Fire regime characteristics in relation to physiography at local and landscape scales in Lake States pine forests

Forest Ecology and Management 454 (2019) 117651

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Fire regime characteristics in relation to physiography at local and landscape scales in Lake States pine forests

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Jed Meunier , Nathan S. Holoubek, Megan Sebasky Wisconsin Department of Natural Resources, Division of Forestry, 2801 Progress Road, Madison, WI 53716, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Disturbance Fire history Physiography Pinus resinosa Pre-settlement Red pine Topography Wisconsin

Examining fire regime characteristics across temporal and spatial scales is critical to understanding relationships between fire and landscape physiography. In the Lake States (Wisconsin, Michigan, Minnesota) we have often relied on either broad extrapolation from local studies, and/or interpretations of coarse-scale Euro settlement era records tied to landscape-scale physiography to understand complex fire regimes. In this study, we used detailed fire frequency and origin-to-first-scar (OFS, patterns of fire-scar formation) intervals to evaluate fire characteristics in relation to physiography at local and landscape scales. We found frequent fires (mean fire return intervals, MFRI = 8 years) and widespread fire years were common among our sites. OFS intervals were also less than half as long (µ = 18.3) as intervals often deemed necessary for seedlings to survive fires (ca. 50 years). We found no differences in either MFRI or OFS intervals nor physiographic effects (topography and water features) at broad ecological landscape scales. Most variability in OFS was accounted for at a site scale with increased water features and topographical ruggedness both resulting in shorter OFS intervals (trees surviving and recording fires at a younger age). We found few differences in MFRI’s among sites, which ranged from 4 to 13 years. Relationships between fire resistance and stand level physiography, which was highly variable, may have a greater role than recognized in forest successional stages and stand dynamics.

1. Introduction The importance of physiographic variables, like water features or topographical ruggedness, to forest disturbance processes is well established (Foster and Boose, 1992; Flatley et al., 2011; Rogeau et al., 2018). Topography interacts with fire as both a physical and ecological process and directly and indirectly influences fire behavior (e.g., Rothermel, 1983) and fire regimes (Beaty and Taylor, 2001; Taylor and Skinner, 2003). However, it is often unclear how topographical controls change geographically and across spatial scales. Identifying scale dependency in ecological variables is important for determining the appropriate resolution at which to analyze and identify ecological relationships (Stambaugh and Guyette, 2008). Fire regimes are generally controlled at regional scales by climate, but increasingly influenced by physiography at smaller spatial scales (Bergeron, 1991; Swetnam and Betancourt, 1998; Beaty and Taylor, 2001). Although broad-scale and/or high-severity fire events are less influenced by topography (Morgan et al., 2001; Flatley et al., 2011), in much of the Lakes States where detailed disturbance data is lacking, understanding of disturbances have relied on proxy data like Eurosettlement era survey records tied to broad-scale physiography (Manies

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and Mladenoff, 2000; Schulte and Mladenoff, 2001; Lorimer and White, 2003; Schulte and Mladenoff, 2005). In northern Michigan, for example, survey records were coupled to landscape-scale physiography (e.g., topography, wetlands and water, soils) to delineate fire regime characteristics (Cleland et al., 2004). The ecological landscapes that were dry and flat with few water bodies or other fire breaks were delineated as the most frequent, large, and severe fire regimes (Cleland et al., 2004). However, Whitney (1986), also using landscape scale survey data for Michigan, failed to find any relationship between the incidence of fire and topography or natural firebreaks, and in Wisconsin, spatial patterns of severe fire had stronger associations with topographic effects at local rather than regional scales (Schulte et al., 2005). General Land Office (GLO) survey notes rely on interpretation of coarse scale tree patterns and cannot detect scale dependent relationships between topography and fire which are likely most important in small (100–102 ha) to medium (103–104 ha) sized fires (Schulte and Mladenoff, 2005; Povak et al., 2018). Similarly, survey notes cannot reliably detect small scale or low to moderate-severity fires (Schulte and Mladenoff, 2001). Most research on spatial patterns of fire has been in forests of the western US; usually in dry montane, wet subalpine, or boreal forests

Corresponding author. E-mail address: [email protected] (J. Meunier).

https://doi.org/10.1016/j.foreco.2019.117651 Received 20 June 2019; Received in revised form 17 September 2019; Accepted 21 September 2019 0378-1127/ Published by Elsevier B.V.

