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Keywords:

  • anatomy;
  • leaf nitrogen concentration;
  • life history trait;
  • nitrification ratio;
  • nitrogen mineralization;
  • pH;
  • phenology;
  • plant height

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    The main objective of this study was to predict the responses of vascular plant species to atmospheric nitrogen deposition and enhanced soil nitrogen levels. The study was carried out in deciduous forests located in three regions of southern Sweden. The abundance of vascular plants, as well as soil pH and nitrogen mineralization rates, were studied in a total of 661 sample plots.
  • 2
    We calculated an ecological measure (Ndev value) for all species based on their observed vs. expected nitrification ratios at a given soil pH, and compared its accuracy in predicting abundance changes with results using life history traits. Data from long-term field studies and fertilization experiments were used for validation.
  • 3
    Ndev values were positively correlated between neighbouring regions. Values for the southernmost region (Skåne) were also positively related to the changes in species frequency observed in large-scale flora surveys and permanent plot studies in that area and with species changes reported from Central Europe. Values from one of two other regions were also consistent. Ndev values from Skåne (but no other region) predicted species responses in short-term fertilization experiments.
  • 4
    No life history trait was as good a predictor as Ndev, although plant height, leaf anatomy, leaf nitrogen concentration and phenology showed significant correlations. Attributes related to taxonomy, life form, relative growth rate and habitat type showed no agreement with the changes in species abundance.
  • 5
    We predict that species with the following attribute syndrome will increase in abundance in response to enhanced nitrogen levels: those favoured by a high soil nitrification ratio relative to other species at a given soil pH, tall stature, hydro- to helomorph anatomy, high leaf nitrogen concentration and a late phenological development.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Many ecosystems are exposed to extensive human impact that causes various kinds of environmental change, for example the drainage of wetlands, the abandonment of grazing and mowing in formerly managed grasslands and the deposition of toxic pollutants. An important task of ecology is to study the effects of these changes on plant and animal life and to predict the responses of species, especially of those that might be affected negatively.

Scientists in the industrialized and densely inhabited countries of Europe and North America have paid much attention to the problems caused by the atmospheric deposition of excess nutrients and acidifying substances (e.g. Bobbink et al. 1998; Fenn et al. 1998). In Sweden, where the well-being of forests is of fundamental importance for society, the effects of nitrogen input on the health and productivity of forests (Binkley & Högberg 1997) and on the plant species richness of forest ecosystems (Falkengren-Grerup 1995; Kellner & Redbo-Torstensson 1995) are of particular concern. Repeated analyses have been performed using data from permanent or semi-permanent plots (Falkengren-Grerup 1995; Liu & Bråkenhielm 1996), or large-scale floristic surveys (Tyler & Olsson 1997), and regional comparisons undertaken between polluted and less polluted areas (Rosén et al. 1992; Bråkenhielm & Liu 1995; Falkengren-Grerup et al. 1998; Diekmann et al. 1999).

Fertilization experiments have also been conducted mainly in coniferous forests (Kellner & Mårshagen 1991; Hallbäcken & Zhang 1998; Nordin et al. 1998; van Dobben et al. 1999), but more rarely in deciduous forests (Falkengren-Grerup 1993). Such Swedish studies and colleagues from Central Europe revealed largely consistent positive responses to nitrogen enrichment for a number of species (Epilobium angustifolium, Rubus idaeus) and negative ones for others (Ericaceae, lichens).

We aim to predict the responses of forest vascular plants to enhanced nitrogen levels. Our first approach is based on the assumption that the species’ responses to nitrogen deposition and soil mineralization of nitrogen are correlated with various life history traits, such as life form, anatomy/morphology, physiology and phenology. Recently, there has been a growing interest in classifying species into (functional) groups sharing certain biological attributes, particularly those affecting responses to disturbance (e.g. Lavorel et al. 1997, 1999).

The second approach uses soil chemistry in association with the functional nitrogen index for species (FNIS) proposed by Diekmann & Falkengren-Grerup (1998). The simultaneous deposition of acidifying substances and nitrogen affects both the pH and nitrogen content of the soil, and we assume that these two variables are positively correlated. Plant responses will depend on N availability, but the correlation between mineralization (usually the net potential mineralization) and pH is not clear. Both positive (Kriebitzsch 1978) and negative correlations (Falkengren-Grerup et al. 1998), or no distinct relationship at all (Curtin et al. 1998), have been reported and may partly depend on the pH-range studied. Falkengren-Grerup et al. (1998), for instance, showed that a positive relationship between pH and mineralization in soils with pH (KCl) < 4.0 became negative when soils with increasingly higher pH were included. More general patterns are found for the nitrification and ammonification rates, which increase and decrease, respectively, with increasing pH (e.g. Falkengren-Grerup et al. 1998).

