Rocky pine forests in the High Coast Region in Sweden: structure, dynamics and history
expand article infoJennie Sandström, Mattias Edman, Bengt Gunnar Jonsson
‡ Mid Sweden University, Sundsvall, Sweden
Open Access


Almost all forests in Sweden are managed and only a small fraction are considered natural. One exception is low productive forests where, due to their limited economical value, natural dynamics still dominate. One example is the Scots pine (Pinus sylvestris L.) forests occurring on rocky and nutrient-poor hilltops. Although these forests represent a regionally common forest type with a high degree of naturalness, their dynamics, structure and history are poorly known. We investigated the structure, human impact and fire history in eight rocky pine forests in the High Coast Area in eastern Sweden, initially identified as good representatives of this forest type. This was done by sampling and measuring tree sizes, -ages, fire-scarred trees, as well as dead wood volumes and quality along three transects at each site. The structure was diverse with a sparse layer of trees (basal area 9 m2 and 640 trees larger than 10 cm ha-1) in various sizes and ages; 13 trees ha-1 were more than 300 years old. Dead wood (DW), snags and logs in all stages of decay, was present and although the actual DW (pine) volume (4.4 m3 ha-1) and number of units (53 ha-1) was low, the DW share of total wood volume was 18% on average. Dead wood can be present for several centuries after death; we found examples of both snags and logs that had been dead more than 300 years. Frequent fires have occurred, with an average cycle of 40 years between fires. Most fires occurred between 1500-1900 and many of them (13) during the 1600s. However, fires were probably small since most fire years were only represented at one site and often only in one or a few samples. The rocky pine forests in the High Coast Area are representative of undisturbed forests with low human impact, exhibiting old-growth characteristics and are valuable habitats for organisms connected to sun-exposed DW. Management of protected rocky pine forests may well include small-scale restoration fires and the limited DW volumes should be protected.


boreal, coarse woody debris, dendrochronology, fire history, natural forest, pine heath forest, shingle field pine forest


Human influence on boreal forests has varied considerably over time and has dramatically transformed forests in Fennoscandia during the last centuries (Östlund et al. 1997). The multi-aged, structurally diverse and old-growth forests that once dominated the Swedish forest landscape have today been replaced by young, even-sized and single-aged managed forests (Östlund et al. 1997; Axelsson and Östlund 2001). In forests with low levels of human impact, there are structures and processes, such as various kinds of disturbances, (e.g. wind, fire, insects and browsing) that create a structurally diverse and heterogeneous forest (Bradshaw et al. 2011; Brumelis et al. 2011). However, studies of natural forest structure and dynamics are hampered by the limited availability of unmanaged reference forests (Kuuluvainen et al. 2017).

In Sweden, nutrient poor forests with low productivity (< 1 m3 ha-1 yr-1) have been exempted from regular forestry by the Forest Act since the 1970s (Anonymous 2017). Approximately 4.6 M ha of forest land are left out from management because of low productivity (Fridman and Wulff 2018). Some of these forests occur on nutrient-poor, rocky areas on hilltops, where Scots pine (Pinus sylvestris L.) is the major tree species. Because of the nutrient-poor conditions, the distribution of trees is sparse and the forests are characterized by openness and sun-exposed conditions. There is no exact estimate on how much forest land consists of rocky pine forests; however, they constitute a significant share of the 14% of the Swedish forest land that is covered by unproductive forest (Forestry 2015).

Rocky pine forests can frequently be found in the High Coast Area in the eastern parts of Northern Sweden (Fig. 1). This area is characterized by a steep terrain and rugged topography and in combination with the highest isostatic up-lift in the world after the latest glaciation resulted in recognition as a World Heritage Area. The current land up-lift is 8 mm year-1 and total recovery is almost 300 m in altitude (Berglund 2012). The area that formerly was below sea level has been exposed to wave action and coastal erosion and, in particular, convex land surfaces often consist of bare bedrock and very shallow soil.

Figure 1.

Study area. Location of the study area in the High Coast Area, Västernorrland County, in the southern boreal zone of central Sweden. Forest structure was investigated at eight sites with rocky pine forest (shown by stars, GB Gropberget, PB Porsmyrberget, VB Vårdkallberget, FA Fanön, GA Gårdberget, SU Southern Ulvön, SK Skuleskogen National Park, HU Hummelvik Nature Reserve)

The low productivity in rocky pine forests in the High Coast Area, together with their inaccessibility, partly explains why many of these forests have escaped extensive human use. These forests mostly have continuity in old-growth characteristics with diverse canopy structure, old trees and dead wood. Hence, these types of forests could function as small refuges for organisms dependent on old-growth conditions. For example, many threatened insects are dependent on dead wood (Stokland et al. 2012), including sun-exposed dead wood (Wikars 2015). An example is Chalcophora mariana L., a beetle dependent on sun-exposed dead wood, and currently found at only two sites in Sweden, of which the Skule National Park in the High Coast Area is one of them (Ehnström and Bader 2013).

To date, very few studies about the structure and history of nutrient-poor, rocky pine forests have been made. This is true not only for Northern Europe but also on a more general, global scale. Some studies have been made on rocky black pine (Pinus nigra Arn.) forests in Spain and even though these forests resemble Scots pine forests in northern rocky areas, they constitute a different forest type with a contrasting historical and landscape context (e.g. Fule et al. 2003; Rubiales et al. 2007; Hernandez et al. 2011; Camarero et al. 2013; Ehnström and Bader 2013).

Due to lack of knowledge of the special habitat that rocky, nutrient-poor pine forests constitute and the importance of baseline information on forests with low human impact, we have examined the structure, history of human use and fire history in rocky pine forests in the High Coast Region. We have used a combination of several methods; investigation of the current forest structure, the use of historical records and biological archives (dendrochronology), which allowed us to address the following questions: 1) What characterizes the forest structure and dynamic in rocky pine forests? 2) What is the fire history in the rocky pine forests? 3) To what extent has human use in the past influenced the rocky pine forests?


Study area

The study was conducted within a 15 × 75 km area (approximately at 62.5°–63.1°N, 17.9°–18.7°E, DD) in the High Coast Region situated in Västernorrland County located in the southern boreal zone of Sweden (Fig. 1). The High Coast Region on the east coast of Sweden along the Baltic Sea is characterized by a rugged and steep terrain. The area was covered with ice during the latest ice age and after the ice had melted the area was under sea level for millennia; only the highest hill tops > 285 m.a.s.l. could be seen as small islands (Lundkvist 1986). Most of the plots are situated on hilltops and the altitude ranges from approximately 50 to 230 m, i.e. below the highest coastline. Mean annual temperature in the region is around 3 ° C, mean annual precipitation is 800 mm, the length of the growing season (number of days with temperature > 5° C) is approximately 150 days. The average maximum snow depth is 70 cm and the snow covers the ground 150–175 days every year (SMHI 2016). Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris) are the two dominant tree species in the study area with Scots pine dominating on dry and nutrient poor sites. A general inventory of 26 sites with rocky pine forests was conducted in 2011 in the study area by the County Administration in Västernorrland with the aim to map core areas with high conservation values, e.g. presence of big and old trees, diverse forest structure and abundant dead wood (Salomonsson and Bader 2015). Eight of these 26 study sites with the highest ranking (high conservation values) were selected for this study.

Sampling for stand structure

In August 2014 we established three band transects at each site, resulting in total in 24 transects. We used band transects since they sample spatial heterogeneity better than circular plots. The studied forests are highly heterogeneous and exposed bedrock occurs mixed with parts of more closed forest. Transects were 10 × 100 m for living trees, but for dead wood sampling we expanded plots to 20 × 100 m to ensure a sufficient number of sampled dead wood units.

