Mountain summer farms in Røldal, western Norway vegetation classification and patterns in species turnover and richness

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1 Plant Ecology 170: , Kluwer Academic Publishers. Printed in the Netherlands. 203 Mountain summer farms in Røldal, western Norway vegetation classification and patterns in species turnover and richness V. Vandvik 1, * and H.J.B. Birks 1,2 1 Department of Botany, University of Bergen, Allégaten 41, Bergen, N-5007, Norway; 2 Environmental Change Research Centre, University College London, 26 Bedford Way, London, WC1H OAP, UK; *Author for correspondence Received 19 September 2001; accepted in revised form 28 November 2002 Key words: (Detrended) (canonical) Correspondence analysis, Classification, Cultural landscapes, Diversity, Environmental effects, Generalized linear modelling, Grazing animal effects, Land-use effects, Species richness, Species turnover Abstract This paper discusses vegetation types and diversity patterns in relation to environment and land-use at summer farms, a characteristic cultural landscape in the Norwegian mountains. Floristic data (189 taxa) were collected in m 2 sample plots within 12 summer farms in Røldal, western Norway. The study was designed to sample as fully as possible the range of floristic, environmental, and land-use conditions. Vegetation types delimited by two-way indicator species analysis were consistent with results from earlier phytosociological studies. Detrended correspondence analysis and canonical correspondence analysis show that rather than being distinct vegetation types, the major floristic variation is structured along a spatial gradient from summer farm to the surrounding heathland vegetation. Species richness (alpha diversity) was modelled against environmental variables by generalized linear modelling and compositional turnover (beta diversity) by canonical correspondence analysis. Most environmental factors made significant contributions, but the spatial distance-to-farm gradient was the best predictor of both species richness and turnover. While summer farms reduce mean species richness at the plot scale, the compositional heterogeneity of the upland landscapes is increased, thereby creating ecological room for additional vegetation types and species. Within an overall similarity across scales, soil variables (ph, base saturation, LOI, phosphate and nitrogen) differed considerably in their explanatory power for richness and turnover. A difference between productivity limiting factors and environmental sieves is proposed as an explanation. Species turnover with altitude is relatively low in grasslands as compared to heaths. Introduction Many of the characteristic habitats for wildlife in Europe are semi-natural; they have emerged and are maintained under the effects of human land-use (Fægri 1988; Lawton 1999). Today, the extensive, lowintensity land-use practices necessary to maintain these habitats are no longer economically feasible, and are being discontinued. At the same time, these habitats are being destroyed by new, increased, or intensified human activities such as urbanisation, intensified crop production, overgrazing, pollution, fertilisation, nutrient leaching, and species introductions. The result is a general trend for areas of no- or highintensity land-use to increase at the expense of areas of (traditional) extensive low-intensity land-use (Wallis de Vries et al. 2002). Many semi-natural systems have long ecological histories and maintain high species diversity (Hobbs and Hunneke 1992; Milchunas et al. 1988). Accordingly, the discontinuation of lowintensive land-use has recently been identified as one of the major factors adversely affecting the floras and faunas of Europe (Stanners and Bordeau 1995) including Scandinavia (Bernes 1993; Fremstad and Moen 2001).

2 204 In regions with steep altitudinal gradients, extensive areas necessary for traditional land-use practices were not available in the lowlands, and animals were moved up to the mountains during summer. At summer farms (sæter, støl), domestic animals were gathered in sheds or enclosures for milking and shelter at night, but were allowed to range freely in the mountains during the day. This practice, essentially an altitudinal transhumance system, is known as summer farming in Scandinavia, but is also known from mountainous regions elsewhere in Europe and Asia (Frödin 1929; Reinton 1955, 1957, 1961; Sterten 1974; Rhinschede 1988; Bonard and Dubost 1992; Amiaud et al. 2000). Ethnological (e.g., Reinton 1955, 1957, 1961; and Bryn and Daugstad (2001)), archaeological (e.g., Bjørgo et al. (1992) and Randers and Kvamme (1992)), and pollen-analytical (e.g., Kvamme et al. (1988, 1992)) studies suggest that the practice dates back to prehistoric times, and that the extent of summer-farming areas has varied greatly through time, depending on population size and economy. Summer farming created a landscape with large variation and strong gradients in grazing animal effects, which again resulted in very distinct vegetation patterns (Resvoll-Holmsen 1920; Nordhagen 1943; Spatz 1980; Bonard and Dubost 1992; Austrheim et al. 1999; Bryn and Daugstad 2001; Vandvik and Birks 2002a, 2002b). Weedy communities and productive manured grassland dominate the area immediately surrounding the houses and enclosures. Away from farms, this heavily disturbed and manured vegetation gives way to extensive low-productive perennial grasslands. Different floristic elements meet here; generalist grassland herbs and grasses, more demanding semi-natural grassland specialists, alpine plants that take advantage of the open vegetation structure created by grazing animals, and common subalpine plants. During centuries of continuous low-intensity land-use, these have become characteristic vegetation types. Further away from summer farms, grasslands blend into the surrounding low-alpine dwarf-shrub heaths or subalpine birch woodland. At the landscapescale, summer farms appear as habitat islands that are scattered throughout extensive sub- and low-alpine heaths and woodlands. In the mountains of Norway, the total biomass harvested by grazing and mowing has decreased by 61% from 1939 to 1996 (Edelmann 1997). There is no uniform trend, however, as the intensity of land-use varies at all spatial scales, from regions and landscapes to individual summer farms. Today, many summer farms are in different stages of secondary succession after abandonment (Spatz 1980; Austrheim et al. 1999; Austrheim and Eriksson 2001; Vandvik and Birks 2002a). Despite their importance both for traditional agricultural practices and as an integral part of the mountain landscape, and the threat posed by recent landuse changes, summer farms in the Scandinavian mountain range have, until recently, received surprisingly little attention in the ecological literature (Nordic Council of Ministers 1989; Austrheim and Eriksson 2001). The work presented here was initiated to contribute in the effort (Olsson 1998; Austrheim et al. 1999; Austrheim 2001, 2002) to reduce this gap in knowledge. This paper is the third in a series that aims to provide a factual ecological basis for the conservation and maintenance of biodiversity in this characteristic cultural landscape. In the two previous papers we investigated the relative importance of land-use and environment for local (within-farm) and regional (between-farm) floristic patterns (Vandvik and Birks 2002a) and used species traits to identify the ecological process by which these vegetation patterns are created and maintained (Vandvik and Birks 2002b). The current study has two major aims. First, we provide a classification of the main vegetation types within summer-farms in Western Norway that can serve as a link to, and comparison with, earlier phytosociological studies. We also evaluate the effectiveness of classification for summarising variability in summer farm vegetation. Landscape diversity may be broken down into hierarchical components: two of these are compositional turnover among habitats (between-habitat, or beta diversity) and species richness within each of these (within-habitat, or alpha diversity) (Whittaker 1975; Huston 1994). We have previously focused on the first of these, compositional turnover. The second aim of this paper is therefore to extend the focus to consider both components and their responses to different environmental and landuse variables in order to evaluate more fully the consequences for diversity at the landscape-scale. Study area The Suldal valley (59 50 N, 6 50 E) runs southwest to northeast into the Hardangervidda plateau in western Norway. The valley has a characteristic glacial topography with flat bottom, steep hillsides, and