In the long term, the airborne deposition of acidifying substances and nitrogen is likely to lead to acidification and eutrophication and will therefore cause the line representing the relationship between soil acidity and N content to shift up and towards the left (Fig. 1). Species favoured by an increasing nitrogen supply at a given pH (such as ‘species a’ in Fig. 1), or tolerant of increasing soil acidity at a given level of nitrogen (‘species b’) will be favoured because conditions are closer to their optima, whereas those below and to the right of the line are likely to be at a disadvantage. A single soil variable that reflects both N and pH is desirable for making these predictions: it must increase significantly along the pH gradient and be affected by nitrogen deposition. Both the nitrification rate and the nitrification ratio (nitrification rate/ammonification rate × 100) show a positive, curvi-linear correlation with pH and are the most responsive mineralization measures (Falkengren-Grerup et al. 1998; Diekmann et al. 1999), but the ratio is dimensionless and therefore more suitable for comparisons between regions.

image

Figure 1. Theoretical relationship between soil nitrogen availability and pH in Swedish deciduous forests and its response to eutrophication and acidification (dotted line). Species a and b have ecological optima to the left of or above the line and are therefore favoured.

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We do not claim that the nitrification ratio is an accurate measure of the amount of plant available nitrogen in the soil, but it does reflect the amount and form of nitrogen over a broad pH spectrum. Ammonium predominates in acid soils and nitrate at higher pH, but total nitrogen varies little with pH (Falkengren-Grerup et al. 1998). Many species in our flora are favoured by a specific nitrogen form, both in laboratory and field studies for the flora studied here (Falkengren-Grerup 1998; Olsson & Falkengren-Grerup 2000). Our FNIS index, describing optimum conditions for species abundance in terms of a negative relationship with ammonium and a positive one with nitrate (Diekmann & Falkengren-Grerup 1998), is a similar measure to the nitrification ratio.

We aimed to develop a measure that reflects the average soil status with respect to nitrogen and pH at which individual species occur, which can then be used to predict the responses of vascular plants to enhanced nitrogen deposition. Validation of this measure against results obtained from monitoring and permanent plot studies, as well as from nitrogen fertilization experiments, would allow its accuracy to be compared with predictions using life history traits.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

STUDY AREAS AND DATA SETS

We studied deciduous forests in three regions of southern Sweden south of the Limes Norrlandicus:

  • Skåne, the southernmost province in Sweden, with an annual mean temperature of 7–8 °C and an annual mean precipitation of 550–750 mm.
  • Eastern Småland, a province about 200 km north-east of Skåne, with somewhat lower values for temperature (6–7 °C) and rainfall (500–600 mm).
  • Other parts of south Sweden up to 600 km north-east of Skåne, with data mainly from the provinces Öland, Uppland, Västergötland, Södermanland and Gotland. Annual mean temperatures range from 5 °C to 7 °C, the yearly precipitation varies between 450 mm in the south-east and more than 900 mm in the westernmost provinces.

The deposition of acidifying substances (especially sulphate) and nitrogen varies between the study areas, and in general is highest in Skåne, decreasing sharply towards the north-east. According to model data for 20 × 20 km2 grids from Lagner et al. (1996), total N deposition averages 17 kg ha−1 year−1 in the studied plots in Skåne, 10 kg ha−1 year−1 in Småland and 7–10 kg ha−1 year−1 in region 3. There is, however, a considerable variation in all regions.

Data from a total of 661 plots of various types of deciduous forest were used, comprising 194 sampled in Skåne in 1993 (Brunet et al. 1996), 156 in Småland in 1993 (Å. Rühling, unpublished vegetation data) and 311 in region 3 surveyed in 1987 and between 1989 and 1992 (Diekmann 1994). Phytosociologically, all plots can be assigned to the class Querco-Fagetea, characterized by a mixture of tree species, namely Acer platanoides, Alnus spp., Carpinus betulus, Fraxinus excelsior, Quercus spp. (often dominant), Tilia cordata and Ulmus spp. The most common shrubs encountered were Corylus avellana, Crataegus spp., Juniperus communis, Lonicera xylosteum and Ribes alpinum. The field layer usually is fairly species-rich, including a large number of graminoids and forbs, occasionally also some ferns.

All plots were selected and sampled according to the Braun-Blanquet approach, but details of field methods nevertheless differed between the regions (plot size, separation of vegetation layers, registration of woody taxa, vernal species and bryophytes, and cover percentage scale, see Diekmann 1994; Brunet et al. 1996; Diekmann et al. 1999). We used cover percentage values for the assessment of species abundance, and as these are not directly dependent on plot size we assumed that neither its variation (100–500 m2) nor different sampling dates in these stable forests would distort the results. All estimates of species abundance were transformed onto a 1–9 ordinal scale according to van der Maarel (1979). The nomenclature of species follows the new checklist of Swedish vascular plants by Karlsson (1997).

LIFE HISTORY TRAITS

In the absence of known correlations with responses to nitrogen deposition we chose a range of traits (Table 1) for which data are available. Regenerative traits, such as seed mass, dispersal mode or ability of clonal growth, were assumed not to be relevant here.

