Landscapelevel variability in historical ... - Wiley Online Library

Journal of Vegetation Science 25 (2014) 386–401

Landscape-level variability in historical disturbance in primary Picea abies mountain forests of the Eastern Carpathians, Romania Miroslav Svoboda, Pavel Janda, Radek Bace, Shawn Fraver, Thomas A. Nagel, Jan Rejzek, s, Jan Douda, Karel Boublık, Pavel Samonil, ch Cada, Martin Mikola Vojte Volodymyr Trotsiuk, kora, Petr Uzel, Jirı Zelenka, Vıt Marius Teodosiu, Olivier Bouriaud, Adrian I. Birisß, Ondrej Sy k & Jirı Lehejcek Sedla

Keywords bark beetle outbreak; blowdown; dendroecology; disturbance regime; forest dynamics; multidimensional scaling; natural disturbance; Norway spruce; old-growth forest; spatiotemporal variability; temperate forest Nomenclature Kubat et al. (2002) Received 18 March 2013 Accepted 29 June 2013 Co-ordinating Editor: John Morgan

Svoboda, M. (corresponding author, [email protected]), Janda, P. ([email protected]), Ba ce, R. ([email protected]), Rejzek, J. ([email protected]), s, M. ([email protected]), Mikola Cada, V. ([email protected]), Trotsiuk, V. ([email protected]), kora, O. ([email protected]), Sy Uzel, P. ([email protected]), Zelenka, J. ([email protected]), k, V. ([email protected]) & Sedla Lehej cek, J. ([email protected]): Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, Praha 6 – Suchdol, Prague, 16521, Czech Republic Fraver, S. ([email protected]): Department of Forest Resources, University of Minnesota, St. Paul, Minnesota, 55108, USA Nagel, T.A. ([email protected]): Biotechnical Faculty, Department of Forestry and Renewable Forest Resources, University of Ljubljana, Vecna Pot 83, Ljubljana, 1000, Slovenia Douda, J. ([email protected]) & Boublık, K. ([email protected]): Faculty of Environment, Czech University of Life Sciences Prague, Kamycka 129, Praha 6 – Suchdol, Prague, 16521, Czech Republic

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Abstract Questions: How have the historical frequency and severity of natural disturbances in primary Picea abies forests varied at the forest stand and landscape level during recent centuries? Is there a relationship between physiographic attributes and historical patterns of disturbance severity in this system?

Location: Primary P. abies forests of the Eastern Carpathian Mountains, Romania; a region thought to hold the largest concentration of primary P. abies forests in Europe’s temperate zone. Methods: We used dendrochronological methods applied to many plots over a large area (132 plots representing six stands in two landscapes), thereby providing information at both stand and landscape levels. Evidence of past canopy disturbance was derived from two patterns of radial growth: (1) abrupt, sustained increases in growth (releases) and (2) rapid early growth rates (gap recruitment). These methods were augmented with non-metric multidimensional scaling to facilitate the interpretation of factors influencing past disturbance. Results: Of the two growth pattern criteria used to assess past disturbance, gap recruitment was the most common, representing 80% of disturbance evidence overall. Disturbance severities varied over the landscape, including stand-replacing events, as well as low- and intermediate-severity disturbances. More than half of the study plots experienced extreme-severity disturbances at the plot level, although they were not always synchronized across stands and landscapes. Plots indicating high-severity disturbances were often spatially clustered (indicating disturbances up to 20 ha), while this tendency was less clear for lowand moderate-severity disturbances. Physiographic attributes such as altitude and land form were only weakly correlated with disturbance severity. Historical documents suggest windstorms as the primary disturbance agent, while the role of bark beetles (Ips typographus) remains unclear. Conclusions: The historical disturbance regime revealed in this multi-scale study is characterized by considerable spatial and temporal heterogeneity, which could be seen among plots within stands, among stands within landscapes and between the two landscapes. When the disturbance regime was evaluated at these larger scales, the entire range of disturbance severity was revealed within this landscape.

Journal of Vegetation Science Doi: 10.1111/jvs.12109 © 2013 International Association for Vegetation Science

Landscape-level disturbance in Picea forests

M. Svoboda et al. Samonil, P. ([email protected]): Department of Forest Ecology, Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Brno, 65720, Czech Republic Teodosiu, M. ([email protected]) & Bouriaud, O. ([email protected]): Forest

Research and Management Institute, Station C^ampulung Moldovenesc, Calea Bucovinei 73b, C^ampulung Moldovenesc Suceava, 725100, Romania