3 205 hanging side-valleys, and is surrounded by mountains reaching m above sea level (a.s.l.) (Figure 1). Bedrock consists predominantly of Precambrian basement gneiss, with phyllitic Cambrio-Ordovician sedimentaries and amphibolites present locally (Naterstad et al. 1973). The climate is sub-oceanic, with high autumn precipitation and winter snowfall, and relatively small temperature amplitude (ca. 18 C). Two meteorological stations, Røldal at 393 m a.s.l. and Svandalsflona at 1200 m a.s.l., record annual precipitation of 1444 mm and 1245 mm, respectively (Førland 1993), and July mean temperatures of 14.1 C and 9.8 C, respectively (Aune 1993). Steep relief and variable topography cause local climatic variation within the broad-scale altitudinal climatic gradient (Sterten 1974). The climatic forest limit is ca. 800 m a.s.l on north-facing slopes, and 900 m on south-facing slopes, but is heavily depressed around summer farms. The sub-alpine forest consists almost exclusively of Betula pubescens, and tall-herb and tall-fern communities are prominent on the steep hillsides. Above the forest limit the vegetation is mainly dwarf-shrub heath dominated by Vaccinium myrtillus, Calluna vulgaris, and Empetrum hermaphroditum (Odland 1981). The summer-farming areas are located at m a.s.l (Reinton 1955, 1957, 1961). Methods Field and laboratory methods Twelve summer farms, representing a chronosequence from farms in use today to farms abandoned ca. 10, 20, and 40 years ago were selected (Figure 1). In m plots were placed subjectively so as to sample the major floristic variation at each farm, including heavily grazed vegetation, fenced infields, and surrounding heath or woodlands (Figure 1). The cover-abundance of each species in the plots was estimated using the Domin scale (Dahl 1957). Nomenclature follows Lid (1985) for vascular plants, Smith (1978, 1990) for bryophytes, and Krog et al. (1980) for lichens. For each farm, data on land-use history were based on interviews, and altitudes were recorded from maps. The size of the farm was measured as the average distance from the milking shed or other animal assembly point, to where the semi-natural grasslands gave way to the surrounding heath or woodland vegetation. As animals range freely during the day, but are gathered at night, the spatial gradient extending from farm centres to surrounding vegetation was assumed a priori to parallel a gradient of decreasing animal influence on vegetation. Plot position relative to farm centre is therefore considered as a surrogate for plot-scale grazing pressure. In order to standardise this variable between farms, distance from plot to farm centre was divided by farm size for each individual farm. Slope and aspect of each plot were measured, and potential solar irradiation calculated (Oke 1987). This radiation index is used as an estimate of plot-scale local climate. Soil chemical analyses were carried out on air-dried soil passed through a 2-mm sieve. Soil ph, loss-on-ignition (LOI), total nitrogen (Kjeldahl method), phosphate extractable by a lactic acid solution, and sodium, potassium, calcium, and magnesium extractable by ammonium acetate were measured using standard procedures (Røsberg 1984). Soil phosphate was logtransformed prior to numerical analysis (Palmer and Dixon 1990; Palmer 1993), and cation availability was expressed as two summary variables, cation exchange capacity and base saturation (Røsberg 1984). The Kjeldahl method estimates total soil N, but as the bulk of soil N is bound to the organic fraction, and only a small proportion is available to plants (as NH 4 + or NO 3 depending on soil ph) (Etherington 1975; Schroeder 1984), this is not an ecologically relevant parameter for the plants. Following Dahl et al. (1967) and Økland (1988) we therefore standardised the Kjeldahl N by using the N:LOI ratio as a parameter for available nitrogen in the soil. Abbreviations, units, and statistical characteristics of each of the measured and derived explanatory variables are given in Table 1. Numerical data analyses To enable comparison with phytosociological studies, vegetation types were delimited by two-way indicator species analysis using TWINSPAN V. 2.1b (Hill (1979), modified by C. J. F. ter Braak and H. J. B. Birks). To evaluate the explanatory power of each division in the hierarchical classification, TWINDEND V. 0.4 (J. M. Line & H. J. B. Birks, unpublished) was used to calculate the mean internal dispersion (or heterogeneity) of the TWINSPAN groups at each division level as a percentage of the mean dispersion of the total data (cf. Orloci (1967)). Additionally, the distinctness of the TWINSPAN vegetation classes was evaluated by a detrended correspondence analy-

4 206 Figure 1. Map of the study area in Røldal, and its location within the Suldal valley. The investigated summer farms and the Valldalsvatnet and Votna reservoirs are marked. Contour intervals are 150 metres. sis (DCA, Hill and Gauch (1980)), with detrendingby-segments and non-linear rescaling of axes. Ordinations were also used to estimate species turnover, and to quantify compositional responses to the explanatory variables. The length of the floristic gradients was estimated by the DCA. The compositional responses to the explanatory variables were quantified by canonical correspondence analyses (CCAs, ter Braak (1986, 1987)), with one environmental or land-use variable at a time entered as the

