Patch mosaic of developmental stages in central European natural forests along vegetation gradient

Forest Ecology and Management 330 (2014) 17–28

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Patch mosaic of developmental stages in central European natural forests along vegetation gradient Kamil Král a,⇑, Sean M. McMahon b, David Janík a, Dušan Adam a, Tomáš Vrška a a b

Department of Forest Ecology, The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Lidická 25/27, CZ-602 00 Brno, Czech Republic Smithsonian Institution’s Forest Global Earth Observatory, Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, MD 21037-0028, United States

a r t i c l e

i n f o

Article history: Received 5 February 2014 Received in revised form 12 June 2014 Accepted 15 June 2014

Keywords: Developmental stage Stand mosaic Patch pattern Mean patch size Forest dynamics Spatial analysis

a b s t r a c t The shifting mosaic of patches in different phases of forest development is a widely used framework for describing stand dynamics, structure and biodiversity in European temperate forests. In spite of the common application of patch mapping of developmental stages/phases, an objective and quantified evaluation of patch mosaics has been missing. This approach identifies patches of forest stand according to a developmental trajectory, from Growth, through an Optimum stage to Breakdown. Here we present the first attempt to compare quantitative and qualitative characteristics of patch mosaics of stand developmental stages using three decades of extensive data in five study sites along a vegetation gradient. We do this using the same, observer independent method based on an artificial neural network classifier. We also used the historical stem position datasets to evaluate the change of mosaic characteristics in time. Resulting patch patterns were analyzed by standard mosaic metrics commonly used in landscape ecology, evaluating area, shape, aggregation and connectivity of patches. The mean patch size of the mosaic of four developmental stages showed a relatively narrow range of 570–800 m2 in all study sites and censuses. The shape of patches in all sites and years had no significant differences, and the aggregation of patches of the same type was similar in all sites at the mosaic level. Conversely, we did find some stage-specific patterns. For example, the Growth stage was usually the most abundant (covering 25–50% of the stand), and had the highest mean patch size, ranging between 590 and 2800 m2. The Growth stage patches also had the most complex shapes. On the contrary, the Breakdown stage usually had the opposite values, forming constantly small (250–720 m2), simple and scattered patches in the mosaic. These basic traits were found in all study sites and were stable in time. We also found some common trends in the dataset, such as increasing mean patch size of the Breakdown stage along the altitudinal vegetation gradient. The complex Steady State stage was generally more abundant than expected according to results of other studies and thus might indicate processes that have not been well described in previous, subjective, applications of the patch mosaic paradigm. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction The European approach to the study of forest dynamics has largely been based on the patch dynamics model introduced by Watt (1947), which is characterized by a sequentially shifting fine-scale mosaic of patches in different phases of a successional cycle. Watt’s seminal idea of patch dynamics developed into two recent concepts of forest dynamics description: the gap phase concept (e.g. Brokaw, 1982; Runkle, 1981) and the patch mosaic concept that gave rise to several verbal models of the forest cycle (e.g. Emborg et al., 2000; Koop, 1989; Korpel, 1995; Leibundgut, 1959; Mayer ⇑ Corresponding author. Tel.: +420 605 205 086; fax: +420 541 246 001. E-mail address: [email protected] (K. Král). http://dx.doi.org/10.1016/j.foreco.2014.06.034 0378-1127/Ó 2014 Elsevier B.V. All rights reserved.

et al., 1976; Zukrigl et al., 1963). The gap phase model focuses on small disturbances, while the patch mosaic models distinguish particular stand developmental stages and/or phases. The patch mosaic models have been criticized as being too simplistic (Gratzer et al., 2004; Podlaski, 2008) and were questioned by quantitative spatial analyses (Paluch, 2007; Szwagrzyk and Szewczyk, 2001). On the other hand, both basic assumptions of these patch mosaic models – the sequence of stand developmental stages/ phases and significant and persistent patchiness are found in forest dynamics simulations (e.g. Huber, 2011a; Rademacher et al., 2004). Still, the concept is recently accepted and widely used for description of forest structure and dynamics by many, in particular European, authors (e.g. Diaci et al., 2011; Heiri et al., 2012; Huber, 2011b; Kucbel et al., 2012; Schütz and Saniga, 2011; Winter and

