The role of landscape configuration in plant composition of ... - Wiley

Journal of Vegetation Science 21: 1110–1124, 2010 DOI: 10.1111/j.1654-1103.2010.01213.x & 2010 International Association for Vegetation Science

The role of landscape configuration in plant composition of floodplain forests across different physiographic areas Jan Douda Abstract Questions: What is the relative importance of landscape variables compared to habitat quality variables in determining species composition in floodplain forests across different physiographic areas? How do species composition and species traits relate to effects of particular landscape variables? Do lowland and mountain areas differ in effects of landscape variables on species composition? Location: Southern Czech Republic. Methods: A total of 240 vegetation releve´s of floodplain forests with measured site conditions were recorded across six physiographic areas. I tested how physiographic area, habitat quality variables and landscape variables such as current land-cover categories, forest continuity, forest size and urbanization influenced plant species composition. I also compared how mountain and lowland areas differ in terms of the relative importance of these variables. To determine how landscape configuration affects the distribution of species traits, relationships of traits and species affinity with landscape variables were tested. Results: Among landscape variables, forest continuity, landscape forest cover and distance to nearest settlement altered the vegetation. These variables also influenced the distributions of species traits, i.e. life forms, life strategies, affinity to forest, dispersal modes, seed characteristics, flooding tolerance and Ellenberg indicator values for nitrogen, light, moisture and soil reaction. Nevertheless, physiographic area and habitat quality variables explained more variation in species composition. Landscape variables were more important in lowland areas. Forest continuity affected species composition only in lowlands. Conclusions: Although habitat quality and physiographic area explained more vegetation variability, landscape configuration was also a key factor influ-

Douda, J. (Corresponding author, [email protected]): Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamyćka´ 129, Praha 6 – Suchdol, 165 21, Czech Republic.

encing species composition and distribution of species traits. However, the results are dependent on forest geographical location, with lowland forests being more influenced by landscape variables compared to mountain forests. Keywords: Dispersal limitation; Forest continuity; Forest fragmentation; Forest loss; Forest species; Habitat quality; Human impact; Land-cover classes; Landscape ecology; Riparian forests. Nomenclature: Kuba´t et al. (2002)

Introduction Human impact on forest vegetation is omnipresent in many parts of the world; therefore, it must be considered in studies of forest vegetation responses to environmental variables (Honnay et al. 2005). Various studies have shown that both recent and historical landscape configurations appear to be important factors affecting species composition and abundance in forests (Dzwonko 1993; Bellemare et al. 2002; Honnay et al. 2005). The effect of habitat continuity on species composition has repeatedly been documented, especially in comparisons of recently developed forests (i.e. recent forests) to forests with long continuity (i.e. ancient forests) (e.g. Hermy et al. 1999; Bellemare et al. 2002). In European countries, the most frequently applied threshold for habitat continuity of an ancient forest is 4200 years (e.g. Dzwonko 1993). Forest species with slow colonization rates that lack long-term seed banks are associated with ancient forests (Hermy et al. 1999; Honnay et al. 2005). These dispersal-limited species remain rare long after the development of a recent forest, and their occurrence depends inversely on the distance of the given forest fragment from an ancient forest source (Dzwonko 1993; Vellend 2003; Matlack 2005). The prevalent process driving forest species distribution in forests of various ages seems to be dispersal limitation rather than recruitment limitation resulting from unsuitable habitat conditions (Ehrleń & Eriksson 2000; Ehrleń et al. 2006).

Role of landscape configuration for floodplain forests

Recent land cover on adjacent land influences the species composition of forest stands in several ways. First, the surrounding land cover types act as important donors of species (Guirado et al. 2006). For example, urbanization and arable land in the areas surrounding forests can increase the flow of propagules of ruderal plants, which can subsequently affect the persistence of rare forest species through competition (Honnay et al. 2002; Guirado et al. 2006). Second, the surrounding environmental conditions penetrate into the forest interior (Jules & Shahani 2003; Honnay et al. 2005). This may endanger the existence of rare forest species by creating unsuitable conditions; moreover, it might promote the spread of non-forest species into forest interiors (Guirado et al. 2006). Finally, the surrounding landscape matrix quality can affect pollination, seed dispersion, herbivory and seed predation of forest interior species (Jules & Shahani 2003; Honnay et al. 2005). Forest loss in the surrounding landscape associated with forest fragmentation and the increasing isolation of forest patches appear to be prevalent factors affecting forest vegetation (Vellend 2003; Honnay et al. 2005; Matlack 2005). Forest loss decreases the rate of colonization of forest fragments by forest species and increases the risk of extinction in isolated populations (Dupre´ & Ehrleń 2002; Kolb & Diekmann 2004; Honnay et al. 2005). Many studies have shown that flooding regime (i.e. flood frequency, intensity, duration and timing), as well as depth to the water table, soil acidity and nutrient concentrations, are major factors of habitat quality determining plant composition of floodplain forests (Turner et al. 2004; He´rault & Honnay 2005; Glaser & Wulf 2009). Moreover, at large spatial scale, climate, geology basement and historical migration of plants are considered to be other important factors influencing species composition of floodplain forests across different physiographic areas (Turner et al. 2004; Predick & Turner 2008). Although our knowledge of the effects of habitat quality and landscape configuration on forest species composition is growing, the relative importance of these variables remains insufficiently clarified (Kolb & Diekmann 2004). Obviously, it is difficult to separate these two effects, particularly because landscape configuration is affected by habitat quality and vice versa. Probably the best way to separate the effects of these factors is to compare patterns of species composition across a broad range of environmental conditions and to decompose the variance in species composition into parts attributable to habitat quality, landscape configuration and their overlap. However, most studies so far have dealt with the