5 Table 1. The measured and derived environmental and land-use variables, with the abbreviations and units used. The means, standard deviations (SD), and ranges of the quantitative variables are given. Variables and abbreviations Units Data attributes Mean SD Range Altitude ALT metres Slope SLO degrees Radiation index RI units Loss-on-ignition LOI % of dry soil Relative nitrogen content N % of LOI Phosphate P log(mg/100 g dry soil) ph ph units Cation exchange capacity CEC meq/100 g dry soil Base saturation BASE % of total cations Relative distance to centre RD Years since abandonment YRS years See text for explanations about how these variables have been measured or calculated. 207 explanatory variable, and the first constrained axis tested statistically by Monte Carlo permutation tests (ter Braak 1990) using 299 unrestricted permutations. All ordinations were done using the computer program CANOCO V. 3.12a (ter Braak 1987) with strict convergence criteria (see Oksanen and Minchin (1997)), and down-weighting of rare taxa (ter Braak 1987). Ordination plots were drawn by CANODRAW 3.0 (Šmilauer 1993). Species richness patterns were investigated by means of regression analysis, following Pausas (1994) and Vetaas (1997). Generalized linear models (GLM, McCullagh and Nelder (1989)), as implemented by SAS v (SAS Institute 1997) were used to model species numbers to the quantitative environmental variables. As the response variable consists of discrete data (counts), a Poisson error distribution was assumed, and a log link function used. Models with linear and second-order polynomial terms were fitted for all predictor variables. The statistical significance of the three alternative models for each variable (null, linear, and second-order polynomial) was assessed by testing whether the decrease in residual deviance when an extra term was added to the regression model was greater than the critical value of 2 at = 0.05 with 1 degree of freedom (residual deviance test, McCullagh and Nelder (1989)). The total deviance in species richness that the environmental variables can potentially account for was estimated by multiple regression analyses. First, all significant variables (linear and second-order terms) from the univariate regression analyses were included in the full model. Non-significant terms ( = 0.05, residual deviance test with Bonferroni correction) were then removed one at a time using backward selection (Crawley 1993). As an additional evaluation of the relation between compositional and species richness patterns, GLM models of species richness were also fitted against the DCA axes 1 and 2 plot scores. Results In the m 2 sample plots 189 taxa of macro-lichen, bryophyte, and vascular plant were recorded, with an average of 21 taxa per plot (Appendix 1). Species turnover within the dataset is high; the large eigenvalue of the DCA axes (Table 3) indicates good dispersion of taxa (ter Braak 1987). Axis 1 is 4.85 standard deviations, indicating a complete turnover of species so that plots at the extremes of this axis have, in general, no species in common. Species richness in the data varies from very low to relatively high (7 46 taxa plot 1 ). Below, we first present the results of the plot classifications and relate these to a DCA of plots. We then evaluate and compare patterns in species richness and compositional turnover in the context of the environmental and land-use gradients. Description of the vegetation types at summer farms The TWINSPAN division into groups 1, 2, 3, and 4 (Figure 2) is associated with large decreases in mean dispersion (ca %) (Orloci 1967), and these groups differ substantially in their composition, species richness, soil characteristics, and mean distance

6 208 Figure 2. TWINSPAN and TWINDEND results, with mean dispersion (%) along the vertical axis. Numbers in circles refer to groups 1, 2, 3, and 4. Capital letters A K indicate the subgroups. The TWINSPAN indicator species and their pseudospecies cut-levels for each division (Hill 1979) are indicated. Taxon abbreviations are explained in Appendix 1. to farm centre (Figure 2; Table 2; Appendix 1). Further divisions of the major groups were carried out to ease comparison with earlier phytosociological studies. Group 1 Heaths The 42 plots are located on steep ground away from the farms. The soils are low in P and N (Appendix 1). Vegetation is dominated by dwarf-shrubs (Calluna vulgaris, Vaccinium myrtillus, V. uliginosum). Deschampsia flexuosa, Cornus suecica, and Maianthemum bifolium are characteristic. The dense groundlayer is dominated by Pleurozium schreberi, Barbilophozia lycopodioides, and Dicranum scoparium. Subgroups A, B, and C differ in soil nutrient status and moisture. Subgroup A consists of dry, basepoor plots dominated by Calluna vulgaris with few grasses and herbs, and a ground-layer rich in Cetraria islandica and Cladonia rangiferina. Subgroup B contains moister plots on more organic soils. All dwarfshrubs are constant, and Betula pubescens seedlings are frequent. Trientalis europaea and Potentilla erecta are common. Pleurozium schreberi is prominent. Subgroup C represents tall-herb and tall-fern plots on nutrient-rich soils on steep, relatively inaccessible slopes. The lush, species-rich field-layer is characterised by Hypericum maculatum, Aconitum septentrionale, Bartsia alpina, Anthoxanthum odoratum, Geranium silvaticum, Rubus saxatilis, and Thelypteris limbosperma. Group 2 Poor grasslands The 41 plots occur on relatively flat ground at intermediate distances from farm centres. Soils are minerogenic, with intermediate ph and higher N content than Group 1 (Appendix 1). These infertile grasslands are characterised by Agrostis capillaris, Anthoxanthum odoratum, Carex bigelowii, C. brunnescens, Deschampsia flexuosa, Nardus stricta, Phleum alpinum, and Poa alpigena. Alchemilla alpina and Rumex acetosella are common. The ground-layer is very variable, but often contains Rhytidiadelphus squarrosus and Drepanocladus uncinatus. Subgroup D consists of species-poor plots, (average 17 taxa plot 1 ). Agros-