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Brambach, 2011). The concept is also used as a framework for biodiversity assessment (e.g. Boncina, 2000; Winter and Brambach, 2011) and nature conservation (Bobiec et al., 2000); In particular the Leibungut’s model (Leibundgut, 1959, 1982), Mayer’s model (Mayer, 1976) and the Korpel’s model (Korpel, 1982, 1995) gained considerable popularity. In spite of the common application of these models in practical mapping of stand developmental stages/phases, quantitative evaluation of resulting patch patterns has largely been ignored, leaving the potential for these models incomplete. The exceptions are occasionally reported mean patch size and/or proportion of individual stages/phases in the mosaic. These values vary greatly from ca 600 m2 to about 5000 m2 for the mean patch size of the mosaic (Christensen et al., 2007; Drossler and Meyer, 2006; Peterken, 1996); the stages/phases representation vary from ca 2% to 20% for breakdown phases and from about 10% to ca 50% for growth and optimum phases (Drossler and Meyer, 2006; Emborg et al., 2000; Koop and Hilgen, 1987; Peterken, 1996). Any direct comparison between these metrics, however, is troubled by the fact that the patch mosaics were mapped by various authors using different number of classes, nomenclature and criteria. In this context a study of Winter and Brambach (2011) is very illustrative: They tested consistency of three existing and well-described mapping methods in identical forest stands and found that in approximately two thirds of the cases the three considered methods did not agree on a stage/phase. Besides the different criteria and nomenclature of various mapping methods, in any method that employs some expert judgment or field estimates, also the issue of observer’s subjectivity may play an important role. Developing a more rigorous comparative approach to the analysis requires an objective and repeatable method for delineation of stand patches and assigning these patches to particular developmental stages/phases. Its absence is probably the reason why any extensive analysis of the patch mosaics from different study sites or even forest types have been missing so far. Standovár and Kenderes (2003) reviewed the common weak points of all traditional differentiation and mapping approaches such as: (i) the incomplete compatibility of systems developed by different authors; (ii) the loose definition of the categories used; (iii) the different spatial resolution (grain size) according to the system used; and (iv) the observer-dependent recognition of stages and phases in the field that might cause problems for long-term observations. Maps showing the spatial distribution of developmental phases are thus generally subjective and not reproducible (Commarmot et al., 2005). Several approaches for objectified field-mapping have evolved. Some of them make use of a regular grid that divides a study area into numerous square subplots, where a rule-based classification system is applied (e.g. Drossler and Meyer, 2006; Tabaku, 2000; Winter and Brambach, 2011). An advantage of these ‘grid’ approaches is the ability to preserve the spatial scale (grain size) of mapping. Still, the observer’s subjectivity is the sticking point of comparability of the field-mapping results, even based on the same phase’s concept and mapping method. These challenges were addressed by the GIS based method introduced by Král et al. (2010), where the developmental stages and phases are identified objectively from stem maps using an Artificial Neural Network (ANN). This approach ensures that the same stand structures (observed through the local presence of living and dead trees of given DBH) will always be classified and mapped in the same way. The complete reproducibility of mapping is undoubtedly one of the major benefits of this method. It may be applied to any stem position data set that includes both living and dead trees, thus offering an objective unifying concept for the inter-comparison of numerous studies from different study sites. This paper presents the first attempt to compare quantitative and qualitative characteristics of the patch mosaics of stand

developmental stages, which were mapped in five study sites along vegetation gradient by the same, observer independent method. In order to evaluate also the stability/changeability of the mosaic characteristics in time, the stages were mapped three times in a period of about 35 years (i.e. in 70s 90s and 00s) using historical stem position datasets. General hypotheses and specific questions: The observed patch patterns in spontaneously developing forests may be regarded as ‘footprints’ left by the past action of space–time processes (Fortin et al., 2002; Perry et al., 2002). Analyzing and comparing these patch patterns in different study sites along a vegetation gradient thus might help to understand forest dynamics and their common traits and differences in different forest types. In European temperate forests, strong winds generally act as a major disturbance type, while hurricanes and typhoons known from North American and Southeast Asian temperate forest are absent, as well as large-scale fires known from boreal forests (Fischer et al., 2013). This results in disturbance regime typical of frequent fine-scale and episodic intermediate disturbances (Nagel et al., 2006; Splechtna et al., 2005; Šamonil et al., 2013), where disturbed sites are usually re-colonized by highly competitive, shadetolerant ‘climax’ tree species. Moreover, due to relatively low tree species diversity common for all European temperate forests, the variety of successional trajectories following disturbances is limited. We therefore hypothesize that the major mosaic traits will be comparable across all study sites. On the other hand, along increasing altitude and related climate and vegetation gradients, other disturbance types (e.g., snow and ice breakage and bark beetle) are increasingly relevant (Fischer et al., 2013). This, together with changing species composition including increasing proportion of conifers, especially Picea abies, may lead to gradual changes in some patch mosaic traits and their long-term dynamics. We therefore hypothesize that some patch patterns differ along a vegetation gradient. Finally, ecological processes such as disturbance, colonization, competitive thinning, growth, and senescence typical for particular forest developmental stages/phases operate at different spatial and temporal scales (Bormann and Likens, 1979; Oliver and Larson, 1990). Many of these processes are key components of standdynamics, and would therefore expected to coincide with developmental stages. Because of this, we further hypothesize that patch patterns differ for particular developmental stages and that stage-specific patterns of forests will be evident. To test these hypotheses, we will answer the following questions: (1) What is the size distribution of patches in deciduous and mixed central European temperate forests? (2) What are the qualitative characteristics (e.g. patch shape and spatial distribution) of these patch mosaics? (3) Are the patch mosaic traits of different forest types along the vegetation gradient comparable or do important differences exist? (4) Are the patch patterns specific to particular developmental stages across different forest types? (5) Are the observed patch patterns stable in time? 2. Materials and methods 2.1. Study sites The five study sites are located in three different biogeographical subprovinces (Neuhäuslová et al., 1998) along altitudinal and related climate and vegetation gradient ranging from 150 to 1110 m a.s.l. The vegetation gradient is thus represented by four central European forest types including Pannonian alluvial