To avoid subjectivity in the placement of transects we randomized their placement by using a numbered grid that was placed over a map of the core area at each site and starting points as well as the direction (N, NE, E, SE, S, SW, W or NW) were randomly assigned, but with a minimum distance of 100 m. Within each transect, we recorded × and Y coordinates, species and diameter at breast height (DBH) for all living (stems ≥ 1.3 m high) trees. To describe the spatial distribution pattern, we calculated the distance from each tree to the nearest tree in the × direction and then computed the variance to mean ratio of these distance, i.e. a one-dimensional spatial analysis (Horne and Schneider 1995). We then used a χ2-test to test whether the distribution of trees where random or significantly aggregated. We extracted two increment cores at 30 cm height from each living tree (DBH ≥ 10 cm) using a 6 mm increment borer (Haglöfs, Mora, Sweden). We measured the height of the tallest tree along the transect with a Laser Height Meter (Nikon Forestry 550) at 0 m, 50 m and 100 m.

All snags and logs with a maximum diameter of ≥ 10cm and with their base inside a transect were included. We recorded both base- and top diameters, DBH, length and decay class. We used a four class system for decaying wood: 1 Hard dead wood – The volume of the stem consisting of > 90% hard wood, hard surface, very little impact from wood-decaying organisms; 2 Partly decayed wood – 10–25% of the stems’ volume consists of soft wood, a sheath knife goes through the surface but not through the whole sapwood; 3 Decayed wood – 26–75% of the stems volume consists of soft to very soft wood; 4 Substantially decayed wood – 75–100% of the stems volume consists of soft-very soft wood, a sheath knife can penetrate the whole stem, but a hard core can occur (Esseen et al. 2003). We collected samples for dead wood for dating with a chainsaw, taking only small samples (cookies) rather than whole cross-sections when possible. The dead wood samples were used for age determination and calculating time since death. Naturally created high stumps were recorded as snags. We used the conic-paraboloid formula (Fraver et al. 2007) to calculate the volume of dead wood, a formula that has a greater precision and lower bias than more commonly used formula. However, due to the shape of the trees with many branches and bent, crooked stems, the volume calculations are rough estimates rather than exact values. Cut stumps were not included in dead wood volume calculations but we noted diameter and species and took samples for dating to quantify past harvesting.

Sampling for fire history

In autumn (September-November) 2015 we carried out a comprehensive search of each site for fire scars in stumps, living and dead trees. Two persons visited each site one whole day, resulting in approximately 10 search hours at each site covering the core area and the adjacent forest. Every tree with a fire scar was mapped and samples were later collected with a chainsaw. We only took partial sections whenever possible to avoid unnecessary damage. A total of 52 fire-scarred wood samples were collected and dated from eight sites. An additional six samples from dead wood sampling in 2014 and 3 samples from Skuleskogen, sampled in 2010, also had fire scars and were included, resulting in a total of 61 dated samples. More than half of the samples (33) contained scars from repeated fires and the maximum number of fires in one sample was four.

Dendrochronology and cross-dating

We mounted cores from living trees and cross-sections from dead trees and sanded them with increasingly fine grain size until a fine polish was achieved (down to grain size 400 for all samples and to 600 for some samples) using standard methods (Speer 2010). Ring widths and number of rings (age) were measured using a scanner and the image analyzing software WINDENDRO (version 2014). We used Applequist (1958) pith locator in samples that had missing rings in the core. Age was determined by counting every year ring to the pith. A few samples had a rotten core and we then used the mean growth rate for each site and the DBH to estimate the age. As a measure of growth at each site, we used the average ring width from each tree and its variability for all trees at each site. Samples from living trees with high age and several samples from dead wood that were visually dated with high accuracy were selected to create a master chronology. We used the software program COFECHA (Holmes 1983; Grissino-Mayer 2001) to cross-date our samples and suggested years were always visually double-checked to make sure that distinct marker years (with typical late-wood features) were dated correctly. The program ARSTAN was then used to create a standardized master chronology and tree ring series with questionable dating were eliminated before standardization (Cook and Krusic 2005). The master chronology from living and some dead wood samples resulted in 130 samples covering 818 years (1197–2014) with a mean sensitivity of 0.30 and a relatively strong inter-correlation of 0.515. This master chronology was used to date samples from dead wood and as dead wood samples were dated, they were added to the master chronology. We were able to date 70% of all dead wood samples; 30% of the logs were too decomposed to allow sampling or difficult to date. The dated samples belonged to all decay classes but only a few samples in decay class four were possible to sample and date. The master chronology was also used to date fire scars and because the growth is related to precipitation during the main fire season in June, we noted the average ring width for all the fire years. The final chronology (see Appendix 1) with living as well as dead wood samples with and without fire scars resulted in 248 samples covering 819 years (1197–2015) with a mean sensitivity of 0.29 and an inter-correlation of 0.505 and with a minimum of ten samples, beginning with the year 1431. We also calculated time since death for the dead wood; however, we cannot rule out the possibility that we have overestimated this value as in some cases the outermost parts might have eroded. The computer program FHAES (Fire History Analysis and Exploration System, version 2.0) was used for structuring and analyzing fire data. FHAES was also used to produce fire history graphs.


Site history

Very little written information is available that is relevant for the specific historical use of the rocky pine forests. The general history of the region is, however, relatively well documented (e.g. Lundkvist 1986; Baudou 1995) . Based on findings of old building structures in stone and grave cairns, it is clear that some places in the area have been populated by humans for at least 3,000 years (Baudou 1995). The few early settlers based their livelihood mostly on fishing and hunting (e.g. seal along the coastline) and fishing has been the main income in the region for a long time (Lundkvist 1986). The possibility for mining was explored during the 1700´s at several places along the High Coast Region, e.g. at southern Ulvön and Hemsön (sites SU and PB), and, although it never became an industrial use, the attempts might have impacted the nearby forests to some extent (Andersson 1975; Lindh 1991). When the first large-scale wave of industrial usage of the forests started during the 1800´s, it is unlikely that the nutrient-poor pine forests with rather small trees on the hill-tops along the High Coast also were exploited. In a map of Vårdkallberget from 1851 it is possible to read (in Swedish) that the area close to the hill-top where the rocky pine forests occur is named “Näs by skogsmark” and “Myre by skogsmark” (Renström and Hedström 1985). This means that this area was classified as a common forest resource, free to use for the village members, but to what extent it was actually used is not known. Close to one (VB) of the eight sites, remnants of two old tar pits have been found (Fig. 2). The pits are located just along the coastline and it is unclear whether the wood for the tar was collected nearby or from the pine forest in the hill-top (the hill-top is located approximately 500 m from the tar pit remnants). The area around VB was not populated before 1780 but nearby settlers used the area mainly for fishing even before the settlements (Renström and Hedström 1985; Lundkvist 1986) . We only found a few harvested stumps on our plots, at one plot at GA and in two neighboring plots at the SU site, indicating very limited forestry activities. We found nine stumps at GA and ten stumps at SU, most of them heavily eroded, and we were only able to date six stumps. The outermost year ring from the stumps at SU was dated to 1736, 1827 and 1886 and for GA 1752, 1813 and 1828, corresponding to 200 years since harvest on average.

Figure 2.

Old map from one rocky pine site. Map of Vårdkallberget (VB) from 1851, which shows the classification of the nutrient poor rocky pine forests around the hill-top (marked with a triangle) as “general forest land” and the nearby settlement (marked with a plus sign). Two remnants of tar pits are marked with a star. The widest distance from east to west is approximately 2 km.

Stand structure

Scots pine was the dominating tree species in rocky pine forests and stands were very scattered and open; they had very little mineral soil and the ground was interspersed with bare rock and with a vegetation dominated by lichens and dwarf shrubs (Fig. 3). A general pattern was that the forest became even less dense and increasingly rocky higher up on the mountain. Trees had different sizes and they grow upright but also strongly leaning, some even grow horizontally despite being healthy and alive.

Figure 3.