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site scales. OFS intervals are the period between estimated tree germination and first fire-scars (Baker, 1989). We expected that in landscapes with few barriers to limit fire spread, or change fire behavior, we would find less frequent but severe fires and longer corresponding OFS intervals for pines that survive (and record) their first fire. Presumably, a greater proportion of young trees would be killed (rather than scarred) by more severe fire conditions and trees that survive would be larger, with greater fire resistance, and longer corresponding OFS intervals, when scarred. In contrast, we expected that in landscapes with greater topography and fire breaks we would see more frequent and less severe fires with relatively short OFS intervals. We also anticipated similar fire-physiographic factor relationships across spatial scales; sites within an ecological landscape, and ecological landscapes (Cleland et al., 1997; WI DNR, 2015). Our specific research objectives were to (1) quantify fire frequency and OFS intervals as related to physiographic features at local (site) and ecological landscape scales, and in doing so to also (2) evaluate 50-year thresholds for pine survival of wildfire with historical evidence. Notably, ca. 50-year return interval between low-severity fires in the BWCA (Heinselman, 1973) match the ca. 50-year threshold for young pines to survive fire (Van Wagner, 1970; Rouse, 1988) and forms, in part, the basis of understanding fire history in the Great Lakes Region.

(Flatley et al., 2011) where topographic features have been found to influence fire regimes (Taylor and Skinner, 1998; Rollins et al., 2002; Howe and Baker, 2003). Environmental factors controlling spatial heterogeneity of fire regimes are numerous however, vary through time, and from one ecosystem to another (Cyr et al., 2007; Povak et al., 2018), topography may have a more limited role in other locations (Schulte et al., 2005; Flatley et al., 2011). Fire regimes are often more difficult to ascertain, and fire history studies more limited, in the Great Lakes Region (Meunier et al., 2019). One of the few detailed (stand age structure and fire-scar data) and extensive (ca. 215,000 ha) fire histories in the Great Lakes Region was Heinselman’s (1973) landmark studies in the Boundary Waters Canoe Area (BWCA) of northeastern Minnesota. Heinselman (1973) found that surface fires burned periodically (ca. 40–50 years) followed by stand replacing fire (ca. 150–300 years) with strong effects of physiographic factors on historical fire patterns, particularly lakes, streams and wetlands. Notably, the BWCA landscape is exceptional even among the Lake States in terms of physiographic factors influencing fire spread with > 1000 lakes (some > 4000 ha in size), > 3000 km of rivers and streams, and the highest elevation in Minnesota. In southern Minnesota’s Big Woods, vegetation was most strongly correlated to fire-probability via fuel loading (Grimm, 1984). Fire is a disturbance inseparable from landscape surface fuels which are a link between physiographic features and fire spread across spatial scales. Topographic variation at finer scales influences surface fuel production via factors such as runoff, temperature, and solar radiation, which in turn also affect flammability through fuel curing (Daly et al., 1994; Dubayah and Rich, 1995; Flatley et al., 2011). At meso-scales (50–5000 ha; Moritz et al., 2011), topography and fuels are considered primary factors controlling fire characteristics such as intensity, rate of spread, and severity (Abatzoglou and Kolden, 2013; Birch et al., 2015; Holsinger et al., 2016). Topography has been found to also influence fire frequency (Iniguiz et al., 2008; Bigio et al., 2016; Holsinger et al., 2016). However, fire frequency is potentially the fire regime dimension most likely to be influenced by both top-down (e.g., broad scale climate patterns) and bottom-up (e.g., topography and fuels) controls (Cyr et al., 2007; Guyette et al., 2012). Topography has more often been related to fire severity than fire frequency (Kushla and Ripple, 1998; Cyr et al., 2007; Holsinger et al., 2016). Generally, wildfire frequency and severity are inversely related (Pickett and White, 1985; Turner et al., 1989; Huston, 2003) forming the basis of how fire regimes are defined (Schmidt et al., 2002; Barrett et al., 2010). However, ascertaining fire severity in low to moderate-severity fire regimes is challenging. Even in fire resistant red pine (Pinus resinosa) most small trees with thinner bark can be killed by fires (Gutsell and Johnson, 1996), but after approximately 50 years a minimum bark thickness develops to reduce or prevent lethal cambial damage (Van Wagner, 1970; Ahlgren, 1974). However, pine seedlings have been found to survive a high degree of needle scorch as well as cambial injury (Methven, 1971; Rouse, 1988). Interestingly, some fire-scar studies, including in Minnesota’s BWCA (Heinselman, 1973) and northwestern Quebec (Dansereau and Bergeron, 1993), have relied primarily on thin barked, fire sensitive white cedar (Thuja occidentalis) to reconstruct fire history. It is conceivable that small diameter trees, with thinner bark, could be better recorders of the lowest intensity fires than larger trees, which are resistant to fire-scarring (Baker and Ehle, 2001). Small diameter trees, the very trees the Euro-settlement era GLO surveyors avoided using (Bourdo, 1956), may provide valuable fire severity information and hold unique clues to past fire regimes. Examining variability of fire regime characteristics over space and time is crucial to understanding interplay between landscape patterns and fire (Morgan et al., 2001). In this study we aimed to examine relationships among fire frequency, origin-to-first-scar (OFS) intervals – a relatively new type of fire severity data (Baker and Ehle, 2001; Van Horne and Fulé, 2006), and physiographic features at landscape and