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hardwood forests (Cahnov, Ranšpurk), Carpathian fir-beech forests (Salajka), Hercynian spruce–beech dominated forest Zˇofín and mixed mountain beech–spruce forest of Boubín (Table 1). All of them are strict forest reserves left to spontaneous development in the long-term. 2.1.1. Pannonian alluvial hardwood forests Alluvial hardwood forests are represented by Ranšpurk (48°400 N 16°560 E) and Cahnov–Soutok (48°390 N 16°560 E) national nature reserves, further referred to as Ranšpurk and Cahnov study site respectively. They are located at the confluence of the Morava and Dyje rivers, in the south-eastern part of the Czech Republic belonging to the Pannonian biogeographical province. The elevation above sea level ranges from 150 to 155 m. The entire area is an alluvial floodplain, with undulating sand dunes rising 0.5– 3.0 m above the surrounding area. The geological basement consists of recent Holocene sandy and clayey-loam sediments on fluvial gravels. Soils can usually be classified as Endogleyic Fluvisols Humic and Eutric, Epigleyic Fluvisols Humic and Eutric, or – rarely, on sandy materials – Haplic Arenosols (Michéli et al., 2006). The formation of soils was affected by regular inundations that brought flood sediments. Mean annual temperature is 9.3 °C and mean annual precipitation is c. 517 mm. According to the Braun-Blanquet approach (Braun-Blanquet, 1921), plant communities at the sites are generally classified in the associations Fraxino pannonicae-Ulmetum and Fraxino pannonicae-Carpinetum (e.g. Unar and Šamonil, 2008). Stand structure is reach, dominant and co-dominant canopy trees consist mainly of Quercus robur L. and Fraxinus angustifolia Vahl., but also by Carpinus betulus L., Acer campestre L., Tilia cordata L. and other species, which also form the understory. More detailed description of stand structure including species composition and diameter distributions give Table 2 and Fig. 1; see also Vrška et al. (2006). The localities have been left to spontaneous development, including no removal of deadwood since the beginning of the 1930s (Vrška et al., 2006). 2.1.2. Carpathian fir-beech forests The study site of Carpathian fir-beech forests – Salajka national nature reserve (hereafter Salajka) – lies in the Outer Western Carpathians in the Czech Republic, on the border with Slovakia (49°240 N 18°250 E). The elevation above sea level ranges from 715 to 815 m (Table 1). The terrain is characterized by slopes of various inclinations (up to 20°) and exposures. Geologically, the entire area belongs to the flysch zone of the Western Carpathians, formed of Godulian Upper Cretaceous sandstones. Prevailing soils are siltloam, loam or occasionally clay-loam Haplic Cambisols (Driessen et al., 2001; Michéli et al., 2006). Mean annual total precipitation is 1144 mm, and mean annual temperature is 5.4 °C (Tolasz et al., 2007). The forests usually belong to the Dentario enneaphylli-Fagetum and Dentario glandulosae-Fagetum associations (Ellenberg, 1996; Šamonil and Vrška, 2007). Fagus sylvatica L. and, to a lesser degree Abies alba Mill. and Picea abies (L.) Karsten dominate the tree species composition. Thorough description of stand structure including species composition and diameter

distributions give Table 3 and Fig. 1. The sites have been strictly protected since the mid-1930s, including no felling or removal of deadwood. 2.1.3. Hercynian sub-montane and montane forests Zˇofín and Boubín national nature reserves (hereinafter Zˇofín and Boubín) represent Hercynian mountain spruce-(fir)-beech forests (Table 1). Established in 1838 and 1958 they are the two oldest forest reserve in the Czech Republic and one of the oldest in Europe (Welzholz and Johann, 2007). The Zˇofín is situated in the Novohradské Hory Mts., near the border with Austria. Altitudinal gradient of 735–825 m a.s.l. is formed by predominantly NW gentle slopes. Bedrock is almost homogenous and consists of finely to medium-grainy porphyritic and biotite granite. The dominant soils (classified according to Michéli et al., 2006) at terrestrial sites are: Entic Podzols and Haplic and Dystric Cambisols. At water-affected sites there are Histic and Haplic Gleysols, Endogleyic Stangonosols, or Fibric, Hemic, and Sapric Histosols (Šamonil et al., 2011). Annual average rainfall is 917 mm. Annual average temperature is 4.3 °C. Plant communities can be classified in the following associations: Galio odorati-Fagetum, Mercuriali perennis-Fagetum, Calamagrostio villosae-Fagetum, and Luzulo-Fagetum. Spring-area plant communities can be classified as the alliance Caricion remotae, or as the association Equiseto-Piceetum (Boublík et al., 2009; Braun-Blanquet, 1921). Fagus sylvatica L. predominates in tree species composition, followed by Picea abies (L.) Karsten and an admixture of Abies alba Mill. (Table 3). Diameter distribution of key tree species in the plot in 90s is given in Fig. 1, for recent data see Trochta et al. (2013). Boubin primeval forest lies on Bohemian Massif crystalline formed by primary schists, biotic and mica-schist gneisses. The site is located in the lower part of the north-eastern slope of Boubin Mt. at an altitude of 930–1110 m a.s.l., thus forms the top end of the altitudinal site gradient (see Table 1). Mean annual temperature is 4 °C and mean annual precipitation amount is 1300 mm (Tolasz et al., 2007). Lower elevation soils can be classified according to Driessen et al., 2001 and Michéli et al., 2006 as Endoskeletic Cambisols or Endoleptic Cambisols; Umbric Podzols occur at higher elevations and Arenic Gleysols up to Ombric Histosols occur in terrain depressions and along streams (Vrška et al., 2012). According to a floristico-phytocoenological classification (BraunBlanquet, 1921; Ellenberg, 1996), most plant communities can be classified as Calamagrostio villosae-Fagetum Mykiška 1972 and Calamagrostio villosae Piceetum Hartman and Jahn 1967. Picea abies (L.) Karsten and Fagus sylvatica L. dominate the tree species composition with an admixture of Abies alba Mill., the third principal tree species of locality (Table 3). Comprehensive description of stand structure including development of species composition and diameter distributions give Šebková et al. (2011) and Vrška et al. (2012). 2.2. Datasets We used the results of detailed stem-mapping carried out in 70s, 90s and 00s (Vrška et al., 2001, 2006, 2012). In 70s and 90s the stem positions were measured by tripod-based theodolite. In

Table 1 The study sites with used stem-position datasets. Study site

Census area (ha)

Altitude min. (m a.s.l.)

