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# Multimillennial fire history of northern Finland along a latitude/elevation gradient

Marion Lacand, Hugo Asselin, Gwenaël Magne, Tuomas Aakala, Cécile C Remy, et al.

Remy, Heikki Seppä, Adam A Ali

► To cite this version:

Marion Lacand, Hugo Asselin, Gwenaël Magne, Tuomas Aakala, Cécile C Remy, et al. Multimillennial fire history of northern Finland along a latitude/elevation gradient. Quaternary Science Reviews, 2023, 312, 10.1016/j.quascirev.2023.108171. hal-04895306

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# Multimillennial fire history of northern Finland along a latitude/ elevation gradient

Marion Lacand<sup>a, *</sup>, Hugo Asselin<sup>a</sup>, Gwenaël Magne<sup>b</sup>, Tuomas Aakala<sup>c</sup>, Cécile C. Remy<sup>d</sup>, Heikki Seppä<sup>e</sup>, Adam A. Ali<sup>b</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

**Article history:**

Received 24 February 2023

Received in revised form

2 June 2023

Accepted 2 June 2023

Available online xxx

Handling Editor: P Rioual

**Keywords:**

Boreal forest

Charcoal

Holocene

Climate

Lake sediments

Altitudinal zonation

Latitudinal zonation

## ABSTRACT

In boreal environments, wildfires are expected to decrease in frequency and/or size with latitude/ elevation, mainly in response to climate, as well as fuel availability and type. Furthermore, fire frequency and biomass burned are supposed to have been higher during warm and dry periods of the Holocene (last ~ 11,000 years). We tested these assumptions in northern Finland by using charcoal analysis to reconstruct Holocene regional fire regimes from eight lake sediment sequences sampled within four different environments in terms of elevation, latitude and vegetation type: (1) low latitude/mid elevation coniferous forests (*Pinus sylvestris* and *Picea abies*); (2) mid latitude/low elevation pine forests (*Pinus sylvestris*); (3) mid-high latitude/elevation transitional mixed forests (*Pinus sylvestris* and *Betula pubescens* subsp. *tortuosa*); and (4) high latitude/elevation birch woodlands (*Betula pubescens* subsp. *tortuosa*). As expected, fire frequency decreased with increasing latitude/elevation. Contrary to expectations, fire frequency was not significantly higher in the warm and dry mid-Holocene than in the cool and wet late Holocene, except in transitional mixed forests. Biomass burned was higher in the mid- than in the late Holocene, but only in pine and pine-spruce forests. The opposite was observed in transitional mixed forests and birch woodlands, suggesting that factors other than climate controlled fire regimes in these environments. While climate warming will likely result in more biomass burned in low latitude/elevation coniferous forests, other disturbances (such as insect outbreaks) might play a more important role in high latitude/elevation birch-pine forests and birch woodlands.

© 2023 Elsevier Ltd. All rights reserved.

## 1. Introduction

Climate change strongly affects the dynamics of northern ecosystems, especially in areas close to species or biome distribution limits (Delwaide et al., 2021; Kullman, 2002; Holtmeier and Broll, 2018; Myers-Smith et al., 2011; Stark et al., 2021). In boreal and subarctic ecosystems, climate change increases the risk of severe, large-scale disturbances such as wildfire by lengthening the fire season and by increasing the occurrence of drought events (Ali et al., 2012; Gaboriau et al., 2020; Remy et al., 2019; Timoney et al., 2019).

Differences in vegetation and regional climate due to geographical and topographical characteristics have been reported to affect wildfire severity and frequency in Fennoscandia, i.e., Norway, Sweden, Finland, and the westernmost parts of northern Russia (Austrheim and Eriksson, 2001; Kuuluvainen and Gauthier, 2018; Steijlen and Zackrisson, 1987; Wallenius et al., 2010). A common feature of fire regimes in northern Fennoscandia is long (>300 years) return intervals (Carcaillet et al., 2007; Clear, 2013; Rogers et al., 2015; Steijlen and Zackrisson, 1987; Wallenius et al., 2010). Hence, fires in northern Fennoscandia are less frequent than in boreal forests located further south (Larjavaara et al., 2005). In deciduous forests dominated by mountain birch (*Betula pubescens* subsp. *tortuosa*), the major structuring disturbance is insect outbreaks (Kuuluvainen et al., 2017; Tenow et al., 2007).

Historically, large fires have been a major driver of tree growth

<footnote><sup>a</sup> School of Indigenous Studies, Université du Québec en Abitibi-Témiscamingue, 445 Boulevard de l'Université, Rouyn-Noranda, QC J9X 5E4, Canada</footnote>

<footnote><sup>b</sup> Institut des Sciences de l'Évolution de Montpellier, UMR 5554 CNRS-IRD-Université de Montpellier-EPHE, Montpellier, France</footnote>

<footnote><sup>c</sup> School of Forest Sciences, University of Eastern Finland, Joensuu, Finland</footnote>

<footnote><sup>d</sup> Institute of Geography, Augsburg University, Augsburg, Germany</footnote>

<footnote><sup>e</sup> Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland</footnote>

<footnote>* Corresponding author.
E-mail address: marion.lacand@uqat.ca (M. Lacand).</footnote>
and regeneration in northern Finland (Aakala, 2018). During the past three centuries, fires were more frequent in drier sites dominated by Scots pine (Pinus sylvestris) than in mesic sites dominated by Norway spruce (Picea abies). During the 20th century, wildfires virtually disappeared in coniferous forests dominated by Scots pine or Norway spruce, likely as a result of a drastic reduction in human-induced ignitions due to cultural changes in land use (Wallenius, 2011). Nowadays, northern Finnish landscapes are mostly structured by gap dynamics, i.e., small to medium scale disturbances caused by the death of one or a few trees (Aakala et al., 2009; Aakala, 2018).

