Different response to environmental factors and spatial variables of two attributes (cover and diversity) of the understorey layers

Forest Ecology and Management 258 (2009) 1267–1274

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Different response to environmental factors and spatial variables of two attributes (cover and diversity) of the understorey layers ˜ ez Antonio Gazol *, Ricardo Iba´n Department of Plant Biology, University of Navarra, Irunlarrea s/n, 31008 Pamplona, Spain

A R T I C L E I N F O

A B S T R A C T

Article history: Received 20 January 2009 Received in revised form 11 June 2009 Accepted 15 June 2009

We investigated the relationships among the vertical layers of a temperate forest and the power of environmental and spatial factors to explain the variation in two attributes of shrub and herbaceous layers: cover and diversity. In the study site, 102 square plots with sides of 20 m were established in a stratified random design. Among the environmental factors we studied overstorey related factors, soil attributes and topographic related variables. To use the space as an explanatory variable, we applied the Principal Coordinates of Neighbourhood Matrices method. Variation partitioning with regression analyses was used to discover which variables better explained variation in cover and diversity within the shrub and herbaceous layers. The spatial patterns displayed by cover and diversity in the shrub and herbaceous layer were more similar between both layers than within the same layer. Along the same lines, the amount of variance explained by all the environmental (overstorey, soil and topography) and spatial variables together was higher in the models of cover than in those of diversity. The differences in the explained variation were primarily due to the higher spatial fraction in the models of cover. In general, shrub and herbaceous cover was higher on southern slopes with a more diverse overstorey, high values of soil temperature and low values of litter cover. Otherwise, higher values of shrub and herbaceous diversity were found on steep slopes with low values of leaf litter cover. However, while higher values of shrub diversity were found on southern slopes, herbaceous values were more patchily distributed. The differences in the amount of variation explained by the spatial variables in both attributes (cover and diversity) indicate their different spatial arrangement at the scale which we considered. While values of cover were more continuous in space, those of diversity showed a patchy distribution of higher values. The presence of this spatial component could be interpreted as the importance of seed dispersal or unmeasured environmental variables. The results indicate that the lack of management in temperate forests allows species movement in a heterogeneous environment favouring higher values of cover and diversity in the understory layers. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Shrub layer Herbaceous layer Beech Oak Variation partitioning Spatial ecology

1. Introduction The understorey plays an important role in the functioning of forest ecosystems (Augusto et al., 2003). It is important in the energy flow, carbon dynamics and cycles of some essential nutrients like N and K (Gilliam, 2007). Moreover, although the understorey contains a small part of the total biomass, it usually accounts for the highest number of species (Whigham, 2004). It also has important implications for the regeneration of trees, because it can influence overstorey seedling dynamics (Gilliam, 2007). The importance of the understorey for management and

* Corresponding author. Tel.: +34 948425600; fax: +34 948425619. E-mail address: [email protected] (A. Gazol). 0378-1127/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2009.06.024

conservation plans justifies the number of studies that focus on it (Gilliam, 2007). In temperate forests, many factors have been reported to affect the diversity of the understorey, among which we find environmental conditions (Svenning and Skov, 2002; Ha¨rdtle et al., 2003; Kolb and Diekmann, 2004), perturbations (Onaindia et al., 2004; Ga´lhidy et al., 2006; Naaf and Wulf, 2007), litter layer (Eriksson, 1995; Schimpf and Danz, 1999; Dzwonko and Gawronski, 2002) and intensity and type of management (Graae and Heskjaer, 1997). In managed forests, the understorey is affected by human activity (Graae and Heskjaer, 1997), but untouched ancient forests are good scenarios to study vegetation patterns created by natural dynamics in order to use this knowledge as a baseline for management and conservation planning. Among these factors, the effect of the overstorey has also been studied (Beatty, 1984; Augusto et al., 2003; Thomsen et al., 2005; Estevan et al., 2007) and a general conclusion is