General description of rocky forests in the High Coast Area. Photos from Fanön (FA), Gårdberget (GA) and S. Ulvön (SU), which show the topography, typical characteristics in rocky pine forests with dispersed trees and dead wood in various sizes in rocky terrain, spruce interspersed at concave surfaces and a fire-scarred pine.

The general stand structure at the eight sites was characterized by sparsely distributed trees with a mean basal area of approximately 9 m2 ha-1 and a density of 640 trees ha-1 on average (Table 1). Variance to mean ratio showed that trees both had an aggregated and a random distribution pattern, depending on site (Table 1). The forests mainly consisted of Scots pine, which constituted approximately 90% of all the trees. The second most common tree species was Norway spruce, which constituted 7% on average. Deciduous trees were usually present but in very low density (Table 1). Less than 4% of the trees were deciduous with birch (Betula spp.) as the most common species, while aspen (Populus tremula L.) and rowan (Sorbus aucuparia L.) occurred only sporadically. The average maximum tree height for pine was 7.5 m and varied between 5.5 and 10.1 m at the different sites (Table 1). The average age for living pines varied between 147 and 194 with a mean of 167.5 years and the maximum age was even more varied with the oldest living trees ranging from 276 to 418 years (Table 1) at the eight different sites. The average ring width of living pines varied from 0.52 to 0.68 mm yr-1 between sites (x‒ = 0.55), indicating very low growth rate (Table 2). In addition, there was a considerable within-site variability in tree-ring width, indicating a large variation in growth at the stand level (Table 2).

General stand structure. Stand structure data for eight rocky Pinus sylvestris dominated forests in the High Coast Region in Northern Sweden. Values are means with SE in parenthesis and based on three band transects per site. Site acronyms as in Figure 1.

Study site GB PB VB FA GA SU SK HU
Dbh (cm), Trees ≥ 1.3 m height 11.4 (0.6) 9.8 (1.2) 11.2 (0.8) 11.3 (2.8) 11.1 (1.0) 12.2 (0.7) 12.6 (0.3) 9.3 (0.6)
Basal area (m2 ha-1), Trees ≥ 1.3 m height 11.0 (1.9) 8.8 (0.9) 10.2 (1.1) 8.1 (2.4) 10.2 (1.9) 6.3 (0.8) 10.1 (2.1) 8.8 (0.9)
Maximum height, Pine (m), average 10.1 (0.67) 7.0 (0.27) 6.6 (0.23) 8.4 (0.42) 8.4 (0.45) 6.2 (0.05) 8.0 (0.67) 5.5 (0.25)
Age, Pine (yr), average ≥ 10 cm DBH 194 (16) 157 (10) 182 (10) 147 (4) 157 (15) 189 (21) 156 (30) 158 (24)
Age, Pine (yr), maximum 334 299 372 276 418 403 442 376
No. of living trees ha-1 ≥ 1.3 m height 647 (94) 713 (91) 636 (76) 563 (39) 753 (234) 363 (19) 593 (118) 783 (72)
No. of living trees ha-1 ≥ 10 cm DBH 280 (70) 300 (15) 297 (32) 300 (60) 323 (33) 220 (6) 303 (61) 363 (35)
Spatial distribution (variance to mean ratio, VMR) 1.9* 1.1 1.2 3.3* 1.2 1.7* 1.7* 1.0
Pine, share (%) 77 (15) 95 (3) 95 (1.4) 78 (6) 88 (11) 92.5 (4) 94 (1.3) 98 (2.3)
Spruce, share (%) 20 (13) 4 (2.2) 5 (1.4) 4 (0.2) 9 (7) 6 (4) 4 (0.8) 0 (0)
Deciduous, share (%) 3 (1.9) 1 (1.1) 0 (0) 18 (5.8) 3 (3.4) 1.5 (1.5) 2 (1.1) 2 (2.2)
Dead wood volume (m3 ha-1)
4.8 (1.09) 1.0 (0.49) 1.7 (0.31) 1.9 (0.52) 2.6 (1.30) 1.9 (0.89) 1.5 (0.76) 0.6 (0.21)
Downed 3.3 (1.56) 1.7 (0.29) 1.6 (0.30) 3.3 (1.14) 2.0 (0.15) 2.0 (0.99) 4.0 (1.44) 1.2 (0.52)
Proportion dead wood, basal area (%) 18.9 (1.91) 13.5 (1.69) 16.7 (2.88) 21.7 (5.64) 16.7 (2.64) 26.1 (6.00) 20.3 (8.47) 9.9 (2.61)

Tree-ring width based growth. The growth of living Scots pines measured as average annual tree-ring width in eight rocky Pinus sylvestris dominated forests in the High Coast Region in Northern Sweden. Values are means and SD from all trees > 10 cm DBH at the sites. Site acronyms as in Figure 1.

Study site GB PB VB FA GA SU SK HU
Growth (mm yr-1) 0.53 0.53 0.58 0.68 0.56 0.48 0.55 0.52
SD 0.21 0.20 0.29 0.38 0.26 0.25 0.24 0.19
n 82 85 84 70 83 59 49 89

The tree diameter distribution was positively skewed with small trees (DBH ≤ 10 cm), dominating and with decreasing frequency of trees with increasing size (Fig. 4a). However, even though the large trees were rare, they did exist and there were on average one pine ha-1 larger than 40 cm in DBH and 15 pines larger than 30 cm ha-1 in the studied forests. For trees larger than 10 cm in DBH, medium age classes (75–200 yr) were most common for both pine and spruce (Fig. 4b). Pines were on average 165 years (SD = 75, n = 624) and spruce 144 years (SD = 47, n = 52).

Figure 4.

Categories of tree size and tree age. Summary of the distributions of diameter at breast height (DBH) (a) and age (b) (> 10 cm DBH) for living trees of pine and spruce at the eight sites combined (with mean and SD, n = 8).

All ages are present for pine but really old spruces were lacking. The oldest spruces were a maximum of 275 years old whereas pines were older; approximately 1 tree ha-1 was above 400 years old and 13 trees ha-1 were more than 300 years old. The correlation between age and DBH was generally low (R2 = 0.31 for pine and R2 = 0.11 for spruce) but due to the large sample size significant for both tree species (p < 0.001 and p < 0.05 respectively; Fig. 5). The oldest pine (418 years) was also the largest (48 cm DBH), while for spruce the oldest tree (259 years) was only 11 cm at breast height (Fig. 5). The age span was wide across all diameter classes; for example, the age span of trees with a DBH of 10 cm varied from 28 to 282 years and from 57 to 216 years for pine and spruce, respectively.

Figure 5.

Relationship between size and diameter at breast height (DBH) for pine (a, n = 624) and spruce (b, n = 52). Only trees with a DBH larger than 10 cm are included.

Dead wood

The amount of dead pine wood was low (average of 4.5 m3 ha-1, range: 2–8, SD = 2.0, n=8) and varied between sites, but both logs and snags were present at all sites (Table 1, Fig. 6a). On average, there were 53 pine DW units ha-1 (SD = 12.8, n=8). DW in all decay stages were present and with decay stage 2 as the most common. However, at three sites dead trees in decay stage 1 were more common (FA, GA, HU; Fig. 6b). The average decay class for all dead wood was 2.45 and for dated dead wood samples 1.92. The difference stems from lack of datable samples from dead wood in decay class four. The basal area of DW in relation to total basal area, including living trees, varied between 10–26% at the different sites with an average of 18%. Most dead wood had died fairly recently; almost half of the dated dead wood samples had died during the last 50 years (Fig. 7).

A majority of the DW, 88%, had died during the last 200 years, which corresponds to a DW addition of approximately 2 trees per ha-1 and decade since the beginning of the 1800s. However, there were clear signs that dead wood can remain for several hundred years. The DW did not totally decay even in cases when the logs had been dead more than 300 years and we even found DW that had been dead more than 500 years (Fig. 7). Snags constituted a higher proportion of newly dead wood whereas logs were the most common DW type for units older than 350 years, but important to note is that there were also really old snags present. For DW that had been dead between 100–300 years the two types are equally common (Fig. 7). The average time since death for all dated dead wood units was 106 years (SD = 129, n=220), for snags 92 years (SD = 151, n=109) and for logs 120 years (SD = 129, n=111). The average age for the dated DW was 192 (SD = 82, n=220).