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importance of landscape configuration at the level of a single region, i.e. across a relatively narrow gradient of environmental conditions (e.g. Kolb & Diekmann 2004; He´rault & Honnay 2005). Only a few studies have investigated the effects of landscape configuration on forest species composition across several regions comprising a broad range of environmental conditions (Dupre´ & Ehrleń 2002; Turner et al. 2004; Verheyen et al. 2006). Moreover, the results of several studies indicate significant differences in the relative importance of landscape variables between regions, which are probably associated with variation in human impact (Honnay et al. 2002; Vellend 2003; Verheyen et al. 2006) and productivity between sites (Graae 2000; Verheyen et al. 2006). Floodplain forests have been studied several times with the aim of determining the effects of landscape configuration on species composition (e.g. Turner et al. 2004; He´rault & Honnay 2005; Glaser & Wulf 2009). These communities are suitable for such studies for several reasons. First, floodplain forests often occur as isolated patches surrounded by arable lands, grasslands and urbanized areas. Second, floodplain forests are a suitable place to study the effects of forest continuity in Europe because a large proportion of these forests developed through spontaneous succession after the abandonment of managed wet grasslands in the late 19th and early 20th centuries (Falin´ska 1991). Third, because of the integrating influence of the flooding regime, floodplain forests are well-defined objects of study independent of their tree species compositions. Moreover, the floodplain forests in the present study are unmanaged forests with a natural composition of trees and shrubs. The main aim of this study is to explain the influences of recent landscape configuration (i.e. the influence of current patterns of land cover types) and forest continuity on the species composition of floodplain forests across different physiographic areas. The following questions were asked: (1) What is the relative importance of landscape variables compared to habitat quality variables in determining species composition in floodplain forests? (2) How do species composition and species traits relate to the effects of particular landscape variables? (3) Do lowland and mountain areas differ in the effects of landscape variables on the species composition of floodplain forests?

Methods Study area The study area is located in the southern part of the Czech Republic (13130 0 -15130 0 E, 48130 0 -

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49140 0 N, Fig. 1). It covers ca. 11 000 km2 and comprises a heterogeneous landscape of mountains, uplands and lowlands ranging from about 350 to 1400 m a.s.l. The climate is subcontinental, with annual mean temperatures ranging from 2.51C to 8.21C. The mean annual precipitation is roughly 550 mm in the lowlands and 1250 mm in the mountains (Tolasz et al. 2007). Most of the area is drained by the Vltava River and the Moravska´ Dyje River. Several large tributaries, such as the Otava, Lu&nice and Malsˇ e Rivers, run into the Vltava River. Moreover, many streams and smaller rivers create a dense network of streams across the study area (Fig. 1). Several geological formations occur there, creating both nutrient-poor and nutrient-rich substrates. Delimitation of physiographic areas The comparison of patterns of species composition across a broad range of environmental conditions makes more plausible the partitioning of variance in species composition into parts explained by habitat quality and landscape configuration. To capture a sample with a wide range of ecological conditions, physiographically different areas were delimited. A physiographic area is a synthetic variable composed of its climatic, geological, topographical, hydrological and biogeographical characteristics (Predick & Turner 2008). The physiographic areas were delimited using maps of environmental variables overlaid with a grid of Central European

mapping, with cells (10 0 6 0 ) divided into four 5 0 3 0 (approximately 6 km5.6 km) quadrats. Three maps of environmental factors comprising 33 environmental categories were used for selection of the areas (http://www.geoportal.cenia.cz): (i) climate was divided into ten categories, ranging from cold district to mild district; (ii) potential natural vegetation was divided into eight categories; and (iii) geology was divided into 15 categories of bedrock. The proportion of each environmental category was estimated in 322 quadrats of the grid on a percentage scale. To delimit physiographically different areas, the similarity of quadrats was calculated using the TWINSPAN numerical classification method in the program Juice (Tichy´ 2002), with cut levels of 0, 5, 25, 50 and 80, minimum group size of five and maximum level of division of four. Sixteen clusters were thus established. These clusters were then joined to yield six ecologically well-interpreted areas, each with a size of more than 1000 km2. Thanks to classification based on environmental variables, most physiographic areas were composed of several spatially isolated areas (Fig. 1). The altitudinal gradient has been shown to be the most important factor determining the classification. Physiographic areas A, B and C represent areas from mountain to upland, whereas physiographic areas D, E and F comprise areas from upland to lowland. Finally, in each of the six areas, 10 quadrats of grids were randomly chosen for data collection (Fig. 1).

Fig. 1. Study area with dotted quadrats where data were collected in six physiographic areas; areas A, B, C and D, E, F represent mountain and lowland areas, respectively.


Data collection Sixty quadrats were surveyed once during the summers of 2004-2006. To determine variation in species composition and habitat quality of floodplain forests in each quadrat, four plots (phytosociological releve´s) were placed in the centres of different stands. These stands were selected on the basis of two criteria, both of which had to be satisfied, they had to be: (1) stands on alluvial soils located in floodplains and (2) stands having no obvious sign of recent management activities. Picea abies, Pinus sylvestris and Populus sp. plantations were excluded. The following forest communities were investigated from a phytosociological viewpoint (Douda 2008): riparian forests of Alnion incanae Pawzowski, Sokozowski et Wallisch 1928, riparian willow shrubs and forests of Salicetea purpurae Moor 1958 and alder carr of Alnion glutinosae Br.-Bl. et Tu¨xen ex Westhoff et al. 1946. A total of 240 plots were recorded in this way. The standard plot size used for sampling was 200 m2 (20 m10 m), but smaller plots (100-180 m2) were used if the floodplain forests occurred only in small patches. The non-significant effect of plot size on species composition was verified using canonical correspondence analysis (CCA) (see Table 1). For each plot, a list of vascular plants (shrubs and trees in-

Table 1. Environmental factors considered to be explanatory variables attributable to the given group variables (i.e. habitat quality, landscape configuration). The significant variables (P 5 0.001) shown after CCA analysis using forward selection are marked in bold. Abbreviations: Co (continuous variable), O (ordinal variable), Ca (categorical variable) and B (binary variable). Habitat quality Water table (1, 2, 3, 4; O) Catchment area (m2; Co) Relative flooding elevation to 100-yr stream flow (m; Co) Microtopographical heterogeneity of sample plot (m; Co) Slope (degrees; Co) Base saturation (PCA scores; Co) Soil acidity (PCA scores; Co) Phosphorus, potassium (PCA scores; Co) Landscape configuration Forest cover up to a distance of 1 km from sampling plot (%; Co) Proportion of urbanized area up to a distance of 1 km from sampling plot (%; Co) Presence of arable land up to 100 m from sampling plot (B) Presence of grassland up to 100 m from sampling plot (B) Presence of ancient forest on the site of sampling plot (B) Distance to the forest edge (m; Co) Distance to the nearest ancient forest (m; Co) Distance to the nearest settlement (m; Co) Forest patch size (m2; Co) Physiographic area (A, B, C, D, E, F; Ca) Sampling plot size (m2; Co)