2. Methods 2.1. Study area Our study encompassed much of Wisconsin, USA, spanning a 4degree latitudinal gradient (43°–47°N) and five ecological landscapes (Cleland et al., 1997; WI DNR, 2015, Fig. 1). This study was positioned along a prairie-forest tension zone that includes the Laurentian mixed forest type, itself a transition from Canada’s boreal forest, and southern broad-leafed forest (Fig. 1). These broad ecoregions are in part a result of successive glaciation events that have created diverse ecological landscapes. The ecological landscapes all had sandy soils or sandstone outcrops and contained red pine (P. resinosa) dominated stands. The landscapes were otherwise disparate, lending themselves well to studying differences in disturbance history among broad geographical regions with varying physiography. The ecological landscapes included the: (1) Northwest Sands (5066 km2) – deep, well drained outwash sands, relatively flat to rolling topography with few lakes aside from several concentrations of lakes in the southern portion, (2) Northern Highlands (5390 km2) – a rolling topography with pitted-gravelly sands and abundant natural lakes, rivers, and streams, (3) Northeast Sands (3995 km2) – deep, well drained outwash sands with level to rolling topography and few lakes, (4) Central Sands (8858 km2) – a nearly level glacial lakebed of well drained outwash sands intermixed with wetlands, but with few natural lakes or rivers and very little topography, and (5) Western Coulees and Ridges (24,972 km2) – unglaciated region of silt and loam soils with few natural lakes and wetlands, but complex ridge and valley topography (WI DNR, 2015, Fig. 1). Sites within the Western Coulees and Ridges were comprised of red pine relicts occurring on steep bluffs with exposed sandstone outcrops in an otherwise hardwood dominated landscape that was historically primarily oak savanna. We made additional site comparisons to locations within the BWCA including Seagull, Lac La Croix, and Ramshead Lakes studied by Heinselman (1973), Conrad et al. (1994), Fritts (1995), Graumlich (1995a,b), and Swain et al. (1995; NCDC ITRDB, 2018). The BWCA in northeastern Minnesota, USA borders Ontario, Canada and represents the Boreal-Great Lakes forest ecotone and could differ in fire characteristics from much of the Lake States, which are closer in proximity to the prairie ecozone (Fig. 1). Similarly, the BWCA contains the highest point in Minnesota and is 20% water (ca. 770 km2), thus representing an exceptional landscape in terms of physiographic factors influencing fire spread and behavior, but one commonly with broad geographical 2

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Fig. 1. Study sites (n = 28) among five ecological landscapes in Wisconsin where we examined fire frequency and origin-to-first-scar (OFS) intervals in relation to physiographic variables. Lighter colored pins represent a subset of sites (n = 10) where we have determined exact calendar dates of fires and fire frequency (n = 300 samples). Wisconsin contains multiple North American Ecoregions (Omernik et al. 2000) representing a mix of major vegetation types.

application of its fire regime characteristics (Whitney, 1986; Cleland et al., 2004).