Introduction Natural disturbances strongly influence the dynamics of forest ecosystems (Pickett & White 1985; Turner 2010). Variation in disturbance size, frequency and severity plays an important role in shaping forest structural and compositional heterogeneity at landscape scales (Turner 1987; Fraver et al. 2009; Donato et al. 2012). This heterogeneity, in turn, influences a variety of ecosystem properties and processes, such as nutrient cycling (Turner 1989), carbon pools (Coomes et al. 2012; Seidl et al. 2012), species diversity (Angelstam 1998; Bengtsson et al. 2000; Kuuluvainen 2002; Dittrich et al. 2012) and susceptibility to future disturbances (Bigler et al. 2005). Characterizing the historical range of variability of natural disturbance often requires retrospective studies in primary forests, i.e. forests relatively uninfluenced by human activities. Knowledge of historical natural disturbances also provides the context for gauging the apparent recent increases in disturbance activity (Westerling et al. 2006; Allen et al. 2010; Mitton & Ferrenberg 2012). This issue is especially relevant in temperate Europe’s Picea abies forests, where recent wind and bark beetle disturbances have caused widespread damage (Hais et al. 2009; Jonasova et al. 2010; Lausch et al. 2011; Seidl et al. 2011), raising concerns that human land use and climate change may have altered the disturbance regime in this region. Most previous studies of disturbance regimes in temperate forests have been conducted at the stand level (but see Frelich & Lorimer 1991; D’Amato & Orwig 2008; Fraver et al. 2009). However, understanding the interaction of disturbances, such as severe windstorms and bark beetle outbreaks, requires a landscape-level approach to fully capture the spatial variability inherent in these disturbances. Limitations of previous stand-level studies are in part due to the scarcity of large tracts of primary forest that would permit a landscape-level analysis. In Europe’s temperate zone, for example, many of the remaining primary forests exist as isolated fragments, the result of long-term human land use in this region. However, studying historical disturbances in a number of primary fragments situated within the same landscape or region may reveal a representative characterization of disturbance severity and synchronization over quite large areas (D’Amato & Orwig 2008). Understanding this landscape-level variability is not only important for advancing theories of forest dynamics, but also for ecological forest

Birisß, A.I. ([email protected]): Forest Research and Management Institute, Bd. Eroilor 128, Voluntari Ilfov, 077190, Romania

management, which intends to emulate the historic range of forest structure, composition and processes that result from natural disturbances (Seymour & Hunter 1999; Cyr et al. 2009; Kuuluvainen 2009; Mori 2011). The general objective of this study was to evaluate landscape-level disturbance patterns, using P. abies as our study system. The large number of reported primary P. abies stands in Romania’s Eastern Carpathians (Veen et al. 2010) present an ideal opportunity to study landscapelevel disturbance patterns, using verified dendrochronological methods. This region may hold the largest concentration of primary P. abies forests in Europe’s temperate zone, yet has been little studied with respect to historical disturbance. To this end, we applied dendrochronological methods to a large number of plots over the landscape, thereby providing information about historical disturbances at both stand and landscape levels. Previous stand-level studies for this forest system report a wide range of disturbance regimes throughout central Europe, from small-scale gap dynamics (Szewczyk et al. 2011) to moderate- and high-severity events caused by windstorms and bark beetle outbreaks (Zielonka et al. 2010; Panayotov et al. 2011; Svoboda et al. 2012). We assumed that the synchronization of disturbance evidence from many plots across the landscape suggests large-scale disturbances, while temporally independent disturbance evidence seen only on individual plots suggests localized small-scale events. Further, we suspected that physiographic attributes, such as altitude or slope position, would influence disturbance frequency and severity. Our specific objectives were to: (i) quantify the temporal and spatial pattern of the disturbances, including the range of variability, at the forest stand and landscape level; and (ii) examine the relationship between physiographic attributes and patterns of disturbance severity. Our results will shed much light on the dominant disturbance agents, as well as the variability of the disturbance regime for P. abies forests, and provide important background information for forest management and conservation efforts in the temperate zone of Europe.

Methods Study area We conducted this study in the C alimani and Giumal au Mountains, in the Eastern Carpathian Mountains of


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Romania. The Carpathians are thought to contain the largest tracts of primary forests in Central and Eastern Europe (Veen et al. 2010; Knorn et al. 2012b), including the largest remnants of primary Picea abies forest in temperate Europe, making this region ideal for investigating natural disturbance processes over large spatial scales. We refer to our study areas as ‘primary forest’, in that stands developed under a regime of natural disturbances and show little to no evidence of past human impact, but are not necessarily in an old-growth stage of development. Calimani National Park (hereafter Calimani, 24 000 ha) was officially operative in 2003 (Knorn et al. 2012a,b). It is part of the Calimani Mountains, and the core zone (i.e. strictly protected areas) covers an area of about 16 800 ha. The Nature Reserve Giumal au (hereafter Giumalau) has been protected since 1941, with a size of ca. 310 ha (Lamedica et al. 2011; Teodosiu & Bouriaud 2012). It is part of the smaller mountain range of Giumalau. Primary closed-canopy P. abies mountain forests, the focus of our study, occur on slopes between about 1200 and 1700 ma.s.l. (Popa & Kern 2009; Cenusßa 2010). Picea abies is the dominant tree species, with a lesser admixture of Sorbus aucuparia, and rarely Pinus cembra, Larix decidua, Acer pseudoplatanus and Betula pendula. These forests have a dense understorey dominated by Vaccinium myrtillus, Calamagrostis villosa, Luzula sylvatica and Avenella flexuosa. Plant nomenclature follows Kubat et al. (2002). Average annual temperature in the study region varies from 2.4 to ca. 4.0 °C; precipitation varies between 1100 and 1650 mm annually, and increases with altitude (Food and Agriculture Organization (FAO) Local Climate Estimator New_LocCLim v. 1). Snow is present 139–208 days per year, and contributes up to 500 mm of the total annual precipitation. Bedrock in Calimani is volcanic (andesites) (Seghedi et al. 2005) and crystalinic (phyllite, gneiss) in Giumalau (Balintoni 1996). Soils in the study area are very diverse, including mainly podzols, while cambisols occur very rarely at lower altitudes, leptosols at stony and exposed sites and stagnosols at wet sites (M. Valtera, P. Samonil & K. Boublık unpubl. data).