Figure 6.

Number of logs and snags from pine in rocky pine forests at the eight different sites (SE, n = 3) as well as the total mean (SD, n = 8). The abundance of DW is classified as standing (Snag) or horizontal (Log) (a) and also classified in decay stages (b).

Figure 7.

Time since death for dead wood for snags and logs.

Fire history

Most signs of fire were found in FA where 18 fire years were detected from 12 collected fire-scarred samples (Fig. 8a). FA was also the only site with individual trees that had experienced at least four fires. On average, 1.8 fires per sample from all sites was detected but on FA where most fires occurred, we detected 2.4 fires per sample. We did not find any fire-scarred trees at GB and only 3 fires could be detected at PB. On average, eight fire events per site were detected covering a time period from 1235 to 1923 but the number of detected fires varied between sites (Fig. 8a and Table 2). The period with most fires was during the 1600s and the 1800s when 13 fires occurred across all eight sites (Fig. 8b). We found signs of 47 fires between 1500 and 1900 and we detected very few fires earlier than 1500 and later than 1900. The earliest detected fire occurred in 1235 but as with most dated fires, this fire was only detected at one site (Fig. 8c). However, one of the fire years, 1693, was found at five sites. The fire in 1693 was found in PB, FA, SU, SK and HU but not in GA and VA which are located close to FA, SK and HU (Table 2). GA and FA are situated very close together but the only fire year that is common for the two sites is 1563. Approximately half of the fire-scarred trees (45%) had only one fire scar and trees with most scars (repeated fires) had four scars and was found at FA (see Appendix 2 for detailed information on samples with fire scars). In addition, most of the dated fire years was only detected in one sample (64%). The average fire interval varied between sites (shortest: FA 20.5 years and longest: VB: 65.5 years), but with an average fire interval of 42 years (excluding GB, where no signs of fire were detected) (Appendix 3). We also noted that many fire years had smaller year rings (indicating a dry fire season) than the average sizes in the master chronology. Twenty-eight fires had significantly smaller year rings than average, 25 had average size while only four had unusually large rings (Appendix 4).

Figure 8.

Fires in rocky pine forests. The total number of fires at the eight sites and the average from the sites (a). All detected fires are included, regardless of the number of samples the fire was detected in. Total number of fires are also separated into centuries (b) and individual fire years and their occurrence in the number of areas, with fire years occurring at more than one site highlighted (c).


Forest structure

The studied rocky pine forests are characterized by a very open structure. The low density of trees can partly be explained by the scarcity of water and nutrients, as low productivity is generally connected with low basal area and openness (Liira et al. 2007). The rocky pine forests also have an uneven distribution of tree species, size and age. This creates a heterogenic and diverse environment and a wide age distribution typical for old pine forests (Kuuluvainen et al. 2002). Attributes that usually are associated with old-growth forests are present, such as several canopy layers, high variation in tree sizes and ages and high spatial heterogeneity of tree distribution (irregular distribution of gaps) (Bauhus et al. 2009).

Although the diameter distribution pattern in our study is descending with size, the pattern is not a typical reversed J-shaped curve, common in many undisturbed forest stands (e.g. Parker and Peet 1984; Linder et al. 1997; Kuuluvainen et al. 2002; Rouvinen and Kuuluvainen 2005). In a study by Lilja and Kuuluvainen (2005), three different semi-natural (old-growth) pine stands had all a descending pattern, one that showed a reversed J-shaped pattern and two stands that were more similar to our findings. Rouvinen and Kuuluvainen (2005) found a bimodal pattern in a managed pine stand but a descending pattern, but no sharp reversed J-curve, in a natural pine stand. In a sub-xeric old-growth stand, dominated by pine, Uotila et al. (2001) found a pattern with most trees in intermediate diameter classes and not a clear descending pattern. Since we have no data on regeneration (trees < 1.3 m), we have no clear explanation to why we did not find a clear reversed J-shape on the diameter distribution in our study. It could be that the small trees are less frequent because there are only a few places/spots where trees can establish or it could be influenced by moose (Alces alces), which browses on small trees in winter. Neither did we find a bimodal diameter distribution pattern, often seen in forests with repeated disturbances, such as fires (e.g. Zenner 2005). This could indicate that the fires in this type of forests have been low-intensity fires of small sizes, as discussed below. In addition, the lack of bimodal diameter distribution pattern also could be an effect of lack of fires during the last 150 years.

As well as being a sign of naturalness, the heterogeneity and patchiness of tree distribution may also be influenced by an uneven water and nutrient availability for the trees. A tree that grows where the ground consists of bare rock has a very different potential for growth than trees in a concave patch covered with mineral soil, which probably can explain why the trees grew spatially irregularly at four of the sites. The trees generally have a low growth and high variability between trees at all of the sites, indicating also heterogeneity in growth. The high variability in growth between trees clearly contributes to the limited correlation between age and DBH. Kuuluvainen et al. (2002) found a stronger correlation between age and size; their R2 was 0.58 for pine. However, they had a wider range of stand types included in their study and not only nutrient poor pine forest, which could explain the higher predictability. Nevertheless, they also found a lower correlation for spruce (R2=0.36) than for pine, similar to our findings.

The average maximum tree height is generally low in rocky pine forests. The maximum tree height is related to height growth when the tree is young (Ryan and Yoder 1997), which in turn is influenced by genetic and environmental factors (Junttila 1986; Kaya et al. 1999; Pensa et al. 2005). Tree height seems to depend on a combination of temperature, precipitation and nutrient availability (Jansons et al. 2015) and the environment in the rocky pine forests is characterized by low water and nutrient availability and can probably partly explain the low mean maximum tree height. Other factors that might influence tree height in the rocky pine forests are wind-exposure and openness (Tomczak et al. 2014).

The presence of old trees is one of the key features of natural forests (Östlund et al. 1997; Andersson and Östlund 2004). Old trees are relatively common in the studied rocky pine forests and we found approximately 13 pine trees ha-1 older than 300 years and as many as 70 pines ha-1 older than 200 years. As a comparison, Edwards and Mason (2006) found approximately 20 and 60 trees ha-1 older than 200 years in two different old-growth pine stands in Scotland. In another Scottish old (and open) forest, only about 6 trees ha-1 older than 250 years were found (Summers et al. 2008). This is in line with the findings of Kuuluvainen et al. (2002); they found approximately 5–10 trees ha-1 older than 250 years in an old-growth pine forest. The oldest living pines in our plots were 418 years but we know that living trees older than 600 years (data not shown) existed in the studied area. In line with our findings, in old-growth forests in Finland, ages of more than 500 years for pine and almost 300 years for spruce were found (Kuuluvainen et al. 2002). In a study from a similar area, Hornslandet, approximately 200 km south from our study area, the authors actively searched for very old pine trees and found the oldest recorded pine in Sweden with an age of more than 750 years (Andersson and Niklasson 2004).

There are not many deciduous trees in the rocky pine forests and although birches (the most common deciduous species in the area) can tolerate rather dry conditions (Sutinen et al. 2002), regular drought may well limit the abundance of deciduous trees. In addition, it is likely that the density of deciduous trees is limited by the presence of moose (Alces alces L.), which can reduce the abundance of several deciduous species (e.g. rowan and aspen) when present (Edenius et al. 2002). The abundance of moose has since the 1970s massively increased in northern Sweden (Cederlund and Bergström 1996) and moose is today abundant throughout the whole of Sweden with the highest densities in the world (Skogforsk 2016). Moose is highly abundant also in the rocky pine forests in the High Coast Region and likely have a negative impact on the abundance of deciduous trees.