2.2. Data collection and analysis We collected sections from remnant stumps, and partial sections from snags and fire-scarred living trees at 10 cm height (for one of the cut surfaces) within natural origin red pine dominated stands. Stands were either old-growth or had been harvested in the cutover period (ca. 1860–1910) but had minimal recent disturbance (e.g., logging) and contained pre-Euro-settlement era stumps. Multiple stands comprised a site which ranged in size from areas with small pine relicts in the Western Coulees and Ridges to more extensive forests. In all cases, we made efforts to sample across a range of red pine dominated stands with comparable areas (0.12–2.41 ha, µ = 0.76 ha). Samples were collected from randomly placed plots (Meunier et al., 2019) as well as opportunistically by searching the vicinity of plots (within ~200 m) for additional samples. In the laboratory, we sanded samples until the cellular structure of xylem was clearly visible with magnification (Grissino-Mayer and Swetnam, 2000) and determined origin-to-first-scar (OFS) intervals (Baker and Ehle, 2001) for all samples that contained pith and for which the earliest fire-scars were identifiable (Fig. 2). We considered the pith date at approximately 10 cm height to be the year of tree establishment, or origin (Brown et al., 2008). Using a caliper, we recorded the radius from pith to first fire-scar (to nearest 0.1 cm) and estimated the proportion of circumference scarred. As part of a larger fire history study we crossdated and assigned exact calendar dates to 300 fire-scar samples spanning a subset of 10 sites among five ecological landscapes (Fig. 1). We calculated mean fire return intervals (MFRI) within sites for all years with at least two recording samples and fire injuries replicated twice (Grissino-Mayer, 1999) using Fire History Analysis and Exploration System software version 2.0. (FHAES, Sutherland et al., 2014). We also generated median, minimum, maximum, and Weibull median probability intervals (WMPI). We assigned ring-boundary scars (dormant season position) to the year containing the earlywood immediately following firescars. We used a Kruskal-Wallis one-way analysis of variance (ANOVA)

Fig. 2. Examples of both healed over and open fire-scars on cross sections from (a) red pine (P. resinosa) and (b) white pine (P. strobus) samples used to determine origin-to-first-scar (OFS) intervals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and Dunn’s Multiple Comparisons Procedures to test for differences in MFRI among sites as well as to test whether the mean percentage of firescarred trees (a proxy for fire severity) differed among sites. We also calculated a similarity of fire occurrence (burns occurring in the same year) among ecological landscapes and among sites using the Jaccard Index of similarity (Romesburg, 1984). We examined physiographic features in relation to OFS intervals to determine if there were differences among the five ecological landscapes with diverse physiographic features. Features we examined included topographical ruggedness and the proportion of a given area (site or ecological landscape) composed of water features (lakes and

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Table 1 Fire history characteristics for subset of 10 sites including fire return intervals for fires recorded on at least two samples and two trees recording fires. Sites

Area (ha)

No. samples

No. fires

No. Intervals

Years

*MFRI

**

NW Sands Inch Lake Totogatic River

20 20

34 27

127 206

16 25

1668–2018 1699–2018

11 8

9 7

3–47 1–25

N. Highlands Cathedral Point Finnerud Pines

5 36

24 39

60 147

5 15

1784–2018 1673–2018

7 11

8 10

2–21 1–36

NE Sands Wolf Lane Camp Bird

25 3

16 17

109 141

13 20

1797–2018 1731–2018

6 7

6 7

3–16 3–14

C Sands Wildcat Ridge Bruce Mound Levis Mound

32 35 25

49 59 18

216 219 188

14 19 38

1712–2018 1660–2018 1602–2018

6 13 4

5 10 3

2–16 2–33 1–17

W Coulees Pine Bluff

4

17

168

31

1657–2018

5

5

1–17

WMPI

Min-max interval

* MFRI is the Mean Fire Return Interval. Only one site (Cathedral Point) had slight differences in mean and median values with normal or Weibull distributions (e.g., MPI = 7, WMI = 10). ** We report WMPI (Weibull Median Probability Interval) which is where half of the probability distribution is below, and half above the value.

wetlands combined), which are considered to directly affect fire spread and behavior and broadly determine fire regimes (Dansereau and Bergeron, 1993; Cleland et al., 2004). We determined the proportion of land classified as either open water or wetland using WiscLand 2.0 (Wisconsin Department of Natural Resources, 2016) level 1 data and quantified topographical roughness with a terrain ruggedness index (TRI; Riley et al., 1999) using python 2.7. TRI calculates the amount of elevation difference between adjacent cells of a digital elevation model (DEM), in this case a 10 m DEM from the Wisconsin DNR. We scaled TRI values from 0 (level) to 10 (highly rugged). We completed all geospatial processes using ArcGIS 10.4.1 (ESRI, Redlands, CA). We summarized TRI for each ecological landscape using zonal statistics tool resulting in a continuous variable of terrain roughness. We evaluated the same physiographic variables at a local (site) scale by mapping all samples we collected within sites and placing a 250 m buffer around each sample point. We then merged buffers within each site (n = 28) to create discrete polygons representing our sites. We wanted to capture the area searched and sampled for fire history information ( x¯ = 24 ha, Table 1) but also the neighboring features influencing fire behavior and spread. We used site polygons to define areas (20–125 ha, µ = 54 ha) for calculating proportion of wetlands/ water and TRI at a local (site) scale. We examined correlations among parameters (TRI and water) with Pearson correlation coefficient tests and evaluated statistical differences between site and ecoregion level parameters with t-tests. We compared OFS among ecoregions using an unbalanced ANOVA in PROC GLM using SAS 9.4 (SAS Institute Inc., Cary, NC). We also compared the variability in OFS explained by ecological landscape factors versus site factors to determine which spatial scale was appropriate for further investigation. We used Generalized Estimating Equations (PROC GENMOD) to further evaluate potential relationships between physiographic features and OFS intervals. We used quasi-likelihood model selection criterion (QICu, Pan, 2001), a modification of Akaike’s Information Criterion (Akaike, 1974) appropriate for GEE models, to identify which variables should be retained and competitively rank regression models. We calculated TRI and proportion of wetlands/water using the same methodologies for sites in the BWCA. We were interested in how physiography in BWCA sites, for which fire history characteristics have been determined and often extrapolated across the region, compared to ours. We used a 1 m DEM resampled to be consistent with 10 m Wisconsin DEM data to calculate TRI, and used a 2011 National Land Cover Database (NLCD, USGS, 2014) raster to determine the proportion of wetlands/water for BWCA sites.