Altitude max. (m a.s.l.)

Mean annual temp. (°C)

Mean annual prec. totals (mm)

Years of census

Cahnov Ranšpurk Salajka Zˇofin Boubín

17.3 22.3 19.0 71 (74.5) 46.7

150 152 715 735 930

153 155 815 825 1110

9.3 9.3 5.4 4.3 4.0

517 517 1144 917 1300

730 , 730 , 740 , 750 , 720 ,

940 , 940 , 940 , 970 , 960 ,

060 060 070 080 100

GPS northing (u)

GPS Easting (k)

48°390 48°400 49°240 48°400 50°040

16°560 16°560 18°250 14°420 17°15

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Table 2 Tree species composition in alluvial hardwood sites in 90s according to tree count, basal area and volume. Tree speciesa

Quercus robur Fraxinus angustifolia Acer campestre Carpinus betulus Tilia cordata et platyphylla Ulmus laevis Ulmus minor Alnus glutinosa Populus alba

Cahnov Species composition (%) according to:

Ranšpurk Species composition (%) according to:

Count

Basal area

Volume

Count

Basal area

Volume

13.5 25.6 21.6 23.1 9.5 2.7 2.1 0.0 0.2

31.5 34.9 10.3 13.9 6.7 1.4 0.5 0.0 0.2

34.9 36.8 8.0 12.1 6.1 1.1 0.3 0.0 0.2

3.4 17.2 32.4 31.5 5.2 4.2 0.1 1.3 0.3

15.2 32.0 20.9 17.7 4.2 5.1 0.1 1.1 1.3

18.8 35.0 17.0 15.9 4.1 4.3 0.0 0.9 1.6

a Individuals of other tree species as Pyrus communis, Crataegus monogyna, Sambucus nigra, Malus sylvestris, Populus tremula, Rhamnus catharticus and Salix sp. were also present in single sites.

Fig. 1. Distribution of dominant tree species in the five relative DBH classes used in the ANN classification (90s datasets); (a) spruce–fir–beech sites, (b) alluvial hardwood sites.

00s the Field-Map technology (http://www.fieldmap.cz) was employed. The relatively larger areas of study sites (tens of hectares) are advantageous for the intended patch mosaic analyses. At all study sites, census of all standing and downed trees with a minimum diameter at breast height (hereafter DBH) of 10 cm was performed. For the purposes of this paper, we used a datasets that contain tree position coordinates (X, Y), species, DBH and tree status (live/dead). For lying deadwood only distinguishable stems are recorded, woody debris of tree branches is neglected (analogically to living trees where only stem positions are recorded). 2.3. Mapping developmental stages We used the method of developmental stages and phases mapping following Král et al. (2010) that employs as an input data the stem-position maps of living and dead trees and therefore can be applied on corresponding datasets. By focal filtering of stem position GIS layers, local distributions of both live and dead tree counts and tree basal areas across five general diameter classes were calculated. In beech-dominated sites the original definition of the

DBH classes (Král et al., 2010) was used, i.e. class I: DBH < 25 cm (recruits), class II: 25 cm  DBH < 45 cm (thin trees), class III: 45 cm  DBH < 65 cm (mature trees), class IV: 65 cm  DBH < 85 cm (dominant trees) and class V: DBH  85 cm (thick trees). In alluvial hardwood forests, the definition of the DBH classes altered in the two groups of dominant tree species. For robust species represented in particular by pedunculate oak and narrow-leaved ash (and other associated species such as elm, linden and poplar) the original DBH classes were preserved. For smaller-statured species represented in particular by European hornbeam and field maple (and associated species as wild pear and alder) the definition of the DBH classes was adjusted according to their DBH range and height curves (Vrška et al., 2006), taking into account the DBH where the trees usually reach the canopy (class III). These DBH classes were defined as follows: class I: DBH < 20 cm, class II: 20 cm  DBH < 35 cm, class III: 35 cm  DBH < 50 cm, class IV: 50 cm  DBH < 65 cm and class V: DBH  65 cm. The distribution of trees in these relative DBH classes was determined separately for every square meter in the stand and its circular surroundings – diameter of the moving filter was 21 m; mapping step 1 m (this

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K. Král et al. / Forest Ecology and Management 330 (2014) 17–28 Table 3 Tree species composition in spruce–fir–beech sites in 90s according to tree count, basal area and volume. Tree speciesa

Fagus sylvatica Picea abies Abies alba Acer pseudoplatanus Sorbus aucuparia Ulmus glabra a

Zˇofín Species composition (%) according to:

Salajka Species composition (%) according to:

Boubín Species composition (%) according to:

Count

Basal area

Volume

Count

Basal area

Volume

Count

Basal area

Volume

65.2 32.2 1.8 0.6 0.1 0.2

47.8 46.8 4.6 0.7 0.0 0.2

51.5 42.8 4.8 0.7 0.0 0.2

71.7 9.8 16.5 1.7 0.2 –

60.8 10.4 27.7 1.0 0.1 –

60.9 8.9 29.3 0.9 0.0 –

41.9 55.4 2.4 0.3 0.0 0.0

35.6 60.1 4.2 0.1 0.0 0.0

40.4 54.8 4.7 0.1 0.0 0.0

Individuals of other tree species as Acer platanoides, Betula pendula, Populus tremula and Salix sp. were also present in single sites.