Dead wood and human impact

The CWD volume is very low and corresponding to levels in managed forest (Jonsson et al. 2016; Ylisirniö et al. 2012) and at a much lower level than what usually is common in natural forests with no or little human impact (e.g. Rouvinen and Kuuluvainen 2001; Karjalainen and Kuuluvainen 2002; Koster et al. 2005; Shorohova and Kapitsa 2015). Given the low tree density it is, however, more relevant to compare the proportion of dead trees to living trees. In the rocky pine forest the CWD share of the total basal area (living and all dead wood combined) is 18% on average, which is in line with several other pine-dominated forests with old-growth characteristics (e.g. Sippola et al. 1998; Siitonen et al. 2000; Rouvinen and Kuuluvainen 2001). By contrast, Karjalainen and Kuuluvainen (2002) found a higher proportion (32%) of the volume of CWD in forest stands on dry soils. The variation in CWD:live wood volumes is greater in pine dominated forests than in spruce forests (Shorohova and Kapitsa 2015). We found a slightly lower proportion of dead wood in this study, but it should be noted that the ratio between dead and living trees is driven primarily by tree life span (mortality rate) and decay rates. Even though decay rates are slow in the studied rocky pine forests, it is to be noted that tree life span is likewise very long.

Charcoal and tar production has been common in the area a long time and the CWD quality in rocky pine forests is particularly suitable for tar production. Situated < 1 km from our sampling sites, two old remnants of tar pits have been found at one site, but there were no reported signs of old tar pits at the other seven sites. Hence, we cannot totally rule out the possibility that some CWD has been used for char and/or tar production in the studied area because these activities tend not to leave any visible traces behind.

The most common decay stage in this study is stage 2, which is in line with other pine forests with a high degree of naturalness (Karjalainen and Kuuluvainen 2002). The late decay stages were not as common in the study area as the most decayed wood (decay stage 4) constituted approximately only 12% of the total amount of CWD. In other studies of pine forests with high degree of naturalness the distribution of CWD in decay classes was more even (Rouvinen and Kuuluvainen 2001) or later decay classes were also more common (Sippola et al. 1998). One explanation for the relatively low presence of CWD in late decay stage found in this study could be reoccurring small fires because fire consumes logs in later decay stages to a higher extent than logs in earlier stages (Eriksson et al. 2013). It seems that CWD is accumulating slowly and can remain for very long periods in dry conditions. This makes every CWD unit very valuable and even a small outtake can have a large impact on the CWD-depending organisms. The slow decay rate also explains why there is really old CWD; some has been dead for 300–500 years, in the rocky pine forest. In addition, we were not able to sample and date the most decayed dead wood, so the result is probably an underestimation of the amount of very old CWD.

Only two of the sites had signs of past cutting. A low frequency of manmade stumps indicates a high degree of naturalness (Uotila et al. 2001; Rouvinen et al. 2005). The outermost year rings on the six dated stumps varied between 1736 and 1886. This suggests that harvesting took place a very long time ago, although not necessarily giving the exact year of harvest since all stumps lacked bark and some erosion of the outer parts cannot be excluded. Consequently, the years should be regarded as the earliest possible cutting year but the actual cutting could have happened later. Nevertheless, there is evidence that the limited cuttings that did occur happened during the 1800s, or even earlier, and with no recent harvesting during the last 100 years. It is unlikely that rocky pine forests were targeted by industrial forestry since trees with large diameter are rare and the accessibility is very low. However, it is possible that settlers used the pine forests at small-scale for certain construction details, e.g. window frames, for which dense high quality wood was preferred. Such limited harvest/extraction is unlikely to leave any long-lasting visible traces.

Fire history

Surprisingly, many fires were detected in the rocky pine forests. There is not much fuel on the ground in the studied rocky pine forests, bare rock is common and trees are scattered. On the other hand, the ground surface easily becomes very dry and vegetated areas are mainly composed of reindeer lichens and dry mosses, which together with scattered dwarf shrubs potentially could carry a ground fire during dry years. Not many fires were detected before 1500. This could partly be a sampling artefact due to the limited number of fire scarred old trees and snags, but different climate and lower human population size cannot be ruled out as explanations for lower fire frequency in the beginning of our time series. Many fires happened during the 1600s, a pattern also observed by others (Zackrisson 1977; Niklasson and Granström 2000; Wallenius et al. 2004). The year 1693 stands out as a major fire year; a fire year that has also been documented by others (Drobyshev et al. 2014).

There was a population increase after the Black Death plague during the 1600s and several “slash and burn” immigrants from Finland also settled down in northern Sweden during this period, which can be an explanation for more frequent fires. However, most of the ”slash and burn” farmers did not settle down along the coast area where fishing was the main livelihood but rather in the inland areas where the land was more suitable for the ”slash and burn” cultivation technique (Lundkvist 1971). One fire happened in 1721 in Hummelvik nature reserve, a year when Russians made raids where they burned hundreds of farms along the coastline in the studied area (Lundkvist 1971; Lindh 1991). A village just outside Hummelvik nature reserve is mentioned as being burned (Lundkvist 1971), which could potentially explain the fire 1721 since the year ring has an average size. Unusually small year rings have been shown to be closely related to low precipitation in June in the Rocky pine forests in the High Coast Region and fire years are influenced by summer temperature and precipitation to a large extent in the northern region (Drobyshev et al. 2012; Drobyshev et al. 2014).

Most fire years were detected only in a few samples, which indicates that the fires in this area and forest type might have been small in size, but not necessarily rare events. A plausible explanation is that there are plenty of small-scale dispersal barriers for fires in this heterogenic landscape. Both bare rock and wet hollows often occur. Moist depressions, swamps and Picea abies patches often do not burn even when nearby dry patches do (Wallenius et al. 2004). The mean fire interval varied between 20.5 and 65.5 years at the different sites (except of one site with no fires found at all). These intervals included fire years that only were detected in one sample, and hence do not necessarily indicate that the whole forest area (site) burned. It is more likely that the fires were small in this heterogenic environment which creates a small-scale spatial variation influencing fire frequency and size (Wallenius et al. 2004). Many trees also survived the fires and many age classes were present. Taken together, this suggests that the fires were not stand-replacing, had low intensity but happened on a regular basis.


The rocky pine forests in the High Coast Region show a high degree of naturalness and possess many old-growth characteristics, e.g. presence of old trees, diverse structure and although the volume of dead wood is low, it constitutes approximately 18% of the total basal area. The dead wood is diverse with a variety of both snag and log sizes and can be present for a long time due to the slow decay rate. The diverse presence of dead wood, e.g. all decay stages represented, indicates that there is a constant supply of dead wood and that these types of forests have natural features. The high degree of naturalness is also supported by the lack of signs of human use; only a few man-made stumps have been found. All sites but one have clear signs of fires and it seems that the fires have happened quite frequently, but have been small in size. It is likely that these type of forests host a specific biota, evolved and adapted to the specific conditions that rocky pine forests constitute.


We are thankful to all landowners for allowing access to the study sites. We would like to thank for their much appreciated help in the field: Julia Hjalmarsson, Miriam Matheis, Jonas Orelund and Håkan Norberg. Thanks also for the valuable help from the County Administration in Västernorrland, especially to Pekka Bader, Jonas Salmonsson and Johan Uebel. We would also like to thank Prof. Lars Östlund for advice regarding signs of human use and for comments that improved the manuscript. Thanks to Prof. Evan Larsson for help with the master chronology and for comments that improved the manuscript. Many thanks also to Prof. Timo Kuuluvainen for comments that improved the manuscript.