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that an increase in the number of species in the overstorey tends to increase understorey diversity (Gilliam, 2007), pointing to its importance in determining understorey composition. Biotic relationships play an important role in the distribution of the overstorey species (Schwarz et al., 2003). Biotic relations have been less intensively studied in the understorey, although phenomena like dispersal or vegetative propagation have been recognized as important factors in the distribution of understorey species (Miller et al., 2002; Svenning and Skov, 2002). Nowadays, biotic relations are frequently studied through neighbourhood relationships (Legendre, 1993). After the seminal paper by Borcard et al. (1992), the elimination of the effect of space from the environment and the determination of biotic relations through space (Schwarz et al., 2003) have been widely used in ecological research. But biotic and environmental factors are scale dependent (Levin, 1992), so the study of different spatial scales would seem to be useful (Borcard and Legendre, 2002). The understorey of temperate forests comprises mainly shrubs, herbs, young trees, saplings, bryophytes and lichens. In biodiversity studies, we can find references treating all these components together (e.g. Svenning and Skov, 2002; Ha¨rdtle et al., 2003). Other authors have focused on a specific layer, for example woody species (e.g. Estevan et al., 2007) or seedlings and saplings (Miller et al., 2002). Finally, it is less usual to find studies treating the forest layers as separate parts in a hierarchical classification (e.g. Houle, 2007), specifically in meso-scale studies (e.g. Augusto et al., 2003). In our opinion, the hierarchical division of the understorey by functional characteristics, for example, growth form or vertical structure, could prove extremely useful for the study of understorey patterns. By using diversity indexes we take into account both species richness and the relative abundance of each species (Wilsey et al., 2005). However, one characteristic of these indexes is that if there is one dominant species and a relatively large number of rare ones, diversity is low but cover is high. However, it is not clear what is more important to improve our knowledge of forest ecosystems, species diversity or species cover. So, if we study both diversity and cover, we could obtain a more complete picture of understorey patterns. In general terms, the influence of the overstorey on the understorey layers is not completely clear (Barbier et al., 2008), nor is it clear whether diversity and cover of herbaceous and shrub layers of the understorey are driven by the same forces. Moreover, we do not know whether these forces are controlled by environmental factors (Whittaker, 1956) or by less well understood biotic ones such as dispersal (Hubbell, 2001). These insights provide sufficient motivation for carrying out studies that could serve to improve our knowledge of understorey patterns. The main objective of our study is therefore to discover what the principal factors are that explain variation in cover and diversity of the herbaceous and shrub layer in an unmanaged temperate forest. To this end, we shall: (1) determine the cover and diversity patterns of the tree, shrub and herbaceous layers and their interrelations and, (2) explain the cover and diversity patterns of the shrub and herbaceous layers using environmental (overstorey, soil and topographic related factors) and spatial variables. 2. Materials and methods 2.1. Study site The study site, called the ‘‘Suspiro’’ watershed, is a bowl-shaped forest of 132 ha open to the east. It is located in Bertiz Natural Park (438100 N, 18360 W), in the northwest of Navarra, Spain (Fig. 1). Heights vary from 600 m in the south- and northwest part, to 200 m in the eastern part of the basin, with an average slope of 37%.

Fig. 1. Situation and topographic map of the study site. The presence of streams and open areas is shown. Each sample plot is represented by a black square proportional to its size.