3. Results We determined OFS intervals for 357 samples (Fig. 2) within 28 sites across five ecoregions (Fig. 1). OFS intervals ranged from two years (n = 7) to 80 years (n = 1) and the mean OFS interval across all samples was 18.6 years with 63% of samples scarred before 19 years. 96% of trees were scarred by the time they reached 50 years old, a commonly assumed minimum age for survival (Van Wagner, 1970; Rouse, 1988, Fig. 3). Mean inside bark diameters at the first scar were 6.2 cm (at 10 cm sample height) and on average 30% of the circumference of trees were scarred (range 5–75%). The majority of samples we collected were red pine (n = 351) with only occasional white pine (P. strobus, n = 2), oak (Quercus spp. = 3, and jack pine (P. banksiana, n = 1) samples. We crossdated and assigned exact calendar dates to fire-scarred samples (n = 300) in a subset of 10 sites as part of a larger ongoing fire history project. Mean, Median, and Weibull probability intervals were similar across sites suggesting normally distributed (skewness < 1) fire

Fig. 3. Tree age at origin-to-first-scar (OFS) for samples (n = 357) across ecoregions of Wisconsin. 96% of all samples were scarred before 50 years of age, which is the age pines are classically understood to be able to withstand fire. Mean OFS (x¯ =19) is the average age at first scar among all study sites and vertical shading represents the complete range of mean fire return intervals (MFRI = 4–13 years) for cross-dated samples (n = 300) across 10 sites for fires recorded on at least two trees within each site. 4

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Fig. 4. Fire history across five ecological landscapes of Wisconsin (NWS; Northwest Sands, NH; Northern Highlands, NES; Northeast Sands, CS; Central Sand Plains, WCR; Western Coulees and Ridges). Vertical lines are fire events from (a) a 300-sample fire history composited by site (horizontal lines, n = 14–59 samples/site, µ = 29 samples/site). Site composited fires (a) are filtered by fires recorded on ≥ two samples with ≥ two recorder trees. (b) Composite chronology across all landscapes is based on the filtered fires (a) that were recorded at ≥ 3 sites.

Fig. 5. Comparison of mean origin-to-first-scar (OFS) interval, standard error, maximum OFS, and minimum OFS among ecological landscapes. There is no significant difference in OFS intervals between ecological landscapes (P = 0.40), although there was high variability within landscapes and particularly among sites.

Fig. 6. Comparison of topographical ruggedness index (TRI) and proportion of water features within each study site (circles, scaled by OFS interval), entire ecological landscapes (X symbols), and the Boundary Waters Canoe Area (BWCA) sites (Heinselman 1973, Conrad et al. 1994, Fritts 1995, Graumlich 1995a, 1995b, Swain et al. 1995). Our study sites generally had lower proportion of areas comprised of water features but contained greater variability of both water features and topographical ruggedness (TRI).