filter size is inherent to the method and different filter sizes would produce different local diameter distributions). These distributions were then recognized by an Artificial Neural Network (ANN) and classified into pre-defined categories (Table 4; for further details see Král et al., 2010). Compared to the original method, the classification algorithm was improved by retraining the ANN using more of newly defined independent training samples (30 training and 30 testing per each class). The used model training samples represent the general traits of the developmental phases of European temperate old-growth forests, irrespective of forest type. For better understanding the concept the model samples are exemplified in Supplementary data. The ANN primarily recognizes eight developmental phases that are subsequently regrouped into appropriate four major developmental stages: Growth, Optimum, Breakdown and Steady State (i.e. hierarchical nomenclature of Korpel’ (1995) modified by Král et al. (2010) was used). Classification results were evaluated by 404 independent evaluation samples (representing real stand structures from Zˇofín plot) using the traditional confusion matrix (Congalton, 1991; Nilsson, 1998; see Tables 4 and 5). We applied this enhanced classification at five study sites with existing stem-position dataset that represent different forest types

Table 4 Confusion matrix of eight developmental phases.

Table 5 Confusion matrix of four developmental stages.

along the vegetation gradient. The ANN classification produced maps of eight developmental phases in four developmental stages for each study site. The detailed definition of particular forest developmental stages and phases is again given by Král et al. (2010). The improved ANN classification eliminated the weakest phase’s recognitions of the pilot version (Král et al., 2010). In the case of developmental phases mapping the user’s accuracy of Growth/Expiration phase classification increased from 30% to more than 90%; for Initial Breakdown phase from 59% to 88% and for Typical Optimum phase from 61% to 100%. Similarly the weak producer’s accuracy for Ageing Optimum phase increased from 30% to almost 84% and for Steady State from 43% to 87%; of course sometimes at the expense of accuracy of other classes. Overall classification accuracy of stand developmental phases however increased by more than 16% to almost 85% (Table 4). Regarding the classification of developmental stages, the accuracy also improved, thought not so abruptly: the overall accuracy increased from almost 80% to more than 90% (Table 5). Regarding individual stages, all are now recognized and mapped with higher user’s accuracy and except for Breakdown stage also with higher producer’s accuracy compared to preceding ANN performance (Král et al., 2010).

22 K. Král et al. / Forest Ecology and Management 330 (2014) 17–28 Fig. 2. The patch mosaic metrics of the four stand developmental stages and total mosaic. The metrics observed in 70s, 90s and 00s in five study sites along the vegetation gradient: PoS – Percentage of Stage, MPS – Mean Patch Size, LPI – Largest Patch Index and MSI – Mean Shape Index. Boxes represent the mean values, whiskers represent the 95% confidence intervals.

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2.4. Analyzing the mosaics All further analyses focused on the four major developmental stages only. The raw mosaics of developmental stages resulting from the classification were smoothed by 7  7 focal majority filter to reduce a noisy ‘salt and pepper’ effect. Such generalized maps were then analyzed in FRAGSTATS 4.1 (McGarigal and Marks, 1995) and in PatchAnalyst 5.0 for ArcGIS software (Rempel et al., 2012) by metrics traditionally used in landscape ecology, recently being adopted also in spatial and forest ecology. The remaining patches smaller than 50 m2 were excluded from the analysis (being empirically regarded as too small to represent a peculiar developmental stage). The following mosaic metrics were calculated at class level (i.e. separately for patches of individual developmental stages) and mosaic level (i.e. from all patches irrespective of the stage): Percentage of Stage (PoS) is the percentage of the total mosaic made up of the corresponding developmental stage, this index is of necessity calculated only at the class level (%); Mean Patch Size (MPS) is the average patch size of the class and/or the mosaic (m2); Largest Patch Index (LPI) is the percentage of the total class/mosaic area that is made up by its largest patch (%); Mean Shape Index (MSI) equals 1 when all patches of the corresponding patch type are circular and increases without limit as the patch shapes become more irregular; Connectance Index (CI) is a percentage of functional joinings between patches of the corresponding patch type, where each pair of patches is either connected or not based on a user-specified distance criterion (7 m distance threshold was used); and Aggregation Index (AI) equals 0 when the focal patch type is maximally disaggregated, it increases as the focal patch type is increasingly aggregated and equals 100 when the patch type is maximally aggregated into a single, com-

(a)

pact patch. Precise definitions including formulas of used mosaic metrics are given by McGarigal and Marks (1995). The differences among mosaic metrics that are calculated as mean or sum values of individual patches forming the mosaic or part of the mosaic (i.e. particular developmental stage) were compared by the bootstrap confidence intervals. The 95% nonparametric bias-corrected and accelerated confidence intervals based on 1000 bootstrap replications (Efron and Tibshirani, 1993) were constructed for mean values of MPS and MSI and for percentages of LPI and PoS. The bootstrap package of R statistical software (R Development Core Team, 2010) was used for these calculations. 3. Results Proportion of developmental stages in the mosaic varies among study sites and varies also in time. In spite of that, some common patterns can be observed in all studied patch mosaics. The Growth stage is usually more abundant and covers usually 25–50% of the stand. The Breakdown stage is less abundant and covers usually only from 10% to 25% of the stand area; though the differences between these two stages are not always statistically significant (Fig. 2). In addition, the proportion of Breakdown stage seems to gradually increase in time and also along the vegetation gradient. The proportion of Steady State in the mosaic varies usually between 15% and 45%. In spruce-fir-beech forests this proportion seems to increase along the vegetation gradient, which is significantly apparent particularly in Boubín. In alluvial hardwood forests the proportion of Steady State slightly increases in time. The proportion of the Optimum stage is highly variable among study sites, ranging from 5% to 40%. Its proportion also varies in time, but no clear trend was detected.