The climatic conditions are characterized by moderately warm summers and mild winters. The long-term annual mean precipitation and temperature are 1525 mm and 14 8C respectively, with a mean monthly low of 7.2 8C in January and a high of 21.6 8C in July (Gobierno de Navarra, 2008). The bedrock consists mainly of silicic shales and schists from the Palaeozoic era. The vegetation in the study site is dominated by an acidophilus beech (Fagus sylvatica L.) forest on the north-facing slopes, while a mixed beech– oak (Quercus robur L.) acidophilus forest grows on the south-facing ones (Bermejo et al., 2008). 2.2. Sampling design and variable survey Sampling was conducted in 102 square plots with sides of 20 m, distributed in a stratified random design (Fig. 1). From May to July 2006 we identified all the vascular plant species present in each plot and assessed visually the abundance of each species using the Braun-Blanquet cover-abundance scale (Westhoff and van der Maarel, 1973). Species were then classified into three groups according to their growth form and vertical structure. Hemicryptophytes, geophytes, therophytes and some herbaceous chamaephytes were classified as herbs; other chamaephytes, nanophanerophytes and saplings of phanerophytes under 3 m tall were grouped as shrubs; and finally, phanerophytes were classified as trees. For identification of plant species and classification of growth forms, we followed Aizpuru et al. (1999). Additionally, the cover of each vertical layer was estimated visually. We calculated an alpha diversity index for each layer in each sample plot. According to van der Maarel (2007), the BraunBlanquet cover-abundance scale was transformed into a quasimetric scale with the formula ln C = (OTV  2)/a, where C is the cover percentage, OTV is the observed Ordinal Transform Value and a is a weighting factor that equals 1.520. With the transformed 0 cover scale, the exponential of the Shannon–Wiener index (eH ) was calculated. We selected this index because it ranges from a minimum of 1 to a maximum that equals the total number of species in each plot when species are equally abundant (Gurevitch et al., 2006).

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Eight variables that might influence plant distribution were surveyed in each sample plot. Aspect was measured with a compass (Silva Clino Master, Silva Sweden) and was used to calculate an exposure index on a scale of 1.0 (northeast-facing slopes) to 1.0 (southwest-facing slopes) using the equation TAspect = cos(45  aspect) (Beers et al., 1966). Elevation was obtained from a topographic map 1:5000 (Gobierno de Navarra, 1995), slope was measured with a clinometer (Silva Clino Master, Silva Sweden), and cover of leaf litter was assessed visually. Soil temperature was measured with a soil penetrating thermometer of 10 cm (Testo 110, Testo Ltd. Portugal) during three sunny days with constant weather in June 2007. To avoid differences in temperature during the day and among days, the temperature was measured every 2 h in three preselected plots (one on the northfacing slope, another on the south-facing one and the last one in the central part of the basin). This allowed us to standardize all the measurements as if they had been performed at the same hour of the middle of the day. Top soil water content was measured with two soil moisture sensors SM200 (Delta-T device, Cambridge, UK) on one dry day in July 2007. Twenty measurements were made in each plot in order to obtain an averaged measure and to control the variability inside the plot. Finally, the presence of streams and open areas was assessed visually. We annotated the UTM coordinates of each sample plot to use the space as explanatory variables. Instead of using the UTM coordinates directly, we applied the Principal Coordinates of Neighbourhood Matrices method. This method is based on a principal coordinate analysis of a truncated pairwise geographic distance matrix between sampling sites. Eigenvalues associated with positive eigenvectos can be used as spatial predictors in multivariate regression (Dray et al., 2006). Moreover, these eigenvalues have the advantage that they are orthogonal to each other and that they represent various structures over the whole range of scales encompassed by the sampling design (Borcard and Legendre, 2002). Firstly, we created a Euclidean distance matrix with the X and Y UTM coordinates of each plot. Then, to decide a threshold value in order to truncate the Euclidean distance matrix, we tried different approaches based on matrix connection techniques (Dray et al., 2006), using the program Passage 1.1.3.4 (Rosenberg, 2001) for this purpose. Finally, the calculation of the principal coordinate variables (from now on PCNM variables) was performed using the program SpaceMaker 2 (Borcard and Legendre, 2004). 2.3. Statistical procedures Geostatistical techniques were applied to make continuous maps of the response variables. We used the geostatistical analysis extension in ArcGis 9.1 (Esri, Redlands, USA) to adjust the semivariograms and create the maps with kriging. Variables were transformed for normality if needed. Similarly, data were detrended if needed and, when anisotropic behaviour was present, directional semivariograms were carried out (Legendre and Legendre, 1998). Finally, maps were created using the adjusted parameters of the theoretical semivariogram. We used the Pearson correlation coefficient to measure both the relationship between cover and diversity of the forest layers and that between the quantitative measured variables: aspect, elevation,