7 Table 2. Summary of the four TWINSPAN groups. The TWINSPAN sample subgroups (A K; Appendix 2) in each group are given, as well as some characteristic species and soil features for each major group. Abbreviations for the environmental variables follow Table 1. Heath Poor grassland Wet vegetation Farm centres ABC DEF G HIJK Calluna vulgaris Carex bigelowii Carex echinata Poa alpigena Vaccinium spp. Anthoxanthum odoratum Potentilla palustris Trifolium repens Betula pubescens Alchemilla alpina Carex nigra Deschampsia cespitosa Maianthemum bifolium Phleum alpinum Viola palustris Poa annua Cornus suecica Rumex acetosella Sphagnum spp. Stellaria media Pleurozium schreberi Carex brunnescens Barbilophozia lycopodioides Rhytidiadelphus squarrosus Dicranum scoparium 209 Low N Intermediate N High CEC High N Low P Low LOI Very high LOI High P Variable ph Intermediate ph High ph High ph tis capillaris is more dominant, and Rumex acetosella, Carex vaginata, and C. nigra are more prominent here than in subgroups E and F. Plots in E have a consistently lower soil nutrient-status than plots in D and F. The field-layer is more open and speciesrich with, on average, 26 taxa plot 1. Several herbs and graminoids, such as Galium saxatile, Polygonum viviparum, Viola palustris, Solidago virgaurea, Leontodon autumnalis, Alchemilla vulgaris coll., Carex pilulifera, C. pallescens, and Luzula frigida are characteristic. The ground-layer is also more species-rich than in D, and Drepanocladus uncinatus and Dicranum scoparium are prominent. Four plots comprise subgroup F. The field-layer is even more open than in E, and Alchemilla alpina, Rumex acetosella, and Campanula rotundifolia are characteristic. The ground-layer is dense, with abundant Rhytidiadelphus squarrosus, Racomitrium elongatum, and Polytrichum alpinum. Group3 Wetvegetation These 11 plots all occur on flat ground near farm centres. Soils are highly organic, with intermediate N content (Appendix 1). Although several wetland species occur in all plots, there are few constants, and the group is rather heterogeneous. Dominants are Carex echinata, C. nigra, and Nardus stricta, but Juncus filiformis, Potentilla palustris, and Viola palustris are also characteristic. Group 4 Farm centers This group contains 36 plots on flat ground near farm centres. Soils have considerably higher ph, P, and N values than the other vegetation types, whereas cation concentrations and organic content are variable (Appendix 1). Species typical of intensively grazed and manured meadows dominate, but the vegetation composition is variable, with few species common throughout. Agrostis capillaris, Deschampsia cespitosa, Poa alpigena, P. annua, Rumex acetosa, and Trifolium repens are characteristic. The groundlayer is sparse. Plots in subgroup H have relatively low P and ph values. Agrostis capillaris is more dominant than elsewhere, and Phleum alpinum, Leontodon autumnalis, and Rumex acetosella are all common. Ground-cover consists mainly of Rhytidiadelphus squarrosus and Brachythecium salebrosum. Subgroup I has prominent Ranunculus repens and soils rich in N with very high ph. Poa alpigena, Rumex acetosa, Stellaria graminea, and Achillea millefolium are also characteristic. Subgroup J has soils with high organic content and very high ph and a dense field-layer dominated by Carex brunnescens, C. nigra, Deschampsia cespitosa, orpoa alpigena. Characteristic herbs include Veronica serpyllifolia, Cerastium fontanum, and Taraxacum spp. Subgroup K has the highest N, P, ph, and base-saturation means in the data. The vegetation is speciespoor (mean 10 taxa plot 1 ), and is dominated by Stellaria media, Poa annua, P. alpigena, and Ranunculus repens. Ruderals, such as Chamomilla suaveolens,

8 210 Polygonum aviculare, Capsella bursa-pastoralis, and Plantago major are characteristic. Vegetation types and floristic gradients The plots are more or less evenly spread in DCA axis 1 and axis 2 space, and although groups 1 4 are relatively non-overlapping in DCA space there is little discontinuity between the TWINSPAN classes. The first two DCA axes explain 11.6% and 5.1% of the total inertia, respectively, which are relatively high considering the large number of taxa, many zero values, and high compositional turnover in the data (ter Braak 1987). DCA axis 1 Taxa at the negative end are nitrophilous and trampling-resistant herbs and grasses (Figure 3a), such as Stellaria media, Polygonum aviculare, and Poa annua. They are followed by taxa typical of mown fields such as Trifolium pratense and Phleum pratense. Some very common (e.g., Agrostis capillaris, Nardus stricta, Rhytidiadelphus squarrosus) as well as some less frequent (e.g., Luzula frigida, Polygonum viviparum, Campanula rotundifolia) species apparently have no affinities for any specific part of the ordination (cf. ter Braak (1987)). These species are typically plants that tolerate a wide range of grazing and management regimes. A different group of taxa, e.g., Carex pallescens, Deschampsia cespitosa, Carex bigelowii, and Phleum alpinum, are related to the ordination axis and have their ecological optima near the centre (ter Braak 1986). At the high positive end there are dwarf-shrubs, as well as Betula pubescens, Pleurozium schreberi, and Maianthemum bifolium. This axis clearly reflects a gradient from manured and trampled vegetation at the farm centre (subgroups H, I, J, and K) through mown or grazed grasslands (subgroups D, E, F, and G) to heaths or forests (subgroups A, B, and C) surrounding the farms (Figure 3b). Correlations between environmental variables and DCA axes (Table 3) confirm that the major floristic gradient parallels the spatial gradient. This floristic gradient is also a gradient of decreasing soil fertility, as DCA axis 1 is negatively correlated to soil N, P, ph, and base saturation. DCA axis 2 Taxa with negative scores (Figure 3a) include Carex echinata, Juncus filiformis, Carex nigra, and Viola palustris. These all favour wet or moist habitats. Taxa with high positive scores include Rumex acetosella, Alchemilla alpina, Veronica offıcinalis, Polytrichum juniperinum, Cladonia rangiferina, and Cetraria islandica, all plants of dry, nutrient-poor habitats. Axis 2 reflects a gradient from wet grasslands (subgroup G) and more nutrient-rich heathland vegetation at the negative end to dry, nutrient-poor grasslands and heaths (subgroups D, E, F) at the positive end (Figure 3b). The second DCA axis is strongly positively correlated to altitude, but also to phyllitic bedrock and years since abandonment, and negatively correlated to LOI and CEC (Table 3). This suggests that the gradient is related to differences between farms, but also a soil moisture gradient independent of DCA axis 1. Species turnover and richness related to ecological and land-use factors The CCA results (Table 4) show that all environmental variables account for statistically significant amounts of variation in the floristic data, but that they differ considerably (by a magnitude of three) in the amount of variation explained, the best variables being relative distance (6% of total variation), slope (5%), ph (4.8%), base saturation (4%), and soil nitrogen (3.9%). The full CCA, with all statistically significant variables included (23.9%), explains much less than the sum of the variances explained by the individual CCA s, indicating that there is considerable covariation between variables. The GLM results show that many environmental variables are also statistically significant predictors of richness, and again they vary greatly in the amount of variation they explain (Table 5). The best predictors, soil P and relative distance to farm centre, explain more than 25% of the deviance, slope explains 21%, and N 9.3%. Altitude, soil organic content, ph, CEC, and base saturation account for small but significant fractions of the deviance. Surprisingly, there is no detectable response in richness to solar radiation or years since abandonment. The response patterns are variable (Table 5, Figure 4a). Responses to relative distance and nitrogen are both humped; the latter model has an optimum when soil N comprises slightly more than 2% of LOI. The response to increasing P is negative: as P increases 10-fold from the poorest to the richest TWINSPAN vegetation groups, the number of taxa per plot, as predicted by GLM, decreases by 1/3. Richness increases with increasing ph, but decreases with increasing soil organic content, CEC, and base saturation. The observed over-

