particularly evident in northern ecozones that are composed of both forested and tundra areas (e.g., the 453 Taiga shield), or in ecozones that encompass two or more distinct climates that yield different fire 454 regimes (e.g., the Boreal shield, where the western half experiences substantially more fire activity than 455 the eastern part). The problem of intra-zone variation was largely avoided in data-driven HFR zones, but

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456 these coarse zones have the inverse problem of not considering important environmental gradients that are 457 known to affect fire regimes, especially in the topographically complex part of the country. A parallel can 458 be made between the FRTs and the U.S. Fire Regime Condition Class zonation (Schmidt et al. 2002), 459 which uses fire data and historical accounts to map potential historical fire regimes on a pixel basis. Both 460 schemes allow for zones with disjunct areas insofar as they share fire regime characteristics.

461 A fire regime zonation system reflecting attributes in both biogeoclimatic and fire regimes can provide a 462 useful framework for understanding the interaction between fire disturbance and ecosystem dynamics (see 463 also Whitman et al. 2015; Stralberg et al. 2018) and, as such, be the most suitable for some land 464 management applications. For example, with the zonation system presented here, forestry operations 465 could better consider the potential fire loss to their projected harvest scheduling and planning (Armstrong 466 1999). Also, carbon emissions due to wildfires (Amiro et al. 2001) could be more accurately assessed for 467 their past and future impacts than by using zones in which fire regimes cannot be appropriately 468 characterized. The conservation of the nationally threatened woodland caribou (Rangifer tarandus 469 caribou) is an emerging issue in Canada. It is directly affected by fire regime characterizations 470 (specifically, estimates of burn rates), in thatDraft caribou habitat should not exceed 35% of the area being 471 disturbed; disturbance includes cut blocks younger than 40 years and burned area younger than 50 years 472 old (Environment Canada 2011). The fire regime zonation proposed here will help determine how the 473 different caribou ranges have been affected by wildfires in the past and help determine the wildfire 474 potential for broad-scale habitat conservation and planning. As we are attempting to establish a baseline 475 from which to assess environmental change in Canada, whether from climate or anthropogenic sources, it 476 is crucial to consider spatial variation in order to properly incorporate regional rates of projected change 477 (Boulanger et al. 2014; Flannigan et al. 2005; Wang et al. 2017).

478 The two-level fire zonation schemes presented here will be useful for a variety of modeling exercises, 479 ranging in spatial extent and resolution. Correlative studies at sub-continental extents in North America 480 have often used zones of coherent fire regime attributes as sampling units (Littell et al. 2009; Parks et al. 481 2014). A slightly different way in which zones have been used in fire–environment models is to produce a 482 general model (e.g., for all of Canada) and summarize the findings by zone a posteriori (Flannigan et al. 483 2005; Magnussen and Taylor 2012). When zones are large enough (i.e., ecozones, HFR zones, FRTs), and 484 hence encompass sufficient environmental variability, it is possible to build independent models for each 485 zone. Parisien et al. (2016) used this approach to evaluate and compare the influence of humans on fire 486 activity across North America. Finer-scaled zones, such as the FRUs, ecoregions, or ecodistricts (i.e., 487 units nested in ecozones) have been used as inputs to simulation modeling that require a representation of 488 homogeneous fire regimes. This is the case, for instance, with the Burn-P3 model (Parisien et al. 2005)

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489 for mapping fire probability; detailed fire regime information must be input for areas (zones) with distinct 490 fire regimes, such as elevation-driven vegetation strata, in order to parameterize the model in a realistic 491 fashion (Wang et al. 2016).

492

493 Limitations and potential improvements

494 The proposed fire regime zonation approach can—and should—be improved as our knowledge of fire– 495 vegetation–climate grows and new data becomes available. For instance, the inclusion of fire severity, fire 496 intensity, and fire type, three components of the fire regime that are missing from our zonation analysis, 497 will help refine fire zones (cf. Whitman et al. 2015) when these data become available for all (or most) 498 fires in Canada. An extended temporal coverage of fire activity will also improve our characterisation of 499 fire regimes, as the period of record currently available is much shorter than the mean fire-return interval 500 for almost all of the study area. Whereas data-quality issues will undermine any zonation technique, this 501 is increasingly less of a problem given the accumulation of high-quality data in recent years. In spite of 502 the comprehensive nature of the data for much of Canada over that past few decades, there is a biome- 503 wise bias whereby prairie areas (largely non-forested)Draft do not benefit from the degree of monitoring and 504 reporting as the forested areas with respect to vegetation fires. Although at this point in time it is difficult 505 (or impossible) to incorporate prairie areas into our zonation scheme, we should strive to achieve this 506 once more comprehensive data is available, especially given the potential risk of grassland fires to human 507 safety (Alexander et al. 2013).

508 The underlying philosophy of the approach presented here is that these zones are changeable and can be 509 updated when warranted, such as was made for the ecoregions in North America (Omernik and Griffith 510 2014). Implicit is the assumption that the fire regime parameters of the observation period (1970-2016) is 511 representative of current fire regime. Although temporal patterns do not indicate a drastic change in fire 512 regimes, we acknowledge that some changes are likely occurring (e.g., Hanes et al. 2019). These 513 modifications in fire dynamics may indeed be more substantial under future weather conditions and 514 vegetation attributes (Boulanger et al. 2014; Terrier et al. 2013). If and where such changes to the fire 515 zones are warranted, a careful re-assessment following the method described here can be undertaken. 516 Regardless of the degree of scrutiny, it is important to keep in mind that, as with most ecologically based 517 zonation, our scheme remains an abstraction of a fundamentally continuous phenomenon. As such, 518 variability is to be expected within a zone. There are, for instance, localized areas that experience 519 relatively few fires (e.g., fire refugia) in zones that may be highly fire prone (Meddens et al. 2018).