searched all the available archival information regarding the history of land use in these areas. Historical data indicate the areas selected for study escaped logging in the 18th and 19th centuries and have been protected since this time. Based on our initial field survey and historical information, we selected five stands in the C alimani Mountains, all within C alimani National Park (Fig. 1). These include a stand of ca. 40 ha (C1) and four stands (C2–C5), each ca. 8–12 ha. In the Gium alau Mountains, we selected a 101ha stand within the Gium alau Nature Reserve (G1); the large size of this stand required that we sample at two intensities (see below). The Calimani and Giumalau landscapes, which are separated by ca. 50 km, each include a broad range of geographic variability regarding elevation, slope and aspect; thus, to some degree, they represent the landscape-scale heterogeneity found in the study region. However, a statistical stratification of stands across the various geographic positions was not possible because of the limited distribution of primary forest fragments. Field and laboratory procedures Sample plots were established in each stand using a stratified random design. Using a GIS, a 100 9 100-m or 141.4 9 141.4-m grid was overlain on each stand. Within

Study site selection Potential study sites were first selected using a previous inventory of primary forest remnants in Romania (Veen et al. 2010). Using a GIS, potential P. abies stands were selected. These stands were then located in the field and surveyed for indicators of naturalness (e.g. coarse woody debris in various stages of decay, pit-and-mound topography) and signs of human impact; stands with evidence of past logging and grazing were avoided, as were stands in close proximity to formerly grazed areas. Additionally, we

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Fig. 1. Study area showing the two landscapes (Calimani, Giumalau) and stands (alpha-numeric codes). The Coordinate Reference System WGS84 was used.



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each grid cell, a circular sample plot (1000 m2) was established at a restricted random position (generated in a GIS) using GPS. Plot centres were restricted to the inner 0.25 and 0.49 ha core in each 1-ha or 2-ha cell, respectively. In Calimani, the 40-ha stand (C1) was sampled using a 1-ha grid cell size (40 plots), whereas stands C2–C5 were sampled with a 2-ha grid cell size (total of 21 plots). Sampling in Giumalau covered 101 ha, with a 1-ha grid cell size in the core 41-ha zone and a 2-ha grid cell size in the remaining area (total of 71 plots). In total, 132 plots were established in the six stands (Table 1). In each 1000-m2 sample plot, we labeled all living trees ≥10 cm diameter at breast height (DBH) and recorded their DBH. Using a random number generator, we selected 25 canopy P. abies trees per plot for increment core extraction. Trees with a significant part of the crown projection receiving direct sunlight from above were classified as canopy trees (Lorimer & Frelich 1989). Suppressed trees were avoided because their growth patterns may lack information important for disturbance history reconstruction (Frelich & Lorimer 1991; Splechtna et al. 2005; Svoboda et al. 2012). One core per tree was extracted 1.0 m above the ground level for radial growth analysis and age determination. We also measured the crown dimension of five randomly selected canopy trees per plot. For each tree

crown, two orthogonal axes were measured for the purpose of predicting crown area from DBH. Finally, physiographic attributes were recorded for each plot, including slope, aspect and elevation. The topographic location of each plot was classified based on its slope position, further reported as ‘land form’ (upper slope – value 1, middle slope – value 2, and lower slope – value 3). On plots (58% of the total number of plots) with a high tree density (>500 stemsha1) and homogenous structure, the plot size was reduced to 500 m2. For these plots, where most trees appeared to be of the same age cohort (based on tree size and stand structure), we also decreased the number of increment core samples to up to 15 per plot to decrease the work load. For plots in stand C1, where the stand structure was homogenous (based on tree size and structure), we extracted cores from an additional one to five trees of advanced age, indicated by their bark, branching and crown characteristics, to supplement the disturbance history reconstruction. The location of such trees was restricted to within 50 m of plot borders. Increment cores were dried, secured on wooden mounts and shaved with a razor blade. For cores that missed the pith, the number of missing rings was estimated using Duncan’s (1989) method. The pith was reached in 72.3% of samples, while the remaining 22.3% and 4.7% were

Table 1. Physiographic parameters, number of the plots and basic stand structural characteristics. Stands

Giumalau G1

Calimani C1

Calimani C2

Calimani C3

Calimani C4

Calimani C5

Mean elevation (ma.s.l.) Range of elevation (ma.s.l.) Mean slope (°) Geographic position (E°, N° in WGS84) Size of sample area (ha) Number of plots Mean tree density (Nha1) Mean DBH (cm) Mean height (m) Mean basal area (m²ha1) % suppressed trees (SE) % suppressed projection (SE) % trees showing gap recruitment % trees showing release % of showing multiple release