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slope, leaf litter cover, soil temperature and soil moisture. The significance of each correlation coefficient was tested using 9999 unrestricted permutations in order to avoid problems of the p value due to the spatial autocorrelation of the variables. The analyses were done with the function corPerm2 (Legendre, 2005) of the R statistical language (R Development Core Team, 2007). Variation partitioning (Borcard et al., 1992) was used to discover the fraction of variation in cover and diversity of the herbaceous and shrub layers explained by the environmental and spatial variables. The environmental matrix was created including together the overstorey related factors (tree diversity, tree cover and open areas), soil attributes (leaf litter, soil temperature and soil moisture) and topographic related variables (T-aspect, slope, elevation and presence of streams). Firstly, we selected a set of environmental and spatial variables explaining a significant portion of variation in each one of the response variables (a + b and b + c, respectively). Then, we determined the fraction of variation explained by the whole set of variables (a + b + c). Partial regression was used to determine the fraction of variation explained by the environmental (fraction a) and the spatial variables (fraction c) when removing their interaction (fraction b). After that, another variation partitioning of the a + b fraction was performed to determine the relative contribution of the overstorey, soil and topographic related factors to this environmental fraction. The ‘‘leaps’’ library (Lumley, 2008) of the R statistical language (R Development Core Team, 2007) was used to select the sets of explanatory variables. The variation partitioning analyses were carried out with the ‘‘vegan’’ library (Oksanen et al., 2007) and the ‘‘Stats’’ package of the R statistical language (R Development Core Team, 2007). The environmental and response variables were transformed if needed. After that, all the quantitative environmental variables were standardized to have zero mean and unit variance. 3. Results 95 species of vascular plants, accounting for 1408 presences, were used in the analyses (see Appendix 1): 67 species were coded as herbs, 21 as shrubs and young trees, and 12 as trees. Table 1 summarizes the cover and the exponential of the Shannon–Wiener diversity index for the shrub and the herbaceous layers. Values of cover and diversity of each stratum were heterogeneously distributed within the basin (Figs. 2 and 3). The mean and range of the overstorey, soil and topographic related variables are summarized in Table 2. Regarding spatial variables, the selected distance to truncate the Euclidean distance matrix was 143.5 m, determined with the minimum spanning tree method. The spatial matrix contained 65 PCNM variables representing the spatial structure of the basin at different scales. Variables 1–11 were classified as broad-scale ones because they represent spatial patterns at distances higher than 500 m; variables 12–44 were coded as medium-scale for representing patterns between 200 and 500 m; lastly, variables 45–65 were classified as fine-scale ones because they represent spatial patterns at distances lower than 200 m. The correlation coefficients between cover and diversity of the forest layers are shown in Table 3. Cover and diversity of the tree layer were found to have a negative relationship. The relationship

Table 1 Summary of the response variables studied in the shrub and the herbaceous layers. Layers

Attribute

Mean value

Range

Transformation

Shrub_C Shrub_eH Herb_C Herb_eH

Percentage cover Exponential Shannon–Wiener’s index Percentage cover Exponential Shannon–Wiener’s index

23  3 3.67  0.21 27  3 4.41  0.34

0–95 0.00–8.43 0–90 0.00–15.67

Arcsine(x1/2) None Arcsine(x1/2) log10(x + 1)

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Fig. 2. Maps of cover and diversity of the shrub layer. Maps were obtained by interpolating respectively the values of percentage cover and the exponential Shannon–Wiener diversity index of each sample plot. In both maps, a second order trend was eliminated before creating the map. Cover: 0,047981*Hole Effect(401,41) + 0,046226*Nugget. Diversity: 1,2158*Hole Effect(279,88) + 2,2954*Nugget.