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clusive) was recorded. The cover of species was recorded using the nine-step ordinal Braun-Blanquet scale (Westhoff & van der Maarel 1973). For each plot, I determined landscape variables and habitat quality variables, which have been shown to have important effects on species composition, especially in floodplain forests (Turner et al. 2004; He´rault & Honnay 2005; Guirado et al. 2006; summarized in Table 1). The proportion (in %) of forests and urbanized areas up to 1 km from the centre of each plot was estimated using colour aerial photographs taken in 2004 (pixel size 1 m; http:// www.geoportal.cenia.cz) in ArcMap 9.2 (http:// www.esri.com). Landscape elements such as roads, rooftops and sidewalks indicated urbanized areas. Other land-cover classes, such as arable land and grassland, were not estimated at the 1-km scale because they were clearly negatively correlated with forest size. However, the presence of arable land and grassland up to 100 m from the centres of plots was recorded to determine the influence of land-cover classes immediately adjacent to floodplain stands. As the next attributes determining current landscape configuration, the size of individual floodplain forest patches as well as distance from the centre of each plot to a non-forest area (nearest forest edge) and to the nearest edge of a settlement were measured. To determine forest continuity, the presence of ancient forest (considered as areas continuously forested since the end of the 18th century) within the plots was determined using the maps of the First, Second and Third Military Survey of the Habsburg Empire from 1764 to 1783, 1836 to 1852 and 1877 to 1880, respectively (at a scale of 1:25 000-28 800; http://www.geoportal.cenia.cz). Of 62 plots assigned to ancient forests, 35 were located in lowland areas and 27 in mountain areas, as defined hereinafter. Simultaneously, to assess the importance of potential sources of forest species, the distance to the nearest edge of an ancient forest from the centre of each plot was also measured on historical maps. Soil samples were collected below the litter layer in August 2009. Five 15-cm-deep soil cores from each stand were mixed and passed through a 2-mm sieve. All soil analyses were performed on the o2mm fraction of soil. Soil samples were analysed for pH, organic matter content (OM), total nitrogen (Ntot), plant available phosphorus (P), calcium (Ca), magnesium (Mg) and potassium (K). Soil pH was measured in a solution of 5 g of soil and 25 ml of distilled water with a standard glass electrode, OM by loss on ignition (5501C for 4 h) and Ntot by acid decomposition and absorption spectrophotometry. Plant available P, Ca, Mg and K were extracted in

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Mehlich’s solution and measured using absorption spectrophotometry. The water table in the centre of each plot was described in terms of the depth of the reductive soil layer, which reflects the upper limit of the water table (He´rault & Honnay 2005). If the reductive layer depth could not be determined (e.g. in sandy soils), the elevation of releve´s relative to the common water level in the stream was measured. A four-member ordinary scale was applied for measurement: 1 5 4100 cm, 2 5 61-100 cm, 3 5 20-60 cm and 4 5 o20 cm. The microtopographic heterogeneity of each sample plot was measured as the difference between the maximum and minimum elevations of the plot. The flooding regime was characterized by the elevation of each plot relative to the 100-year flood elevation (Turner et al. 2004). Because data on floods were missing for most streams, 100-year relative flood elevation was modelled for each plot based on a 0.6-m digital terrain model and 100-year stream flow using HEC-RAS 2.2 software (US Army Corps of Hydraulic Engineers 2002). For the other characteristics of habitat quality influencing the water regimes of sites, the slope of the site (in grade) was recorded using a clinometer and the catchment area of the stream, which characterized stream size, was calculated using a 0.6-m digital terrain model and the watershed function in ArcMap 9.2 (http://www.esri.com). Data analysis Principal components analysis (PCA) was performed to summarize the variation among, in many cases, correlated soil properties. Site scores of the three principal components axes explaining the largest part of the variation in the data were used for further testing (Table 2). The concentrations of Ca, Mg and Ntot and proportion of OM were strongly positively correlated with the PC1 (Base saturation)

axis, pH, portion of OM and concentration of Ntot was correlated with the PC2 (Soil acidity) axis, and concentrations of P and K with the PC3 (Phosphorus and potassium) axis (Table 2). The overall pattern of variation in the species composition of floodplain forests was found with the help of detrended correspondence analysis (DCA), detrended by segment. In order to interpret DCA results in terms of environmental gradients, the significant environmental variables (resulting from the CCA analysis hereinafter) were passively plotted onto a DCA ordination diagram as supplementary variables. CCA was used to test the gross and net effects of explanatory variables on species composition. Forward selection was carried out with all environmental variables (Table 1) to determine the minimal set of variables that significantly contribute to the explained variance. Due to the high probability that variables will be significant predictors by chance alone (Lepsˇ & Sˇmilauer 2003), the significance level was adjusted for the number of variables tested, using Bonferroni correction, to P 5 0.001. Thus, only variables that substantially altered species composition were selected with forward selection (Lepsˇ & Sˇmilauer 2003). Subsequently, gross and net effects were calculated with only the significant variables arising from forward selection using partial CCAs. The gross effect denotes the total amount of variance explained by a given environmental variable. The net effect denotes the unique contribution of a given environmental variable without the effects of the remaining environmental variables that are used as covariates. The gross and net effects of habitat quality and landscape configuration were expressed as the total variation explained by significant variables attributable to the given group variable (Table 1). Consequently, the percentage of net shared variation was calculated for habitat quality,

Table 2. Factor loadings of a PCA ordination for the soil data collected in floodplain forests in August 2009. Values in bold indicate the most important soil variables for each axis (factor loading 40.6). Variable name

Variance explained Organic matter (%) pH Phosphorus (mg L 1) Potassium (mg L 1) Calcium (mg L 1) Magnesium (mg L 1) Total nitrogen (mg L 1)