96

Aggregation Index

95

94

93

92

91

90

1973 1994 2006 2.5

Connectance Index

(b)

Cahnov

1973 1994 2006

1974 1994 2007

Ranšpurk

Salajka

1973 1994 2006

1974 19942007

1975 1997 2008

1972 1996 2010

Žofín

Boubín

2.0

1.5

1.0

0.5

0.0

1973 19942006 Cahnov Growth

Ranšpurk Optimum

Salajka Breakdown

1975 1997 2008

19721996 2010

Žofín Steady State

Boubín Total

Fig. 3. The measures of patches aggregation and connectance for the four stand developmental stages and total mosaic observed in 70s, 90s and 00s in the five study sites: (a) Aggregation Index; (b) Connectance Index.

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Concerning Mean Patch Size, the most striking trait of the mosaic is the fact that at the mosaic level the MPS is very similar in all study sites and studied periods (see class ‘Total’ in MPS in Fig. 2). It ranges in relatively narrow interval from 570 m2 to 800 m2 and observed differences are statistically insignificant. Conversely, MPS of individual developmental stages are highly variable. They are often highest for Growth stage (590–2800 m2) and lowest for Breakdown stage (250–720 m2), thus the MPS of the Breakdown stage is mostly significantly smaller than that of the Growth stage. MPS of Optimum stage and the Steady State are quite changeable and for both vary from about 250 m2 to 1500 m2. The MPS of the Breakdown stage in all sites and the MPS of the Steady State in spruce–fir–beech dominated stands seem to increase along vegetation gradient. Generally similar pattern may be observed also in the Largest Patch Index (Fig. 2). At the mosaic level the LPI ranges from 3% to 22%, for the Growth stage (5–56%) and the Optimum stage (4–40%) is usually significantly higher, while for the Breakdown stage (2–13%) significantly lower and much less variable. In the Steady State it ranges from about 3% to 28%. Also in the Mean Shape Index the relative differences among individual stages are usually similar, thought often less significant. The patches of the Growth stage have higher shape complexity than the patches of the Breakdown stage, which are mostly of the simplest shape (although gradually increasing along the vegetation gradient). Compared to its size metrics the MSI of the Steady State is relatively high. The MSI of the entire mosaic remains significantly stable, ranging between 1.5 and 1.6 across all study sites and observation periods. The spatial distribution of patches of individual developmental stages was evaluated by the Aggregation Index and the Connectance Index (Fig. 3). At the mosaic level the level of aggregation is similar for all study sites. The patches of the Growth stage and the Optimum stage appears to be usually the most aggregated; the patches of Breakdown stage are usually less aggregated, thought in some sites the aggregation of the Steady State is lower. Relatively high aggregation shows the patches of Steady State in the mountain spruce–beech forest of Boubín. Similarly, the patches of the Growth stage are usually more connected, while the patches of the Breakdown stage are usually little connected. In alluvial hardwood forests in 90s and 00s and in the mountain Boubín forest also the Steady State shows relatively high connectivity. The general level of patches connectance in particular sites is stable in time, but it seems to be consistently slightly lower in the most elevated spruce–beech sites of Zˇofín and Boubín. Indeed, Boubín in general shows the most site specific deviations from the common mosaic pattern. These are defined by the

long-term significantly lowest proportion of the Optimum stage and the consistently highest proportion of the Steady State. 4. Discussion 4.1. Common traits of the mosaics and their steadiness in time The similarity among sites of mean metrics of the size (MPS) and shape (MSI) of patches indicate an existence of generally similar ecological processes operating at similar spatial scales in all studied sites. The consistency of these metrics in time however does not signify that the sites are still the same. On the contrary, well documented histories of the sites detail significant changes in species composition and severity of recent disturbances. The alluvial hardwood forest of Cahnov and Ranšpurk for example experienced dieback of old ‘pasture’ generation of Quercus robur and significant emergence of Carpinus betulus and Acer campestre after elimination of spring floods since late 70s (Janík et al., 2008, 2011). The Carpathian site Salajka similarly experienced dieback of human induced old abundant generation of Abies alba from 70s to 00s (Vrška et al., 2009). The Zˇofín forest was strongly affected by the Kyrill windstorm in 2007 (Šamonil et al., 2013; Šebková et al., 2012) and Boubín less severely but significantly by the windstorm Emma in 2008 (Šebková et al., 2011). Moreover, all spruce–fir–beech dominated sites are affected by long-lasting growing dominance of beech (Vrška et al., 2012). The observed stability of the mean mosaic metrics is thus also a sign of high resilience of natural temperate forest stands in terms of their spatial structure. The found mean patch sizes of the mosaic (570–800 m2) well agree with estimates from the recent literature: Emborg et al. (2000) distinguishing five phases reported the MPS of Suserup Skov in 1992 as 834 m2, this number was confirmed ten years later by Christensen et al. (2007), who found the MPS of 809 m2, although nearly half of the area changed the phase in between. Drossler and Meyer (2006) and Tabaku (2000) found mean patch sizes of Slovak, German and Albanian virgin forests about 300– 460 m2 distinguishing nine phases. Already Korpel’ verbally described area of the basic stand textural elements of 200– 700 m2 (Korpel’, 1989). These areas are however about 10 times lower compared to patch sizes resulting from practical field mapping of developmental stages/phases using the original Korpel’s approach in the Czech Republic and Slovakia (e.g. Vrška et al., 2006, 2012; Korpel’, 1995) or using the Leibundgut’s nomenclature in Croatia (Mayer and Neumann, 1981). Possible explanation of these discrepancies is given by Peterken (1996), who states that the texture map of Cˇorkova Uvala (Mayer and Neumann, 1981)

Fig. 4. The mosaic of developmental stages in Carpathian fir–beech forest of Salajka from 70s to 00s.