9 Figure 3. Ordination of (a) taxa and (b) samples on DCA axes 1 and 2. The two axes account for 16.7% of the inertia (= variance) in the floristic data. The different symbols on (b) represent TWINSPAN sample subgroups A K (Appendix 1). Taxon abbreviations are explained in Appendix

10 212 Table 3. Pearson correlations between the environmental variables and DCA ordination axes 1 and 2. Abbreviations for the environmental variables follow Table 1. = p < 0.05, = p < 0.01, = p < DCA = detrended correspondence analysis, SD = standard deviation units. Axis 1 Axis 2 Gradient length (SD) Eigenvalue ALT SLO RI LOI N P ph CEC BASE RD YRS PHYL AMPH MOWN Table 4. Canonical correspondence analyses (CCA) results showing the explanatory power of each individual explanatory variable: the eigenvalue of the first (constrained) axis, its p-value based on Monte Carlo permutation tests, and % explained is the amount of floristic variation that the individual variable accounts for. The bottom row shows the variance explained by a CCA including all explanatory variables after forward selection. Predictor Eigenvalue p % variance variables explained none 4.63 ALT SLO RI LOI N P ph CEC BASE RD YRS Full CCA dispersion (deviance/df >> 1), even from the best univariate models, indicates that although these models are statistically significant, no individual environmental variable is capable of predicting variation in richness. The multiple regression results (Table 5) confirm this. Although the final environmental multiple regression model explains an appreciable amount of variation in richness (49.3%, Figure 4b), the large number of significant terms makes ecological interpretation difficult. There are considerable similarities in the explanatory power of the different environmental variables for species turnover and richness patterns (Spearman s rank correlation 0.65, p < 0.05). Accordingly, the main floristic gradients are also gradients in species richness: plot scores in the two-dimensional DCA diagram (Figure 3b) account for 43.8% of the variation in species richness (Table 5). There are some striking differences, however. Soil ph and base saturation rank higher as explanatory factors for species turnover than for richness, whereas the situation is the opposite for phosphorus and soil organic content (Tables 4 and 5). Discussion Is the TWINSPAN classification comparable to phytosociological studies and is it an effective representation of the vegetation patterns? Comparisons of data obtained with different aims and methods may not give a realistic picture of the real similarities and differences between study areas and times. Nevertheless, an attempt is made to compare Nordhagen (1943) study on alpine vegetation in Sikilsdalen and Knatterud s (1974) study on summer farms in Grimsdalen, both in eastern Norway, with the semi-natural grassland vegetation types (TWIN- SPAN subgroups D to K) from Røldal in western Norway. Our primary TWINSPAN division delimits grasslands from heathlands. This division closely agrees with Nordhagen (1943) delimitation of vegetation types characteristic of summer farms. The poor grasslands (subgroups D, E, and F), along with subgroup H loosely resemble Nordhagen (1943) Nardeto-Agrostion tenuis, which he describes as summer farm grasslands and considers being dependent on fertilisation and grazing. The vegetation is dominated by Festuca rubra, Poa alpigena, Agrostis capillaris, Nardus stricta, or Deschampsia cespitosa, and is divided into three associations, mainly on the basis of dominants. Knatterud (1974) describes several com-

11 Figure 4. (a) Scatter plots of species richness (y axes) against selected environmental and land-use variables (x axes). Regression results and parameter estimates are given in Table 4. (b) Scatter plot of predicted number of species (y axis) against observed number of species (x axis) from the environmental multiple regression model, which explains 43.5% of the deviance. Variables and parameter estimates are given in Table