Wildfires have an effect on tree species composition in Fennoscandia (Aakala et al., 2018; Molinari et al., 2020b; Rolstad et al., 2017; Stivrins et al., 2019; Wallenius et al., 2002). Higher fire frequencies tend to promote Scots pine over birch and Norway spruce (Hannon et al., 2021; Kuuluvainen et al., 2017; Molinari et al., 2020b). Mixed forests with conifers and birch as well as coniferous forests with Norway spruce are known to burn less often but to experience more severe fires than forests dominated by Scots pine (Ohlson et al., 2011; Wallenius et al., 2002, 2004, 2010). In Scots pine stands, thick bark and the self-pruning of low branches limit fire spread to the canopy, hence leading to a regime of low intensity surface fires (Kullman, 2001a; Gromtsev, 2002; Johnston et al., 2015; Rogers et al., 2015; Wallenius et al., 2010). Such low-intensity surface fire regimes tend to occur more frequently in Scots pine than in Norway spruce forests, to a large extent driven by differences in fuel moisture content of the ground layer (Lindberg et al., 2021).

Norway spruce is a fire-sensitive species which thrives in the prolonged absence of fire (Kullman, 1995b; Seppä et al., 2009a). This species has low-lying branches that maintain soil humidity and low flammability in the understory (Kullman, 2001a). However, under fire conducive weather conditions, low-lying branches can act as ladder fuel and favour fire spread to the canopy (Kullman, 2001a). A spruce stand will regenerate following a severe crown fire, provided that seed trees survive within or close to the burned area (Kuuluvainen et al., 2002). In such a case, post-fire succession usually starts with birch, later replaced by spruce (Kuuluvainen et al., 2017). Close to the treeline, in birch-dominated woodlands, fires are infrequent because of low fuel availability and flammability (Hannon et al., 2021; Kuuluvainen et al., 2017; Molinari et al., 2020b; Rogers et al., 2015). Little is thus known about the structuring role of fire in mountain birch woodlands in Fennoscandia. Nevertheless, compound disturbances, such as extreme weather events, insect outbreaks, as well as browsing by reindeers, can have lasting ecological consequences on tree mortality and regeneration success (Glückler et al., 2022; Holtmeier and Broll, 2019; Kuuluvainen et al., 2017; Vindstad et al., 2022).

Several studies have highlighted the effects of human activities on landscape structure in Fennoscandia, notably through fire use in agricultural or forest management practices, as well as accidental fire ignition (Clear, 2013; Bergman et al., 2013; Freschet et al., 2014; Kuuluvainen et al., 2017; Hörnberg et al., 2018; Pitkänen et al., 2001; Rolstad et al., 2017; Stivrins et al., 2019). Local populations established in the region simultaneously with vegetation following deglaciation (Kuuluvainen et al., 2017). While permanent agricultural activities in sedentary settlements further South have had greater effects on the environment than the temporary human activities of reindeer herders in the North (Carcaillet et al., 2007; Hörnberg et al., 2006; Khorasani et al., 2015; Kuosmanen et al., 2018; Molinari et al., 2018), low-density activities carried out over a long period nevertheless influenced landscapes (Freschet et al., 2014; Östlund et al., 2015; Staland et al., 2011). Although it is difficult to quantify the human influence on fire regimes in northern Fennoscandia, it is highly likely that anthropogenic

activities affected forest understory structure and composition (Egelkraut et al., 2018; Josefsson et al., 2010; Kuuluvainen et al., 2017; Staland et al., 2011), and that human use of fire shaped the landscapes (Cogos et al., 2019, 2021a, 2021b; Hörnberg et al., 2018; Kuuluvainen et al., 2017).

Most of our understanding of wildfire behaviour (e.g., spread and extent) and its effect on vegetation in northern Finland has been developed under the narrow, modern-day range of environmental conditions, creating substantial uncertainty in our forecasts of how climate change will alter future wildfire regimes. A more comprehensive interpretation of the relationship between area burned and atmospheric warming is required to improve our ability to forecast how climate and wildfire will interact to shape ecosystem functioning. Describing past fire-climate interactions through palaeoecological studies helps broaden the range of studied climatic conditions, and thus better predict the effects of ongoing climate change on future fire regimes (Clear et al., 2014; Randerson et al., 2006). Thus, in this study we tackled the following questions: how did the fire regime parameters (frequency, severity and size) change through time in response to climate along a latitude/elevation gradient in the boreal forest of northern Finland? Which other factors affected fire dynamics (vegetation, soil moisture, topography, etc.)? We hypothesized (H1) that fire frequency would decrease with latitude/elevation due to reduced fuel availability and flammability in what are nowadays high latitude/ elevation open woodlands, compared to low latitude/elevation closed-crown forests (Hannon et al., 2021; Kuuluvainen et al., 2017; Ohlson et al., 2011; Wallenius et al., 2002, 2004, 2010). We also hypothesized (H2) that fire frequency and/or biomass burned would be higher during the warmer/drier periods of the Holocene as recorded in other parts of the boreal zone (Ali et al., 2012; Clear et al., 2014; Drobyshev et al., 2016; Hély et al., 2020; Novenko et al., 2018).