PC1

PC2

PC3

Base saturation

Soil acidity

Phosphorus and potassium

37.2% 0.68 0.35 0.14 0.16 0.89 0.84 0.69

26.5% 0.66 0.80 0.24 0.28 0.33 0.36 0.64

15.5% 0.13 0.11 0.75 0.65 0.07 0.14 0.19


landscape configuration and physiographic area using the method of variance partitioning (Lepsˇ & Sˇmilauer 2003). All CCAs were followed by Monte Carlo permutation tests for all canonical axes (999 permutations were used). PCA, DCA and CCA analyses were performed using the CANOCO 4.5 package (Lepsˇ & Sˇmilauer 2003). To eliminate the influences of rare species, which may distort the resulting pattern of species distribution, only species recorded in more than five plots were used in the analyses. The strong influence of dominant species was eliminated using square-root transformation. Size of catchment area was log-transformed before performing the analyses. Variance inflation factors were below 2.5, indicating low multicollinearity among the environmental variables (Lepsˇ & Sˇmilauer 2003). To explain the effects of particular landscape variables on species composition, plant life traits reflecting key processes in fragmented landscapes (Hermy et al. 1999; He´rault & Honnay 2005) were assigned to individual species. Raunkiaer life forms, CSR strategies, flooding tolerance and Ellenberg indicator values for nitrogen, light, moisture and soil reaction were taken from Ellenberg et al. (1992). Dispersal mode was assigned according to Frank & Klotz (1990). Type of reproduction and pollen vector were taken from the BiolFlor database (Klotz et al. 2002). Species affinity to the forest was identified in accordance with Chytry´ & Tichy´ (2003). Forest species (species that prefer forests), nonforest species (species that prefer non-forest communities) and indifferent species (species that occur equally in forest and non-forest communities) were distinguished. The seed weight, number, size and terminal velocity taken from the BiolFlor (Klotz et al. 2002) and LEDA (Kleyer et al. 2008) databases were used to describe seed characteristics. The potential seed number per square meter with 100% cover of a given species was used to quantify seed number (Kleyer et al. 2008). To assess seed dormancy, the longevity index of Thompson et al. (1998), with a range from 0 (non-dormant seeds) to 1 (dormant seeds), was calculated for each species when at least five records were present in the database of Thompson et al. (1997). Particular life traits were included in analyses as continuous, ordinal or binary variables (see Table 4). Relationships between plant traits and the net effects of the significant landscape variables were tested with generalized linear models (GLMs). The independent variable was species scores on the first canonical axis of partial CCA, representing the net effect of the given landscape variable, and the dependent variable

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was values of a particular life trait (Lososova´ et al. 2006). Separate tests for all species and forest species as defined above (Chytry´ & Tichy´ 2003) were performed to determine whether responses to landscape variables also differ within forest species. To separate the effects of landscape variables and phylogenetic relatedness among species on distribution of life traits, I used the method of phylogenetic correction published by Desdevises et al. (2003) and used for plant life trait patterns by Lososova´ et al. (2006) and Ku¨ster et al. (2008). Phylogenetic relatedness among species may influence the distribution of species traits along environmental gradients because closely related species more often have similar habitat requirements and tend to be more alike in terms of their life traits than do unrelated species. Thus, independence of life traits of related species cannot be assumed (Desdevises et al. 2003). Phylogeny was calculated based on the phylogenetic tree available in the BiolFlor database (Klotz et al. 2002). First, in order to express relatedness among species, a patristic distance matrix based on the number of nodes between species in the phylogenic tree was used to calculate classical (metric) multidimensional scaling (cMDS) (Lososova´ et al. 2006). Second, in order to choose the cMDS dimensions related to particular life traits, GLMs were calculated with species scores on the cMDS dimensions as independent variables and values of each life trait as dependent variable. Only the most important cMDS dimensions that expressed more than 85% of the total variation were used in these regressions (Lososova´ et al. 2006). Subsequently, the subset of cMDS dimensions that were found with the help of the Akaike information criterion (AIC) to be the best predictors was assigned to each life trait (Lososova´ et al. 2006). Finally, the effects of landscape variables on distribution of each life trait, corrected for relatedness among species, were calculated using GLMs. In these models, values of each life trait were used as dependent variable, with species scores on the first canonical axis of partial CCA representing the net effect of a given landscape variable as the independent variable, and species scores on the selected subset of cMDS dimensions as covariates. A separate phylogenetic correction was calculated for forest species. In all GLMs, a Gaussian distribution was used to test continuous and ordinal life traits, whereas a binomial distribution was applied for binary life traits (Table 4). Continuous life traits were log(x11) transformed or arcsin squareroot transformed if necessary to meet the assumption of GLMs. The cMDS and GLMs were

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calculated using the statistical program R (R Development Core Team 2009). To find whether the effects of landscape configuration on the species composition of floodplain forests are area dependent, I quantified the net effect of each significant explanatory variable within two distinct areas (i.e. mountain and lowland areas). Each of these areas comprises three physiographic areas: mountain area (areas A, B, C) and lowland area (areas D, E, F) (Fig. 1). Forward selection (P 5 0.001) and partial CCAs were calculated for each area, analogous to the analysis of the whole data set.

Results In this study, a significant response of floodplain forest species composition to habitat quality, physiographic area and landscape configuration was found. All of these environmental variables were shown to be important in the results for variance partitioning (Fig. 2b). Landscape configuration had a smaller effect on species composition than did habitat quality and physiographic area. However, the effects of these variables also partly overlapped. Habitat quality was more associated with particular physiographic areas than landscape configuration (Fig. 2b). The high importance of the significant explanatory variables was also supported by the results of DCA (Fig. 3). The first and second ordination axes corresponded to physiographic area and other environmental variables associated with habitat quality and landscape configuration, except for ancient forest presence. Nevertheless, the presence of ancient forest was captured by the third and fourth ordination axes (data not shown). In spite of the lower percentage of variability explained by landscape configuration, its effect on the species composition of floodplain forests was important (Fig. 2a, Table 3). The significant variables were: forest cover that characterizes forest fragmentation and the isolation of forest patches, the presence of ancient forest, indicating the historical continuity of the forest during at least the last 200 years, and distance to the nearest settlement, which represents decreasing human impact along an urban–rural gradient. Surprisingly, some landscape variables that are commonly mentioned as important for forest species composition, such as forest patch size, distance to forest edge and presence of particular land-cover classes adjacent to study plots,

Fig. 2. Percentage variation in species composition explained by environmental variables using CCA for (a) gross (black bars) and net (white bars) effects of particular variables of habitat quality and landscape configuration (dotted bars), and (b) relative importance of group variables (i.e. habitat quality, landscape configuration) and physiographic area expressed as percentages. 5 P 5 0.001; 5 Po0.01.