12 214 Table 5. Results from GLM regressions of species richness on the explanatory variables. For each model the parameter estimates for (a) first and (b) second-order terms, their standard deviations (SD), the residual deviance, and the associated degrees of freedom (DF), the p-value from the residual deviance test, and the percentage of the total deviance that is accounted for (% deviance explained) are given. The bottom rows show the test results and percentage deviance accounted for by multiple backwards-selected regression models on the environmental variables, and on the DCA compositional turnover gradients. NS = not significant. Abbreviations for environmental variables follow Table 1. Predictor variables Parameter estimates Residual deviance DF p % Deviance explained a SD B SD none ALT SLO RI NS 0.0 LOI N P ph CEC BASE RD YRS NS 0.0 Multiple regression: RD + N + SLO + ph + BASE Multiple regression: DCA1 + DCA munities within Nordhagen s Nardeto-Agrostion tenuis. TWINSPAN divisions are based on the full species composition, and as a result subgroups D, E, F, and H contain plots which differ ecologically (Appendix 1; Figure 3b) but not with respect to dominants, and comparisons cannot be extended to the subgroup level. The wetland vegetation (Group 3) resembles Knatterud s Carex nigra sociation. TWIN- SPAN subgroup I resembles Nordhagen s Ranunculus repens-poa trivialis sociation and Knatterud s Poa pratensis coll. sociation. TWINSPAN subgroup K closely resembles Nordhagen s Stellaria media- Capsella bursa-pastoris sociation and Knatterud s Stellaria media sociation. TWINSPAN subgroup J is very heterogeneous (Figure 2), and although the vegetation is clearly related to other subgroups within the farm centres (Figure 3b; Appendix 1), it has no obvious previously described analogue. In phytosociological studies, typical or representative vegetation types are selectively sampled (Nordhagen 1943) and as a result phytosociological tables typically show marked floristic discontinuities. Although the Røldal sample plots were also selected subjectively, our aim was not to look for distinct vegetation types but simply to sample as much as possible of the total variation. The above discussion shows that there are similarities between the TWIN- SPAN and the phytosociological classification, indicating that these probably represent real and important patterns in the vegetation. Further, the fall in mean dispersion within groups down through the TWINSPAN hierarchy indicates that at least some of these divisions have considerable explanatory power. Still, all groups have high within-group dispersions, indicating that they are not homogeneous (Figure 2), very few species occur exclusively in one or a few TWNSPAN groups (Appendix 1), and variation between TWINSPAN groups in the ordination diagram is gradual with considerable overlap (Figure 3b). Seen together, these results indicate that groups are not distinct vegetation units. However, if the groups are viewed as reference points in a continuum rather than as real entities, the classification has some explanatory power. Some differences between summer farms in eastern and western Norway Some species found at summer farms in eastern Norway (Nordhagen 1943; Knatterud 1974; Austrheim et al. 1999; Austrheim 2001, 2002) were not found, or were much less common in Røldal in western Norway. Several of these, e.g., Betula nana, Polygala amarella, Potentilla crantzii, Trollius europaeus, Vi-

13 215 ola biflora, and V. rupestris, have their distributional centres east of the Scandes (Mossberg 1992; Lid and Lid 1994; Pedersen 1990). Furthermore, some species with a western distribution in Norway, e.g., Galium saxatile, Festuca vivipara, and Carex pilulifera, occur exclusively, or are more common, in Røldal than in summer farms further east. There also seem to be systematic differences between the summer-farm soils. The ph values in eastern Norway are higher by ca. 0.7 ph units, base saturation is higher by 20%, and soil N is slightly higher, whereas soil LOI is ca. 20% lower (Knatterud 1974; Austrheim et al. 1999). There may be at least three explanations for these differences: First, the study areas have different site characteristics such as bedrock. Second, the higher precipitation and lower summer temperatures in an oceanic climate (Fægri 1960) decrease the rate of mineralisation of organic matter, whereas leaching is accelerated (Etherington 1975). The result is lower ph, base saturation, and soil N, and higher organic content in the west. Third, soil acidity has changed during the 20 years separating Knatterud (1974) investigation from the more recent studies. Soil acidification in southern Sweden (Tyler 1987) and southern Norway (Dahl 1988; Bjørnestad 1991) has occurred during the last few decades. Several hypotheses have been put forward to explain this phenomenon, including the decrease in pastoral activities, an increase in atmospheric acid deposition, and longterm successional changes (see Birks et al. (1990)). Diversity at different scales: patterns in richness and turnover at summer farms Most ecological factors can account for significant fractions of species richness and turnover in the data, but the factors differ greatly in explanatory power. Land-use history and incoming radiation appear to have weak effects on either aspect of diversity, whereas the spatial position along the farms-to-surroundings gradient and slope have considerable explanatory power both for turnover and richness patterns. Altitude and soil chemical variables occupy intermediate positions, and differ, to some extent, in their explanatory power for the two diversity components. Interestingly, P, LOI, and N rank higher as predictors for species richness than for turnover, whereas the situation is the opposite for soil ph, base saturation, and CEC (Tables 4 and 5). These two groups of variables coincide with two partially independent chemical gradients in soils (Vandvik and Birks 2002a), as the first group reflects a soil biology complex related to decomposition rate and organic inputs from manure etc., whereas the second reflects a soil reaction or cation availability complex. N and P are main limiting nutrients for plant growth in unfertilised montane heaths and grasslands (e.g., Baadshaug (1983) and Ellenberg (1988)), and hence productivity is expected to increase with the availability of these nutrients. The relationship between productivity and species richness has received considerable attention in the ecological literature (Grime 1979; Huston 1994). Humped relationships between productivity and species richness are common, and the decrease in richness towards high-productivity environments is often explained by increased competition for light (Huston and DeAngelis 1994; Grytnes 2000). Both the humped (N) and the decreasing (P) richness responses observed in this study can potentially be accounted for by the model, simply by assuming that we have sampled different parts (the hump and the high productivity end) of the model. Judging by the much higher explanatory power of the soil P model (Table 5), we could suggest that this nutrient is the more important limiting factor within these grasslands. The potential importance of these variables are further supported by their wide ranges; the low values from heath and poor grasslands are comparable to upland heaths (Selsjord 1966), coniferous forest (Dahl et al. 1967), and moorlands (Gorham 1953), whereas the high values from the central grasslands exceed those of fertilised lowland hay meadows (Losvik 1993) and crop fields (Aase 1981). The soil reaction complex, in contrast, relates more strongly to species turnover than to richness and may therefore be interpreted more as environmental sieves (sensu Zobel (1992, 1997)) that function to exclude subsets of the local species pool from the local community rather than as productivity-limiting factors. The simple, coarse-grained quantification of the spatial gradient from farm centre to surrounding heathland which was simply the distance in metres divided by the average farm diameter is one of the best predictors of compositional patterns (Tables 1, 3 and 4) and species-richness patterns (Table 5). The vegetation at summer farms is thus spatially structured along this farm-to-surroundings gradient, but this spatial gradient is also strongly negatively correlated to the soil biology complex (LOI, P, and N), and positively correlated to plot slope (see Table 3). The strong covariation between these complexes (Ta-