## 2. Material and methods

### 2.1. Study area

The study was conducted in the Inari and Utjoski municipalities of northeastern Finland. The area covers more than 10,000 km² around Lake Inari, between 68°N and 70°N and 25°E and 29°E (Fig. 1). Lake Inari is located approximately 120 m above sea level (a.s.l.) and the highest point of the study area has an elevation of about 580 m a.s.l. The average annual temperature, based on the last 60 years, is -0.5 °C (around 11.5 °C in summer). The average annual rainfall is 490 mm, including 230 mm during the growing season (mid-May to mid-September) (Finnish Meteorological Institute, 2022). The bedrock (greywacke and argillite) is mostly covered by a podzolized moraine layer resulting from the last glaciation (Geological Survey of Finland, 2022).

The geography and topography of the Lake Inari region influence the distribution of land cover. First, the study area is subject to both oceanic and continental climates (Vajda, 2007). The influence of the Gulf Stream and the Norwegian Sea from the northwest buffers temperatures and maintains a certain humidity compared to boreal ecosystems at similar latitudes in Canada and Siberia. The relief of the Scandinavian Alps is less pronounced than in Sweden or Norway (highest point <1500 m a.s.l.), and is characterised by gently rolling hills which form a basin around Lake Inari, partly retaining incoming precipitation from the east (Seppä et al., 2009b). Second, the area experiences both subarctic and subalpine conditions (Seppä et al., 2004; Vajda, 2007). The region is located 250 km north of the Arctic Circle. The polar insolation implies important variations between north and south slopes during the growing season, and presence of a snow cover the rest of the year. The
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (A) Digital elevation model (DEM) of the study area showing the locations of the sampled lakes. The DEM was produced with open-source raster data (resolution of 10 × 10 m) obtained from the National Land Survey of Finland (https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/elevation-model-10-m). Dark green is coniferous forest, light green is broadleaf forest and yellow is woodland or shrubland. The information on vegetation was obtained from open-source raster data (resolution 20 × 20 m) from the CORINE Land Cover 2018 dataset produced by the Finnish Environment Institute (https://ckan.ymparisto.fi/dataset/%7B0B4B2FAC-ADF1-43A1-A829-70F02BF0C0E5%7D). (B) Side view of the studied toposequence, following the red line on the map above, showing changes in tree species dominance along a latitude and elevation gradient.</figcaption></figure>

distribution of the main tree species (*Pinus sylvestris*, *Picea abies* and *Betula pubescens*) along the subalpine/subarctic zonation is detailed in Table 1.

## 2.2. Holocene climate and vegetation variations

In northern Finland, the Holocene can be divided into three broad distinct climatic periods: the cold and humid early Holocene, which ended with the “8.2 ka” event (Seppä et al., 2009b; Wastegård, 2022); the warm and dry mid-Holocene from 8200 to 4200 cal yr BP (Magne, 2019; Seppä et al., 2009b; Veski et al., 2004; Wastegård, 2022); and the cool and wet late Holocene after 4200 cal yr BP (Seppä et al., 2009b; Seppä and Birks, 2001; Walker et al., 2019; Wastegård, 2022). The beginning of the early Holocene was mostly characterised by tundra or shrubland vegetation dominated by lichens, graminoids and low-lying shrubs (Clear, 2013; Jensen and Vorren, 2008). During the mid-Holocene there was widespread expansion of pine forests (Clear, 2013; Kullman, 1995a, 2008a; Väliranta et al., 2011), with spruce establishment in more humid sites such as in the southeastern part of the study area (Remy et al., 2023). Finally, spruce abundance increased in the late Holocene in response to climate cooling and humidity, at the

expense of pine and other more thermophile species (Kullman, 1995b, 2001a, 2001b; Kuuluvainen et al., 2017; Corine Land Cover 2018, 2022). The current northern limit of spruce distribution is located to the southeast of Lake Inari (Corine Land Cover 2018, 2022) and the species probably never reached more northern locations in the past (Helmens et al., 2000; Hicks, 1993, 2001; Seppä et al., 2009a; Seppä and Hammarlund, 2000; Väliranta et al., 2011). Scots pine and birch forests are found all the way to the northern tip of Finland (Kuuluvainen et al., 2017), although pine is limited to elevations under 300–500 m a.s.l. (Rousi et al., 2018).

## 2.3. Sampling design

Prior to fieldwork, several lakes were pre-selected using a Geographic Information System (QGIS 3.16.14) based on elevation, latitude, watershed characteristics, land cover and accessibility (<1 km from a road). Eight lakes with sufficient water depth to avoid sediment mixing in spring and fall, and without rocks in the bottom (which prevent sediment coring) were selected for sampling (Fig. 1, Table 2). Lakes Rosalia, Charly (unofficial names) and Pikku Härkäjärvi (official name, hereafter “Pikku”) were sampled during summer 2017 (Magne, 2019; Remy et al., 2023), whereas
<table><caption>**Table 1**
Main tree species and understory composition of lower and upper subalpine/subarctic environments in northern Finland.</caption><thead><tr><th></th><th>Lower subalpine/subarctic</th><th>Upper subalpine/subarctic</th></tr></thead><tbody><tr><td>Main tree species</td><td>Xeric soils<br>Scots pine (Pinus sylvestris)<br>(Kuuluvainen et al., 2017).<br><br>Shrubs: Calluna vulgaris, Vaccinium vitis-idaea, Vaccinium myrtillus and Empetrum hermaphroditum<br>(Kuuluvainen et al., 2017).<br><br>Mosses and lichens: Pleurozium schreberi, Cladonia arbuscula and Cladonia rangiferina (Kuuluvainen et al., 2017).</td><td>Mountain birch (Betula pubescens subsp. tortuosa) (Kullman, 2002; Kuuluvainen et al., 2017).<br><br>Shrubs and graminoids: Empetrum hermaphroditum, Vaccinium vitis-idaea, V. myrtillus, Linnaea borealis, Deschampsia flexuosa (Stark et al., 2021).<br><br>Mosses and lichens: Pleurozium schreberi, Dicranum spp., Cladonia rangiferina and Cladina mitis (Holtmeier and Broll, 2018).</td></tr></tbody></table>