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was a simplification of very complex patterns in which each tree could almost be regarded as a separate developmental phase. The field-drawn maps probably used to be simplified for practical reasons, as the complex fine-scale stand structures are difficult to document with a lack of spatial quantitative information. The approx. 10 times larger grain of practical field mapping was confirmed when we compared the field-drawn maps with ANN performance in the same sites (unpublished results). Last but not least, the detected mean patch size of the mosaic corresponds well to the irregular repetitive patch pattern (usually of 400–1100 m2 in size) identified recently by the quantitative analysis of spatial variability of general stand variables (e.g. local stand density and basal area) in beech-dominated natural forests, including Zˇofín and Salajka sites (Král et al., 2014). 4.2. Specific patch patterns of developmental stages We should keep in mind that part of the similar pattern, which is repeatedly observed through different mosaic metrics (Fig. 2), is caused by correlation among these metrics. This effect is natural and was described by Riitters et al. (1995). For example higher abundance of any class (PoS) is likely to produce bigger patches (MPS, LPI) and larger patches further tend to have more complex shape (MSI). More abundant classes are also likely to have higher connectivity (CI). This effect however does not deny an existence of following stage specific patterns. Firstly, the Breakdown stage is usually the least abundant, have the lowest mean patch size and size variability, simple shape and generally lowest variability of all metrics. The Growth stage usually shows quite opposite traits. Its occurrence is usually higher, mean patch sizes are bigger as well as the size variability. Its patches are also usually more complex, aggregated and connected (Figs. 2–4). Consequently, the Growth stage often acts as a stand matrix with small, scattered inclusions of Breakdown and larger patches of other stages (Fig. 4). Direct comparison of patch sizes and percentages of individual phases with other published results could be misleading due to substantial differences in various systems used; still, most authors agree that the phases related to growth are usually largest and the most abundant while the breakdown related phases are the least abundant and the smallest (Christensen et al., 2007; Drossler and Meyer, 2006; Emborg et al., 2000; Koop and Hilgen, 1987; Winter and Brambach, 2011). These findings are not surprising considering that the time needed for tree growth is usually much longer than the time of tree dieback and decomposition. The constantly small patches of the Breakdown stage may be also explained by the fact that the patches formed by more severe stand replacing disturbances are, at least in our system, mapped as the initial phase of the Growth stage (if there are no trees susceptible to further breakdown left) as happened in Zˇofín after Kyrill windstorm in 2007. These larger disturbances were thus demonstrated by very high LPI of the Growth stage in Zˇofín in 00s (Fig. 2). The values of other mosaic metrics were more or less equalized in the large area of the study site (71 ha), which in this case probably played an important role. In a smaller site a disturbance of this severity might also change the generally stable mosaic metrics of MPS and MSI. Returning back to the Breakdown stage, the observed mean patch sizes of 250–720 m2 are on the other hand still much larger than the mean gaps sizes (about 90 m2) reported from central European temperate mixed forests (e.g. Kenderes et al., 2009). This difference is an inevitable consequence of fundamental differences between the gap phase concept and the patch mosaic concept. While in the first even the smallest gaps are included in the model per se, in the traditional patch mosaic concept (Leibundgut, 1959; Mayer, 1976; Korpel’, 1995) death of one or few canopy trees create the patch of the Breakdown stage together with surrounding

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canopy trees that remain. Several small clustered canopy gaps thus may form single patch of the Breakdown stage. The areal proportions and other metrics of the Optimum stage and the Steady State are not so typical and are more variable in time and among study sites. In comparable sizes the patches of Steady State seem to have more complex shape compared to Optimum stage. Traditional silviculture literature claims that ‘plenter phase’ (the close counterpart to the Steady State in our concept) can rarely be found in natural forests because it represents a short transition phase between the Breakdown stage and the Optimum stage (e.g. Mayer, 1976; Schütz, 2001); similar conclusions were made using simulations in Huber (2011a). Our findings, however, do not support these claims. In some sites, the Steady State is quite abundant and seems to complement the Growth stage. For example since the 90s in Cahnov, Ranšpurk and Boubín the Steady State has become almost a matrix of the mosaic, which is usually formed by the Growth stage (more about potential causes in Section 4.3). A simple regression analysis showed that within individual sites the occurrence of the Steady State and the Growth stage are the most negatively correlated, indicating that they complement each other. 4.3. Trends in time and along vegetation gradient One of the trends recognized in patch patterns is changing proportion of the Breakdown stage that gradually increases along the vegetation gradient and also in time, especially between 70s and 90s (Fig. 2). This trend is reflected also in enlarging mean patch size and increasing mean shape index. Although this trend is perceptible in all these metrics, the increase is statistically significant in multiple cases only for the mean patch size (both along vegetation gradient and in time). The trend along altitudinal vegetation gradient might be explained by the changing disturbance regime towards increasing frequency and/or severity of wind disturbances (Brázdil et al., 2004) and increasing role of other disturbance types as European spruce bark beetle outbreaks (Ips typographus L.) and snow/ice breakages (Fischer et al., 2013). The simultaneous increase of the Breakdown stage in time, especially from 70s to 90s in all sites is probably a result of several independent phenomena. In the alluvial hardwood forests of Cahnov and Ranšpurk it was the simultaneous dieback of the European white elm (Ulmus leavis Pall.) caused by the Dutch elm disease (Ophiostoma novo-ulmi Bras.) and in particular the dieback of old abundant cohort of pedunculate oak (Janík et al., 2008) originating from the 17th and 18th century, when the open-canopy floodplain forests were massively used for forest grazing of pigs (Vrška et al., 2006). The old oak cohort has been at the end of the life-span and its dieback has been accelerated by the decrease of the water table level caused by elimination of natural spring inundations after 1976 (Penka et al., 1985). A similar story took place in the fir–beech forest of Salajka. The old and abundant generation of silver fir (Abies alba) established in the 17th and 18th century, when the Carpathian top-hill meadows and surrounding forest edges were used for cattle grazing and litter raking, which favored regeneration of fir (Vrška et al., 2009). From 70s to 90s was the dieback of abundant old firs in Western Carpathians accelerated by the air pollution (Tesarˇ and Krecˇmer, 2001), which increased the proportion of the Breakdown stage in the site. The basal area of living firs dropped in this period from 13.9 m2 ha1 in 70s to 7.4 m2 ha1 in 90s and to 7.1 m2 ha1 in 00s, hence nearly to a half. The old firs co-dominating the stand were replaced by the new recruits and ingrowth of beech, which is reflected also in decreasing proportion of Optimum stage and increasing proportion of the Growth stage (Figs. 2 and 4). Other recognized trends are related to the Steady State, which is increasing in PoS and MPS either in time in alluvial hardwood forests (Cahnov, Ranšpurk) or along altitudinal vegetation gradient in