Fig. 3. Maps of cover and diversity of the herbaceous layer. Maps were obtained by interpolating respectively the values of percentage cover and the exponential Shannon– Wiener diversity index of each sample plot. In both maps, a second order trend was eliminated before creating the map. Cover: 0,018356*Exponential(216,51) + 0,064557*Nugget. Diversity: 0,020903*Exponential(182,61) + 0,063143*Nugget.

between diversity of the tree layer and cover and diversity in the shrub and herbaceous layer was positive although the relationship to herbaceous diversity was non-significant. Cover and diversity in the shrub and herbaceous layers were positively related but they showed higher correlations between attributes of different layers than within the same layer. The lowest correlation was found between cover and diversity of the shrub layer.

The correlation between the six quantitative environmental variables, excluding tree cover and diversity, can be seen in Table 4. We found positive correlations between aspect and soil temperature. Additionally, both variables were negatively correlated with leaf litter cover and elevation. Similarly, aspect and slope were both negatively correlated to soil moisture. The correlation coefficients between the other variables were non-significant.

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Table 2 Summary of the explanatory environmental variables. Variable

Type

Units

Mean value

Overstorey Tree cover (tree_C) Tree diversity (tree_eH) Open areas (gaps)

Quantitative Quantitative Dummy

Percentage cover

Topography Aspect (TAspect) Elevation (height) Slope (slope) Streams (streams)

Quantitative Quantitative Quantitative Dummy

Scale (1 to 1) Meters above sea level Sexagesimal degrees

Soil Leaf litter (litter) Soil temperature (temp) Soil moisture (moist)

Quantitative Quantitative Quantitative

Percentage cover Centesimal degrees Millivolts

3.1. Shrub layer The mean values of cover and diversity of the shrub layer were 23  3% and 3.67  0.21 respectively (Table 1). The environmental variables selected to explain variation in cover of the shrub layer (fraction a + b) were, in order of selection, leaf litter cover, elevation, tree diversity and open areas. These together explained 37.6% of this variation (Fig. 4a). Among these variables, leaf litter and elevation had a negative relationship to shrub cover. However, tree diversity and open areas had a positive relationship to shrub cover. In the spatial model (fraction b + c), 17 spatial PCNM variables were selected, which explained 54.4% of variation in shrub cover. Among the spatial variables, only medium- and broad-scale ones were selected. The fraction of variation explained by the environmental and spatial variables together (fraction a + b + c) was 62.9%. The environmental variables explained 8.5% of variation in shrub cover (fraction a) when we eliminated their joint effect with the spatial ones. The selected spatial variables without the combined effect of the environmental ones (fraction c) explained 25.3% of shrub cover variance. Consequently, the amount of variance in shrub cover

Table 3 Correlation coefficients between cover (C) and diversity (eH) of the tree, shrub and herbaceous layers.

Tree C Tree eH Shrub C Shrub eH Herb C Herb eH

Tree C

Tree eH

Shrub C

Shrub eH

Herb C

Herb eH

1

0.203* 1

0.247* 0.376** 1

0.256* 0.320** 0.067 1

0.211* 0.342** 0.447** 0.437** 1

0.073 0.095 0.402** 0.324** 0.232* 1

*

The significance of each correlation coefficient against 9999 unrestricted permutations is indicated with p < 0.05. ** The significance of each correlation coefficient against 9999 unrestricted permutations is indicated with p < 0.01. Table 4 Correlation coefficients between the six quantitative environmental variables. TAspect TAspect Height Slope Litter Temp Moist