intervals rather than right skewed for which WMPI is statistically more robust (Grissino-Mayer, 2001). We report mean fire return intervals (MFRI) throughout this text unless otherwise specified. MFRI within our subset of 10 sites ranged from 4 to 13 years, with both of those extremes occurring in the Central Sands ecological landscape (Table 1, Fig. 4). MFRI among sites were surprisingly similar to one another, and to average MFRI across all sites/landscapes (8 years). We found no differences in MFRI among landscapes and the only significant difference in MFRI among sites was for one site (Levis Mound in the Central Sands), which was different from three other sites among 45 individual site comparison combinations. Notably, MFRI’s are conservative estimates that included the period of fire exclusion, and relatively long MFRI’s (Fig. 4). There were 12 fire years (1717, 1736, 1774, 1780, 1798, 1809, 1827, 1831, 1841, 1847, 1850, and 1860) that were recorded in three or more sites and across multiple landscapes (Fig. 4). Among sites and ecological landscapes TRI was negatively correlated with water (-0.61, P < 0.001). Our sites generally had higher TRI than the entirety of the ecological landscapes they were contained

within (Fig. 6), with significant differences in the Central Sands (P < 0.001), Northwest Sands (P = 0.033), and Western Coulees and Ridges (P = 0.010). Aside from the Northwest Sands, the proportion of all water features within sites was also significantly different than the entire ecological landscapes (P < 0.001). In the Northern Highlands, there were more water features within local sites than the ecological landscape, but the Western Coulees and Ridges, Northeast Sands, and the Central Sand Plains sites had fewer water features than their respective ecological landscapes (P < 0.001). We found no differences in OFS intervals between ecological landscapes (Figs. 4, 5). While OFS intervals were similar among sites and ecological landscapes, variation in OFS intervals was better explained at local, site level (P = 0.070), whereas ecological landscape scale factors had little explanatory value (P = 0.460). Our models investigating physiographic factors’ influence on OFS yielded one top-ranked model (QICu = -2526.32) with all other models outranked (> 9 QICu). We found that greater proportion of area in local water features (P = 0.004, β = −1.002, SE = 0.354) and higher TRI (P = 0.028, β = −0.048,

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primarily low density stands (Bolliger et al., 2004; Meunier et al., 2019) and even relatively poor sites in the Lakes States likely have greater available moisture for growth than many semi-arid western U.S. forests. Regional differences in OFS intervals could also include timing of fires. Experimental prescribed burns in advanced white pine regeneration in Virginia, for example, resulted in disparate levels of mortality in spring (80%) versus fall burns (46%, Drews and Fredericksen, 2013). Seasonality of fires in the Great Lakes Region is not well known, but studies have described variability in historical timing, and a preponderance of latewood fires that likely burned in fall (Sands and Abrams, 2011; Guyette et al., 2016). There is also a paucity of information on historical ignition sources in this region. Researchers have tended to attribute ignition sources to Native Americans (Muzika et al., 2015; Guyette et al., 2016; Johnson and Kipfmueller, 2016), but lightning density is comparable to much of the southwestern U.S. (NLDN, 2018) where fires were historically frequent, and primarily lightning ignited (Allen, 2002). Relatively frequent and similar fire return intervals among disparate landscapes suggests that regardless of source, ignitions were common and not a limiting factor of fire regimes. There is a long history of defining fire regimes in the Lake States based on Euro-settlement survey proxy data tied to landscape scale physiography (Grimm, 1984; Leitner et al., 1991; Cleland et al., 2004) and while the landscape context of our sites varied greatly, we found little difference in either OFS or fire return intervals among landscapes (Table 1, Fig. 5). The landscapes that differed most in terms of topographic ruggedness, for example, were the Central Sands (lowest TRI, 0.48) and the Western Coulees & Ridges (highest TRI, 3.62) yet both had similar fire return intervals (8 and 5 respectively), no differences in OFS intervals, and the most similar fire occurrences (Jaccard similarity index 0.233). Similarly, the landscape with most similar fire occurrence to the Northern Highlands, which had the highest amount of water of any landscape, was the Northeast Sands, which next to the Central Sands had the least amount of water of any landscape. Even the Northeast Sands ecological landscape with few lakes and gentle terrain did not have significantly different OFS intervals than the Western Coulees and Ridges, a landscape dominated by rough terrain. Most variability in physiography was at the site scale, however rough topography and water features did not predictably change fire frequency or OFS intervals (Fig. 6). We expected that with greater topographical relief we would see slope driven differential heating leading to leeward scarring (Gutsell and Johnson, 1996) and OFS intervals more similar to mountainous regions of the western U.S. and dissimilar to our sites with less topography, but this was not the case. One potential explanation is a greater importance of wind driven fire events (Schulte and Mladenoff, 2005) that may function similarly across spatial scales in the Lake States. Wind could have different physical processes of differential heating and scarring. Wind also has the potential to create disturbance legacies impacting fuel characteristics and microclimate conditions that effect the probability, intensity, and severity of fires (Cannon et al., 2017). We suspect that the high degree of variability between OFS intervals and physiography among sites could be in part due to heterogeneous fuel characteristics locally (e.g., fuel loading and moisture), which influence fire behavior and severity. Notably, sites used to reconstruct fire history in the Boundary Waters Canoe Area Wilderness (Heinselman, 1973; Conrad et al., 1994; Fritts, 1995; Graumlich, 1995a,b; Swain et al., 1995; NCDC ITRDB, 2018) represent a narrow, and somewhat exceptional, range of physiography and with important differences in fire frequency making broad application of its fire regime characteristics problematic (Fig. 6). Importantly, all fires for which we determined OFS intervals for were likely low- to moderate-severity at the scale of individual trees. However, our findings also suggest that historically low-severity fires were primary forcing agents of stand development (Meunier et al., 2019). Fire both promotes tree regeneration (White, 1985) and kills young seedlings, thus successful tree regeneration is influenced by the net outcome of these competing roles (Baker and Ehle, 2001) which