1434

1556

1626

1484

1557

1558

1249–1606

1468–1657

1599–1653

1415–1549

1505–1601

1512–1598

27 25.4679, 47.4421 101

21 25.2587, 47.1136

38 25.2837, 47.1450

22 25.1928, 47.0663

28 25.2037, 47.0650

20 25.2255, 47.0687

40

8

12

12

10

71 470

40 597

4 368

6 837

6 412

5 432

32.3 23.1 43.9

32.2 23.8 51.3

36.5 19.7 46.4

24.3 20.8 44.6

38.2 24 53.2

39.7 23.7 61

24.0 (13.1)

29.9 (14.4)

3.6 (2.3)

31.0 (8.9)

4.2 (4.6)

8.8 (4.3)

15.0 (10.0)

21.3 (11.3)

2.1 (1.6)

22.5 (7.3)

2.5 (2.7)

4.6 (2.5)

79.6

77.3

79.6

96.7

85.8

85.0

14.9

19.0

17.5

2.7

11.0

12.6

5.5

3.7

2.9

0.6

3.2

2.4


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50% of the boundary line. To be considered a valid release, the elevated growth rate also had to be sustained for at least 7 yrs (Fraver et al. 2009); shorter-term recoveries to prior growth rates were not considered releases. Finally, we discounted apparent releases shown on trees determined to have been in the canopy at the time of the disturbance, as such growth increases would suggest the loss of a neighbouring, not overtopping tree, the focus of our work (see Lorimer & Frelich 1989). Trees were assumed to have been in the canopy if their diameter at the time of the event exceeded 25 cm, which was determined by logistic regression (as above applied to gap recruitment criteria) of canopy and suppressed tree diameters. For a small number of cores, radial growth patterns did not meet the gap recruitment or release criteria described above. The inferred disturbance dates on these samples were categorized as either gap-recruited or released based on their overall radial growth pattern (Lorimer & Frelich 1989). Samples with a declining, flat or parabolic pattern were included into the gap-recruited group and samples with an ambiguous or irregular pattern were categorized as released trees, following Lorimer & Frelich (1989). Finally, because P. abies is shade-tolerant, especially during the juvenile life stage, more than one disturbance may be



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needed to attain canopy status (Lorimer & Frelich 1989). Thus, multiple major releases from the same tree were allowed in the disturbance chronologies. However, from the total number of trees, only 3% showed more than one major release. Moderate releases were included in the chronologies only when no other releases were present on a core. Construction of the disturbance chronologies The number of growth releases and gap recruitment events were converted to total canopy area disturbed in each decade following the approach and rationale of Lorimer & Frelich (1989), thereby producing a disturbance chronology for each plot. This conversion scales the evidence of disturbance according to the tree’s current crown area; it is required because the magnitude of disturbance inferred from small-crowned trees differs from that inferred from large-crowned trees. Current crown areas were predicted from current DBHs using a regression equation developed from these same data. We calculated a composite disturbance chronology, based on all disturbance evidence, for Calimani and Giumalau separately. However, because the composite is based on a large number of plots, each with a unique disturbance history, it represents only the most general history of disturbance. We therefore constructed additional disturbance chronologies, one for each stand, based on plots within stands (as opposed to trees within plots). As such, these chronologies express the proportion of plots (within stands) showing light (to 20% canopy disturbed), moderate (20.1–40%), heavy (40.1–60%) or extreme (>60%) disturbance severities within each decade (categories based on Frelich & Lorimer (1991), except for the highest category, added here (see example of plot level disturbance chronology in Appendix S1). The composite chronologies were truncated when the total number of trees dropped below 20%, while the plot level disturbance chronologies were truncated when the number of trees in a decade dropped below 20% of the total number of trees per plot. The last decade in the analyses was 1980–1990 because of the length of time needed for trees to respond to disturbance events. Finally, because we ultimately found that (1) gap recruitment was the most common form of disturbance response and (2) recruitment periods were quite protracted, spanning decades following disturbance, we sought a means of presenting the disturbance evidence that better captures these long periods of gap recruitment. To this end, we summed plot-level disturbance evidence over three consecutive decades (assuming protracted recruitment as the explanation for these lengthy periods). Thus, for each plot, the three-decade running

sum was used to refine our inferences about past disturbance. Periods with sums >60% or 80% canopy disturbance were selected. We take the first decade in each of these selected periods to represent the timing of the disturbance. Plots with a proportion over 60 and 80% were tallied. When a plot reached 80%, it was not included in the 60% class. The periods with the highest number of plots meeting the 60 and 80% criteria are then shown for each stand in Table 2. Longer periods of tree establishment for disturbance severity assessment have similarly been used in sub-alpine forests of other regions. For example, Veblen et al. (1994); Kulakowski & Veblen (2006) and DeRose & Long (2012) used a 40-yr period of tree establishment to identify stand-replacing fires in the Rocky Mountains, USA. Landscape-scale ordination analysis of the disturbance chronologies To facilitate interpretation of the disturbance history at the landscape level, we performed non-metric multidimensional scaling (NMDS), which is a nonparametric, unconstrained ordination technique. NMDS is more suitable for analysing disturbance chronologies than is principal components analysis, because decadal disturbance rates are typically non-normally distributed and discontinuous in scale (D’Amato & Orwig 2008). The NMDS was based on a plot-by-decade matrix (132 plots, 22 decades), with the proportion of canopy area disturbed occupying matrix cells. Ordinations were performed in R 2.12.0 (R Foundation for Statistical Computing, Vienna, AT) with the Table 2. Proportion of plots with extreme disturbance rates over 60 and 80% in three consecutive decades in individual stands. For each plot, a running window summing the disturbance rates over three consecutive decades was calculated, and plots summing over 60 and 80% were selected (see Methods). The periods with highest number of plots meeting the 60 and 80% criteria are shown for each site. Number of plots with heavy and extreme disturbance rate was 70 from a total of 132 plots. Disturbance severity