Table 1.  Life history traits used in this study
Taxonomic groupFive categories: woody species = 1, herbaceous dicotyledons = 2, Poaceae = 3, monocotyledons except Poaceae = 4, Pteridophytes = 5.
Life formRaunkiaer system, based on the position of regenerating buds over the inactive season; here distinguishing six categories: chamaephytes, geophytes, hemicryptophytes, nanophanerophytes, phanerophytes and therophytes. Data source: Oberdorfer (1990)
Plant heightMean height of adult plant; in m. Data source: Rothmaler (1986)
AnatomyFor statistical analysis transformed to ordinal values as follows: hydromorph = 1, helomorph = 2, hygromorph = 3, mesomorph = 4, scleromorph = 5, plus intermediate classes. Data source: Lindacher (1995)
Maximum potential relative growth rate, RGRmaxIn week−1. Data source: Grime & Hunt (1975)
Current nitrate reductase activity, NRAIn µmol g−1 fresh weight h−1. Data sources: Havill et al. (1974); Lee & Stewart (1978); Kinzel (1982); Gharbi & Hipkin (1984); Gebauer et al. (1988); Högbom & Ohlson (1991)
Nitrogen concentration of the leaves, N-concIn mg g−1 dry weight. Data sources: Höhne (1962, 1963, 1970, 1978); Janiesch (1973); Dinic & Misic (1975); Höhne et al. (1981); Aronsson (1984); Robinson & Rorison (1985); Gebauer et al. (1988); Falkengren-Grerup (1990)
PhenologyThe phenological development is described as flowering time (ferns: time of full vegetative development) in the form of ordinal numbers from 1 (flowering in early spring) to 9 (flowering in late summer). For the large majority of species (but not in many vernal geophytes) shoot and leaf development precede flowering; therefore, flowering time largely reflects vegetative development. Data source: Dierschke (1983)
Habitat typeTwo categories: specialists = species with high shade tolerance usually found in closed forests; generalists = species with lower shade tolerance mainly confined to forest edges and openings. Data source: Oberdorfer (1990)

MEASUREMENTS

Mixed soil samples were collected from all plots in 1993 (Skåne and Småland) and 1995 (region 3), by combining five top soil cores (0–5 cm, taken below the litter layer). In the laboratory, the fresh soil was passed through a 6-mm sieve prior to chemical analysis. For determination of pH, 10 g fresh soil (15 g in region 3) were extracted using 50 mL 0.2 m KCl, shaken for 2 hours and measured, after sedimentation, with an electrometric glass electrode. Organic matter content was analysed as loss on ignition (LOI) by ashing the samples at 600 °C in a muffle furnace. Potential net nitrogen mineralization was determined by incubating the samples at, on average, 48% of water holding capacity (60% in region 3) at 18 °C for 15 weeks. After incubation, 10 g of fresh soil were extracted using 50 mL 0.2 m KCl, after which the contents of ammonium (minNH4+) and nitrate (minNO3) were measured by flow injection analysis. The mineralization values were then related to LOI and expressed in µg N g−1 (dry organic matter) day−1. For a more detailed description of the methods used, see Falkengren-Grerup et al. (1998).

We did not consider the initial amounts of nitrogen, because earlier studies had shown that they only are a small fraction of the values after incubation (cf. Falkengren-Grerup et al. 1998). Measurements from a further 38 localities in south-west Sweden confirmed that minNO3 and minNH4+ differed only by an average of 3.6% and 7.8%, respectively, when corrected for initial N concentrations. For the nitrification ratio (%minNO3) the mean difference was only 0.7%. Nor did we add ammonium or nitrate to the incubated samples, because the needed amounts to simulate realistic deposition levels would again represent only a tiny fraction of the accumulated N. We are aware that labor atory measurements of mineralization do not always reflect the situation in the field, where both climatic and edaphic factors may constrain rates. However, the only way to obtain mineralization data for a large number of sites is to carry out simplified laboratory experiments.

STATISTICAL METHODS

The regional data were kept separate for statistical analysis, as they differed considerably in nitrogen deposition rates, and provided three independent test data sets.

First, we determined the general relation between soil percentage minNO3 and soil pH. As there was a sigmoid relationship between the two variables (cf. Falkengren-Grerup et al. 1998) our first attempt was to transform the nitrification ratio using the equation x= loge (%minNO3/[1 – %minNO3]). However, neither the relation between the loge-transformed variable and pH, or any relation using other types of transformation, were well described by simple linear regression. We therefore determined the relationship between the two variables by applying the method of running means: pH values were sorted in ascending order and defined as R1 (lowest value) to Rn (highest value), with their corresponding percentage minNO3 values denoted N1 to Nn. The expected percentage minNO3 value (Nexpected) of soils of a given pH (Rz) was then calculated by:

  • Nexpected(Rz) = (Nz–5 + Nz–4 + Nz–3 + Nz–2 + Nz–1 + Nz + Nz+1 + Nz+2 + Nz+3 + Nz+4 + Nz+5)/11 (eqn 1)

We then calculated species indices as the weighted averages of the soil nitrification ratios and pH values in all plots where the species were present (cf. Diekmann & Falkengren-Grerup 1998):

  • Nindex/observed = (x1Nx2N2+ … +xnNn)/(x1 + x2 + … + xn) (eqn 2)
  • Rindex/observed = (x1Rx2R2+ … +xnRn)/(x1 + x2 + … + xn) ( eqn 3)

where x1, x2, … , xn are the species’ abundance values in individual plots. A low Nindex/observed would indicate a species that is largely restricted to sites with low nitrification ratios.