520

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521 6. CONCLUSION

522 In the forested areas of Canada, where wildfires are usually the main natural disturbance, it is essential to 523 consider this process for almost any land-management decisions and activities. Because fire regimes tend 524 to vary over fairly large areas—with the exception of particularly rugged terrain—it is useful to delineate 525 these areas into zones. As an impetus to build an upgraded fire regime zones for Canada, we present here 526 an approach that is rigorous yet flexible. This work builds on previous fire zonation systems (Boulanger 527 et al. 2012, 2014) by integrating additional data and by generating more spatially resolved boundaries of 528 ecological zones, insofar as these boundaries corresponds to natural breaks in wildfire patterns (Mansuy 529 et al. 2014). Central to this method is the idea that any relevant information that can help produce more 530 sensible zones should be incorporated, even if these are qualitative in nature. Furthermore, the two-level 531 system we propose can accommodate a wide range of intended uses, for either ecological analysis or 532 land-management planning. 533 534 Draft

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633 Mansuy, N., Boulanger, Y., Terrier, A., Gauthier, S., Robitaille, A., and Bergeron, Y. 2014. Spatial 634 attributes of fire regime in eastern Canada: influences of regional landscape physiography and 635 climate. Landsc. Ecol. 29(7): 1157-1170. doi:10.1007/s10980-014-0049-4. 636 Marcoux, H.M., Gergel, S.E., and Daniels, L.D. 2013. Mixed-severity fire regimes: How well are they 637 represented by existing fire-regime classification systems? Can. J. For. Res. 43(7): 658-668. 638 doi:10.1139/cjfr-2012-0449. 639 McKenney, D., Hutchinson, M.F., Kesteven, J., and Venier, L. 2001. Canada’s plant hardiness zones 640 revisited using modern climate interpolation techniques. Can. J. Plant. Sci. 81(1): 129-143. 641 doi:10.4141/P00-030. 642 McMahon, G., Wiken, E.B., and Gauthier, D.A. 2004. Toward a scientifically rigorous basis for 643 developing mapped ecological regions. Environ. Manage. 34(1): S111-S124. 644 doi:10.1007/s00267-004-0170-2. 645 Meddens, A.J., Kolden, C.A., Lutz, J.A., Smith, A.M., Cansler, C.A., Abatzoglou, J.T., Meigs, G.W., 646 Downing, W.M., and Krawchuk, M.A. 2018. Fire Refugia: What are they, and why do they 647 matter for global change? BioScience 68(12): 944-954. doi:10.1093/biosci/biy103. 648 Meidinger, D. and Pojar, J. 1991. Ecosystems of British Columbia, Special Report Series Vol. 6. 649 Research Branch, Ministry of Forests, Victoria, British Columbia, Canada. 650 Meyn, A., Schmidtlein, S., Taylor, S.W., Girardin, M.P., Thonicke, K., and Cramer, W. 2010. Spatial 651 variation of trends in wildfire and summer drought in British Columbia, Canada, 1920–2000. Int. 652 J. Wildland Fire 19(3): 272-283. doi:10.1071/WF09055. 653 Morgan, P., Hardy, C.C., Swetnam, T.W., Rollins, M.G., and Long, D.G. 2001. Mapping fire regimes 654 across time and space: understanding coarse and fine-scale fire patterns. Int. J. Wildland Fire 655 10(4): 329-342. doi:10.1071/WF01032.Draft 656 Natural Regions Committee. 2006. Natural regions and subregions of Alberta. Government of Alberta. 657 Pub. No. T/852. Edmonton, Alberta, Canada. 658 O'Neill, R., Hunsaker, C., Timmins, S.P., Jackson, B., Jones, K., Riitters, K.H., and Wickham, J.D. 1996. 659 Scale problems in reporting landscape pattern at the regional scale. Landsc. Ecol. 11(3): 169-180. 660 doi:10.1007/BF02447515. 661 Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V., Underwood, E.C., 662 D'amico, J.A., Itoua, I., Strand, H.E., and Morrison, J.C. 2001. Terrestrial Ecoregions of the 663 World: A New Map of Life on Earth, A new global map of terrestrial ecoregions provides an 664 innovative tool for conserving biodiversity. BioScience 51(11): 933-938. doi:10.1641/0006- 665 3568(2001)051[0933:TEOTWA]2.0.CO;2. 666 Omernik, J.M. 1987. Ecoregions of the conterminous United States. Annals of the Association of 667 American Geographers 77(1): 118-125. doi:10.1111/j.1467-8306.1987.tb00149.x. 668 Omernik, J.M. Ecoregions: a framework for managing ecosystems. In The George Wright Forum, Vol. 12 669 (1). 1995. JSTOR. pp. 35-50. 670 Omernik, J.M. 2004. Perspectives on the nature and definition of ecological regions. Environ. Manage. 671 34(1): S27-S38. doi:10.1007/s00267-003-5197-2. 672 Omernik, J.M. and Griffith, G.E. 2014. Ecoregions of the conterminous United States: evolution of a 673 hierarchical spatial framework. Environ. Manage. 54(6): 1249-1266. doi:10.1007/s00267-014- 674 0364-1. 675 Oris, F., Asselin, H., Ali, A.A., Finsinger, W., and Bergeron, Y. 2013. Effect of increased fire activity on 676 global warming in the boreal forest. Environ. Rev. 22(3): 206-219. doi:10.1139/er-2013-0062. 677 Parisien, M.-A., Hirsch, K., Lavoie, S., Todd, J., and Kafka, V. 2004. Saskatchewan fire regime analysis. 678 Information Report NOR-X-394. Natural Resources Canada, Canadian Forest Service, Northern 679 Forestry Centre, Edmonton, Alberta, Canada. 680 Parisien, M.-A., Kafka, V., Hirsch, K., Todd, J., Lavoie, S., and Maczek, P. 2005. Mapping wildfire 681 susceptibility with the BURN-P3 simulation model. Information report NOR-X-405. Natural 682 Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, 683 Canada.