1

Height

Slope **

0.301 1

0.033 0.007 1

Litter 0.372 0.032 0.056 1

Temp **

Moist **

0.547 0.536** 0.080 0.372** 1

0.295** 0.181 0.268** 0.111 0.111 1

** The significance of each correlation coefficient against 9999 unrestricted permutations is indicated with p < 0.01.

91  1 1.25  0.35

0.29  0.63 396  8 23  1

74  0 12.2  0.7 506  7

Range

Transformation

20–100 1.00–2.91

Inverse (1/x) Inverse (1/x) None

1.00 to 1.00 262–635 2–42

log10(x + 1.5) None None None

10–99 10.3–13.4 320–674

loge((1/x) + 1) None None

explained by the joint effect of space and environment (fraction b) was 29.1% and the unexplained fraction was 37.1%. The partition of the a + b fraction showed that 11% of the environmental fraction was due to the pure effect of the overstorey related factors (tree diversity and open areas). Similarly, 12% of this fraction was attributable to the pure effect of the topographic variable elevation. The soil factor leaf litter cover contributed 4% to this fraction. The overlap between the overstorey and topographic variables accounted for 3% of the fraction. Finally, 8% was attributable to the interaction between overstorey and soil. The pattern of diversity of the shrub layer was different to that observed for cover (Fig. 2). Aspect, slope, leaf litter cover and soil temperature were the selected environmental variables (fraction a + b), explaining together 27.4% of shrub diversity variance (Fig. 4b). Aspect, slope and soil temperature showed a positive relationship to shrub diversity, whereas leaf litter had a negative relationship to it. The selected spatial PCNM variables (fraction b + c) explained 33.0% of variation in shrub diversity. Among these variables, broad-scale ones were the most important. The overall amount of explained variance in shrub diversity (fraction a + b + c) was 37.2%. The environmental variables, after their spatial structure was removed (fraction a), explained 4.2% of shrub diversity variance. Similarly, the selected spatial variables (fraction c) were able to explain 9.8% of this variance. So, fraction b amounted to 23.2% of the variance and the percentage of shrub diversity variance that remained unexplained was 62.8%. The soil variables (leaf litter cover and soil temperature) contributed 6% to the environmental fraction (a + b), and 9% was due to the topographic variables (aspect and slope). The interaction between both sets of variables was 12%. 3.2. Herbaceous layer The mean values of cover and diversity in the herbaceous layer were 27  3% and 4.41  0.34 respectively (Table 1). Leaf litter, soil temperature and tree diversity were the selected environmental variables (fraction a + b), which explained 36.6% of cover variance in the herbaceous layer (Fig. 4c). Leaf litter showed a negative relationship to cover of the herbaceous layer. In contrast, soil temperature and tree diversity had positive relationships to herbaceous cover. The spatial fraction (b + c) was created with 13 PCNM variables, which were able to explain 59.8% of cover variance. All the spatial scales were represented in the selected set of spatial variables. The overall amount of explained variation (fraction a + b + c) was 63.4%. The amount of variance explained by the environmental variables without spatial structure (fraction a) was 3.5%. On the other hand, the spatial PCNM variables without the effect

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Fig. 4. Variation partitioning results of cover and diversity of the shrub (a and b) and herbaceous (c and d) layers. The percentage variation explained by the environmental (fraction a), spatial variables (fraction c) and their interaction (fraction b) is indicated in the diagrams. The sign of the environmental variables shows their positive or negative effect. The significance of each correlation coefficient against 9999 unrestricted permutations is indicated (*p < 0.05 and **p < 0.01). The partition of the environmental fraction (a + b) is provided, showing the relative contribution of the overstorey, soil and topographic related variables. Area shown not to scale.

of the environmental ones (fraction c) explained 26.8% of variation in the cover of the herbaceous layer. The amount of variance explained by the spatially structured environment (fraction b) was 33.1% and the variance that remained unexplained (fraction d) was 36.6%. The partition of the a + b fraction showed that the overstorey factors contributed 2% to the variance explained and the soil factors (leaf litter cover and soil temperature) explained 26% of this fraction, while 9% of the environmental fraction was due to the interaction between both sets of variables. The map of diversity of the herbaceous layer displays a different pattern to that observed for cover (Fig. 3). The selected set of environmental variables (fraction a + b) included the presence of open areas and streams, leaf litter cover, slope and soil moisture, which explained 35.7% of herbaceous diversity variance (Fig. 4d). All the variables except leaf litter cover had positive relations to diversity. Four spatial PCNM variables were selected (fraction b + c) and explained 12.5% of variation in diversity. Two of them represent spatial patterns at broad-scale and the two others at medium-scale. The overall amount of explained variance of herbaceous diversity (fraction a + b + c) was 41.5%. The environmental variables without spatial structure (fraction a) explained 29.0% of variation in diversity. The amount of variance explained by the spatial PCNM variables alone (fraction c) was 5.8%. The fraction of variation explained by the joint effect of space and environment (fraction b) was 6.7%, so 58.5% of the variance in herbaceous diversity remained unexplained. 8% of the environmental fraction was explained by the pure effect of the overstorey variable presence of gaps. The pure effect of the soil (leaf litter cover and soil moisture) and topographic variables (slope and presence of streams) were 7% and 15% respectively. The overstorey showed an overlap with the soil variables that contributed with 6% to the environmental fraction. Finally, 1% of this fraction was explained by the overlap between soil and topography.