SE = 0.0218) both resulted in shorter OFS intervals with younger trees surviving and recording fire events. 4. Discussion Our study sites, spanning five different ecological landscapes, multiple biomes, and disparate physiography, had surprisingly similar fire characteristics including fire frequencies (MFRI = 8 years, 10 sites), and origin-to-first-scar (OFS) intervals. Frequent fires, over extended periods, in red pine dominated forests were the rule not the exception in every site and landscape we evaluated as was near universal fire exclusion ca. 1910s–20s (Fig. 4). Additionally, extensive fire years were common, with ≥12 fire years from 1717 to 1860 burning across multiple sites and ecological landscapes (Fig. 4). Even when only considering these 12 most widespread fire years the MFRI was 11 years from 1717 to 1860. Fire frequencies we found were similar to dry, montane forests of the western US; however, OFS intervals were on average less than half as long (µ = 18.6, Swetnam and Baisan, 1996; Heyerdahl et al., 2008). Mean OFS intervals on the Mogollon Rim of Arizona, for example, were 101.5 years (80.5% > 50 years, Van Horne and Fulé, 2006) and 51 years in the Black Hills of South Dakota (40.9% of intervals > 50 years, Brown et al., 2008), both frequent fire landscapes. Similarly, mean OFS intervals within Ozark Highlands (Missouri and Arkansas, USA) shortleaf pine were 45.5 years (n = 91); however, most trees there had survived at least one fire in the first 20 years of growth (Stambaugh et al., 2007). While scarring is primarily a recorder of low- to moderate-severity fires (non-stand-replacing, Romme, 1980) and we did not evaluate the role of stand replacing mortality of overstory trees in this study, we found that historically (ca. 1600–1900) pine seedlings and saplings (mean inside bark diameter at 19 years was 6.4 cm) commonly survived and were scarred by fires. OFS intervals were less than half as long as commonly assumed ca. 50-year minimum age threshold for pines to survive fires (Figs. 3, 4, Van Wagner, 1970; Ahlgren, 1974). Fire frequency has strong effects on modeled successional stages, but fire resistance, the capacity to absorb disturbance and remain largely unchanged (DeRose and Long, 2014), may have a greater role than previously recognized and a one-sized-fits-all approach to pines gaining fire resistance may not be applicable and instead depend on site conditions (Wade et al., 2000). Our data suggests that ca. 50-year survival threshold of pines that has been used extensively in state-transition models that quantify rate and effects of succession and disturbance on landscapes (e.g., LANDFIRE, 2015), may not be applicable in many areas. Notably, the time from germination to first scar does not represent a fire-free period and most trees studied that experienced fire, including small trees, survived and were not damaged by fire (Collins and Stephens, 2007; Stephens et al., 2010). Explanations for small trees surviving fire (and not forming scars) have included nonuniform consumption of fuel throughout burns and low accumulation of litter at the base of small trees; and where new scars were formed, significant associations with presence of woody debris were found (Stephens et al., 2010). Generally, moderately wet climates are most fire prone due to greater fuel production but also periodic dry spells (Sauer, 1952; Meyn et al., 2007; Krawchuk and Moritz, 2011). It is conceivable that in much of the Lake States there was sufficient moisture for greater fuel production, but also dry spells for burning, promoting frequent (low to moderately severe) fires resulting in earlier and higher rates of scarring, but not necessarily in complete mortality of young pines. Alternative explanations for relatively short OFS intervals should be considered though. In western ponderosa pine, for example, densely grown trees tended to have thinner bark and less fire resistance than open grown trees (Barkley and Powell, 2011). Trees with higher growth rates (on more productive sites) may also reach fire resistance earlier (Stambaugh et al., 2007). Historically red pine in the Lake States were 6