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684 Parisien, M.-A., Parks, S.A., Miller, C., Krawchuk, M.A., Heathcott, M., and Moritz, M.A. 2011. 685 Contributions of ignitions, fuels, and weather to the spatial patterns of burn probability of a boreal 686 landscape. Ecosystems 14(7): 1141-1155. doi:10.1007/s10021-011-9474-2. 687 Parisien, M.-A., Miller, C., Parks, S.A., DeLancey, E.R., Robinne, F.-N., and Flannigan, M.D. 2016. The 688 spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 689 11(7): 075005. doi:10.1088/1748-9326/11/7/075005. 690 Parisien, M.-A., Parks, S.A., Krawchuk, M.A., Little, J.M., Flannigan, M.D., Gowman, L.M., and Moritz, 691 M.A. 2014. An analysis of controls on fire activity in boreal Canada: comparing models built 692 with different temporal resolutions. Ecol. Appl. 24(6): 1341-1356. 693 Parks, S.A., Parisien, M.A., Miller, C., and Dobrowski, S.Z. 2014. Fire activity and severity in the 694 western US vary along proxy gradients representing fuel amount and fuel moisture. PLoS One 695 9(6): e99699. doi:10.1371/journal.pone.0099699. 696 Parminter, J. 1995. Biodiversity guidebook - Forest Practices Code of British Columbia. BC Ministry of 697 Forests and BC Environment, Victoria, British Columbia, Canada. 698 Price, D.T., Alfaro, R., Brown, K., Flannigan, M., Fleming, R., Hogg, E., Girardin, M., Lakusta, T., 699 Johnston, M., and McKenney, D. 2013. Anticipating the consequences of climate change for 700 Canada’s boreal forest ecosystems. Environ. Rev. 21(4): 322-365. 701 R Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for 702 Statistical Computing, Vienna, Austria. 703 Rossum, S. and Lavin, S. 2000. Where are the Great Plains? A cartographic analysis. The Professional 704 Geographer 52(3): 543-552. 705 Saucier, J.-P., Grondin, P., Robitaille, A., and Bergeron, J.-F. 2003. Vegetation zones and bioclimatic 706 domains in Quebec. Comité sur la carteDraft des régions écologiques, Gouvernement du Québec, 707 Ministère des Ressources naturelles, Québec, Canada. 708 Schmidt, K.M., Menakis, J.P., Hardy, C.C., Hann, W.J., and Bunnell, D.L. 2002. Development of coarse- 709 scale spatial data for wildland fire and fuel management. Gen. Tech. Rep. RMRS-GTR-87. US 710 Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, 711 Colorado, United States. 712 Schoennagel, T., Veblen, T.T., and Romme, W.H. 2004. The interaction of fire, fuels, and climate across 713 Rocky Mountain forests. AIBS Bulletin 54(7): 661-676. doi:10.1641/0006- 714 3568(2004)054[0661:TIOFFA]2.0.CO;2. 715 Scholtz, R., Fuhlendorf, S.D., Leis, S.A., Picotte, J.J., and Twidwell, D. 2018. Quantifying variance 716 across spatial scales as part of fire regime classifications. Ecosphere 9(7): e02343. 717 doi:10.1002/ecs2.2343. 718 Stocks, B.J., Mason, J.A., Todd, J.B., Bosch, E.M., Wotton, B.M., Amiro, B.D., Flannigan, M.D., Hirsch, 719 K.G., Logan, K.A., Martell, D.L., and Skinner, W.R. 2002. Large forest fires in Canada, 1959- 720 1997. Journal of Geophysical Research: Atmospheres 107: 8149. doi:10.1029/2001JD000484. 721 Stralberg, D., Wang, X., Parisien, M.A., Robinne, F.N., Sólymos, P., Mahon, C.L., Nielsen, S.E., and 722 Bayne, E.M. 2018. Wildfire‐mediated vegetation change in boreal forests of Alberta, Canada. 723 Ecosphere 9(3): e02156. doi:10.1002/ecs2.2156. 724 Ter-Mikaelian, M.T., Colombo, S.J., and Chen, J. 2009. Estimating natural forest fire return interval in 725 northeastern Ontario, Canada. For. Ecol. Manage. 258(9): 2037-2045. 726 doi:10.1016/j.foreco.2009.07.056. 727 Terrier, A., Girardin, M.P., Périé, C., Legendre, P., and Bergeron, Y. 2013. Potential changes in forest 728 composition could reduce impacts of climate change on boreal wildfires. Ecol. Appl. 23(1): 21- 729 35. doi:10.1890/12-0425.1. 730 Tymstra, C., Flannigan, M.D., Armitage, O.B., and Logan, K. 2007. Impact of climate change on area 731 burned in Alberta’s boreal forest. Int. J. Wildland Fire 16(2): 153-160. doi:10.1071/WF06084. 732 Viereck, L.A., Dyrness, C., Batten, A., and Wenzlick, K. 1992. The Alaska vegetation classification. Gen. 733 Tech. Rep. PNW-GTR-286. US Department of Agriculture, Forest Service, Pacific Northwest 734 Research Station, Portland, Oregon, United States.