60–80% Percentage of plots

Important decades

Percentage of plots

Important decades

Giumalau G1

25

11

Calimani C1

30

1800–1830 1900–1930 1950–1980 1840–1870 1900–1930

1800–1830 1900–1930 1950–1980 1840–1870 1900–1930

Calimani C2 Calimani C3 Calimani C4

0 0 50

Calimani C5

60


Over 80%

1800–1830 1900–1930 1820–1850

50 0 100 0

1930–1960

0

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‘vegan’ library and ‘mgcv’, using the ‘metaMDS’ function, along with the Bray–Curtis similarity metric and two dimensions. Physiographic attributes (land form, slope steepness, slope aspect, altitude) were then passively projected onto the ordination space to examine their relationship with disturbance patterns, using the ‘envfit’ function. This method produces vectors that represent the most rapidly changing direction for a given variable and have lengths proportional to the strength of the correlation between it and the ordination. Significance was calculated using 9999 permutations. Aspect values were transformed using the following formula: aspect = cosine (45-azimuth degrees) + 1 (see D’Amato & Orwig 2008). This formula transforms values so as to be maximal on northeastern slopes and minimal on southwestern slopes. To quantify the severity of the disturbance regime per plot we created a disturbance index (DI), based on the Shannon index as follows, DI ¼

N X

pi logpi

ð1Þ

i¼1

where pi is the proportion of canopy area disturbed belonging to the ith decade and N is the number of decades. The maximum theoretical value of DI reaches 0 (for 100% canopy area disturbed in a given decade, i.e. highseverity disturbance regime), while the minimum theoretical value for 20 decades reaches ca. 3 (indicative of a low-severity disturbance regime with similar canopy area disturbed in all decades). The DI was likewise included into the ordination using a function that creates a DI surface, shown with contour lines in ordination space. Spatial aspects of disturbance Spatial aspects of disturbance, synchronization of past disturbances and DI values among plots were analysed using matrix correlation via Mantel’s test. The test evaluates the correlation between two plot-by-plot matrices (132 plots, 22 decades), one containing the geographic distance between all pairs of plots, another containing a measure of similarity between plots based on disturbance metrics. Matrix correlation allowed us to assess the importance of plot location, specifically if plots close to each other share similar disturbance histories and metrics. If we found that both synchronization of past disturbances and DI values were clustered in space, we tested if similarity in DI was confounded by similarities in the synchronization of past disturbances using a partial Mantel test. The Mantel test was conducted in R 2.12.0 under library ‘vegan’ and ‘ade4’. The Bray–Curtis distance was used as a similarity measure among plots based on 9999 replicates in a Monte Carlo test. Tests were performed only for stands with

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adequate numbers of plots, i.e. Giumalau and Calimani stand C1.

Results Forest structure Picea abies dominated the forest canopy on all plots, representing ca. 99% of plot basal area overall. Data pooled from all plots revealed a living tree basal area of 47.3 m2ha1, and density of 518 treesha1 (the two landscapes did not differ appreciably). Less than 1% (22 of ca. 3500) of trees exceeded 300 yrs in age, with the maximum at 380 yrs. Structure and general conditions for each stand are shown in Table 1. Disturbance histories Composite disturbance chronologies for the Giumalau (N = 71 plots) and Calimani (N = 61 plots) are shown in Fig. 2. Of the two growth rate criteria used to assess past disturbance (growth releases and gap recruitments), gap recruitment was by far the most common for all stands (Table 1), representing 80% of disturbance evidence overall. Even during disturbance peaks gap recruitment events remained prevalent, typically being more numerous than releases (Fig. 2). Particularly noteworthy is a disturbance peak beginning in ca. 1810 at Giumalau that consists almost exclusively of gap recruitment events (Fig. 2). The composite disturbance chronology in Fig. 2, which combines a large number of plots, necessarily masks plotto-plot variability. Therefore we created a set of chronologies in which the proportion of plots, per stand, experiencing various disturbance severities is expressed for each decade (Fig. 3). These chronologies better capture the temporal variability in disturbance severity and thus support the result of the ordination analysis. Both Calimani C1 and C3 showed extreme severity disturbance (>60% of trees showing disturbance evidence) on a portion of plots, although in different decades for each stand (Fig. 3). Highseverity disturbances in Calimani C1 were also confirmed with evidence of disturbance among the trees of advance age sampled outside plots (Appendix S2). These trees showed peaks in disturbance that clearly matched those in the C1 disturbance chronology. In contrast, Calimani stands C2, C4 and C5 had lower disturbance rates, with most plots recording light disturbance (60% (extreme). For each stand, the chronologies were truncated on the plot when the number of trees fell below 20% of the total. The vertical lines show the periods with extreme disturbance rates from Table 2.