<table><caption>**Table 2**
Location and characteristics of the sampled lakes.</caption><thead><tr><th></th><th>Skaida</th><th>Purnu</th><th>Muotka</th><th>Ahven</th><th>Inka</th><th>Rosalia</th><th>Charly</th><th>Pikku</th></tr></thead><tbody><tr><td>Elevation (m a.s.l)</td><td>318</td><td>227</td><td>214</td><td>200</td><td>154</td><td>139</td><td>195</td><td>188</td></tr><tr><td>Latitude</td><td>69°37'67.16"N</td><td>69°34'58.82"N</td><td>69°24'70.57"N</td><td>69°27'0.19"N</td><td>68°98'10.59"N</td><td>68°54'46.08"N</td><td>68°47'2.04"N</td><td>68°46'38.31"N</td></tr><tr><td>Longitude</td><td>26°26'64.45"E</td><td>27°19'73.84"E</td><td>26°81'12.91"E</td><td>26°81'89.45"E</td><td>26°98'60.35"E</td><td>28°22'9.119"E</td><td>28°32'2.039"E</td><td>28°19'18.85"E</td></tr><tr><td>Lake area (ha)</td><td>2.92</td><td>3.16</td><td>0.67</td><td>3.38</td><td>0.54</td><td>3.02</td><td>1.75</td><td>3.74</td></tr><tr><td>Lake:catchment ratio</td><td>0.21</td><td>0.49</td><td>0.22</td><td>0.22</td><td>0.07</td><td>0.17</td><td>0.29</td><td>0.11</td></tr><tr><td>Water depth at sampling location (m)</td><td>1.20</td><td>1.43</td><td>2.90</td><td>3.20</td><td>2.44</td><td>9.0</td><td>5.2</td><td>6.4</td></tr><tr><td>Length of the sediment sequence (cm)</td><td>97</td><td>89</td><td>201</td><td>68</td><td>216</td><td>142</td><td>143</td><td>130</td></tr><tr><td>Vegetation type in the watershed</td><td>Low birch bushes and shrubs</td><td>Birch woodland</td><td>Mixed pine and birch forest</td><td>Mixed pine and birch forest</td><td>Pine forest</td><td>Pine forest</td><td>Pine forest with spruce</td><td>Pine forest with spruce</td></tr><tr><td>Environment type</td><td>High latitude/ elevation</td><td>High latitude/ elevation</td><td>Mid-high latitude/elevation transitional</td><td>Mid-high latitude/elevation transitional</td><td>Mid latitude/Low elevation</td><td>Mid latitude/Low elevation</td><td>Low latitude/Mid elevation</td><td>Low latitude/Mid elevation</td></tr><tr><td>Dominant slope aspect</td><td>South-West</td><td>East</td><td>South-West</td><td>South-West</td><td>East</td><td>South</td><td>South</td><td>South-East</td></tr></tbody></table>
lakes Skaida, Purnu, Muotka, Ahven and Inka (unofficial names) were sampled during summer 2021.

Sediments were sampled at the deepest point of each lake where no aquatic vegetation was present to avoid bioturbation and shore erosion. The water-sediment interface (ca. 30 cm) was collected using a Kajak-Brinkhurst (KB) gravity corer, subsampled at a 0.5 cm resolution and stored in plastic bags. Deeper sediments were extracted with a Russian corer and stored in PVC tubes. All sediments were kept in cold conditions (4 °C) until analyses.

## 2.4. Age-depth models

Radiocarbon dates by ¹⁴C accelerator mass spectrometry (AMS) were mainly obtained from bulk gyttja, and only for Lake Skaida was the age-depth model supported by one date from macro-remains (Table SM1). No date showed inconsistency, indicating no bias due to a carbon-reservoir effect (Björck et al., 1998; Grimm et al., 2009). Indeed, there were no carbonate rocks in the lake catchments, the gyttja sequences were homogeneous.

The age-depth models were developed using the “Bacon” R package v.4.0.3 based on Bayesian statistical methods (Blaauw and Christen, 2011; R Core Team, 2021) and using a Gaussian distribution to handle dating errors. The dates were calibrated with the IntCal13 database (Reimer et al., 2016). Over the time interval considered in this study, there is no significant difference between the IntCal13 database we used and the more recent IntCal20 database (Fig.SM3). Ages were interpolated at contiguous 0.5-cm depth intervals and all dates are expressed in calibrated years before present (cal. yr BP).

## 2.5. Lake classification

In order to reconstruct regional fire histories, the sampled lakes were grouped into pairs to be representative of four different environments in terms of elevation, latitude and vegetation type (Seppä, 1996; Figure SM1). The changes in vegetation along the gradient correspond to a thermal gradient (Körner, 2021), with roughly equivalent effects of elevation and latitude (Montgomery, 2006). Lakes Skaida and Purnu are in high latitude/elevation environments characterised by birch woodlands. Lakes Ahven and Muotka are representative of mid-high latitude/elevation transitional environments characterised by mixed pine-birch forests. Lakes Rosalia and Inka are located in mid latitude-low elevation pine forests. Lakes Charly and Pikku are representative of low latitude-mid elevation mixed coniferous (pine and spruce) forests.