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735 Wang, X., Parisien, M.-A., Taylor, S.W., Perrakis, D.D.B., Little, J., and Flannigan, M.D. 2016. Future 736 burn probability in south-central British Columbia. Int. J. Wildland Fire 25(2): 200-212. 737 doi:10.1071/WF15091. 738 Wang, X., Parisien, M.-A., Taylor, S.W., Candau, J.-N., Stralberg, D., Marshall, G.A., Little, J.M., and 739 Flannigan, M.D. 2017. Projected changes in daily fire spread across Canada over the next 740 century. Environ. Res. Lett. 12(2): 025005. doi:10.1088/1748-9326/aa5835. 741 Weber, M.G. and Flannigan, M.D. 1997. Canadian boreal forest ecosystem structure and function in a 742 changing climate: impact on fire regimes. Environ. Rev. 5(3-4): 145-166. doi:10.1139/a97-008. 743 Whitman, E., Batllori, E., Parisien, M.-A., Miller, C., Coop, J.D., Krawchuk, M.A., Chong, G.W., and 744 Haire, S.L. 2015. The climate space of fire regimes in north-western North America. J. Biogeogr. 745 42(9): 1736-1749. doi:10.1111/jbi.12533. 746 Wiken, E.B. 1986. Terrestrial ecozones of Canada. Ecological land classification series, No 19. 747 Environment Canada, Hull, Quebec, Canada. 748 Wotton, B.M., Nock, C.A., and Flannigan, M.D. 2010. Forest fire occurrence and climate change in 749 Canada. Int. J. Wildland Fire 19(3). doi:10.1071/wf09002. 750 Wu, Z., He, H.S., Yang, J., and Liang, Y. 2015. Defining fire environment zones in the boreal forests of 751 northeastern China. Sci Total Environ 518-519: 106-116. doi:10.1016/j.scitotenv.2015.02.063. 752 753 Draft

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754 FIGURE CAPTIONS 755 756 Figure 1. Spatial distribution of the 60 Fire Regime Units (FRUs) and the Canadian provinces. The FRUs 757 are identified by a number between 1 to 60 (the fire metrics for each FRU are summarized in the 758 Appendix, Table 1A). AB: Alberta, BC: British Columbia, MB: Manitoba, NB: New Brunswick, NS: 759 Nova Scotia, NT: Northwest Territories, NU: Nunavut, ON: Ontario, QC: Quebec, SK: Saskatchewan, 760 YT: Yukon Territory. See Table A1 in the Appendix for detailed fire statistics.

761 Figure 2. Fire regime description of the Fire Regime Types (FRTs). (a) National statistics of fire regimes; 762 (b) Seasonality: spring (more than 65% of area burned between April 1st and June 21st), summer (more 763 than 65% of area burned between June 22nd and September 30th), and mixed (both seasons comprise 764 between 35 and 65% of area burned); (c) Ignition cause: human (more than 65% of fires are human 765 caused), lightning (more than 65% of fires are lightning caused), and mixed (both ignitions cause between 766 35 and 65% of fires); (d) Fire size in ha; (e) Fire frequency: number of fire in Mha-1 year-1; (f) Burn rate: 767 percentage of burned area in year-1. Metrics were obtained by calculating the median of each fire regime 768 descriptor per year, using fire data ≥ 50 ha forDraft 1970-2016, and then averaged to result in one value per 769 zone (or for all of forested areas of Canada in the table). The black dots beside the legends indicate the 770 classes at national scale for each metric.

771 Figure 3. The illustration chart for a hierarchical qualitative zonation method used to delineate the Fire 772 Regime Units (FRUs), in the forested area of Canada. For simplicity, only major variables are presented. 773 Maps at national and regional levels illustrate the stepwise procedure in identifying homogeneous fire 774 regime areas and establishing the boundaries of FRUs.

775 Figure 4. Spatial distribution of the 15 Fire Regime Types (FRTs). The fire activity index (FA) is a 776 combination of fire size, fire frequency, and burn rate, from low (FA1) to extreme (FA5). The seasonality 777 index (SE) indicates the season in which more than 65% of the total annual areas are burned: spring 778 dominated (SEsp), summer dominated (SEsu), or mixed (SEmx) when no season dominates. The ignition 779 cause index (IC) indicates the type of ignition that cause more than 65% of the annual number of fires: 780 human caused (IChm), lightning-caused (IClt), or mixed (ICmx) when no cause dominates. The index (m) 781 indicates if the FRT is part of the mountain group. See Table A2 in the Appendix for detailed fire 782 statistics.

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784 APPENDIX 785 786 LIST OF DATA SOURCES AND REFERENCES 787 788 This list (non-exhaustive) contains the main elements used in the qualitative delineation process of FRU. 789 790 TOPOGRAPHY 791 Canadian Digital Elevation Model (CDEM), resolution 1:50 000 792 Natural Resources Canada: https://open.canada.ca/data/en/dataset/ 793 Orthoimages of Canada 2005-2010, resolution of 10-20m 794 Natural Resources Canada: https://open.canada.ca/data/en/dataset/ 795 796 VEGETATION 797 Hardiness zones in Canada Draft 798 Natural Resources Canada: http://www.planthardiness.gc.ca/?m=1&lang=en 799 Alberta Backfilled Wall-to-Wall Vegetation Layer (Version 5), 2015 800 Alberta Biodiversity Monitoring Institute, Alberta, Canada. 801 http://abmi.ca/home/data-analytics/ 802 Natural disturbance bibliography for British Columbia, by J. Parminter (2014) 803 https://www.for.gov.bc.ca/hfd/pubs/Docs/Tr/TR080.htm 804 805 ECOLOGICAL ZONATION SYSTEMS 806 BEC zones, British Columbia, Canada. 807 https://www.for.gov.bc.ca/hre/becweb/ 808 Yukon Bioclimate zones and subzones, Yukon, Canada. 809 https://geoweb.gov.yk.ca/geoportal/ 810 Natural regions and subregions of Alberta 811 https://open.alberta.ca/publications/3487069 812 813 CLIMATE and WEATHER 814 Alberta Biodiversity Monitoring Institute, Alberta, Canada.