The general relationship calculated between soil percentage minNO3 and pH was then used to determine the Nindex/expected for all species based on their Rindex/observed. For example, if the average soil percentage minNO3 at pH 5 was 90%, a species with an Rindex/observed= 5 was assigned a Nindex/expected of 90%. The deviation from expectation (Ndev) was calculated as

  • Ndev = Nindex/observed − Nindex/expected (eqn 4)

Thus, if the species above had a Nindex/observed of 95%, its Ndev would be + 5%. Such a positive Ndev value indicates a species that is observed on sites where the average soil percentage minNO3 is higher than expected from site pH (‘species a’ and ‘species b’ in Fig. 2), whereas a negative Ndev value (‘species c’) occurs at sites with a lower percentage minNO3 than expected. Statistically, the Ndev values may be regarded as the error part of the line describing the relation between the pH indices and percentage minNO3 indices of species. To validate our prediction that species with a high positive value of Ndev would be favoured by nitrogen deposition we compared values for species in areas exposed to naturally or experimentally increased deposition, where changed abundances have been reported.

image

Figure 2. Theoretical relationship between pH index and percentage NO3 index. Species a and b, with positive Ndev values, are observed on sites that have, on average, higher percentage minNO3 than expected from their site pH, whereas species c occurs at a lower percentage minNO3 than expected and has a negative Ndev value.

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The Ndev values and the data on long-term trends of species in Skåne were normally distributed, whereas some data on life history traits were not. Therefore, and in order to enable us to compare the predictive power between life history traits and Ndev values, parametric and non-parametric methods were used side by side. All statistical analyses were carried out with the program MINITAB (Anonymous 1998).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

ndev VALUES OF SPECIES

Species indices for percentage minNO3 and pH showed highly significantly positive correlations in all regions (Skåne: r2 = 0.827, n = 76; Småland: r2 = 0.691, n = 71; region 3: r2 = 0.907, n = 94, all P < 0.001).

Table 2 lists the Ndev values of the most common herbaceous species, separately for the three regions. The highest positive Ndev values were observed for the two ferns Athyrium filix-femina (20%) and Dryopteris carthusiana (17%) in Skåne and Galeopsis tetrahit/bifida (20%; non-flowering plants of these species are not distinguishable) in region 3. The highest negative values were found for Melampyrum pratense (−14%) and Viola reichenbachiana (−13%) in region 3 and Vaccinium vitis-idaea (−14%) in Småland. Some species showed positive Ndev values throughout all regions (Deschampsia cespitosa, Rubus idaeus, Stellaria nemorum and Urtica dioica), while others were consistently negative (e.g. Anemone hepatica, Carex digitata, Festuca ovina, Melampyrum pratense, Primula veris, Rubus saxatilis and Vaccinium vitis-idaea). Species whose responses varied with region included Galeopsis tetrahit/bifida (Ndev changed from negative to positive along the deposition gradient) and Dryopteris carthusiana (Ndev decreased). The majority of species, however, showed rather low Ndev values (between + and −5%). Considering all species with at least 10 occurrences in a region, there were similar response patterns in neighbouring regions, i.e. positive correlations between Ndev values for Skåne and Småland (r2 = 0.154, P = 0.007, n = 46) and Småland and region 3 (r2 = 0.118, P = 0.005, n = 64), but not Skåne and region 3 (r2 = 0.026, P = 0.193, n = 68).

Table 2. Ndev values in percentage for the most common species excluding woody taxa and vernal species not registered in all regions, given separately for the three regions. Species with values marked – were either absent or rare (less than 10 occurrences) in the region concerned. Species’ pH index was calculated for the three regions together. Observed species’ responses in different studies are shown under (a)–(e), where data are available for species: (a) Number of sites in permanent plot studies in deciduous forests in Skåne (Falkengren-Grerup 1995) at which species were increasing (+), stable (=) or decreasing (−). (b) A comparison of large-scale flora surveys of grid squares covering all of Skåne between 1938 and 1967 and 1989–96 (Tyler & Olsson 1997) showed no significant change in frequency (0) or a significant positive or negative change of 16–30% (1), 31–45% (2), 46–60% (3), 61–75% (4) or more than 75% (5). (c) A re-analysis of a total of 93 permanent circular plots from all ecosystems in two parishes in Skåne was undertaken between 1958 and 1964 and 1989–93 and the change in number of plots with the species present given (Oredsson 1999). (d) Species responses in nitrogen fertilization experiments in deciduous and coniferous forests in Sweden were classified as in (a) (Kellner & Mårshagen 1991; Falkengren-Grerup 1993; Hallbäcken & Zhang (1998); van Dobben et al. 1999). (e) Changes in species frequency or abundance observed in permanent plot studies in Central European forests (Trepl 1982; Kuhn et al. 1987; Rost-Siebert & Jahn 1988; Medvecka-Kornas & Gawronski 1991; Rodenkirchen 1992; Thimonier et al. 1992, 1994) were classified as increasing (+), decreasing (−), or unclear results (+ −)
SpeciespH indexNdevObserved species response
SkåneSmålandRegion 3(a)(b)(c)(d)(e)
  1. a = Carex pairaeiC. spicata; b = Dryopteris dilatataD. expansa; c = only Galeopsis tetrahit, d = Juncus conglomeratusJ. effusus; e = mainly St.nemorum. ssp. montana; f = mainly St. nemorum ssp. nemorum; g = both Viola reichenbachianaV. riviniana.