  • Andersson A (1975) Vegetationskartering av södra Ulvön. Härnösand.
  • Andersson M, Niklasson M (2004) Rekordgammal tall på Hornslandet i Hälsingland. Svensk Botanisk Tidskrift 98: 333.
  • Andersson R, Östlund L (2004) Spatial patterns, density changes and implications on biodiversity for old trees in the boreal landscape of northern Sweden. Biological Conservation 118(4): 443–453.
  • Anonymous (2017) Skogsvårdslagen, Skogsvårdsstyrelsen.
  • Applequist MB (1958) A simple pith locator for using with off-center cores. Journal of Forestry 56(2): 141.
  • Baudou E (1995) Norrlands forntid: ett historiskt perspektiv. Skytteanska samf., Bjästa.
  • Bradshaw RHW, Josefsson T, Clear JL, Peterken GF (2011) The structure and reproduction of the virgin forest: A review of Eustace Jones (1945). Scandinavian Journal of Forest Research 26(S10): 45–53.
  • Brumelis G, Jonsson BG, Kouki J, Kuuluvainen T, Shorohova E (2011) Forest Naturalness in Northern Europe: Perspectives on Processes, Structures and Species Diversity. Silva Fennica 45(5): 807–821.
  • Camarero JJ, Manzanedo RD, Sanchez-Salguero R, Navarro-Cerrillo RM (2013) Growth response to climate and drought change along an aridity gradient in the southernmost Pinus nigra relict forests. Annals of Forest Science 70(8): 769–780.
  • Cederlund G, Bergström R (1996) Trends in the moose—forest system in Fennoscandia, with special reference to Sweden. Conservation of Faunal Diversity in Forested Landscapes. Springer, 265–281.
  • Cook E, Krusic P (2005) Program ARSTAN: a tree-ring standardization program based on detrending and autoregressive time series modeling, with interactive graphics. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY.
  • Drobyshev I, Niklasson M, Linderholm HW (2012) Forest fire activity in Sweden: Climatic controls and geographical patterns in 20th century. Agricultural and Forest Meteorology 154–155: 174–186.
  • Drobyshev I, Granström A, Linderholm HW, Hellberg E, Bergeron Y, Niklasson M (2014) Multi-century reconstruction of fire activity in Northern European boreal forest suggests differences in regional fire regimes and their sensitivity to climate. Journal of Ecology 102(3): 738–748.
  • Edenius L, Bergman M, Ericsson G, Danell K (2002) The role of moose as a disturbance factor in managed boreal forests. Silva Fennica 36(1): 57–67.
  • Edwards C, Mason WL (2006) Stand structure and dynamics of four native Scots pine (Pinus sylvestris L.) woodlands in northern Scotland. Forestry 79(3): 261–277.
  • Ehnström B, Bader P (2013) Åtgärdsprogram för jättepraktbagge. Naturvårdsverket Rapport 6584.
  • Eriksson AM, Olsson J, Jonsson BG, Toivanen S, Edman M (2013) Effects of restoration fire on dead wood heterogeneity and availability in three Pinus sylvestris forests in Sweden. Silva Fennica 47(2): 954.
  • Esseen P-AGA, Ståhl G, Sundquist S (2003) Fältinstruktion för nationell inventering av landskapet i Sverige, NILS. SLU, Institutionen för skoglig resurshållning och geomatik, Umeå.
  • Fraver S, Ringvall A, Jonsson BG (2007) Refining volume estimates of down woody debris. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 37(3): 627–633.
  • Fule PZ, Heinlein TA, Covington WW, Moore MM (2003) Assessing fire regimes on Grand Canyon landscapes with fire-scar and fire-record data. International Journal of Wildland Fire 12(2): 129–145.
  • Hernandez L, Rubiales JM, Morales-Molino C, Romero F, Sanz C, Gomez Manzaneque F (2011) Reconstructing forest history from archaeological data: A case study in the Duero basin assessing the origin of controversial forests and the loss of tree populations of great biogeographical interest. Forest Ecology and Management 261(7): 1178–1187.
  • Jansons A, Matisons R, Zadina M, Sisenis L, Jansons J (2015) The effect of climatic factors on height increment of Scots pine in sites differing by continentality in Latvia. Silva Fennica 49(3): 1262.
  • Jonsson BG, Ekström M, Esseen P-A, Grafström A, Ståhl G, Westerlund B (2016) Dead wood availability in managed Swedish forests – Policy outcomes and implications for biodiversity. Forest Ecology and Management 376: 174–182.
  • Karjalainen L, Kuuluvainen T (2002) Amount and diversity of coarse woody debris within a boreal forest landscape dominated by Pinus sylvestris in Vienansalo wilderness, eastern Fennoscandia. Silva Fennica 36(1): 147–167.
  • Kaya Z, Sewell M, Neale D (1999) Identification of quantitative trait loci influencing annual height-and diameter-increment growth in loblolly pine (Pinus taeda L.). Theoretical and Applied Genetics 98(3–4): 586–592.
  • Koster K, Jogiste K, Tukia H, Niklasson M, Mols T (2005) Variation and ecological characteristics of coarse woody debris in Lahemaa and Karula National Parks, Estonia. Scandinavian Journal of Forest Research 20(sup6): 102–111.
  • Kuuluvainen T, Mäki J, Karjalainen L, Lehtonen H (2002) Tree age distributions in old-growth forest sites in Vienansalo wilderness, eastern Fennoscandia. Silva Fennica 36(1): 169–184.
  • Kuuluvainen T, Aakala T, Varkonyi G (2017) Dead standing pine trees in a boreal forest landscape in the Kalevala National Park, northern Fennoscandia: Amount, population characteristics and spatial pattern. Forest Ecosystems 4(1): 12.
  • Liira J, Sepp T, Parrest O (2007) The forest structure and ecosystem quality in conditions of anthropogenic disturbance along productivity gradient. Forest Ecology and Management 250(1–2): 34–46.
  • Lilja S, Kuuluvainen T (2005) Structure of old Pinus sylvestris dominated forest stands along a geographic and human impact gradient in mid-boreal Fennoscandia. Silva Fennica 39(3): 407–428.
  • Lindh T (1991) Hemsöns historia, D.1. Hemsö idrottsförening, Älandsbro.
  • Lundkvist T (1971) Boken om Säbrå: kommunens och socknarnas historia: Säbrå, Häggdånger, Stigsjö, Viksjö, Hemsö. Härnösand: Ångermannia, Härnösand.
  • Lundkvist T (1986) Boken om Säbrå: Kommunens och socknarnas historia: Säbrå. Häggdånger, Stigsjö, Viksjö, Hemsö, Angermannia, Härnösand.
  • Östlund L, Zackrisson O, Axelsson AL (1997) The history and transformation of a Scandinavian boreal forest landscape since the 19th century. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 27(8): 1198–1206.
  • Pensa M, Salminen H, Jalkanen R (2005) A 250-year-long height-increment chronology for Pinus sylvestris at the northern coniferous timberline: A novel tool for reconstructing past summer temperatures? Dendrochronologia 22(2): 75–81.
  • Renström A, Hedström B (1985) 15 byar i Kramfors kommun. Värns, Vibyggerå socken: historisk bakgrund, byggnadsinventering, förslag till förnyelse och bevarande.
  • Rouvinen S, Kuuluvainen T (2001) Amount and Spatial Distribution of Standing and Downed Dead Trees in Two Areas of Different Fire History in a Boreal Scots Pine Forest. Ecological Bulletins 2001: 115–127.
  • Rouvinen S, Kuuluvainen T (2005) Tree diameter distributions in natural and managed old Pinus sylvestris-dominated forests. Forest Ecology and Management 208(1–3): 45–61.
  • Rouvinen S, Rautiainen A, Kouki J (2005) A relation between historical forest use and current dead woody material in a boreal protected old-growth forest in Finland. Silva Fennica 39(1): 21–36.
  • Rubiales JM, Garcia-Amorena I, Genova M, Manzaneque FG, Morla C (2007) The Holocene history of highland pine forests in a submediterranean mountain: The case of Gredos mountain range (Iberian Central range, Spain). Quaternary Science Reviews 26(13–14): 1759–1770.
  • Salomonsson J, Bader P (2015) Skoglig naturvårdsinventering – av ett urval hällmarker längs Västernorrlands kust. The County Administrative Board of Västernorrland, Härnösand Rapport 2015: 2.
  • Shorohova E, Kapitsa E (2015) Stand and landscape scale variability in the amount and diversity of coarse woody debris in primeval European boreal forests. Forest Ecology and Management 356: 273–284.
  • Siitonen J, Martikainen P, Punttila P, Rauh J (2000) Coarse woody debris and stand characteristics in mature managed and old-growth boreal mesic forests in southern Finland. Forest Ecology and Management 128(3): 211–225.
  • Sippola AL, Siitonen J, Kallio R (1998) Amount and quality of coarse woody debris in natural and managed coniferous forests near the timberline in Finnish Lapland. Scandinavian Journal of Forest Research 13(1–4): 204–214.
  • Speer JH (2010) Fundamentals of tree-ring research. The University of Arizona press, Arizona.
  • Summers RW, Wilkinson NI, Wilson ER (2008) Age structure and history of stand types of Pinus sylvestris in Abernethy Forest, Scotland. Scandinavian Journal of Forest Research 23(1): 28–37.
  • Sutinen R, Teirilä A, Pänttäjä M, Sutinen M-L (2002) Distribution and diversity of tree species with respect to soil electrical characteristics in Finnish Lapland. Canadian Journal of Forest Research 32(7): 1158–1170.
  • Tomczak A, Jelonek T, Pazdrowski W (2014) Characteristics of selected morphological traits of trees in mature Scots pine stands exposed to wind. Sylwan 158: 183–191.
  • Uotila A, Maltamo M, Uuttera J, Isomäki A (2001) Stand Structure in Semi-Natural and Managed Forests in Eastern Finland and Russian Karelia. Ecological Bulletins 49: 149–158.
  • Wallenius TH, Kuuluvainen T, Vanha-Majamaa I (2004) Fire history in relation to site type and vegetation in Vienansalo wilderness in eastern Fennoscandia, Russia. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 34(7): 1400–1409.
  • Wikars L (2015) Åtgärdsprogram för skalbaggar på äldre död tallved. Naturvårdsverket Rapport 6629.
  • Volkov A, Gromtsev A, Sakovets V (1997) Climax forests in the north-western taiga zone of Russia: natural characteristics, present state and conservation problems. In: Preprint of report for the meeting of the Learned Council Forest Research Institute, Karelian Research Centre, Russian Academy of Sciences.
  • Ylisirniö AL, Penttilä R, Berglund H, Hallikainen V, Isaeva L, Kauhanen H, Koivula M, Mikkola K (2012) Dead wood and polypore diversity in natural post-fire succession forests and managed stands – Lessons for biodiversity management in boreal forests. Forest Ecology and Management 286: 16–27.
  • Zenner EK (2005) Development of tree size distributions in Douglas‐fir forests under differing disturbance regimes. Ecological Applications 15(2): 701–714.