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815 http://abmi.ca/home/data-analytics 816 Climate Atlas of Canada, Prairie Climate Centre, University of Winnipeg, Manitoba, Canada. 817 https://climateatlas.ca/ 818 Temperature and precipitation data from the National Ecological Framework. 819 Environment Canada: http://sis.agr.gc.ca/cansis/nsdb/ecostrat/index.html 820 ACER Association for Canadian Educational Resources Mississauga, Ontario, Canada. 821 http://www.acer-acre.ca/publications-and-research/research 822 AdaptWest project, Current and projected climate data for North America (CMIP5 scenarios) 823 https://adaptwest.databasin.org/pages/adaptwest-climatena 824 USGS Geosciences and Environmental Change Science Center 825 https://gec.cr.usgs.gov/effectsfigures/index.html 826 827 828 829 Draft 830 831

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832 TABLES CAPTIONS IN APPENDIX 833 834 Table A1. Attributes and fire metrics of the 60 Fire Regime Units (FRU). Spring and Summer columns 835 give the distribution, in percent, of area burned during the fire season, i.e. during spring (April 1st to June 836 21st) and summer (June 22nd - September 30th). Lightning and Human columns give the distribution, in 837 percent, of the number of fires depending on the cause. The median of each fire metric was calculated by 838 year, using fire data ≥ 50 ha for 1970-2016, and then averaged to obtain one value per FRU. Blue values 839 show the three highest items for each category.

840 Table A2. Attributes and fire metrics of the 15 Fire Regime Types (FRT). Spring and Summer columns 841 give the distribution, in percent, of area burned during the fire season, i.e. during spring (April 1st to June 842 21st) and summer (June 22nd - September 30th). Lightning and Human columns give the distribution, in 843 percent, of the number of fires depending on the cause. The median of each fire metric was calculated by 844 year, using fire data ≥ 50 ha for 1970-2016, and then averaged to obtain one value per FRT. Red values 845 show the three highest items for each category.Draft

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846 FIGURE CAPTIONS IN APPENDIX 847 848 Figure A1. Fire regime description of Fire Regime Units (FRUs). (a) Population density in number of 849 person per 100 ha; (b) Seasonality: spring (more than 65% of area burned between April 1st and June 850 21st), summer (more than 65% of area burned between June 22nd and September 30th), and mixed (both 851 seasons comprise between 35 and 65% of area burned); (c) Ignition cause: human (more than 65% of fires 852 are human caused), lightning (more than 65% of fires are lightning caused), and mixed (both ignitions 853 cause between 35 and 65% of fires); (d) Fire size in ha; (e) Fire frequency: number of fire in Mha-1 year-1; 854 (f) Burn rate: percentage of burned area in year-1. Metrics were obtained by calculating the median of 855 each fire regime descriptor per year, using fire data ≥ 50 ha for 1970-2016, and then averaged to result in 856 one value per zone (or for all of forested areas of Canada in the table). The black dots beside the legends 857 indicate the classes at national scale for each metric.

858 Figure A2. Geographical distribution of fire polygons (≥ 50 ha) between 1970 and 2016 in Canada, across 859 (a) Ecozones, (b) Homogeneous Fire Regime (HFR) zones, and (c) Fire regime Types (FRT) zones. The 860 illustration is adapted from Hanes et al. (2018).Draft

861

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862 REFERENCES IN APPENDIX 863 864 Bailey, R.G. 2010. Fire regimes and ecoregions in Cumulative watershed effects of fuel management in 865 the western United States. General technical report RMRS-GTR-231. USDA Forest Service, Rocky 866 Mountain Research Station, Fort Collins, CO. 867 Beaudoin, A., Bernier, P.Y., Guindon, L., Villemaire, P., Guo, X.J., Stinson, G., Bergeron, T., 868 Magnussen, S., and Hall, R.J. 2014. Mapping attributes of Canada’s forests at moderate resolution 869 through kNN and MODIS imagery. Can. J. For. Res. 44(5): 521-532. doi:10.1139/cjfr-2013-0401. 870 Bernier, P., Gauthier, S., Jean, P.-O., Manka, F., Boulanger, Y., Beaudoin, A., and Guindon, L. 2016. 871 Mapping Local Effects of Forest Properties on Fire Risk across Canada. Forests 7(8). 872 doi:10.3390/f7080157. 873 Bridge, S. 2001. Spatial and temporal variations in the fire cycle across Ontario. OMNR, Northeast 874 Science & Technology. NEST TR-043 ed. pp. 41. 875 DeLong, C. 1998. Natural disturbance rate and patch size distribution of forests in northern British 876 Columbia: Implications for forest management (vol 72, pg 35, 1998). Northwest Science 73(1): U1- 877 U1. 878 DeLong, S. 2010. Land units and benchmarksDraft for developing natural disturbance-based forest 879 management guidance for northeastern British Columbia. BC Min. For. Range, For. Sci. Prog., 880 Victoria, BC Tech. Rep 59. 881 Environment Yukon. 2016. Yukon Ecological and landscape classification and mapping guidelines. 882 Version 1.0. Department of Environment, Government of Yukon, Whitehorse, YT. 883 Erni, S., Arseneault, D., Parisien, M.A., and Begin, Y. 2017. Spatial and temporal dimensions of fire 884 activity in the fire-prone eastern Canadian taiga. Glob Chang Biol 23(3): 1152-1166. 885 doi:10.1111/gcb.13461. 886 Gralewicz, N.J., Nelson, T.A., and Wulder, M.A. 2012. Spatial and temporal patterns of wildfire ignitions 887 in Canada from 1980 to 2006. Int. J. Wildland Fire 21(3). doi:10.1071/wf10095. 888 Grods, J., Francis, S.R., Meikle, J.C., and Lapointe, S. 2012. Regional Ecosystems of West-Central 889 Yukon. Part 1: Ecosystem descriptions. Report prepared for Environment, Government of Yukon by 890 Makonis Consulting Ltd. and Associates, West Kelowna, BC. 891 Hanes, C. C., Wang, X., Jain, P., Parisien, M. A., Little, J. M., & Flannigan, M. D. (2018). Fire-regime 892 changes in Canada over the last half century. Can. J. For. Res., 49(3), 256-269. 893 Lefort, P., Leduc, A., Gauthier, S., and Bergeron, Y. 2004. Recent fire regime (1945–1998) in the boreal 894 forest of western Quebec. Ecoscience 11(4): 433-445. 895 Li, C. 2000. Fire Regimes and their Simulation with Reference to Ontario. In Ecology of a Managed 896 Terrestrial Landscape: Patterns and Processes of Forest Landscapes in Ontario. Edited by A. Perera, 897 D. Euler, and I. Thompson, UBC Press: Vancouver, BC. pp. 115-140. 898 Loehman, R.A., Bentz, B.J., DeNitto, G.A., Keane, R.E., Manning, M.E., Duncan, J.P., Egan, J.M., 899 Jackson, M.B., Kegley, S., and Lockman, I.B. 2018. Effects of climate change on ecological