Aegopodium podagraria5.4 −1.8     1.3++1+11  
Agrostis capillaris4.0   2.9−2.9   −6.0    0    −2+
Alliaria petiolata5.3 −  1.4     1.4 +3   
Anemone hepatica5.1 −3.4−3.9   −2.2 −2    −1 +
Anthoxanthum odouratum3.9 −5.0−1.2   −4.2   0 −14 
Anthriscus sylvestris5.0 −0.6−0.4     0.3   0    +9  
Athyrium filix-femina4.7  19.8     4.1++1    +3 +
Calamagrostis arundinacea4.7 −−8.0     4.6   0    −1+ 
Campanula persicifolia4.2 −  2.7   −1.7 +1    −1  
Campanula rotundifolia4.1 −8.9  7.5   −   0−10  
Cardamine bulbifera5.1 −−9.7     0.6  0   
Carex digitata4.9 −2.0−5.9   −4.2 −3      0
Carex muricata agg.4.4 −  2.5     4.0 +3    +4a  
Carex pilulifera3.6 −1.0   − +1    +8  
Carex sylvatica5.9 −   −3.1+  0  + −
Chelidonium majus4.3 − 13.2   − +2   
Convallaria majalis4.5 −6.4  0.1   −1.4=  0    −7 
Dactylis glomerata4.5   0.2  1.6     1.9   0   
Deschampsia cespitosa5.0   3.5 13.8     1.8=  0    +5+ 
Deschampsia flexuosa3.8 −5.6  1.1     6.0=  0    +4 + −
Dryopteris carthusiana3.9  17.3  9.1   −0.4   0    +3++
Dryopteris dilatata3.5  12.0   − +3    +6b +
Dryopteris filix-mas4.7 −5.1  3.5      4.6++1    +3 +
Elymus caninus5.8 −−1.9      0.8   0   
Epilobium angustifolium3.7   6.6   −+  0+11++
Equisetum sylvaticum4.8 −     7.2   0    +1  
Festuca gigantea5.1   6.2   −0.9 +2  
Festuca ovina4.0−11.7−6.5   −3.7   0−13  
Fragaria vesca4.7   0.5−1.9   −8.1   0−18 
Galeopsis tetrahit+bifida4.0 −2.5  15.2    20.4++1c    +7 +
Galium aparine5.2   1.9   −3.5 +2    +7 + −
Galium boreale4.2 −  9.1    11.3 −1    −4  
Galium odoratum5.1  13.3   −1.4+3   
Galium saxatile3.6   1.3   −   0    −5 +
Geranium robertianum5.0   2.8−5.2   −1.8   0    +2 +
Geranium sylvaticum5.4 −1.1−1.4   −1.1    0   
Geum urbanum5.3 −4.1−0.3   −0.1 +1    +1 +
Hieracium sect. Hieracium4.3 −  0.5     1.3        0 
Hieracium sect. Vulgata4.3 −  4.4   −7.4  −12  
Holcus mollis3.7   5.2   −   0+12 +
Hypericum maculatum4.0 −  2.4   −1.2   0    −3  
Juncus effusus3.9  11.6   −   0    +3d +
Lamiastrum galeobdolon4.1   2.9   −=−1      0 +
Lathyrus linifolius4.2 −6.8  2.9   −1.3   0    −3 
Lathyrus vernus5.3 −   −0.8 −3   
Luzula pilosa4.0 −6.9−6.0     9.0=  0    +4 + −
Maianthemum bifolium4.5 −4.0  0.3   −7.9=+1    +2
Melampyrum nemorosum4.7 −−9.7      4.5     
Melampyrum pratense4.1 −5.1−5.7  −13.9   0    −3+ −
Melica nutans4.8 −4.1−3.5   −4.2   0    +1  
Melica uniflora4.9 −3.0−3.4   −4.4+  0  + −
Mercurialis perennis5.2  1.7 −4.0   −0.8  0    −1  
Milium effusum4.9  8.6  15.4   −2.1++3  + −
Moehringia trinervia4.3−1.4  12.6     6.3 +2    +9 +
Molinia caerulea3.2−4.4 −     1.3   0    +3  
Mycelis muralis4.9  4.6  10.5   −1.0++2    +5  
Oxalis acetosella4.3−3.6 −0.1     2.0=  0    −2
Poa nemoralis4.7−6.4 −0.7   −2.4+  0    +2+ −
Polygonatum multiflorum5.4−1.3 −     0.1+1      0 + −
Polygonatum odoratum4.4 −1.0   −3.2 −3  +1  
Polypodium vulgare4.1 −6.0   −5.8   0−15  
Potentilla erecta3.9−1.1−11.3   −   0−24 +
Primula veris5.4 −8.7   −5.0 −1   
Pteridium aquilinum3.9−1.5 −4.3   −   0    −6 
Rubus idaeus3.9  7.2   5.9   15.2++1+11++
Rubus saxatilis5.2−3.5 −3.4   −4.0  1    −1  
Rumex acetosa4.0−2.3   7.2   −5.3   0−15  
Sanicula europaea5.2 −   −8.4 −3   
Solidago virgaurea4.5 −9.1   −7.6 +1    −5
Stachys sylvatica5.6 −     3.0   0    −1 +
Stellaria graminea4.1−3.8 −1.4   −   0−12  
Stellaria holostea4.3−3.0 −     1.0++4  
Stellaria media4.2−3.5   1.0   −   0    −8+ 
Stellaria nemorum4.7 12.0e −     6.5f+  0 = 
Taraxacum sect. Ruderalia5.4−6.0   6.2   −0.6      −3 +
Trientalis europaea3.5−2.8 −   − +1    +1++
Urtica dioica5.0  4.1 −     5.1+  0+13 +
Vaccinium myrtillus3.8−5.3 −1.2     3.5   0    −5 
Vaccinium vitis-idaea3.6−8.9−13.5   − −1−16 
Veronica chamaedrys4.4−5.1 −4.0   −1.4   0    −9 
Veronica officinalis4.2−4.8   0.7   11.1   0    −9 
Vicia sepium4.9−2.3   1.8   −2.5   0  
Viola reichenbachiana5.3  −12.6 −2  + −
Viola riviniana4.8   0.1   −3.3g  0    −1 