## 2.6. Charcoal analyses

Macroscopic charcoal particles (>160 µm) were extracted, counted and measured in contiguous 0.5 cm thick samples. Samples were prepared by submerging 1 cm³ of sediment into a solution of 5% sodium hexametaphosphate (Na₆O₁₈P₆), 5% potassium hydroxide (KOH) and 10% sodium hypochlorite (NaClO) smoothly shaken for 24h to facilitate deflocculation and bleaching of non-charred organic matter. Samples were then sifted using a 160 µm sieve, and macroscopic charcoal particles were manually sorted and photographed under a 20 × stereo microscope equipped with a digital camera. Charcoal particles were counted and measured with the WinSEEDLE image analysis software (WinSEEDLE 2016, Regent Instruments Inc.). Raw charcoal data (sum area) were extracted from WinSEEDLE for statistical analyses.

## 2.7. Fire history reconstructions

Fire histories were reconstructed using the CharAnalysis 1.1

software, following Higuera (2009) (https://github.com/phiguera/CharAnalysis). Total charcoal concentration (mm²·cm⁻³) was converted into charcoal accumulation rate (hereafter CHAR, mm²·cm⁻²·year⁻¹) using the sediment accumulation rate determined by the age-depth model. To remove biases induced by variations in sedimentation rates and to compare fire histories between sites, we interpolated individual CHAR series (CHARint) using a constant time resolution (42 years) corresponding to the median sample resolution of the eight lakes (between 17 and 85 years; Table SM2). The CHARint series were then filtered by removing low frequency signals corresponding to variations in charcoal production and sedimentation processes using a robust lowess under various time window widths (CHAR background; hereafter Cbackground) to obtain residual high frequency series (CHARpeak; hereafter Cpeak). Following Brossier et al. (2014), we used the smallest window width yielding a signal-to-noise index (SNI) > 3 (Figure SM2).

Cpeak series (Cint-Cbackground) were decomposed into two subpopulations: CHAR noise (hereafter Cnoise), representing variability due to sediment sampling and naturally occurring noise, and CHAR fire (hereafter Cfire), representing significant charcoal peaks, using a Gaussian mixture model according to a locally defined threshold. To determine if the two populations were from the same distribution or not, a goodness-of-fit test (hereafter GOF) was used. The 99.9th percentiles of the Cnoise distribution was chosen as a threshold. Each Cpeak exceeding the threshold was considered a local fire event.

We calculated the local fire frequency (hereafter FF; Fire·1000year⁻¹) at each site, corresponding to the number of fires per millennium, with a kernel density estimation procedure based on a 500-year smoothing bandwidth using the R package ‘paleofire’ v. 1.2.3 (Ali et al., 2012; Blarquez et al., 2014). We obtained the regional fire frequency (hereafter RegFF; Fire·1000year⁻¹) by averaging the individual smoothed series. We assessed the significance of changes in RegFF using a bootstrap procedure with 999 iterations (BCI, 90%).

To estimate the regional biomass burned (hereafter RegBB) we pooled and smoothed (using a 500-year window) the CHARint series by (1) rescaling initial CHARint using min-max transformation, (2) homogenizing the variance using Box-Cox transformation, and (3) rescaling the values to Z-scores (Power et al., 2008). We assessed the significance of changes in RegBB by using a bootstrap procedure with 999 iterations (BCI; 90%).

Following Ali et al. (2012), we added a constant equal to 1 to RegBB and RegFF and computed the ratio (RegBB/RegFF = FS index), indicative of fire size and/or severity. The FS index corresponds to the mean biomass burned per fire. As severe fires were rare compared to surface fires in the region (Aakala, 2018; Remy et al., 2023), for simplification, we interpreted the FS index as referring to fire size. We assessed the significance of changes in the FS index by using a bootstrap procedure with 999 iterations (BCI; 90%).

## 2.8. Statistical analyses

To compare Holocene regional fire frequencies between the four pairs of sites located along the latitude/elevation gradient and to determine if differences recorded between the four groups were significant, Kruskal-Wallis and Dunn's tests were used on each regional fire frequency reconstruction.

Box plots were used to compare regional fire parameters (RegFF, RegBB, FS index) between time periods with Kruskal-Wallis tests. As the early Holocene was only recorded at the Muotka and Ahven sites, comparisons were only made between the mid- and late Holocene. All numerical and statistical analyses were performed with R (v.4.0.3, R Core Team, 2021). Boxplots were done with
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Regional fire frequency distributions through the Holocene at each environment along the latitude/elevation gradient: birch woodlands (blue; Lakes Skaida and Purnu), transitional mixed birch-pine forests (purple; Lakes Ahven and Muotka), pine forests (green; Lakes Inka and Rosalia), and pine-spruce forests (red; Lakes Pikku and Charly). The central dots in the violin plots show the median regional fire frequency (fire.1000yr⁻¹). Asterisks represent the results of a Kruskal-Wallis test between the two corresponding groups: \*p ≤ 0.05; \*\*\*p ≤ 0.001. Median fire return intervals are shown on top for each environment.</figcaption></figure>

package 'ggpubr' (Kassambara and Kassambara, 2020) and Dunn's tests were performed with package 'FSA' (Ogle and Ogle, 2017).

# 3. Results

## 3.1. Age-depth models

The oldest basal sediments were dated around 12,800 cal yr BP at Lake Muotka (Table 2). According to the Bayesian age-depth models, mean sedimentation rates varied between 0.0075 cm yr⁻¹ (Ahven) and 0.0267 cm yr⁻¹ (Inka). The median resolution of the sediment accumulation rates varied between 171 yr cm for Lake Ahven and 35 yr cm for Lake Inka. All age-depth models are rather linear and none shows anomalous sedimentation patterns except Lake Charly around 2000 cal yr BP (Figure SM3).