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900 disturbance in the Northern Rockies. In Climate Change and Rocky Mountain Ecosystems. Springer. 901 pp. 115-141. 902 MacKenzie, W.H. 2012. Biogeoclimatic ecosystem classification of non-forested ecosystems in British 903 Columbia. Tech. Rep. No. 68, British Columbia Ministry of Forests, Victoria. 904 Macias Fauria, M. and Johnson, E.A. 2007. Climate and wildfires in the North American boreal forest. 905 Philosophical Transactions of the Royal Society B: Biological Sciences 363(1501): 2315-2327. 906 doi:10.1098/rstb.2007.2202. 907 Magnussen, S. and Taylor, S.W. 2012. Inter- and intra-annual profiles of fire regimes in the managed 908 forests of Canada and implications for resource sharing. Int. J. Wildland Fire 21(4). 909 doi:10.1071/wf11026. 910 Mansuy, N., Boulanger, Y., Terrier, A., Gauthier, S., Robitaille, A., and Bergeron, Y. 2014. Spatial 911 attributes of fire regime in eastern Canada: influences of regional landscape physiography and 912 climate. Landsc. Ecol. 29(7): 1157-1170. doi:10.1007/s10980-014-0049-4. 913 Perera, A.H., Baker, J.A., Band, L.E., and Baldwin, D.J. 1996. A strategic framework to eco-regionalize 914 Ontario. In Global to Local: Ecological Land Classification. Springer. pp. 85-96. 915 Portier, J., Gauthier, S., Robitaille, A., and Bergeron, Y. 2017. Accounting for spatial autocorrelation 916 improves the estimation of climate, physical environment and vegetation’s effects on boreal forest’s 917 burn rates. Landsc. Ecol. doi:10.1007/s10980-017-0578-8.Draft 918 Price, D.T., McKenney, D., Joyce, L., Siltanen, R., Papadopol, P., and Lawrence, K. 2011. High- 919 resolution interpolation of climate scenarios for Canada derived from general circulation model 920 simulations. Information Report NOR-X-421. Natural Resources Canada, Canadian Forest Service, 921 Northern Forestry Centre, Edmonton, AB. 922 Rollins, M.G., Keane, R.E., and Parsons, R.A. 2004. Mapping fuels and fire regimes using remote 923 sensing, ecosystem simulation, and gradient modeling. Ecol. Appl. 14(1): 75-95. 924 Simard, A.J. 1973. Forest fire weather zones of Canada. Environment Canada, Canadian Forestry Service, 925 Headquarters, Ottawa, ON. 926 Strong, W., Zoltai, S., and Ironside, G. 1989. Ecoclimatic regions of Canada. Ecological land 927 classification series. Canadian Wildlife Service, Ottawa, ON. 928 Syphard, A.D. and Keeley, J.E. 2015. Location, timing and extent of wildfire vary by cause of ignition. 929 Int. J. Wildland Fire 24(1). doi:10.1071/wf14024. 930 Ter-Mikaelian, M.T., Colombo, S.J., and Chen, J. 2009. Estimating natural forest fire return interval in 931 northeastern Ontario, Canada. For. Ecol. Manage. 258(9): 2037-2045. 932 doi:10.1016/j.foreco.2009.07.056. 933 Wang, X., Thompson, D.K., Marshall, G.A., Tymstra, C., Carr, R., and Flannigan, M.D. 2015. Increasing 934 frequency of extreme fire weather in Canada with climate change. Clim. Change 130(4): 573-586. 935 doi:10.1007/s10584-015-1375-5. 936 Wang, Y. and Anderson, K.R. 2011. An evaluation of spatial and temporal patterns of lightning-and 937 human-caused forest fires in Alberta, Canada, 1980–2007. Int. J. Wildland Fire 19(8): 1059-1072.

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938 Whitman, E., Parisien, M.-A., Thompson, D.K., Hall, R.J., Skakun, R.S., and Flannigan, M.D. 2018. 939 Variability and drivers of burn severity in the northwestern Canadian boreal forest. Ecosphere 9(2): 940 e02128-n/a. doi:10.1002/ecs2.2128. 941 Whitman, E., Batllori, E., Parisien, M.-A., Miller, C., Coop, J.D., Krawchuk, M.A., Chong, G.W., and 942 Haire, S.L. 2015. The climate space of fire regimes in north-western North America. J. Biogeogr. 943 42(9): 1736-1749. doi:10.1111/jbi.12533. 944 Yagouti, A., Boulet, G., Vincent, L., Vescovi, L., and Mekis, E. 2008. Observed changes in daily 945 temperature and precipitation indices for southern Québec, 1960–2005. Atmosphere-Ocean 46(2): 946 243-256. 947

948

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Draft Figure 1. Spatial distribution of the 60 Fire Regime Units (FRUs) and the Canadian provinces. The FRUs are identified by a number between 1 to 60 (the fire metrics for each FRU are summarized in the Appendix, Table 1A). AB: Alberta, BC: British Columbia, MB: Manitoba, NB: New Brunswick, NS: Nova Scotia, NT: Northwest Territories, NU: Nunavut, ON: Ontario, QC: Quebec, SK: Saskatchewan, YT: Yukon Territory. See Table A1 in the Appendix for detailed fire statistics.