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were likely diverse in the Lake States. Butson et al. (1987) suggested that red pine populations in Ontario, Canada on marginal sites they studied were different from the more common higher quality sites. The authors found marginal sites had continual recruitment, greater longevity, and high survivorship of fires as opposed to even-aged, shorter lived red pine on more favorable sites they studied (Butson et al., 1987). It is likely that red pine ecology, and fire ecology varies more widely with site variation than commonly recognized. It follows that red pine disturbance ecology may also vary geographically. Wisconsin pine forests, for example, are closer in proximity to tallgrass prairie than boreal forests, each with very different characteristic disturbances. Lake States’ pine forests were likely nuanced with complex interactions between fire frequency, physiography, fuels, and pine survival and recruitment. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We thank the WI DNR Office of Applied Science, Division of Forestry, and USFWS Pittman-Robertson Wildlife Restoration Program for supporting this work. We would like to acknowledge the many field technicians who helped us collect data including B. Selz, D. Ladd, J. Lois, S. Kovach, C. Sutheimer, A. Lenoch, M. Ruminski, and M. Hertisch. We are particularly indebted to M. Peters for assistance with python code. We also thank anonymous reviewers for manuscript improvements. References Abatzoglou, J.T., Kolden, C.A., 2013. Relationships between climate and macroscale area burned in the western United States. Int. J. Wildland Fire 22, 1003. Ahlgren, C.E., 1974. Effects of fires on temperate forests: north central United States. In: Kozlowski, T.T., Ahlgren, C.E. (Eds.), Fire and Ecosystems. Academic Press, New York, pp. 195–223. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723. Allen, C.D., 2002. Lots of lightning and plenty of people: an ecological history of fire in the upland southwest. In: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C, pp. 142–193. Baker, W.L., 1989. Effect of scale and spatial heterogeneity on fire-interval distributions. Can. J. For. Res. 19, 700–706. Baker, W.L., Ehle, D.S., 2001. Uncertainty in surface-fire history: the case of ponderosa pine forests in the western United States. Can. J. Appl. For. 3, 76–80. Barkley, Y., Powell, D.C., 2011. Predicting mortality in ponderosa pine after a wildfire. University of Idaho Extension, Moscow, ID. http://articles.extension.org:80/pages/ 58547/predicting-mortality-in-ponderosa-pine-after-a-wildfire. Barrett, S., Havlina, D., Jones, J., Hann, W., Frame, C., Hamilton, D., Schon, K., Demeo, T., Hutter, L., Menakis, J., 2010. Interagency fire regime condition class guidebook. Version 3.0, USDA Forest Service: www.frcc.gov. Beaty, R.M., Taylor, A.H., 2001. Spatial and temporal variation of fire regimes in a mixed conifer forest landscape, southern Cascades, California, USA. J. Biogeography 28, 955–966. Bergeron, Y., 1991. The influence of island and mainland lakeshore landscapes on boreal forest fire regimes. Ecology 72, 1980–1992. Bigio, E.R., Swetnam, T.W., Baisan, C.H., 2016. Local-scale and regional climate controls on historical fire regimes in the San Juan Mountains, Colorado. For. Ecol. Manage. 360, 311–322. Birch, D.S., Morgan, P., Kolden, C.A., Abatzoglou, J.T., Dillon, G.K., Hudak, A.T., Smith, A.M.S., 2015. Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests. Ecosphere 6, 1–17. Bolliger, J., Schulte, L.A., Burrows, S.N., Sickley, T.A., Mladenoff, D.J., 2004. Assessing ecological restoration potentials of Wisconsin (U.S.A.) using historical landscape reconstructions. Restoration Ecol. 12, 124–142. Bourdo Jr., E.A., 1956. A review of the General Land Office survey and of its use in quantitative studies of former forests. Ecology 37, 754–768. Brown, P.M., Wienk, C.L., Symstad, A.J., 2008. Fire and forest fire history at Mt Rushmore. Ecol. Appl. 18, 1984–1999. Butson, R.G., Knowles, P., Farmer Jr., R.E., 1987. Age and size structure of marginal, disjunct populations of Pinus resinosa. Ecology 75, 685–692. Cannon, J.B., Peterson, C.J., O’Brien, J.J., Brewer, J.S., 2017. A review and classification

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