Calimani stand C3, all plots experienced moderate to extreme disturbance, which we interpret as a standreplacing event, with the disturbance patch at least as large as the stand (12 ha; data not shown). Similarly, an apparent cluster of plots registering moderate to extreme disturbance between 1800 and 1830 can be seen at Giumalau; however, the lack of data from many plots during that time limits a complete interpretation of the pattern. In contrast, the spatial pattern during periods of light to moderate disturbances was less pronounced. For example, in Calimani stand C1, the period 1840–1870 showed a

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variable spatial pattern of disturbance over a 14-ha area (i.e. 14 plots), and at Giumalau the spatial pattern was similarly variable during the two moderate peaks in disturbance activity (1900–1930, 1950–1980; Fig. 4 but see also Fig. 5 and Appendix S3). The ordination analysis (Fig. 5, Appendices S3–S6) of the disturbance chronologies confirmed marked variability of the disturbance histories. At Calimani, the ordination explained 98% (non-metric fit) of the variance in the raw data (ordination stress = 0.15). The ordination analysis showed somewhat distinct clusters of plots in ordination



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space, the result of similar histories of high-severity disturbance, particularly evident for sites C3 and C1 (Fig. 5). However, there was no clear evidence for synchronization in disturbance histories among individual sites in Calimani. The clusters of plots in C3 and partly C1 were located in ordination space with high values of the DI. When projected onto ordination space, the DI increased in all directions from low values in the centre of ordination space (minimum possible value 3 for low disturbance severity), such that plots with higher DIs were located away from this central space (maximum possible value 0 for high disturbance severity). DIs for Calimani showed significant correlation with ordination axes (P < 0.001, r2 = 0.42). A large proportion of plots belonged to the zone with DI values > 1.5, indicating a history of relatively high disturbance severity. At Giumalau, plots form a broad distribution in ordination space, without distinct groups reflecting similar disturbance histories (Appendix S3). This ordination explained 96% (non-metric fit) of the variance in the raw data (ordination stress = 0.21). Ordination analysis revealed only partial effects of physiographic attributes on plot disturbance histories. In Calimani, both plot altitude (r2 = 0.37, P < 0.001) and land form (r2 = 0.35, P < 0.001) were correlated with ordination axes. Altitude was negatively and land form positively correlated with disturbance severity (Fig. 5), suggesting that plots with higher altitude showed a lower DI, and conversely, plots on lower slope positions showed a higher DI. In Giumalau, only altitude (r2 = 0.29, P < 0.001) was correlated with ordination axes, with no clear relationship with DI (Appendix S3). No other physiographic attributes were significantly (P > 0.05) correlated with ordination axes in either Giumalau or Calimani.

Discussion The historical disturbance regime revealed in this comprehensive study (ca. 3500 increment cores collected from 132 plots) is characterized by considerable spatial variability, which could be seen among plots within stands, among stands within landscapes and between the two landscapes. A wide range of disturbance severities was also documented, ranging from periods of light disturbances (presumably tree-fall gaps) to periods of heavy to extreme disturbance (including stand replacement), the latter showing clustering up to 20 ha. In the discussion that follows, we first consider in detail our interpretation of the historical disturbance regime in this region and then we address methodological challenges encountered in studies such as this. Landscape-level disturbance history interpretation The spatial and temporal variation in disturbance severity documented in this study yields new insight into landscape-scale disturbance processes in mountain P. abies forests of Europe’s temperate zone. Analysis of disturbance severity complemented by NMDS ordination revealed a gradient of disturbance severity across the landscape. In part, our results challenge the traditional model of P. abies forest dynamics in Europe’s temperate zone, which places emphasis on small-scale, canopy gap processes (Korpel’ 1995). More than half of the 132 plots showed evidence of past extreme severity disturbance based on the analysis that summed disturbance evidence over three consecutive decades (see Table 2). By comparison, if we base our interpretation on the decadal disturbance chronology, 12% of the plots had evidence of extreme severity disturbance. The

Fig. 4. Maps of disturbance rates for Giumalau (G1) and Calimani (C1) for periods with the most severe disturbances. To capture prolonged recruitment periods, disturbance rates were summed in three consecutive decades for selected periods. The size of the circle represents disturbance class: 0%, 0.1– 20%, 20.1–40%, 40.1–60%, >60% canopy area disturbed per plot. Crosses represent plots without information due to younger tree ages. Journal of Vegetation Science Doi: 10.1111/jvs.12109 © 2013 International Association for Vegetation Science

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Fig. 5. Non-metric multidimensional scaling (NMDS) ordination of disturbance histories from the Calimani landscape, based on a matrix of decadal disturbance rates. Individual stands are designated in the ordination (see Table 1). Isolines show the gradient in the disturbance index (DI, see Methods). The DI increases in all directions from a low in the centre of ordination space (minimum possible value 3 for lowdisturbance severity), such that plots with higher DIs were located away from this central space (maximum possible value 0 for high-disturbance severity). Vectors show passively projected physiographic characteristics of plots and the DI index, where only those with r² > 0.1 are shown. The vector orientation shows the direction of the gradient, and its length is proportional to the correlation between the variable and the ordination.