Fig. 3. DCA ordination of communities of floodplain forests with significant environmental variables (Table 1) fitted ex post as passive variables. First and second DCA axes explained 6.1% and 4.8% of the total variability, respectively. Abbreviations: Impanol, Impatiens nolitangere; Caltpal, Caltha palustris; Dryocar, Dryopteris carthusiana; Ranurep, Ranunculus repens; Aegopod, Aegopodium podagraria; Filiulm, Filipendula ulmaria; Angesyl, Angelica sylvestris; Rubuida, Rubus idaeus.

did not contribute significantly to the explained variance (Table 1). Within all species data, forest, mesophilous (i.e. species with lower Ellenberg indicator values for moisture) and shade-demanding species responded to all landscape variables (Table 4). They were more common in ancient forests, in stands surrounded by a landscape with high forest cover and in forests far


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Table 3. The 20 species with the best fits to the net effects of particular landscape variables in the CCA. Species are listed according to their scores on the first CCA ordination axis. Positive scores indicate species affinity to the presence of ancient forest, high forest cover or long distance to the nearest settlement. Negative scores indicate species affinity to the presence of recent forest, low forest cover or short distance to the nearest settlement. Forest species are marked in bold (according to Chytry´ & Tichy´ 2003). An extended version of this table is provided in Appendix 1. Presence of ancient forest Species Lemna minor Primula elatior Lysimachia nemorum Viola reichenbachiana Carex sylvatica Luzula pilosa Ulmus glabra Circaea alpina Geum rivale Mycelis muralis Chrysosplenium alternifolium Fraxinus excelsior Ajuga reptans Oxalis acetosella Moehringia trinervia Athyrium filix-femina Phalaris arundinacea Filipendula ulmaria Ranunculus auricomus agg. Cirsium heterophyllum

Forest cover Axis 1 score 1.14 1.10 1.08 1.08 1.04 0.76 0.76 0.71 0.62 0.58 0.42 0.42 0.24 0.23

Fit

Species

0.020 0.042 0.042 0.039 0.031 0.027 0.026 0.030 0.022 0.036 0.040

Milium effusum Carex sylvatica Campanula patula Daphne mezereum Carex remota Carex hirta Alchemilla vulgaris agg. Cardaminopsis halleri Fagus sylvatica Festuca rubra agg. Chrysosplenium alternifolium 0.043 Dryopteris filix-mas 0.023 Acer pseudoplatanus 0.033 Ajuga reptans

0.22 0.16 0.22 0.24 0.51

0.030 0.025 0.021 0.040 0.028

0.58

0.020 Crataegus sp.

Athyrium filix-femina Geum urbanum Prunus padus Sambucus nigra Salix fragilis

from the nearest settlement. Myrmecochorous species (e.g. Luzula pilosa and Viola reichenbachiana), trees at the expense of shrubs and species with a few large seeds (e.g. Paris quadrifolia) had a positive affinity to ancient forests. In contrast, recent forests without historical continuity were characterized by light-demanding species, shrubs, species with a large number of small seeds (e.g. Lychnis flos-cuculi) and species of wet meadows, such as Cirsium heterophyllum and Filipendula ulmaria. With decreasing forest cover in the surrounding landscape, the presence of species with a large number of seeds (e.g. Geum urbanum and Lythrum salicaria) increased in forests (Table 4). Similarly, endozoochorous shrubs (e.g. Crataegus sp. and Sambucus nigra), species with competitive strategy and light-demanding species of forest fringes, such as Galium aparine and Geum urbanum, were more often observed. Conversely, plants growing in landscapes with more forest cover were more often autochorous species (e.g. Carex sylvatica and Cardaminopsis halleri), flooding intolerant species and species employing CSR strategy. Species common to disturbed sites, such as Carex brizoides, Juncus effusus and Tripleurospermum inodorum, appeared to be characteristic of

Settlement distance Axis 1 score

Fit

Species

Axis 1 score

Fit

1.42 1.08 1.03 0.98 0.97 0.90 0.84 0.66 0.65 0.58 0.46

0.042 0.034 0.045 0.033 0.083 0.026 0.028 0.026 0.030 0.027 0.047

Ulmus glabra Viola riviniana Galeobdolon montanum Acer pseudoplatanus Caltha palustris Galium palustre Carex brizoides Juncus effusus Equisetum sylvaticum Stellaria alsine Carex canescens

0.68 0.51 0.49 0.32 0.17 0.18 0.22 0.26 0.32 0.40 0.50

0.021 0.030 0.022 0.030 0.022 0.023 0.023 0.031 0.032 0.054 0.022

0.41 0.30 0.29

0.55 0.56 0.59

0.018 0.029 0.017

0.18 0.23 0.34 0.41 0.52

0.026 Cirsium heterophyllum 0.026 Potentilla erecta 0.034 Tripleurospermum inodorum 0.029 Salix aurita 0.035 Dryopteris carthusiana 0.044 Betula pendula 0.054 Bistorta major 0.028 Holcus mollis

0.59 0.60 0.60 0.63 0.69

0.021 0.035 0.020 0.025 0.048

1.00

0.044 Prunus spinosa

0.81

0.018

forests adjacent to settlements. In view of specific life traits, hemicryptophytes, anemochorous (e.g. Betula pendula and Bistorta major) and epizoochorous species (e.g. Galium palustre), species with a large number of seeds (e.g. Juncus effusus and Tripleurospermum inodorum) and wind-pollinated species were more common in such forests (Table 4). Interestingly, acidophytes and oligotrophic species (e.g. Agrostis canina and Carex canescens) common in short oligotrophic grasslands also occurred, especially in the vicinity of settlements. Floodingintolerant species and species with a few large and heavy seeds with high terminal velocity (e.g. Fagus sylvatica and Paris quadrifolia), species pollinated by insects and self-pollinated species (e.g. Galeobdolon montanum and Viola riviniana) occurred in forests far from settlements. A similar pattern of species traits was found within forest species data, although some traits were not significant (Table 4). Moreover, the frequencies of endozoochorous and flooding-tolerant species and species with competitive strategy increased in recent forests, whereas species with CSR strategy were associated with ancient forests. Tree species occurred more often in forests surrounded by a landscape with less forest cover.