14 216 ble 3), Vandvik and Birks (2002a) makes ecological interpretation difficult. In a functional analysis of summer-farm vegetation, we have shown that species trait responses along this gradient strongly suggest that decreasing animal effects disturbance and manuring are of overriding importance as a causal factor behind these patterns (Vandvik and Birks 2002b). The high explanatory power of slope, on the other hand, arises because of differential land-use in a rugged landscape; farms are located on relatively level ground surrounded by steep hillsides (Appendix 1), (Vandvik and Birks 2002b). Further support for this was found in exploratory ordinations on the grassland and heath data subsets, where slope was not very important and mainly related to a secondary soil-moisture gradient within these two major vegetation types. According to the relative distance GLM model (Table 5, Figure 4a), maximum plot richness (29 taxa plot 1 ) occurs in heathlands (at relative distance = 1.89), and the number of species is, in fact, lower at the summer farm centres than in the surroundings. Species turnover is high, however (Table 3), and strongly related to the farms-to-surrounds spatial gradient (RD CCA model (Table 4)), indicating that farm centres and heathlands have very few species in common. Even if the ecological distance between the intermediate grasslands (TWINSPAN subgroups D, E, F, and G) and the heaths is smaller (ca. 1.5 standard deviations, Figure 3b), many species have their main distribution in, or are confined to, summer farm grasslands (Appendix 1). The question of how summer farming affects diversity is therefore scale-dependent. Summer farming reduces mean species richness at the plot scale (alpha diversity, Whittaker (1975)), but increases the compositional heterogeneity (beta diversity, Whittaker (1975)) of the upland landscapes, thereby creating ecological room for additional vegetation types and species. The overall effect is therefore to increase the total flora (gamma diversity) in the sub- and low-alpine regions. decrease of ca. 9% per 100 m) is appreciable (Table 5; Figure 4a). These results contradict the claim (i.e., Sterten (1974)) that topography is of greater importance than altitude for local climate in these rugged landscapes. The floristic response to altitude could be enhanced by two underlying geographic patterns: (1) the almost universal pattern of decreasing species number with altitude (Whittaker 1975; Huston 1994; Odland and Birks 1999) being especially pronounced at these altitudes because of the loss of many forest species in the transition from sub-alpine forest to alpine heath, or (2) the loss of many ruderals and other species typical of human-influenced vegetation with altitude, as many of these are temperate lowland species that are assumed to be intolerant of cold climate (Holzner 1978). In exploratory analyses on heath and grassland data subsets (not shown) we found that the response to the altitudinal gradient is stronger in the heaths than in the semi-natural grasslands, suggesting that explanation (1) is the most important. This is supported by Austrheim (2002) who evoked mass effect (Shmida and Wilson 1985) and source-sink processes (Pulliam 1988) to explain the presence of typical lowland species at high-elevation sites and consistently low compositional turnover in perennial grasslands with altitude. Acknowledgements We thank Hilary Birks, Gunnhild Jaastad, Birgitte Jonsgard, Klaus Høiland, Per Magnus Jørgensen, Mary Losvik, Bjørn Moe, and Ole Reidar Vetaas for critical comments on earlier versions of this manuscript, Marianne Nygaard for help with lichen identifications, and Beate H. Ingvartsen for assistance with the figures. Local and regional climate Both the broad-scale (altitude) and the local (radiation) climatic variables are statistically significant, but not very strong predictors of species turnover (Table 4), whereas only altitude was significantly related to richness (Table 5). The altitudinal range is relatively narrow (224 m), and when this is taken into account, the floristic response and the decrease in richness with altitude (the GLM estimate implies a

15 217 Appendix 1 Table A1. Two-way TWINSPAN table. The means of quantitative environmental variables, and the proportions of nominal environmental variables for each sample group are listed at the top of the table. Each taxon is assigned to a constancy class (I IV) depending on whether the taxon occurs in 0 20%, 21 40%, 41 60%, 61 80%, or % of the samples in the group. Means of non-zero Domin values were calculated for each group, the number is in bold in the table if the taxon is dominant. Abbreviations for taxon names elsewhere in the text and figures are the first letters of vascular plant names and first letters of bryophytes and lichen names. Exceptions are Poa alpigena (Poa alg) and Rumex acetosella (Rum acl). Abbreviations for the environmental variables follow Table 1. Heaths Poor grasslands Wet Farm centers TWINSPAN group A B C D E F G H I J K Binary number Number of samples ALT SLO RI LOI N P ph CEC BASE RD YRS Mean number of species Aconitum septentrionale I 2 III 2.8 I 2.0 Polytrichum juniperinum V 2.9 I 3.8 I 5.0 II 5.0 II 3.0 Calluna vulgaris II 5.3 IV 3.9 III 2.8 I 2.0 I 2.0 I 1.0 I 2.0 Empetrum hermaphroditum IV 4.6 V 3.6 II 3.0 I 2.0 I 2.0 II 4.0 I 1.0 Juniperus communis III 1.5 IV 1.9 I 2.0 II 0.5 Salix lapponum I 1.5 I 2.0 I 0.5 Sorbus acuparia II 1.6 I 1.0 Vaccinium vitis-idaea IV 4.0 V 3.2 II 2.3 II 3.8 I 1.0 I 2.0 Molinia caerulea I 2.3 I 3.0 Luzula pilosa II 2.7 II 1.5 I 2.0 Cornus suecica II 2.7 IV 3.1 III 1.3 I 3.0 I 2.0 I 3.0 Lycopodium annotinum II 3.6 I 0.8 Ptilidium ciliare II 2.5 III 2.3 I 2.0 II 3.0 Pleurozium screberi V 6.7 V 3.4 II 2.0 I 5.0 III 2.6 Dicranum fuscescens I 2.0 I 2.0 II 2.0 Cladonia gracilis II I 2.0 Cladonia rangiferina IV 2.3 I 2.0 I 2.0 I 2.0 II 2.0 Cetraria islandica V 3.2 II 1.9 II 2.3 I 3.0 III 2.8 Phyllodoce caerulea I 2.0 II 2.0 Vaccinium uliginosum V 3.9 IV 4.2 IV 3.1 I 0.5 II 2.4 III 2.1 Geranium sylvaticum I 2.0 III 3.2 I 0.5 I 1.0 Coeloglossum viride I 0.5 I 1.0 I 0.5 Maianthemum bifolium V 3.0 IV 3.0 V 2.6 I 3.5 II 2.7 Melampyrum pratense I 1.0 III 1.8 III 2.2 I 2.0 Rubus saxatilis II 1.9 Blechnum spicant I 5.0 I 1.8 I 2.0 Gymnocarpium dryopteris II 2.3 III 1.8 I 2.0 Thelypteris limbosperma I 0.5 III 4.4