occurrence of past high-severity disturbance was especially evident in the Calimani landscape, where evidence of stand-replacing events was found over areas as large as 20 ha. Similarly, recent work in other primary P. abies forests in Europe’s temperate region reported evidence of wind disturbance, ranging in size from several to hundreds of hectares (Svoboda & Pouska 2008; Svoboda et al. 2010, 2012; Zielonka et al. 2010; Panayotov et al. 2011; Cada et al. 2013). This study provides additional evidence that stand-replacing disturbances are part of the natural disturbance regime in P. abies forests. Although we found strong evidence of stand-replacing disturbances within stands, there was less evidence that such events were synchronized throughout the entire landscape. Several periods (e.g. from 1800 to 1830 and 1900 to 1930) with high-severity disturbance were common to only three out of six study sites. Within stands, however, plots that recorded damage during these periods of high-severity disturbance were often clustered in space (see Fig. 5). Previous studies have documented clustered

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patterns of high-severity disturbances in landscapes where windstorms were the most severe disturbance agent (Frelich & Lorimer 1991; Foster & Boose 1992; Kulakowski & Veblen 2002; D’Amato & Orwig 2008; Fraver et al. 2009). The prevalence of wind as a disturbance agent could potentially explain lower synchronicity in standreplacing events in this study, given its patchy nature within a landscape (Panayotov et al. 2011). Nevertheless, even in mountainous regions with complex topography, windstorms can cause stand-replacing disturbances over large continuous areas up to thousands of hectares (Kulakowski & Veblen 2002; Zielonka et al. 2010). Furthermore, it is well documented that damage caused by windstorms can vary considerably across landscapes, due to variability in the storm itself, as well as interactions with forests and landscape features (e.g. Foster & Boose 1992; Frelich & Reich 1995; Kulakowski & Veblen 2002; Panayotov et al. 2011; Stueve et al. 2011). The relationship between physiographic features and the spatial pattern of disturbance was analysed without straightforward results. Altitude and land form were correlated with the ordination gradient, but only in Calimani. The low number of plots covering a range of physiographic conditions over the landscape is likely a limitation of this aspect in our study. Several other studies have found a relationship between disturbance history and topographic exposure, especially when focusing on the effects of windstorms (Sinton et al. 2000; Kulakowski & Veblen 2002; D’Amato & Orwig 2008; Panayotov et al. 2011). In the Rila Mountains of Bulgaria, for example, Panayotov et al. (2011) report increasing likelihood of wind-throw on particular slope aspects and with increasing altitude in P. abies forests. Many of the plots throughout the studied landscape did not show evidence of severe disturbance, but rather a history of low- and intermediate-severity disturbances. A good example is the 1950–1980 period in Giumalau, when a significant number of plots showed light to moderate disturbance with considerable spatial variation throughout the stand (see Fig. 4). Tree mortality during periods of light and moderate disturbances could result from several processes. For example, moderate severity wind damage or bark beetle outbreaks can cause a range of mortality patterns at the stand scale, from scattered, single trees to small patches of catastrophic damage (Woods 2004; Worrall et al. 2005; Nagel & Svoboda 2008). Fungal colonization of old trees could also cause direct mortality or indirectly increase vulnerability to wind or bark beetle attack (Worrall et al. 2005). Based on our results, moderate severity disturbance events that remove 20–40% of the canopy in a given decade affected a similar portion of the landscape, as did extreme disturbances. Similarly, Stueve et al. (2011) suggest that the



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Fig. 6. Historical disturbances in Romania for the period covered in this study, summed per decade (as midpoints). We include here only severe disturbances with a regional impact on mountain forests. In many cases no details were available regarding the exact location and extent of damage. Information compiled from Avram (1983), Ichim (1988) and Popa (2007, 2008).

aggregate damage of intermediate severity disturbances is similar to that of large, catastrophic events that occur very infrequently in forested landscapes of the Great Lakes region, USA. Taken together, these findings suggest that periodic disturbances that cause intermediate severity damage at stand scales may be an important component of the disturbance regime in temperate forest regions. Our results therefore highlight the considerable variability in disturbance severity in both space and time over the studied landscape. That is, we documented a continuum of canopy damage ranging from small-scale gap dynamics, to localized damage from intermediate severity disturbance, and to stand-replacing events over large areas of individual stands. Notably, previous studies in P. abies forests of Europe’s temperate zone have reported a range of disturbance severities at the stand level. For example, some studies have reported gap dynamics (Holeksa & Cybulski 2001; Panayotov et al. 2011; Szewczyk et al. 2011), while others have reported stand-replacing disturbances (Zielonka et al. 2010; Panayotov et al. 2011; Svoboda et al. 2012). However, when disturbance patterns are evaluated at the landscape scale, as in this study, the entire range of disturbance severity can be found within a given landscape. This heterogeneity in disturbance severity at local and regional scales has also been documented in temperate forests of North America, where large areas of primary forests occur (e.g. Frelich & Lorimer 1991; Foster & Boose 1992; Veblen et al. 1994; Bergeron 2000; D’Amato & Orwig 2008; Fraver et al. 2009).