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Table 4. Relationships between the net effects of landscape variables and species traits expressed as coefficients (intercept) of the generalized linear models. Analyses were calculated separately for all species and forest species. Numbers of all species and of forest species that possess particular traits are given in parentheses after trait names. For continuous and ordinal variables, these indicate numbers of species for which values of the trait were available. Phylogenetically corrected P-values of significant traits are shown after slashes. 5 Po0.001; 5 Po0.01; 5 Po0.05; ns 5 non-significant; – 5 not calculated. Abbreviations: Co (continuous variable), O (ordinal variable), B (binary variable). Presence of ancient forest

Life form (B) Hemicryptophyte (133/28) Terophyte (17/-) Phanerophyte (16/13) Nanophanerophyte (20/6) Geophyte (33/11) Life strategy (B) C (76/20) CS (46/16) CSR (62/20) Forest affinity (B) Non-forest species (85/-) Forest species (57/-) Indifferent species (63/-) Flooding tolerance (B) Flooding tolerant species (51/6) Dispersal mode (B) Hydrochory (20/-) Autochory (58/17) Anemochory (127/30) Endozoochory (25/9) Epizoochory (86/13) Myrmecochory (31/9) Seed characteristics (Co) Seed longevity (128/28) Seed weight (164/43) Seed number (132/33) Seed size (159/42) Terminal velocity (134/33) Reproduction (B) Seed (64/21) Seed and vegetative (135/35) Pollen vector (B) Insect (128/35) Selfing (85/26) Wind (62/17) Ellenberg indicator value (O) Nitrogen (173/45) Soil reaction (129/41) Moisture (173/50) Light (192/55)

Forest cover

Settlement distance

all species

forest species

all species

forest species

ns ns 1.767/ 2.635/ ns

ns – ns 12.972/ ns

ns ns ns 2.017/ ns

ns – 2.068/ns ns ns

ns ns ns

2.272/ ns 2.517/

0.895/ns ns 1.214/

2.120/ns ns ns

ns ns ns

ns ns ns

2.577/ 3.315/ ns

– – –

1.332/ 1.645/ ns

– – –

1.528/ 2.477/ ns

– – –

ns

4.583/ns

1.771/

4.526/

1.068/

ns

ns ns ns ns ns 1.329/

– ns ns 5.313/ ns 3.447/

ns 1.045/ ns 1.238/ns ns ns

– ns ns ns ns ns

ns ns 1.604/ ns 1.046/ ns

– ns 1.860/ns ns ns ns

ns ns 1.300/ 0.142/ ns

ns ns ns ns ns

ns ns 0.620/ ns ns

ns ns 1.340/ ns ns

ns 0.584/ 0.688/ns 0.190/ 1.080/

ns ns ns ns ns

ns ns

ns ns

ns ns

ns ns

ns ns ns

ns ns ns

ns ns ns

ns ns ns

ns ns 0.806/ 2.038/

ns ns 1.127/ns 1.989/

ns ns 1.526/ 1.754/

ns ns 1.720/ 2.013/

Phylogenetic correction did not substantially influence distribution patterns of life traits along gradients of landscape variables. Only a few significant relationships between landscape variables and life traits disappeared after correction (Table 4). Differences in the significant environmental variables and amounts of vegetation variability explained by individual environmental variables were recorded between mountain and lowland areas (Fig. 4). At the same time, similar species pools were recorded in both areas (i.e. more than 90% of the 205 total species were identical), which shows that

all species 0.945/ns ns ns ns ns

ns ns 1.618/ 1.203/ 1.207/ns 2.142/ 1.868/ 1.371/ 1.915/

forest species ns – ns ns ns

ns ns ns 2.065/ ns ns ns 1.387/ 1.729/

responses of the same species to landscape variables differed between mountains and lowlands. In lowland areas, the species composition of floodplain forests was altered mainly by habitat quality and landscape configuration, whereas the effect of physiographic area was smaller. Conversely, a smaller effect of landscape configuration and major effects of habitat quality and physiographic area were found in mountain areas. The presence of ancient forest was significant only in the lowlands. Similarly, forest cover appeared to have a stronger influence in lowland areas


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Fig. 4. Net effects of variables of habitat quality (black bars) and landscape configuration (dotted bars) and relative importance of group variables (i.e. habitat quality, landscape configuration) and physiographic area for (a) lowland and (b) mountain areas expressed as percentages. 5 P 5 0.001; 5 Po0.01; 5 Po0.05; ns 5 non-significant.

but was also significant in the mountains. Different variables associated with urbanization were important in both areas. In the lowlands, the cover of urbanized area was significant, while distance to the nearest settlement was shown to be important in the mountains.