16 218 Table A1. Continued Heaths Poor grasslands Wet Farm centers TWINSPAN group A B C D E F G H I J K Binary number Number of samples Thelypteris phegopteris II 2.6 IV 4.4 I 2.0 Barbilophozia floerkei III 2.0 II 3.0 III 2.0 I 2.0 Cladonia bellidiflora II 2.0 I 2.0 I 2.0 I 2.0 Salix herbacea I 5.0 II 4.0 I 2.0 I 3.0 Vaccinium myrtillus V 5.1 V 4.1 V 3.9 III 2.5 IV 2.2 II 0.5 I 3.0 I 1.0 I 0.5 Pyrola media I 4.0 I 0.5 III 1.2 I 2.0 Barbilophozia lycopodioides IV 2.0 IV 2.2 V 2.4 III 2.0 III 2.0 I 2.0 Polytrichum commune II 2.4 I 3.0 I 3.0 I 4.0 Hylocomium splendens I 2.0 II 2.0 II 3.0 I 5.0 I 2.0 Hylocomium pyrenaicum I 2.0 I 2.8 II 2.8 I 2.0 III 2.0 I 2.0 Dicranum scoparium IV 2.0 III 2.5 II 4.3 I 2.0 III 2.0 II 2.0 I 2.0 I 2.0 Cladonia furcata I 2.0 I 2.0 II 2.0 I 2.0 I 2.0 Betula pubescens I 4.0 V 3.9 V 4.4 I 0.5 II 1.8 IV 2.0 II 3.5 Hieracium pilosella II 2.0 II 1.3 III 1.4 I 3.0 I 1.8 I 2.0 Salix glauca I 0.5 II 1.1 II 2.3 I 2.0 II 2.3 Trientalis europaea II 2.3 V 2.4 V 2.6 II 1.8 V 2.3 II 2.3 Scirpus caespitosus II 1.6 I 2.0 I 4.0 I 2.5 I 7.5 Solidago virgaurea II 1.5 I 2.5 I 3.0 I 1.0 II 1.9 I 3.0 I 2.0 Polytrichum formosum I 2.0 I 2.0 I 2.0 I 2.0 I 2.0 Deschampsia flexuosa V 3.1 V 2.9 V 2.8 IV 2.6 V 3.3 V 3.8 II 3.0 Carex vaginata III 2.8 II 2.3 III 2.2 III 4.9 II 2.3 II 4.7 Nardus stricta V 4.0 V 3.4 III 3.7 IV 4.3 V 4.9 III 3.0 V 4.3 IV 2.3 II 3.3 II 2.0 Potentilla erecta II 1.8 V 2.7 V 2.7 I 2.5 IV 2.7 V 2.9 I 3.0 I 2.5 I 2.0 Sibbaldia procumbens I 1.5 I 2.0 III 3.5 Festuca ovina I 4.7 II 4.0 I 2.0 Carex pilulifera II 1.8 I 2.8 I 2.0 I 5.0 IV 2.7 II 1.5 I 2.0 Luzula sudetica I 1.0 I 2.0 I 1.5 II 2.5 III 1.4 Carex bigelowii II 2.7 II 2.3 IV 3.7 IV 3.1 II 4.0 I 2.0 II 1.8 Galium saxatile I 0.5 I 2.0 I 5.0 II 3.3 I 3.0 Anthoxanthum odoratum III 1.9 III 2.9 V 2.6 V 3.8 V 4.0 III 3.0 II 2.3 I 2.0 III 2.9 Luzula frigida II 1.7 II 1.8 I 2.0 I 2.0 IV 2.5 II 2.0 II 2.5 Alchemilla alpina III 3.2 II 2.0 V 1.9 III 4.2 IV 3.1 V 6.0 II 2.7 I 2.0 II 1.8 I 2.0 Polygonum vivparum I 2.0 I 2.0 IV 2.2 II 2.8 IV 2.7 II 3.0 IV 2.1 II 2.3 II 0.8 Viola palustris I 2.0 I 2.0 V 2.6 II 3.5 V 2.8 V 3.6 II 3.0 Polytrichum alpinum I 2.0 I 1.5 II 2.0 II 2.0 III 5.5 Campanula rotundifolia I 2.0 I 0.5 III 1.8 I 3.0 IV 3.7 I 3.0 II 1.8 Racomitrium elongatum II 2.0 I 2.0 IV 2.4 I 3.0 I 2.0 V 4.3 II 3.0 I 2.0 Veronica offıcinalis II 2.0 II 2.3 II 2.3 II 2.4 III 2.0 II 2.7 Drepanocladus uncinatus I 2.7 II 2.0 I 3.7 III 3.9 I 2.0 II 3.0 I 2.0 Rhytidiadelphus squarrosus III 3.0 III 2.3 IV 2.6 V 4.8 V 4.1 IV 6.0 IV 2.9 V 3.8 III 3.9 III 3.0 Carex brunnescens II 2.0 III 3.1 III 2.5 II 2.0 I 5.5 III 5.0 Carex nigra II 2.0 I 2.5 I 2.0 IV 3.6 III 2.6 V 4.0 II 3.7 II 4.0 IV 3.2 I 2.0 Agrostis capillaris II 3.0 III 2.6 IV 2.6 V 4.5 V 3.2 V 3.5 IV 3.1 V 6.7 V 4.4 III 3.8 III 3.7 Hypericum maculatum I 0.5 IV 2.9 I 3.2 I 0.5 I 2.5 Lescuraea incurvata II 2.0 I 2.0 II 2.0 Selaginella selaginoides I 1.0 I 2.0 I 2.0 I 2.0 Filipendula ulmaria I 3.0 I 4.0 I 0.5