ACCURACY OF PREDICTION OF ndev SPECIES’ VALUES

These Ndev values were compared with data on the long-term trends of species abundance and on the observed responses of species to experimental nitrogen addition (Table 3). Overall, changes in permanent plots in Skåne showed no correspondence to the Ndev values (Wilcoxon rank sum test, W = 91.0, P = 0.516), although experimental data showed a better agreement (Table 3, column a vs. column d). With few exceptions, species responding positively to fertilization had positive Ndev values, while species responding negatively had negative Ndev values (W = 61.0, P = 0.015), giving a significant effect in Skåne, but only an overall tendency in Småland and region 3 (Table 3).

Table 3.  Comparison of the accuracy of prediction of the floristic changes observed in various types of studies (Table 2, columns a–e), using species Ndev values and various life history traits (Table 1). The floristic changes are either expressed on an interval scale (columns b and c) or as classes (increasing or decreasing abundance, columns a, d and e). Different statistical tests were applied: regression analysis (r2) for the correlation of floristic changes and life history traits (both expressed on an interval scale); one-way anova (F) for the comparison of floristic changes (interval scale) between classes of life history traits; Wilcoxon rank sum test (W) for the comparison of life history traits (interval scale) between the two groups of increasing and decreasing species (species with stable abundance excluded); χ2-test for the comparison of life history traits (classes) between the two groups of increasing and decreasing species. – = no test applied because of too few observations
 (a) Permanent plots in deciduous forests, Skåne(b) Flora survey of grid squares, Skåne(c) Permanent plots in two parishes, Skåne(d) Fertilization experiments, Sweden(e) Permanent plots in forests, Central Europe
Ndev valuesW = 91.0r2 = 0.114r2 = 0.152W = 61.0W = 429.0
SkåneP = 0.516P = 0.006 (n = 64)P = 0.003 (n = 55)P = 0.015P = 0.004
Ndev values   W = 53.0W = 168.0
Småland   P = 0.134P = 0.087
Ndev values   W = 44.0W = 236.0
Region 3   P = 0.074P = 0.004
Taxonomic groupF4,128 = 2.21F4,128 = 2.43
  P = 0.073P = 0.054  
Life formF4,128 = 1.30F4,92 = 1.16χ2 = 3.070
  P = 0.273P = 0.334 P = 0.381 (d.f. = 4)
Plant heightW = 141.5r2 = 0.087r2 = 0.159W = 79.5W = 444.5
 P = 0.085P = 0.001 (n = 126)P < 0.001 (n = 91)P = 0.083P = 0.114
AnatomyW = 133.0r2 = 0.002r2 = 0.101W = 47.5W = 286.5
 P = 0.375P = 0.948 (n = 123)P = 0.003 (n = 87)P = 0.565P = 0.025
RGRmaxr2 = 0.043r2 = 0.066W = 81.0
  P = 0.279 (n = 29)P = 0.204 (n = 26) P = 0.056
NRAr2 = 0.029r2 = 0.158W = 80.5
  P = 0.371 (n = 30)P = 0.061 (n = 23) P = 0.096
N-concW = 85r2 = 0.013r2 = 0.240W = 35.0W = 138
 P = 0.130P = 0.482 (n = 41)P = 0.005 (n = 31)P = 0.927P = 0.014
PhenologyW = 115r2 = 0.034r2 = 0.033W = 59.5W = 290
 P = 0.017P = 0.043 (n = 122)P = 0.096 (n = 86)P = 0.406P = 0.528
Habitat typeχ2 = 2.663F1,128 = 1.15F1,92 = 1.61χ2 = 3.616χ2 = 1.145
 P = 0.103 (d.f. = 1)P = 0.285P = 0.208P = 0.057 (d.f. = 1)P = 0.703 (d.f. = 1)

The comparison of two large-scale flora surveys of grid squares in Skåne covering all ecosystems between 1938 and 1967 and 1989–96 carried out by Tyler & Olsson (1997) gave information on long-term trends for a much larger number of species. When we used the Ndev values from Skåne to predict these trends we found a highly significantly positive correlation (Table 3b, Fig. 3a), despite a large scatter in Ndev values for those species with no long-term change in abundance. A second study from Skåne, also including a wide array of ecosystems, is the re-analysis of a total of 93 permanent circular plots (25 m in radius) from two parishes in the north of the province, Matteröd (1964–89) and Norra Sandby (1958–93; Oredsson 1999). The observed changes in species frequency for both parishes combined (and for individual parishes, data not shown) were again highly positively correlated with regional Ndev values (Table 3c, Fig. 3b).

image

Figure 3. Accuracy of prediction of long-term trends observed in Skåne using species Ndev values. (a) Comparison of large-scale flora surveys of grid squares between 1938 and 1967 and 1989–96 (see Table 2b). (b) Re-analysis of permanent plots from the parishes of Norra Sandby (1958–93) and Matteröd (1964–89) in northern Skåne (see Table 2c). Dots are labelled with species abbreviations except for most of those taxa for which no or little change in frequency was observed.