## 3.2. Fire frequency variation along the latitude/elevation gradient

All regional fire frequency reconstructions are significantly different from each other. Fire events are less frequent in birch (Skaida/Purnu) and birch-pine (Ahven/Muotka) environments than in pine (Inka/Rosalia) and pine-spruce (Pikku/Charly) environments (Fig. 2). Accordingly, median fire return intervals (mFRI) are longer in birch and birch-pine environments than in pine and pine-spruce environments (Fig. 2).

## 3.3. Variations in fire regime according to long-term climate oscillations

In birch woodlands found at the highest latitude/elevation (Skaida/Purnu; Fig. 3 A, B, C), RegFF varied between 2.58 and 0.97 fire·1000 yr⁻¹ during the mid-Holocene (Fig. 3 A.b.). In the Late-Holocene, RegFF reached a low point around 3000 cal yr BP and then raised between 2500 and 1000 cal yr BP (2.40 fire·1000yr⁻¹). Values finally decreased until the present day. Overall, the distribution of RegFF values recorded during the mid-Holocene did not significantly differ from that of the late Holocene (Fig. 3 A.a.). The regional biomass burned (RegBB) gradually increased throughout

the Holocene (Fig. 3 B.b.) and was significantly lower in the mid-Holocene than in the late Holocene (Fig. 3 B.a.). The regional fire size (RegFS) was highly variable during the Holocene, reaching its highest values between 4000 and 3000 cal yr BP (Fig. 3 C.b.). Generally, the distribution of RegFS values did not significantly differ between the mid-Holocene and the late Holocene (Fig. 3 C.a.).

In transitional mixed birch-pine forests (Ahven/Muotka; Fig. 3 D, E, F), RegFF gradually increased from 8000 cal yr BP to reach a maximum of 3.28 fire·1000 y⁻¹ around 5300 cal yr BP (Fig. 3 D.b.). Then, RegFF slowly declined to 1.07 fire·1000 y⁻¹ around 2000 cal yr BP. After that, individual fire frequency tendencies diverged. Overall, RegFF was significantly higher during the mid-Holocene compared to the late Holocene (Fig. 3 D.a.). RegBB (Fig. 3 E.b.) gradually increased from -1.81 to 1.01 throughout the Holocene, with the highest value (1.21) recorded around 1000 cal yr BP. Generally, RegBB was significantly lower in the mid-Holocene than in the late Holocene (Fig. 3 E.a.). RegFS also gradually increased throughout the Holocene, from 0.70 to 2.12, with the highest value (2.18) recorded around 2000 cal yr BP (Fig. 3 F.b.). Overall, RegFS was significantly lower in the mid-Holocene than in the late Holocene (Fig. 3 F.a.).

In pine forests found at mid-latitudes and low elevations (Inka and Rosalia; Fig. 3 G, H, I), RegFF was relatively stable in the mid-Holocene and showed more amplitude of oscillation during the late Holocene (Fig. 3 G.b.); it hit its lowest point around 3000 cal yr BP. Generally, RegFF did not significantly differ between the mid-Holocene and the late Holocene (Fig. 3 G.a.). RegBB sharply increased around 8000 cal yr BP, remained stable until 3500 cal yr BP, and then gradually decreased till the present day (Fig. 3 H.b.). Overall, RegBB was significantly higher in the mid-Holocene than in the late Holocene (Fig. 3 H.a.). RegFS gradually increased during the first part of the mid-Holocene, then strongly oscillated afterward (Fig. 3 I.b.). Generally, RegFS did not significantly differ between the mid-Holocene and the late Holocene (Fig. 3 I.a.).

Finally, in the pine-spruce coniferous forests found at low latitudes and mid-elevations (Charly and Pikku; Fig. 3 J, K, L), RegFF oscillated during the Holocene and hit its lowest value (1.30 fire·1000 y⁻¹) around 3000 cal yr BP (Fig. 3 J.b.). Overall, RegFF did not significantly differ between the mid-Holocene and the late Holocene (Fig. 3 J.a.). RegBB varied between -1.33 and 0.96 during the mid-Holocene and the first part of the late Holocene, before gradually decreasing (Fig. 3 K.b.). Generally, RegBB was significantly higher during the mid-Holocene compared to the late Holocene (Fig. 3 K.a.). RegFS reached its maximum between 3700 and 2500 cal yr BP (Fig. 3 L.b.). Overall, RegFS was significantly higher in the mid-Holocene than in the late Holocene (Fig. 3 L.a.).

# 4. Discussion

The results confirmed our first hypothesis (H1), as fire frequency tended to decrease from lower to higher latitudes/elevations. However, our second hypothesis (H2) was mostly rejected, as fire frequency was higher in the warm and dry mid-Holocene than in the cool and wet late Holocene only in the transitional mixed forests. Biomass burned was higher in the mid-Holocene than in the late Holocene (as predicted), but only in the pine and pine-spruce forests at low-mid latitudes/elevations. The opposite pattern was observed at higher latitudes/elevations (in transitional mixed forests and birch woodlands).