4. Discussion 4.1. Relationships between cover and diversity patterns in the forest layers Cover and diversity were negatively correlated in the tree layer. This fact is a consequence of the different characteristics of the two dominant species, beech and oak. Canopies dominated by beech are denser, with lower light penetration that excludes most of the accompanying sub-canopy tree species. Conversely, oak forms a more open canopy, allowing other tree species coexistence (Graae et al., 2004). In the understorey, the observed spatial patterns points to the independence between cover and diversity in the shrub layer, whereas in the herbaceous layer cover and diversity were slightly related. On the one hand, the highest number of species found in our study site was in the herbaceous layer (69 species). Some common species of temperate forests are microsite dependent (Ga´lhidy et al., 2006) and the size of the sample plots seems to be large enough to enable the presence of different microsites (Beatty, 1984). In this sense, species coexist in environmentally heterogeneous areas, giving as a result higher values of cover and diversity in both layers. On the other hand, areas with more homogeneous environmental conditions enable the development of species that become locally common, giving as a result higher values of cover independently of diversity. In this sense, the higher values of cover of herbaceous and shrub layers found on southern slopes could be related to the dominance of the common herb Deschampsia flexuosa and shrubs from the Ericaceae family (data not shown). 4.2. Determinants of shrub and herbaceous cover Diversity of the tree layer favoured shrub and herbaceous cover. High diversity in the tree layer creates environmental hetero-

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geneity (Svenning and Skov, 2002). Some shrub and herbaceous species could reach their optimum values of cover in those areas with a mixed canopy (Graae et al., 2004). In this sense, Ha¨rdtle et al. (2003) found that in acidophilus beech forests some understorey species were favoured by the presence of oak species in the tree layer. The presence of a diverse overstorey could also have a positive effect on the decomposition of litter because the leaf litter layer depends on the overstorey composition, topography and soil chemistry (Sariyildiz et al., 2005). In temperate forests, the litter layer imposes a barrier preventing the passage of light (Schimpf and Danz, 1999) and acts as a physical impediment for small seed germination (Eriksson, 1995), thus affecting understorey species composition (Dzwonko and Gawronski, 2002). In this sense, the presence of a diverse overstorey could increase the light reaching the forest floor enabling a faster decomposition of litter. There were also environmental variables which only affected cover values in one of the layers. The positive effect of gaps on shrub cover could be related to the higher values of light found in gaps, which enables the development of beech saplings (Rozas, 2003) and of a reduced number of shrubs like Rubus ulmifolius and Hypericum androsaemum, increasing the values of cover independently of those of diversity. Otherwise, the positive effect of soil temperature on herbaceous cover could be related to the decomposition of litter. Soil temperature was higher on southern slopes where the values of radiation tend to be higher in these latitudes. Most of the spatial structure of shrub and herbaceous layer covers (around 30%) was due to the spatial structure of environmental drivers. However, there is also a relevant fraction of unexplained spatially structured variance. This can be attributed to non-measured environmental parameters as pH (Kolb and Diekmann, 2004), but also to the effect of spatially structured biotic relations (Legendre, 1993; Dray et al., 2006). Moreover, historical forest cover has been recognized to be an important factor determining the current species distribution (Graae et al., 2004) and thus we must exercise caution when interpreting the spatial component. Due to the scale limitations imposed by the study lag (distance among plots), seed dispersal seems to be the most reasonable biotic interaction, because other interactions like competition or facilitation have a finer scale domain. Therefore, the high spatial fraction could indicate the importance of dispersal processes. Seed dispersal is obviously dependent on the seed source, so in places where shrub and herbaceous cover were higher, seed rain could increase. Although it has been argued that in temperate forests common species have low dispersal capabilities (Brunet and von Oheimb, 1998), the continuity of the forest in space and time could favour seed dispersal and the maintenance of the soil seed bank (Olano et al., 2002). Regarding scale, broad- and medium-scale variables were those that better explained variations of cover in both layers. However, the model of herbaceous cover has an important set of variables representing fine-scale patterns that could indicate the structure of cover of this layer at finer scales than the cover of shrubs.