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Changes in species abundance observed in permanent plot studies of Central European forests were well predicted by Ndev in Skåne and region 3 and marginally so in Småland (Table 3e).

ACCURACY OF PREDICTION OF SPECIES’ RESPONSES USING LIFE HISTORY TRAITS

There was a tendency of differences in Ndev to be correlated with taxonomic groups (P = 0.054 and 0.073), but not with Raunkiaer life forms (Pc. 0.3–0.4). Most ferns inhabiting mesic to moist forest sites, such as Athyrium filix-femina and Dryopteris spp., showed a marked increase in abundance, whereas those from dryer habitats (Polypodium vulgare, Pteridium aquilinum) showed decreasing trends (Table 2). No marked differences were found between broad-leaved and narrow-leaved grasses.

Species with increasing abundance were generally taller than those with decreasing abundance, e.g. Anthriscus sylvestris, Epilobium angustifolium and Rubus idaeus vs. Fragaria vesca, Potentilla erecta and Viola spp. The correlations between plant height and the long-term trends of species in Skåne were highly significant using mean (Table 3, Fig. 4a), minimum or maximum height.

image

Figure 4. Accuracy using different life history traits of species to predict long-term trends observed in the re-analysis of permanent plots in northern Skåne (Table 2c). (a) Plant height (mean, in m). (b) Anatomy, for statistical analysis transformed to ordinal values, see Table 1. (c) RGRmax (week−1). (d) NRA (µmol g−1 (fresh weight) h−1). (e) N-conc (mg g−1 (dry weight)). (f) Phenology, for statistical analysis ordinal values were used, see Table 1. Regression statistics in the top of each figure.

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There was a negative association between anatomy (expressed as a score that increases with increasing degree of scleromorphy; Table 1) and species changes in only two types of studies (Table 3, columns c and e). Hydro- to helomorph species showed largely increasing trends, whereas mesomorphs (such as Festuca ovina, Polypodium vulgare and Potentilla erecta) tended to decline (Fig. 4b shows data from Table 3c).

Among the physiological life history traits, RGRmax and NRA had a rather low accuracy of prediction, even though both variables tended to be consistently higher in species with increasing abundance (Table 3, Fig. 4c, d). In contrast, the leaf nitrogen concentration of species was significantly correlated with abundance trends, but only in permanent plot studies in Skåne and in Central Europe (Table 3, columns c and e, Fig. 4e). The species with the three highest nitrogen concentrations (Aegopodium podagraria, Anthriscus sylvestris and Urtica dioica) showed marked increases in abundance.

Species with a late phenological development tended to increase in abundance in the long-term studies in Skåne at the expense of early developing species (Table 3, columns a–c; Fig. 4f). Whereas there was a considerable scatter among the late spring and summer species, it is striking that nearly all herbaceous species flowering in early spring (mostly geophytes, such as Anemone spp., Corydalis spp., Gagea lutea, Ranunculus ficaria and Viola spp.) showed negative trends.

True forest species (specialists) and species of edges, clearings and meadows (generalists) showed no differences in their long-term trends (Table 3, habitat type). It is, however, remarkable that many generalist taxa characteristic of shady and fertile forest fringes, such as Aegopodium podagraria, Anthriscus sylvestris, Moehringia trinervia and Urtica dioica, were among the species with the highest increase in abundance. Many typical meadow species, on the other hand, appeared to decrease, for example Rumex acetosa and Stellaria graminea.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

THE ECOLOGICAL MEANING AND PREDICTIVE ACCURACY OF nDEV SPECIES’ VALUES

Three observations make us believe that the Ndev values have an ecological meaning:

  • 1
    The Ndev values of the most common species were positively correlated between neighbouring regions.
  • 2
    They were significantly positively correlated with species changes in frequency observed in long-term studies in Skåne and to some extent in those in Central Europe.
  • 3 They showed a good agreement with the responses of species found in fertilization experiments.

The third result in particular indicates that Ndev is indeed related to the species’ abilities to respond to enhanced nitrogen levels, although the same conclusion may be drawn from (2), since nitrogen deposition and enrichment of ecosystems (Bobbink et al. 1998), as well as soil acidification and forest management (Falkengren-Grerup et al. 1998; Diekmann et al. 1999), have occurred over recent decades. Some species with positive Ndev scores appear to increase in all the regions, including several ferns (Athyrium filix-femina, Dryopteris spp.) and species such as Epilobium angustifolium and Rubus idaeus, while others with negative scores, such as Maianthemum bifolium and Vaccinium spp., show consistent decreases.