## 4.1. Fire frequency variations along the latitude/elevation gradient

As expected (H1), fire frequency generally decreased with latitude/elevation. This may be explained by variations in fuel abundance and vegetation type induced by the temperature gradient.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Local and regional fire frequency, biomass burned and fire size index for birch woodlands (blue; ABC), birch-pine forests (purple; DEF), pine forests (green; GHI), and pine-spruce forests (red; JKL). Boxplots show the value distribution of each regional fire regime component during the mid-Holocene (8200-4200 cal yr BP) and the late Holocene (4200 cal yr BP to present). Asterisks above box plots represent the results of Kruskal-Wallis tests: \*p ≤ 0.05; \*\*p ≤ 0.01; \*\*\*p ≤ 0.001; ns: not significant.</figcaption></figure>
Decreasing biomass productivity due to increasingly harsh growing conditions at higher latitudes/elevations affects fuel availability (Molinari et al., 2020b). In addition, species composition at higher latitudes/elevations includes birch, which is a deciduous tree and therefore produces less flammable litter than spruce and pine, as observed elsewhere in the boreal zone (Blarquez et al., 2015; Hannon et al., 2021; Hély et al., 2020; Girardin et al., 2013; Molinari et al., 2020b; Remy et al., 2019). The slight difference in fire frequency between pine forests (mid-latitude/low elevation) and pine-spruce forests (low latitude/mid elevation) is likely due to moisture conditions, as pines generally grow on drier sites and produce drier litter (Lindberg et al., 2021).

## 4.2. Fire return intervals

The median fire return intervals measured in the study area (411–578 years) are longer than those previously observed in northern Fennoscandia (200–370 years; Carcaillet et al., 2007; Clear et al., 2013; Pitkänen et al., 2003; Wallenius et al., 2010), southern Finland (75–125 years; Pitkänen and Huttunen, 1999), and other boreal forest ecosystems in North America (150–400 years; Fisher, 2020; Hoecker et al., 2020; Girardin et al., 2019; Remy et al., 2017; Senici et al., 2015) or Siberia (95–250 years; Barhoumi et al., 2021; Glückler et al., 2021). Longer fire return intervals in northern Finland could be explained by multiple factors such as (1) higher humidity due to the oceanic influence (Vajda, 2007) or to the proximity of large Lake Inari; (2) fewer frequency of lightning strikes (Larjavaara et al., 2005); and (3) lower human pressure (Molinari et al., 2018). Species composition (trees and understory) could also be part of the explanation, as some species are more adapted to – or even promote – shorter/longer fire intervals (Rogers et al., 2015). Furthermore, northern Finland has many fire breaks such as elevation/slope changes and presence of rock outcrops or lakes (Clear et al., 2013). Finally, mire-rich landscapes, frequent in northern Finland, have longer fire return intervals than mesic or xeric sites (Seppä, 2002).

## 4.3. Regional fire activity during the Holocene

Regional fire activity interpretations are limited for the early Holocene because only four sites displayed information before 8000 cal yr BP (Ahven, Muotka, Inka, Rosalia). Nevertheless, the results show moderate to high fire frequencies with little biomass burned and small fire sizes. This is consistent with the cold and humid climatic conditions during this period, as well as with low fuel availability corresponding to the gradual afforestation phase following deglaciation (Clear, 2013; Helmens, 2019; Hoecker et al., 2020; Molinari et al., 2020b; Seppä et al., 2002).

Fire frequencies (RegFF) during the warm and dry mid-Holocene did not significantly differ from those of the cool and humid late Holocene, contrary to our second hypothesis (H2), except in transitional mixed birch-pine forests at mid-high latitudes/elevations. One explanation for the higher fire frequencies in transitional mixed forests during the mid Holocene could be the increased proportion of Scots pine. Indeed, the mid-Holocene is known as the period of greatest Scots pine extension in the study area, reaching the present tree line (Bjune et al., 2004; Carcaillet et al., 2012; Helama et al., 2004; Molinari et al., 2020b; Seppä and Hammarlund, 2000). This "coniferization" of the landscape favoured fire occurrence (Brown and Giesecke, 2014; Hannon et al., 2021; Harrison et al., 2021; Hély et al., 2020; Molinari et al., 2020b; Remy et al., 2019). Around 5000 cal yr BP, fire frequency started to decrease and was generally lower during the cool and humid late Holocene, as documented in previous studies (Bjune et al., 2004; Seppä, 1996; Seppä and Hammarlund, 2000). The lowest RegFF values recorded

around 2000 cal yr BP in mixed birch-pine forests correspond to a momentary decrease of the pine component (and increase of birch) recorded in pollen data from lake sediments sampled in a similar environment, 50 km away from Ahven and Muotka (Seppä and Hammarlund, 2000). This supports the idea of a positive correlation between fire frequency and pine proportion in the landscape.

Regional biomass burned (RegBB) and regional fire size index (RegFS) were higher in the mid-than in the late Holocene coincident with H2, but only in pine and pine-spruce forests of lower latitudes/elevations. Conversely, birch woodlands and mixed birch-pine forests at high and mid-high latitudes/elevations recorded higher RegBB and RegFS during the late Holocene. This suggests that factors other than climate controlled fire severity at mid-high and high latitudes/elevations. Human influence was limited before the Medieval Warm Period (Clear, 2013), and thus an anthropogenic effect on fire regimes at millennial timescales and at the regional spatial scale seems unlikely. It is possible that insect outbreaks in the birch forests became more frequent in the late Holocene (Harr et al., 2021; Holtmeier and Broll, 2019; Jonsson, 2004; Nygaard et al., 2022; Vindstad et al., 2019), increasing fuel availability (Glückler et al., 2022; Karlsson et al., 2004; Nygaard et al., 2022; Tenow et al., 2004). However, little is known about the long-term dynamics of insect outbreaks and their interactions with the fire regimes during the Holocene in northern Fennoscandia. New palaeoecological proxies allowing to reconstruct insect abundance at centennial to millennial time scales could help in this regard, such as butterfly wing scales (Navarro et al., 2018) and DNA metabarcoding (Napier and Chipman, 2022).