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The model of herbaceous diversity was the most complex in the number of environmental variables selected and so we should exercise caution when interpreting the causes. The higher number of species found in this layer made it possible to find species with a preference for moist conditions (Ha¨rdtle et al., 2003) as well as other heliophilous and generalist species with preferences for habitats with high radiation and bare soils (Ga´lhidy et al., 2006). In our opinion, the selection of variables such as gaps and streams, without spatial continuity in our study design, may indicate that some herbaceous species are microsite dependent (Ga´lhidy et al., 2006). The distribution of some shrub and herbaceous species in microsites with size lower than our fine study limit made difficult to find their spatial structure, especially in the herbaceous layer. The PCNM variables only represent spatial structures whose fine limits are constrained by the minimum distance between sample plots (Borcard and Legendre, 2002). Moreover, the consideration of those microsite-dependent species in the context of a heterogeneous environment could obscure the influence of dispersal processes in the distribution of these species (Honnay et al., 2001). 4.4. Concluding remarks and implications for conservation and management Our study serves as an example of the different behaviours of cover and diversity in the understorey layers. The overstorey, soil and topographic variables selected were more similar between cover and diversity in different layers than in the same layer. Moreover, the spatial component was higher in the models of shrub and herbaceous cover than in those of diversity, pointing to their different spatial configuration. The distribution of the understorey species is driven by environmental heterogeneity (Whittaker, 1956) as well as by dispersal mechanisms (Hubbell, 2001), as was suggested by Svenning and Skov (2002). Conservation and management plans must take into account the different response of cover and diversity of the herbaceous and shrub layers, considering which factors, and at which spatial scales, are influencing the composition of both layers. The structure and dynamic of large forested areas without management, as in Bertiz Natural Park, favours the presence of different microsites (Beatty, 1984) and species dispersal (Brunet and von Oheimb, 1998) resulting in the coexistence of places with higher cover and diversity in both layers. Acknowledgements The authors thank J.M. Olano for discussion and advice in an earlier version of this manuscript. We are also grateful to the Parque Natural ‘‘Sen˜orı´o de Be´rtiz’’ for giving permission to conduct this research within the protected area. The research was partially supported by Fundacio´n Caja Navarra (programa ‘‘Tu´ eliges, Tu´ decides’’) as well as by Fundacio´n Universitaria de Navarra and by a predoctoral grant from the Asociacio´n de Amigos de la Universidad de Navarra to Antonio Gazol. In addition, we thank Jaime Urı´a for his help in the field work.

4.3. Determinants of shrub and herbaceous diversity Steep slopes favoured higher values of shrub and herbaceous diversity. On the one hand, the presence of pronounced slopes could enable litter movement downward, favouring the presence of bare soils (Facelli and Pickett, 1991). On the other hand, when these slopes are south-facing they could enable higher values of light. Therefore, shrub diversity was favoured by the higher light levels on the southern steep slopes while herbaceous diversity was positively affected by the bare soils created by the movement of litter down slopes (Facelli and Pickett, 1991).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2009.06.024. References Aizpuru, I., Aseginolaza, C., Uribe-Echebarrı´a, P.M., Urrutia, P., Zorrakin, I., 1999. Claves ilustradas de la flora del Paı´s Vasco y territorios limı´trofes. Servicio Central de Publicaciones del Gobierno Vasco, Vitoria-Gasteiz.

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