Appendix 1

Master chronology, marker years and ring widths

Abbreviations: ++ much bigger than normal; + bigger than normal; - Smaller than normal; -- much smaller than normal. All other years with ring widths are of average size. Bold years means that they are valuable marker years (unusually thin latewood = TH, unusually thick and dark latewood = DL, unusually thick latewood = B).

Year Ring width Year Ring width Year Ring width Year Ring width Year Ring width
1204 + 1329 + 1464 - 1647 + 1874 --
1207 - 1331 + 1466 -- 1648 + 1878 +
1209 - 1332 + 1468 + 1656 + 1881 --
1210 -- 1333 - 1473 -- 1659 -- 1886 +
1212 + 1335 + 1476 + 1665 + 1888 --
1213 -- 1339 - 1484 ++ 1666 - 1890 ++
1214 + 1347 - 1490 - 1667 - 1891 -
1216 + 1348 - 1491 + 1673 + 1896 +
1218 - 1351 - 1494 - 1686 + 1901 B
1219 - 1353 + 1498 + 1687 + 1902 --
1223 + 1357 + 1503 - 1693 -- 1907 +
1228 + 1358 + 1505 + 1696 - 1911 -
1229 ++ 1362 - 1506 - 1698 - 1913 + B
1230 + 1363 - 1508 - 1713 - 1917 -
1233 -- 1364 + 1509 -- 1723 + 1918 -
1234 -- 1366 -- 1511 - 1726 - 1922 +
1235 - 1368 + 1512 + 1736 - 1923 +
1239 - 1370 - 1513 + 1742 - 1924 ++
1243 + 1372 + 1514 - 1747 - 1925 +
1244 ++ 1373 + 1525 ++ 1749 - 1928 +
1247 + 1376 ++ 1528 + 1752 + 1929 + DL
1252 ++ 1381 + 1531 - 1771 - 1933 --
1256 -- 1382 ++ 1533 - 1776 + 1934 -
1259 - 1384 - 1535 + 1777 ++ 1936 --
1261 + 1385 - 1538 -- 1778 + 1939 B
1262 + 1386 - 1541 + 1781 - 1940 --
1263 - 1388 -- 1543 - 1786 - 1943 -
1265 -- 1389 - 1547 + 1792 + 1945 +
1267 ++ 1391 - 1550 -- 1793 + 1946 +
1268 + 1394 + 1551 + 1794 B 1947 +
1269 - 1396 ++ 1554 -- 1795 -- 1953 +
1274 -- 1398 - 1561 ++ 1805 ++ 1954 +
1275 -- 1401 + 1562 + 1806 + 1955 -
1278 ++ 1403 + 1568 -- 1810 - 1957 ++ DL
1279 + 1404 ++ 1572 -- 1816 - 1959 --
1283 -- 1406 ++ 1578 + 1821 B 1960 --
1285 ++ 1411 -- 1583 + 1822 - 1961 -
1287 - 1418 -- 1585 + 1827 + 1962 +
1291 - 1420 -- 1588 -- 1828 + 1969 --
1293 - 1422 - 1590 -- 1831 - 1972 +
1296 + 1423 + 1594 + 1832 -- 1974 +
1297 - 1425 + 1601 -- 1835 - 1976 -- TH
1298 + 1426 + 1603 - 1840 + 1984 +
1299 - 1427 + 1606 - 1844 + 1987 +
1302 -- 1428 + 1611 + 1847 -- 1991 +
1303 + 1430 - 1615 - 1850 + 1992 --
1304 -- 1432 -- 1623 + 1851 + 1997 --
1305 + 1434 + 1625 + 1853 -- 2000 +
1307 + 1442 - 1626 + 1859 TH 2002 -
1309 + 1446 -- 1628 + 1860 B 2004 +
1312 -- 1451 + 1630 - 1861 -- 2008 --
1319 + 1454 + 1631 -- 1862 - 2009 --
1321 + 1455 - 1633 - 1866 + 2011 + B
1324 - 1459 - 1638 + 1868 + 2012 +
1325 -- 1462 + 1642 - 1869 + 2014 +
10 or more samples from 1431
Total no. of samples 248
Age span 1197–2015
Total no. of years 819
Total no of rings 56329
Mean age 227
Sensitivity 0.29
Series intercorrelation 0.505