## 4.4. The 4000 cal yr BP pivot

Shortly after the transition between the mid- and late Holocene, a synchronous decrease in regional fire frequency occurred at all latitudes/elevations around 4000 cal yr BP. This same pivot point in fire history was reported in other regional fire activity reconstructions in Fennoscandia (Clear, 2013; Drobyshev et al., 2016; Hannon et al., 2021) and has been attributed to a change in climatic conditions induced by decreased insolation (Drobyshev et al., 2016; Magne, 2019; Wastegård, 2022; Seppä et al., 2009b). The transition between the mid- and late Holocene was characterised by many rapid climatic changes, including the 4.2 ka and 3.7 ka cold events which drove numerous environmental changes (Wastegård, 2022). Stable isotope composition of lake waters in Fennoscandia also shows a synchronous shift in precipitation patterns between 4200 and 3000 cal yr BP, from drier to wetter conditions (Andersson et al., 2010; Helmens, 2019; Jessen et al., 2005; Väliranta et al., 2011; Wastegård, 2022). Furthermore, pollen and chironomid-based mean July air temperature reconstructions demonstrate a drop in seasonal temperature at the time (Bjune et al., 2004; Helmens, 2019; Luoto et al., 2014; Magne, 2019; Seppä et al., 2009b; Wastegård, 2022). Various other environmental and climate reconstructions from lake sediments provided geochemical and geophysical evidence of this transitional climatic period in Fennoscandia (Helmens, 2019; Jessen et al., 2005; Wastegård, 2022).

Cooler and more humid climatic conditions in the late Holocene affected vegetation composition and structure. In coniferous stands, the 4000 cal yr BP pivot corresponds to the expansion of Norway spruce at lower latitudes/elevations in the regional landscapes (Clear et al., 2014; Remy et al., 2023; Seppä et al., 2009b). The expansion of Norway spruce during the late Holocene was associated with a shift in fire regime, from high-frequency large surface fires when pine was more dominant in the landscape, to low-frequency small crown fires (Hoecker et al., 2020; Molinari et al., 2020a; Pitkänen et al., 2003; Remy et al., 2023; Steijlen and Zackrisson, 1987).
After collapsing at the 4000 cal yr BP pivot, fire frequency increased again at all latitudes/elevations, although not synchronously. Higher regional fire frequencies were also recorded around 1000 cal yr BP (i.e., during the Medieval Warm Period, MWP) in other studies in Fennoscandia (Wallenius et al., 2010; Pitkänen et al., 2001), Siberia (Barhoumi et al., 2021) and Canada (El-Guellab et al., 2015; Girardin et al., 2019; Hély et al., 2020; Kelly et al., 2013). Around the North Atlantic region, several records and proxies show a distinct warm episode corresponding with the MWP between 1050 and 850 cal yr BP (Wastegård, 2022). The MWP conditions resulted in high biomass productivity and fuel availability, in turn promoting conditions favourable to fire ignition (Tiljander, 2005), but likely not fire spread because of the generally humid conditions, thus allowing spruce to persist in the landscape (Remy et al., 2023). More fire ignitions during the MWP can be explained by a higher frequency of storm events with lightning strikes due to climatic instability attributable to variations in the North Atlantic sea surface temperature (Barhoumi, 2019; Drobyshev et al., 2016; Remy et al., 2023; Veraverbeke et al., 2017).

## 5. Conclusion

At the temporal scale of the Holocene, we demonstrated that fire frequency decreased with latitude/elevation in northern Finland, mainly due to changes in fuel abundance and vegetation type (gradual decrease of biomass production, and shift in dominance from coniferous to broadleaved tree species). Contrary to expectations, the regional fire frequency did not significantly differ between the warm and dry mid-Holocene and the cool and wet late Holocene at three of the four studied latitude/elevation zones. Only birch-pine forests showed higher fire frequencies in the mid-Holocene. It is biomass burned (and thus fire size) that showed the most marked variations between the mid- and late Holocene. However, while the difference was as expected at lower latitudes/ elevations (more biomass burned in coniferous stands during the mid-Holocene), it showed the opposite trend at higher latitudes/ elevations (more biomass burned in birch-dominated stands during the late Holocene). While the ongoing climate warming will likely result in more biomass burned in low latitude/elevation coniferous forests, the question remains open as to how it might affect high latitude/elevation birch-pine forests and birch woodlands.

## Author contributions

M. Lacand: Conceptualization, Fieldwork, Methodology, Data curation, Formal analysis, Writing; H. Asselin: Conceptualization, Methodology, Supervision, Funding acquisition, Writing, Reviewing; G. Magne: Data curation, Reviewing; T. Aakala: Fieldwork, Reviewing; C. Remy: Methodology, Formal analysis, Reviewing; H. Sëppa: Fieldwork, Reviewing; A. Ali: Conceptualization, Fieldwork, Methodology, Supervision, Funding acquisition, Writing, Reviewing.

## Funding

This research did not receive any specific funding from any agency in the public, commercial, or not-for-profit sectors.

## 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.

## Data availability

Data are available on GitHub: https://github.com/MarionLcd/ Fire_Inari_Finland.

## Acknowledgements

We thank Sandrine Canal for her assistance with WinSEEDLE. Thanks also to Laure Paradis and Benoît Brossier for their help with remote sensing data, lake prospecting and fieldwork logistics. We extend our thanks to Dorian Gaboriau for his participation in the 2017 fieldwork.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.quascirev.2023.108171.

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