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spruce–fir–beech forests (Salajka, Zˇofín, Boubín). The trend in time in the floodplain forests is most likely the second consequence of the above described gradual dieback of the co-dominant pedunculate oak and admixed European white elm from 70s to 90s. Increasing available light and elimination of spring floods allowed release and growth of sub-canopy and suppressed individuals of different tree species (Carpinus betulus, Acer campestre, Tilia cordata, Fraxinus angustifolia, etc.) with slightly different ecological requirements and species traits favorable for the development of rich, stratified stand structure of the Steady State. The increasing proportion of the Steady State on the vegetation gradient of spruce–fir–beech forests (represented by Salajka, Zˇofín and Boubin respectively) is in multiple cases statistically significant for Boubín and for enlarging mean patch size of the Steady State also in Zˇofín. The mixture of beech (Fagus sylvatica), spruce (Picea abies) and fir (Abies alba) is reported to be able to form complex stand structure (Schütz, 2002). On the contrary, a dominant proportion of beech, as highly shady and competitive species, may simplify the stand structure (Korpel’, 1995) and may lead to lower occurrence of the Steady State stage. Decreasing proportions of beech and increasing proportions of spruce in the tree-species composition of the sites thus might be one of the possible explanations, although for this supposition we do not have sufficient quantitative support. The role of individual tree species in forming the forest structure thus merits additional attention.

than previously thought. This ‘all in one’ stage is more similar to the recent perception of forest dynamics as a shift from the dynamics of discrete patches to the interactions among tree individuals – so called ‘neighborhood dynamics’ (Gratzer et al., 2004). It also fits with the increasing evidence that sub-canopy dynamics play an important role in dynamics of the tree layer of deciduous and mixed temperate forests (e.g. Christensen et al., 2007; Manabe et al., 2009). Significant proportion of the Steady State and mean patch sizes of all stages give useful background for selection silviculture systems (group selection system in particular). We also demonstrate that even indirect human impact may have long-term effects on the patch mosaic of natural forests. The high inertia of forest ecosystems was manifested in alluvial hardwood forest and fir–beech forest of Salajka, where the historical cattle grazing still influences some mosaic patterns after centuries. Finally, several issues in our opinion deserve further scientific attention: (i) comparison of patch patterns on the finer level of developmental phases; (ii) the role of individual tree species in forming the forest structure of particular developmental stages and phases, and (iii) the multi-temporal comparison of patch mosaic transitions for rigorous verification of the functionality of the model forest cycle. The question is how much does the forest cycle run along a predictable path (the chronological sequence growth—optimum—breakdown), and what is ‘just’ a result of stochastic disturbances?

5. Conclusions We found that the mean size and shape of all patches in the mosaic agreed with our hypothesis that there were no significant differences across study sites. Also the aggregation of patches of the same type was similar in all sites at the mosaic level. These basic traits appear stable in time. We confirmed that patch patterns differ for particular developmental stages, i.e. stage specific patterns exist for the Growth stage and the Breakdown stage. While the Growth stage is usually the most abundant, and has the highest mean patch size, size variability, and the most complex shape, the Breakdown stage usually shows quite opposite traits. These differences at the stages level can be considered as another common feature of forest mosaics and show stability across the decades that our study captures. We also found that our hypothesis that some stage patch patterns differ across elevation gradient and related community composition was supported: mean patch size of the Breakdown stage increased across the vegetation gradient; and the significant correlation between the abundance of spruce in spruce–fir–beech dominated sites and the proportion of Steady State there was found. Finally, we found some temporal trends in the mosaics, e.g. increasing areal proportion of Breakdown stage in all sites especially between 70s and 90s, and increasing proportion and mean patch size of the Steady State in alluvial hardwood forests in the same period. These temporal trends, however, are not another common general trait of the mosaics but demonstrate that some important landscape-scale processes operate differently for different study sites. Overall, we show that an objective and quantified analysis of patch dynamics of European temperate forests over several decades show a combination of important across site commonalities, over and above the unique features of different forest types. Some of the differences across sites can be attributed to gradients, such as elevation and community composition, while others are result from the unique history, location, and quality of the individual sites. This study also shows that the Steady State stage characterized by locally rich and diversified stand structure likely plays a more important role in overall dynamics of European temperate forests

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