Discussion Relationship between landscape configuration, physiographic area and habitat quality Landscape configuration appears to be indispensable in the prediction of forest species composition across physiographic areas and various habitat qualities. Nevertheless, in accordance with studies by Dupre´ & Ehrleń (2002) and Turner et al. (2004), I found that habitat quality and physiographic area explained more variation in the distribution of species than did landscape configuration. The importance of physiographic area as a broad-scale factor reflects variations both in the species pool and in environmental variables such as climate and geology (Turner et al. 2004; Predick & Turner 2008). The most important predictors of species occurrence among the habitat quality variables were soil properties, namely soil acidity and base saturation. In a similar study, Turner et al. (2004) showed the flooding regime (i.e. elevation above the 100-year flow) to be the most important habitat quality factor influencing the distribution of trees in the floodplain forests of the Wisconsin River in the USA. The CCA and DCA analyses showed that the effect of landscape configuration was associated

both with physiographic area and with habitat quality. Both physiographic area and habitat quality may drive the spatial pattern of landscape variables and thus obscure the net effect of landscape configuration. Broad-scale variables determining physiographic areas, such as climate, topography and geology, as well as soil conditions were shown to be important in the prediction of the distribution of land-cover classes (Flinn et al. 2005; Predick & Turner 2008). For example, areas that are difficult to convert into agricultural use, such as mountains or wetlands, are more commonly associated with high forest cover. Effects of landscape configuration and forest history: explanation by species traits Species traits have repeatedly been shown to be important for explaining the effects of landscape variables on species composition in forests (e.g. Hermy et al. 1999; He´rault & Honnay 2005). Nevertheless, the consideration of particular species traits in isolation one by one does not correspond fully to the situation of real organisms. It is evident that life traits are not independent of each other and may interact (He´rault & Honnay 2005). For example, species with heavy seeds are considered to be poor colonizers, although they are able to spread rapidly when dispersed by animals (He´rault & Honnay 2005). Light-demanding species in floodplain forests are more often water-tolerant because the lower crown cover of forest stands is naturally related to waterlogged sites (Douda 2008). Forest shade-demanding species were associated with ancient forests, high forest cover in the surrounding landscape and long distance to the

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nearest settlement. Many authors have suggested that forest species are particularly vulnerable to forest clearance and habitat fragmentation (Honnay et al. 2002; Vellend 2003; Matlack 2005). Many forest species have a poor ability to colonize recent forests, isolated forest fragments and disturbed forest sites. Seed traits such as large seeds, low seed number, short-distance dispersal and short-lived soil seed bank, play an important role in the limited dispersal of species (Dupre´ & Ehrleń 2002; He´rault & Honnay 2005). In this paper, only the distribution of species with a low seed number depended on all landscape variables, whereas the responses of other seed traits were dependent on some particular landscape variables. The common occurrence of wetland and flooding-tolerant species in recent forests, forests surrounded by open landscapes and forests near settlements was recorded. This may be explained by the affinity of these species to frequently disturbed open areas of wet grasslands or areas in the proximity of large rivers and lakes. For example, in recent forests, these species represent ‘‘relicts’’ that survived after grasslands were overgrown and that gradually disappear due to a decrease in their viability in the shady conditions within forests (Falin´ska 1991). Higher proportions of trees, myrmecochorous species and species with large seeds were recorded in ancient forests. Species that are unable to rapidly colonize recently developed forests are commonly associated with ancient forests. Such ‘‘slow’’ species appear to be evolutionarily adapted to local, smallscale disturbances and gradual changes in relatively stable, late successional or virgin forests (Hermy et al. 1999; Bellemare et al. 2002). Long-lived species such as trees benefit from long habitat continuity, which gives them the opportunity to establish a stable population (Glaser & Wulf 2009). Myrmecochorous dispersal is largely associated with species of ancient forests (Hermy et al. 1999). Forest ants move seeds, especially locally within stands (Matlack 2005); moreover, they are very sensitive to habitat interruption and rarely travel through nonforest areas (Mitchell et al. 2002). The more frequent occurrence of forest shrubs recorded in recent forests may indicate their affinity to this particular stage of forest development. As earlier studies indicate, higher light availability facilitates the successful regeneration of shrubs during forest formation (Smidt & Puettmann 1998). A higher proportion of forest endozoochorous species was recorded in recent forests. Endozoochory is considered as a dispersal strategy allowing faster

colonization of recent forests than other dispersal modes (Dzwonko 1993; Hermy et al. 1999; Matlack 2005). The more frequent occurrence of competitive forest species in recent forest and stress-tolerant forest species in ancient forest was discussed as a consequence of higher nutrient and light availability in recent forests, which favour strong competitors (e.g. Hermy et al. 1999). The distributions of species associated with high landscape forest cover are limited by the ‘‘unfavourable’’ conditions of open landscapes, such as high light availability or human pressure (Jules & Shahani 2003; Honnay et al. 2005; Matlack 2005). In this study, the short-distance dispersal autochorous species were more often found in forests surrounded by landscapes with high forest cover. The high connectivity of forests in the landscape may allow them to spread quickly, whereas open areas among forest fragments may limit the dispersal of autochorous species. The common occurrence of endozoochorous species, mainly shrubs, in forest fragments surrounded by open landscape may be explained by the attractiveness of these forests for some animals that feed on seeds or fruits. The movement of these animals among forest fragments and their defecation of the seeds they consume increases the inflow of endozoochorous diasporas into forest fragments and thus the chance of establishing a viable population there (Dzwonko 1993; Bellemare et al. 2002; Matlack 2005). The higher number of species of shrubs, forest trees and other species with a competitive strategy in these stands may be directly conditioned by increased light penetration and agro-nutrients incoming from the surrounding landscape. The negative effect of settlement proximity on the distribution of many forest species is associated with the frequent human-caused disturbances in urban and suburban forests (Burton & Samuelson 2008). This also results in the occurrence of species indicating human intervention, such as forest clearing and pasture (McEvoy et al. 2006; Godefroid et al. 2007). Species with non-specific dispersal agents of seeds and pollen (i.e. wind-pollinated species, anemochorous and epizoochorous species) were more common in forests near settlements, whereas species that are self-pollinated or pollinated by insects were rare in these areas. Few studies dealing with communities of insect pollinators indicate that their diversity and abundance clearly decrease in intensively used urban landscapes (Steffan-Dewenter et al. 2002). Both anemochory and epizoochory as long-distance dispersal modes enable rapid coloni-