Appendix 2

Dated samples with fire scars and fire years

Site and Sample ID Correlation with master Pith year Outermost ring year Age Year of fire1 Year of fire2 Year of fire3 Year of fire4 Sample type Scar direction
No fire scars detected
BrandPO8 0,206 1467 1783 316 1702 Dead
BrandPO12 0,248 1500 1990 490 1660 1693 1804
BrandPO14 0,614 1773 2015 242 1804 Living
VA1d20 0,275 1230 1505 275 1268 1347 Dead
VA1d29 0,338 1671 1846 175 1740
BrandVA1 0,454 1783 2013 230 1804 Living E
BrandVA3 0,562 1580 1838 126 1835 Dead N
BrandVA4 0,468 1661 2015 354 1804 or 1805 Living SE
BrandVA5 0,56 1561 1854 293 1601 1631 1729 Dead W
BrandVA7 0,606 1604 1896 292 1601 Stump W
BrandVA10 0,323 1574 1819 269 1631
BrandVA12 0,273 1731 2015 284 1749 1923 Living E
BrandFA1 0,333 1518 2015 497 1781 Living S
BrandFA4 0,342 1494 1647 153 1533 Stump
BrandFA5 0,163 1526 1990 463 1582 1640 1713 1822 Dead
BrandFA6 0,58 1712 2015 303 1822 Living E
BrandFA7 0,338 1435 1863 428 1527 1563 1693 1819 Dead E
BrandFA8B 0,492 1518 2004 486 1693 1798 1846 1910 Dead N
BrandFA11 0,587 1484 1912 428 1590 1693 1827 (1828) Dead S, N
BrandFA12 0,57 1702 2015 313 1693 1857 Living Fire 1 SE, Fire 2 N
BrandFA16 0,585 1599 1855 256 1689 1781 1822 Dead
BrandFA19 0,296 1407 1546 136 1500 Dead SW
BrandFA20 0,382 1640 1972 332 1640 1693 1781 1846 Dead S
BrandFA22 0,343 1642 1837 195 1693 1781 Living S
Gamlingen 0,397 1419 2014 595 1563 1601 1631 1713 Living
GA1dStump1A 0,596 1409 1736 327 1438 1601 Dead
GA1dStump1B 0,663 1465 1713 248 1601 1713
BrandGA4 0,456 1698 2013 315 1713 1826–1827 Living NW
BrandGA6 0,585 1436 2000 564 1516 1830 Living Fire 1 NW, Fire2 SW
BrandGA10 0,306 1587 1905 318 1667 1830 Living NW
BrandGAlst14 0,146 1773 1996 223 1830 Dead SE
BrandGAX1 0,391 1338 1517 179 1378 Dead
BrandGAX2 0,53 1582 1715 133 1667 Stump N
BrandGAX3 0,3 1447 1723 276 1516 1601 or 1602 1667 Dead W
S. Ulvön
BrandULV1 0,3 1683 1868 185 1729 1864 Living E
BrandULV2 0,237 1745 2015 270 1767 Living E
BrandULV3 0,484 1760 1959 199 1767 Dead S
BrandULV4 0,28 1641 1991 350 1693 Living E
BrandULV5 0,37 1725 2015 290 1767 Living NE
BrandULV6 0,543 1735 1817 82 1794 Living W
BrandULV7 0,561 1749 1925 176 1892 Stump S
BrandULV8 0,423 1587 2015 428 1794 Living E
BrandSKU2 0,305 1428 2004 576 1781-1782 1840 Living W + E
BrandSKU6 0,196 1662 2014 352 1693 1840 Living W
BrandSKU9 0,396 1657 2015 358 1840 Living NE
BrandSKUX1 0,225 1565 1826 261 1631 Dead
BrandSKUX2 0,212 1295 1626 331 1351 1415 1510 Dead W
BrandSKUStump 0,476 1796 161 1652 1693 or 1694 Stump
BrandSKUStump8 0,451 1559 1853 294 1554 1631 1672 Stump W
T4.126 0,617 1598 1808 210 1642 Dead
T5.95 0,338 1203 1546 343 1235 1263 1351 1415 Dead
T6.05 0,484 1421 1753 332 1510 1631 Dead
HU1d02A 0,408 1864 2006 142 1872 Dead
BrandHU5 0,365 1546 1983 437 1568 or 1569 1668 or 1678 1887 Dead SE
BrandHU8 0,451 1411 1759 348 1458 1514 1569 1693 Dead E
BrandHU9 0,201 1648 1918 270 1683 1872 Dead E
BrandHUlst13 0,31 1617 1937 320 1721 Dead NW
BrandHUStump1 0,472 1446 1725 279 1514 1568 (+- 1 yr) 1630 Stump
BrandHUStump2 0,503 1469 1578 109 1514 1630 Stump

Appendix 3

Fire years and interval

Fire years. The individual fire years at the different sites and the number of samples where fires were detected and the average fire interval at each site (calculated as: number of fires/(last fire year minus first fire year)). GB = Gropberget, PB = Porsmyrberget, VB = Vårdkallberget, FA = Fanön, GA = Gårdberget, SU = Southern Ulvön, SK = Skuleskogen National Park, HU = Hummelvik Nature Reserve. Gropberget (GR) did not have any signs of fire.

No signs of fires Fire year n Fire year n Fire year n Fire year n Fire year n Fire year n Fire year n
1660 ±2 yr 1 1268 1 1500 1 1378 ±2 yr 1 1693 1 1235 1 1514 3
1693 1 1347 1 1527 1 1438 1 1729 1 1263 1 1569 3
1702 1 1601 2 1533 1 1516 2 1767 3 1351 2 1630 2
1804 2 1631 2 1563 1 1563 1 1794 2 1415 2 1668 1
1729 1 1582 1 1601 4 1864 1 1510 2 1683 1
1740 1 1590 1 1631 1 1892 1 1554 1 1693 1
1749 1 1640 2 1667 3 1631 3 1721 1
1804 3 1689 1 1713 4 1642 1 1872 2
1835 1 1693 6 1830 4 1652 1 1887 1
1923 1 1713 1 1672 1
1781 4 1693 1
1798 1 1781 1
1819 1 1840 3
1822 3
1827 1
1846 2
1857 1
Average fire interval (yr) 36 65.5 20.5 50 33 46.5 41.5

Appendix 4

Fire years and ring widths

Abbreviations: ++, much bigger than normal; +, bigger than normal; aver, average size; -, smaller than normal; --, much smaller than normal. GB = Gropberget, PB = Porsmyrberget, VB = Vårdkallberget, FA = Fanön, GA = Gårdberget, SU = Southern Ulvön, SK = Skuleskogen National Park, HU = Hummelvik Nature Reserve.

Fire years Site (and number of samples) Ring width in master
1235 SK (1) -
1263 SK (1) -
1268 VB (1) +
1347 VB (1) -
1351 SK (1+1) -
1378+- 2 yr GA (1) aver
1415 SK (2) aver
1438 GA (1) -
1500 FA (1) aver
1510 SK (2) -
1514 HU (3) -
1516 GA (2) aver
1527 FA (1) aver
1533 FA (1) -
1554 SK (1) --
1563 FA (1), GA (1) aver
1569 HU (3) aver
1582 FA (1) aver
1590 FA (1) --
1601 VB (2), GA (4) --
1630 HU (2) -
1631 VB (2), GA (1), SK (3) --
1640 FA (2) -
1642 SK (1) -
1652 SK (1) -
1660 ± 2 yr PB (1) aver (1659 --)
1667 GA (3) -
1668/1669 HU (1) aver
1672 SK (1) aver
1683 HU (1) aver
1689 FA (1) aver
1693 FA (6), SU (1), SK (2), HU (1), PB (1) --
1702 PB (1) aver
1713 GA (4), (FA (1) -
1721 HU (1) aver
1729 VB (1), SU (1) -
1740 VB (1) -
1749 VB (1) -
1767 SU (3) aver
1781 FA (4), SK (1) -
1794 SU (2) aver
1798 FA (1) -
1804 VB (3), PB (2) aver
1819 FA (1) aver
1822 FA (3) -
1827/1828 FA (1) +
1830 GA (4) -
1835 VB (1) -
1840 SK (3) +
1846 FA (2) aver
1857 FA (1) aver
1864 SU (1) aver
1872 HU (2) aver
1887 HU (1) aver
1892 SU (1) aver
1910 FA (1) aver
1923 VB (1) +