Disturbance agents: windstorms or bark beetle outbreaks Because dendrochronological methods alone cannot identify the agents of historical disturbance pulses documented in this study, we searched all available historical archives for information on the occurrence and severity of disturbances during the last 200 yrs in the study region (Fig. 6, Appendix S7). Historical data suggest that windstorms and bark beetle outbreaks were the most likely causes of the disturbance peaks found in this study. Strong windstorms that caused widespread damage were reported throughout the time period covered with our disturbance chronologies, and were more frequent than reported bark beetle outbreaks, which suggest that wind may have been a more important disturbance agent during the past two centuries. (Fig. 6, Appendix S7). Peaks in disturbance severity detected in this study (1800–1830, 1820–1850, 1840–1870, 1900–1930 and 1950–1980) coincided with the disturbances reported in the historical data. However, because of the limited geographic precision of the reported historical disturbances, the connection between individual windstorms and bark beetle outbreaks is difficult to make. Another limitation is that the reliability of historical data decreases further back in time. Nevertheless, there is clear evidence that strong windstorms occur in the region nearly every decade, often several times during a given decade. Consistent with this finding, recent work suggests that Calimani and Giumalau are located within regions of Romania with the highest occurrence and risk of wind disturbance (Popa 2007).


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Methodological considerations Our analyses highlighted two important methodological issues related to the reconstruction of past disturbances. The first relates to gap recruitment, which was the dominant pathway of canopy accession in our study; ca. 80% of trees showed this pattern (see Fig. 2, Table 1). Similar patterns were reported by Svoboda et al. (2012) and Panayotov et al. (2011) in other P. abies forests in Europe’s temperate zone. These findings have important implications for disturbance history reconstruction. Specifically, relying solely on releases from suppression to reconstruct past disturbances could potentially underestimate disturbance rates. A second methodological concern is associated with protracted periods of gap recruitment following disturbances. Several previous studies suggest that recruitment of P. abies regeneration might span several decades following disturbance (Holeksa et al. 2007; Panayotov et al. 2011; Szewczyk et al. 2011; Svoboda et al. 2012). In addition to the slow growth of P. abies seedlings and saplings in mountain environments, there are a number of possible explanations for the prolonged recruitment period seen in the disturbance reconstructions. This pattern could be the result of successive canopy disturbances over several decades, a combination of advance regeneration and newly established seedlings, a lack of sufficiently decayed dead wood for seedling establishment, or factors that influence the survival and growth of regeneration, such as browsing, late frost and heavy snow or ice damage in postdisturbance cohorts. In closed-canopy stands with little to no advance regeneration, seedling establishment and recruitment following stand-replacing disturbance could be particularly slow (Rammig et al. 2006; Svoboda et al. 2012). Recognizing these long periods of post-disturbance recruitment is important for interpreting disturbance histories. That is, quantifying disturbance severity by decade, which is the most common approach (Frelich & Lorimer 1991), would underestimate disturbance severity in cases when post-disturbance regeneration occurs over several decades. Similarly, long establishment periods for disturbance severity assessment have been used in sub-alpine forests in several other regions (Veblen et al. 1994; Kulakowski & Veblen 2006; DeRose & Long 2012).

Conclusions Of the two growth rate criteria used to assess past disturbance, gap recruitment was by far the most common, representing 80% of disturbance evidence overall. Importantly, this recruitment occurred over quite protracted time periods, which spanned at least three decades following disturbance. We identified a range of disturbance severities over the landscape, including periods with the

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stand-replacing events, as well as periods with low- and intermediate-severity disturbances. More than half of the plots across the studied landscape experienced high- to extreme-severity disturbances, although they were not always spatially and temporally synchronized across stands and landscapes. Plots indicating high-severity disturbances were often clustered in space; while this tendency was less clear for moderate- and low-severity disturbances. Standreplacing disturbances were detected in areas up to 10–20 ha, and were likely caused by windstorms. Historical evidence suggests that windstorms played an important role in shaping these forests, while the role of bark beetle outbreaks remains unclear. Intermediate- and low-severity disturbances also played an important role over a large part of the landscape, highlighting the considerable spatial and temporal variation in disturbance severity throughout the landscape. Thus, when the disturbance regime was appropriately evaluated at the landscape scale, as in this study, the entire range of disturbance severity was revealed within this landscape.

Acknowledgements This study was supported by Czech Science Foundation project GACR P504/10/1644. J. Rejzek, M. Mikol as and V. Trotsiuk were supported by the Czech University of Life Sciences, student project CIGA 20114310. We would like Streer, S. Kosobud, K. to thank M. Valtera, J. Lehejcek, C. Svobodov a, M. Reitschmiedov a, L. Mat ej u, M. Cern ıkov a and C. Voln y for assistance in the field. Comments by anonymous reviewers have greatly improved the manuscript. We thank the C alimani National Park authorities, a and local foresters, for administrative especially E. Cenusß support and assistance in the field.

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Supporting Information Additional supporting information may be found in the online version of this article: Appendix S1. Examples of plot-level disturbance chronologies. Appendix S2. The disturbance history reconstruction of old canopy trees in stand C1. Appendix S3. Non-metric multidimensional scaling (NMDS) ordination of disturbance histories from the Giumalau landscape, based on matrices of decadal disturbance rates. Appendix S4. Maps showing the altitude of plots in Giumalau (G1) and Calimani C1. Appendix S5. Maps of disturbance index (DI) values for plots in Giumalau (G1) and Calimani (C1). Appendix S6. Land form maps of plots in Giumalau (G1) and Calimani (C1). Appendix S7. Historical disturbances (windstorm and bark beetle outbreak) in Romania for the period covered by this study.


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