zation of new habitats (Hermy et al. 1999; Matlack 2005) that seem to be advantageous in the dynamic environments near settlements (Lososova´ et al. 2006). Similarly, the presence of species with a large number of small and lightweight seeds with low terminal velocity in the proximity of settlements recorded in this study may be explained as an adaptation to frequent human-made disturbances (Baskin & Baskin 1998). The common occurrence of acidophilous and oligrotrophic hemicryptophytes may be a consequence of long-lasting pasture management in the proximity of settlements. Pasture has been a traditional management method near Czech settlements since the first half of the 20th century (Sa´dlo et al. 2005). As shown by Verheyen et al. (1999), pasture that is maintained for several hundred years has a significantly decreased soil pH and degree of base saturation, probably due to export of nutrient-rich plant material. According to other studies (Lososova´ et al. 2006; Ku¨ster et al. 2008), phylogenetic correction did not significantly influence the resulting pattern of species traits. The small effect of phylogeny in comparison with the effects of environmental factors shows that species traits evolved largely independently in unrelated taxa (Lososova´ et al. 2006). Different effects of landscape configuration in lowland and mountain areas Landscape configuration more strongly affected the species composition of floodplain forests in the lowlands, although habitat quality played a major role in both lowland and mountain areas. Specifically, the presence of ancient forest altered the species composition only in the lowlands. Also, forest cover had a stronger effect in the lowlands but was also significant in mountainous areas. Finally, different variables indicating urbanization were significant in lowland and mountain areas. In the lowlands, the species composition of floodplain forests corresponded to urbanized areas, probably due to the omnipresent human impact in these areas characterized by higher settlement densities (Table 5). In comparison, distance to the nearest settlement was a crucial variable capturing urbanization in less densely populated mountain areas, where human impact is probably more markedly variable along an urban–rural gradient, i.e. from settlements to the surrounding landscape. The small effect of landscape variables on species composition of floodplain forests in mountain

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Table 5. Results of Mann–Whitney U-test and log likelihood ratio test (G-test) describing differences in habitat quality variables and landscape variables between lowland areas (L) and mountain areas (M). 5 Po0.001; 5 Po0.01; 5 Po0.05; ns 5 non-significant.

Water table Catchment area Slope Flooding elevation Base saturation Soil acidity Phosphorus and potassium Settlement distance Forest cover Urbanized area Presence of ancient forest

G-test

U-test

Comparison between areas

– – – – – – – – – – 1.34

7172 6934.5 6750 5998 5594.5 4152 6598 4778.5 4848 5133.5 –

ns ns ns L4M L4M LoM ns LoM LoM L4M ns

areas cannot be explained due to objective limitations of this study. However, several hypotheses should be discussed for future testing. Different effects of particular landscape variables, mainly forest continuity, between areas were discussed as a response to the distinct human impacts and site conditions that characterize these areas. This may influence processes such as the colonization of recent forests by forest species (Graae 2000; Vellend 2003; Verheyen et al. 2006). In this study, mountain areas, which showed smaller effects of landscape variables on species composition, were characterized by lower human impact, i.e. a lower proportion of urbanized areas, longer distance to the nearest settlement and higher forest cover (Table 5), as well as humid and cold climates. More acid, base-poor soils and lower flooding impact distinguished site conditions of mountain forests from those in the lowlands (Table 5). Several studies have found that the recovery process of recent forests takes a shorter time when forest loss is less severe, because the higher proportion of forests in the landscape increases the colonization ability of forest species (Honnay et al. 2002; Vellend 2003; Matlack 2005). Honnay et al. (2002) have shown that colonization is more successful in a forested landscape covered with 40% of forest than in an agricultural landscape with 10% forest. As Vellend (2003) predicted, the recovery process for an agriculture landscape with less than 20% forest would take at least several centuries, even for rapid plant colonizers. Matlack (2005) showed that landscape forest cover particularly influences fast-migrating species, whereas slowmigrating species are rather constrained by their rate of spread.

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Graae (2000) suggested that the weaker effect of the presence of ancient forest on forest species composition in some regions could also result from both less intensive land use in the surroundings of forest fragments (i.e. prevalent grassland environments) and a more humid climate. Both less intensive land use and a more humid climate provide more suitable habitats for the survival of forest species outside of forests or inside small forest fragments. Thus, the differences in species composition between recent and ancient forests may be less pronounced due to increased connectivity of the landscape allowing easier migration of forest species (Graae 2000). The soil conditions of individual regions have been discussed as a possible cause of the weak effect of forest continuity on species composition, but the results of these studies are not consistent (Graae 2000; Verheyen et al. 2006). On one hand, the community recovery of recent forests by forest species has been shown to be faster in unproductive acidic soils, where the dominance of strong competitors is limited (Graae 2000). On the other hand, the rate of species recovery has been found to increase with increasing site productivity, which may advance the reproductive success of colonizers (Verheyen et al. 2006).

Conclusions Significant effects of recent landscape configuration and forest continuity on the forest species composition of floodplain forests were recorded independent of physiographic areas and variations in habitat quality. However, landscape variables had fewer effects than habitat quality and physiographic area. Habitat quality variables have been shown to play a dominant role in the species composition of floodplain forests. Among landscape variables, the presence of ancient forest, forest cover and distance to the nearest settlement affected vegetation patterns. The effects of individual landscape variables differed between mountain and lowland areas; being generally more important in the latter. This study suggests that forest species that are valued by nature conservationists are associated with ancient forests, forests surrounded by landscapes with high forest cover and forests far from the nearest settlement.

Acknowledgements. The author thanks K. Boublı´ k, K. Douda, R. He´dl, J. Kochańkova´, K. Prach, J. Rolecˇek, D. Zeleny´ and two anonymous reviewers for their critical and helpful comments, Z. Lososova´ for help with the calculation of phylogenetic correction, A. Drasˇ narova´ for help in

the lab and P. Szabo´ and M. Hilarie for improving the English of this paper. This work was funded through the grant 42900 1312 3114 from IGA.

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Table S1. More than 40 species with the best fits to net effects of particular landscape variables in the CCA. Species are listed according to their scores on the first ordination axis. Positive scores indicate species affinity to the presence of ancient forest, high forest cover or long distance to the nearest settlement. Negative scores indicate specie affinity to the presence of recent forest, low forest cover or short distance to the nearest settlement. Forest species are marked in bold (according to Chytry´ & Tichy´ 2003). Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

Supporting Information Additional supporting information may be found in the online version of this article:

Received 11 April 2009; Accepted 7 July 2010. Co-ordinating Editor: Dr. Sara Cousins