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Draft Figure 2. Fire regime description of the Fire Regime Types (FRTs). (a) National statistics of fire regimes; (b) Seasonality: spring (more than 65% of area burned between April 1st and June 21st), summer (more than 65% of area burned between June 22nd and September 30th), and mixed (both seasons comprise between 35 and 65% of area burned); (c) Ignition cause: human (more than 65% of fires are human caused), lightning (more than 65% of fires are lightning caused), and mixed (both ignitions cause between 35 and 65% of fires); (d) Fire size in ha; (e) Fire frequency: number of fire in Mha-1 year-1; (f) Burn rate: percentage of burned area in year-1. Metrics were obtained by calculating the median of each fire regime descriptor per year, using fire data ≥ 50 ha for 1970-2016, and then averaged to result in one value per zone (or for all of forested areas of Canada in the table). The black dots beside the legends indicate the classes at national scale for each metric.

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Figure 3. The illustration chart for a hierarchical qualitative zonation method used to delineate the Fire Regime Units (FRUs), in the forested area of Canada. For simplicity, only major variables are presented. Maps at national and regional levels illustrate the stepwise procedure in identifying homogeneous fire regime areas and establishing the boundaries of FRUs.

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Figure 4. Spatial distribution of the 15 Fire Regime Types (FRTs). The fire activity index (FA) is a combination of fire size, fire frequency, and burn rate, from low (FA1) to extreme (FA5). The seasonality index (SE) indicates the season in which more than 65% of the total annual areas are burned: spring dominated (SEsp), summer dominated (SEsu), or mixed (SEmx) when no season dominates. The ignition cause index (IC) indicates the type of ignition that cause more than 65% of the annual number of fires: human caused (IChm), lightning-caused (IClt), or mixed (ICmx) when no cause dominates. The index (m) indicates if the FRT is part of the mountain group. See Table A2 in the Appendix for detailed fire statistics.

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Table A1. Attributes and fire metrics of the 60 Fire Regime Units (FRU). Spring and Summer columns give the distribution, in percent, of area burned during the fire season, i.e. during spring (April 1st to June 21st) and summer (June 22nd - September 30th). Lightning and Human columns give the distribution, in percent, of the number of fires depending on the cause. The median of each fire metric was calculated by year, using fire data ≥ 50 ha for 1970-2016, and then averaged to obtain one value per FRU. Blue values show the three highest items for each category.

FRU AREA POPULATION FIRE SIZE FREQUENCY BURN RATE SPRING SUMMER LIGHTNING HUMAN in Mha per 100 ha in ha Mha -1 year -1 in % year -1 in % in % in % in % 1 11.0 3.84 1241 0.16 0.0426 44.5 55.5 5.2 94.8 2 9.3 15.22 309 0.21 0.0141 87.2 12.8 2.0 98.0 3 4.7 2.86 642 0.15 0.0212 53.8 46.2 27.3 72.7 4 5.8 13.26 673 0.03 0.0021 87.5 12.5 0.0 100.0 5 12.0 0.09 1971 0.16 Draft0.1192 23.0 77.0 65.3 34.7 6 29.9 0.03 2770 0.30 0.1166 20.8 79.2 89.8 10.2 7 31.6 0.02 2911 0.06 0.0194 19.8 80.2 73.3 26.7 8 13.4 0.06 1666 0.91 1.0443 35.2 64.8 84.9 15.1 9 9.2 0.01 2980 1.18 0.8193 54.9 45.1 81.9 18.1 10 20.0 0.26 931 0.64 0.3489 51.5 48.5 73.3 26.7 11 11.2 2.85 2421 0.33 0.1492 80.1 19.9 49.1 50.9 12 19.8 10.33 435 0.13 0.0077 77.0 23.0 25.0 75.0 13 3.2 0.18 1929 0.63 0.2029 38.2 61.8 90.0 10.0 14 25.4 1.44 281 0.34 0.0547 67.1 32.9 35.0 65.0 15 8.9 0.00 1004 0.45 0.0808 46.9 53.1 89.3 10.7 16 23.7 0.03 1657 0.33 0.0845 22.3 77.7 82.8 17.2 17 18.7 0.09 566 2.17 0.8780 43.9 56.1 86.8 13.2 18 18.0 0.34 508 1.16 0.3674 58.2 41.8 69.4 30.6 19 3.5 3.81 1013 0.73 0.0744 80.0 20.0 37.1 62.9 20 12.4 2.66 208 1.03 0.1173 95.0 5.0 7.4 92.6 21 22.0 0.95 247 1.92 0.5961 66.3 33.7 38.4 61.6

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22 11.7 0.28 1107 1.34 0.5813 31.9 68.1 85.4 14.6 23 6.0 0.02 3296 1.06 1.1070 29.2 70.8 88.0 12.0 24 22.8 0.00 1467 0.52 0.1305 9.7 90.3 87.5 12.5 25 15.6 0.02 1072 1.49 0.8197 28.4 71.6 84.9 15.1 26 40.2 0.08 1191 1.88 1.6710 19.1 80.9 94.5 5.5 27 23.8 0.16 943 2.18 2.0186 41.3 58.7 90.4 9.6 28 9.2 1.08 392 0.94 0.4680 74.8 25.2 51.1 48.9 29 1.4 0.12 1120 0.89 0.2068 69.3 30.7 75.4 24.6 30 6.6 2.65 145 0.72 0.0776 79.6 20.4 9.3 90.7 31 6.7 0.01 1090 1.30 0.8981 40.4 59.6 95.8 4.2 32 13.1 0.09 562 0.68 0.1597 54.7 45.3 70.4 29.6 33 8.7 0.03 3347 1.16 1.1041 23.1 76.9 94.6 5.4 34 22.0 0.02 1785 1.14 0.5776 12.9 87.1 94.3 5.7 35 2.1 0.03 1209 1.76 Draft0.3908 17.5 82.5 100.0 0.0 36 1.5 NA NA NA NA NA NA NA NA 37 4.0 0.00 943 0.58 0.0769 34.5 65.5 94.6 5.4 38 7.2 0.00 1569 1.25 0.7173 28.4 71.6 100.0 0.0 39 30.0 0.00 613 0.45 0.0323 9.3 90.7 72.8 27.2 40 11.4 0.00 2074 1.21 0.5308 34.0 66.0 97.6 2.4 41 6.5 0.46 1348 1.16 0.6250 42.7 57.3 88.5 11.5 42 1.2 0.00 1960 0.76 0.1445 14.3 85.7 96.4 3.6 43 7.0 0.01 1044 0.52 0.1155 36.0 64.0 81.3 18.7 44 2.0 0.00 801 0.07 0.0056 20.0 80.0 80.0 20.0 45 8.9 0.04 2579 0.71 0.4446 35.9 64.1 80.0 20.0 46 8.8 0.00 1292 0.46 0.0771 40.9 59.1 62.8 37.2 47 11.4 1.39 332 0.69 0.1313 41.2 58.8 34.5 65.5 48 2.5 0.89 744 1.08 0.0771 7.5 92.5 65.0 35.0 49 3.9 1.05 693 0.77 0.0889 11.0 89.0 46.1 53.9 50 4.7 12.41 357 1.63 0.1686 33.6 66.4 13.0 87.0 51 2.0 164.15 203 0.36 0.0106 7.0 93.0 23.7 76.3 52 6.0 0.01 143 1.86 0.0644 22.0 78.0 65.8 34.2

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53 6.6 0.24 249 0.91 0.1112 22.8 77.2 59.8 40.2 54 1.2 4.35 642 0.57 0.0459 59.9 40.1 33.3 66.7 55 2.9 0.90 400 0.92 0.1196 19.2 80.8 46.5 53.5 56 0.4 9.67 394 2.23 0.1287 49.1 50.9 16.7 83.3 57 3.8 0.00 1567 0.32 0.1244 15.1 84.9 78.5 21.5 58 7.1 0.75 436 1.02 0.0655 77.8 22.2 15.4 84.6 59 1.2 2.28 314 0.50 0.0203 37.3 62.7 22.5 77.5 60 8.5 1.06 121 0.22 0.0034 13.8 86.2 34.2 65.8

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Table A2. Attributes and fire metrics of the 15 Fire Regime Types (FRT). Spring and Summer columns give the distribution, in percent, of area burned during the fire season, i.e. during spring (April 1st to June 21st) and summer (June 22nd - September 30th). Lightning and Human columns give the distribution, in percent, of the number of fires depending on the cause. The median of each fire metric was calculated by year, using fire data ≥ 50 ha for 1970-2016, and then averaged to obtain one value per FRT. Red values show the three highest items for each category.

FRT AREA POPULATION FIRE SIZE FREQUENCY BURN RATE SPRING SUMMER LIGHTNING HUMAN in Mha per 100 ha in ha Mha -1 year -1 in % year -1 in % in % in % in % 1 1.5 6.76 267 0.21 0.0295 66.2 33.8 25.5 74.5 2 76.1 0.03 764 0.28 0.0970 21.8 78.2 85.3 14.7 3 123.1 0.06 741 1.24 0.7565 31.5 68.5 92.3 7.7 4 78.7 0.02 2037 1.14 0.9918 39.8 60.2 86.6 13.4 5 23.9 0.66 432 0.73 Draft0.2746 60.7 39.3 72.2 27.8 6 81.8 0.55 381 2.04 0.7327 57.0 43.0 61.8 38.2 7 40.6 2.31 155 0.91 0.0872 81.3 18.7 13.9 86.1 8 29.7 0.11 984 2.00 1.8097 30.5 69.5 92.4 7.6 9 64.0 0.00 796 0.50 0.0926 38.6 61.4 74.4 25.6 10 19.8 0.00 677 0.35 0.0700 18.0 82.0 77.1 22.9 11 37.0 0.12 1478 1.03 0.5237 39.6 60.4 89.7 10.3 12 26.9 1.70 323 0.66 0.1132 44.6 55.4 33.1 66.9 13 13.9 0.48 183 1.16 0.0918 19.7 80.3 56.4 43.6 14 21.9 12.10 272 1.67 0.1654 33.2 66.8 13.2 86.8 15 5.2 31.71 141 0.25 0.0048 6.7 93.3 28.6 71.4

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Draft Figure A1. Fire regime description of Fire Regime Units (FRUs). (a) Population density in number of person per 100 ha; (b) Seasonality: spring (more than 65% of area burned between April 1st and June 21st), summer (more than 65% of area burned between June 22nd and September 30th), and mixed (both seasons comprise between 35 and 65% of area burned); (c) Ignition cause: human (more than 65% of fires are human caused), lightning (more than 65% of fires are lightning caused), and mixed (both ignitions cause between 35 and 65% of fires); (d) Fire size in ha; (e) Fire frequency: number of fire in Mha-1 year-1; (f) Burn rate: percentage of burned area in year-1. Metrics were obtained by calculating the median of each fire regime descriptor per year, using fire data ≥ 50 ha for 1970-2016, and then averaged to result in one value per zone (or for all of forested areas of Canada in the table). The black dots beside the legends indicate the classes at national scale for each metric.

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Figure A2. Geographical distribution of fire polygons (≥ 50 ha) between 1970 and 2016 in Canada, across (a) Ecozones, (b) Homogeneous Fire Regime (HFR) zones, and (c) Fire regime Types (FRT) zones. The illustration is adapted from Hanes et al. (2018).

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