Ndev values are constructed using the mean field values of percentage minNO3 and pH at which species performance is optimum. Failure to consider the amplitudes around this optimum may explain why the Ndev values fail in some cases to explain responses observed in the field. In general, although correlations of Ndev with the observed species were usually significant, r2 values were rather low. Galium odoratum, for example, attained a high positive Ndev score in Skåne, although its abundance at least in permanent forest plots (Table 2) has decreased. This species obviously prefers relatively nitrate-rich sites, but is unable to tolerate the high hydrogen and aluminium ion concentrations on acid soils (Falkengren-Grerup & Tyler 1992). It may therefore have a skewed response to pH that is not taken into consideration by the current formulation of Ndev. Moreover, the accuracy of Ndev in general is dependent on the homogeneity of the data set used (improved by a high number of sample plots, with the species under consideration present in most –Galium odoratum was present in only 5% of the sites in Skåne, the required minimum – and, above all, a normal or even distribution of values of the measured variables, i.e. pH and percentage minNO3). The responses shown were derived from both fertilization experiments and permanent plot studies from different ecosystems and over different time spans, and cover a strong gradient in nitrogen deposition and accumulation in an area where successional changes in deciduous forests are often slow. The sites may also have been affected by forestry and other management practices, including clear-cutting, thinning, coppicing and grazing, etc. The opening of the forest in particular may have considerable effects on species richness and composition of the herbaceous flora that may be difficult to separate from those of eutrophication. Environmental changes of various kinds may also have different effects on plant species, depending on the vegetation type in question and on the presence of species with high competitive abilities.

Theoretically, a soil with 100% nitrification but a very low rate could have a higher percentage minNO3 value than a soil with a higher production of nitrate and any amount of ammonium, despite its lower Ndev value. However, this is more a theoretical than a practical problem with the use of Ndev as the correlation between minNO3 and percentage minNO3 is positive and highly significant for the studied sites (r2 = 0.512, P < 0.001, n = 648). We therefore believe that percentage minNO3 can be used to represent the complexity of nitrogen availability in a soil: it takes into account the form of inorganic nitrogen produced in the soil and also, indirectly, through the correlation with the nitrification rate, the amount of both ammonium (low percentage minNO3) and nitrate.

SPECIES’ RESPONSES AND LIFE HISTORY TRAITS

Various mechanisms responsible for vegetation changes due to increased nitrogen deposition and availability have been recognized, namely changes in soil chemistry per se, an increased density of the tree canopy (Kellner & Mårshagen 1991), altered patterns of herbivory (Ellenberg 1988) and changes in the mycorrhizal structure of the soil (Arnebrant 1994). The changes in competitive relationships between species, however, appear to be of primary importance (Kellner & Redbo-Torstensson 1995; Bobbink et al. 1998), and these are likely to be affected by life history traits. Using functional traits or groups of species sharing common attributes has the advantage of arriving at more general conclusions than a single-species approach.

Some attributes were, at least partly, correlated with observed responses, although less well than the ecological measure (Ndev) based on soil chemistry. Neither the analysis of taxonomy nor life form gave any clear results. This was not unexpected as both are large and heterogeneous groups. The physiological amplitude of single taxonomic groups such as monocots is very broad, and the same is true for most Raunkiaer life forms (such as the hemicryptophytes) defined on the basis of the position of regenerating buds over the inactive season. Positive and negative changes in abundance were observed in both broad- and narrow-leaved grasses, in contrast to studies showing that narrow-leaved grasses were favoured by nitrogen deposition (Rosén et al. 1992), but consistent with observations that potential growth rate varies with factors other than leaf width (Falkengren-Grerup 1998).

Plant height was the best predictor among the life history traits tested and was also significantly positively correlated with Ndev (for Skåne, for example, r2 = 0.109, P = 0.007, n = 66). This means that tall-growing species are generally favoured by a high nitrogen deposition and percentage minNO3: pH ratio at any given site, at the expense of smaller species; such species are able to overgrow and thereby outshade and outcompete smaller species when conditions are favourable. Height was positively related to RGRmax (r2 = 0.168, P = 0.024, n = 30), an observation also made by Grime & Hunt (1975) in an experimental study of grassland species. Leaf nitrogen concentration showed higher correlations with the observed species trends than the other two physiological traits. In their screening of life history attributes Grime et al. (1997) found a close correlation between foliar nutrient concentrations and the capacity for rapid growth under productive conditions (see also Meerts 1997). The primary strategies of the C-S-R model developed by Grime (1979), on the other hand, did not show any agreement with the observed species trends and do not appear to be good predictors of the species’ responses to nitrogen deposition. The large majority of species in deciduous forests are competitors or stress-tolerant and/or ruderal competitors, and the mean responses of species that were observed did not differ between strategy types.

Plant anatomy was linked to the observed species trends and was also intercorrelated to N-conc (r2 = 0.168, P = 0.009, n = 40; cf. Meerts 1997). Many species with a more or less helomorph anatomy (Aegopodium podagraria, Anthriscus sylvestris, Mercurialis perennis, Urtica dioica) also have a high foliar nitrogen concentration and appear to be favoured by high nitrogen deposition. This coincides with the observation that plants cultivated under nitrogen shortage develop a more xeromorph leaf anatomy than plants cultivated on fertile soil (Müller-Stoll 1947; cited in Ellenberg 1996). In other words, increased nitrogen availability may favour an increasingly hydro-helomorph architecture. A second observation made in grasslands is that fertilization of dry grasslands causes the vegetation to develop a more mesic character (i.e. nitrogen in a way ‘replaces’ water), because plants on nitrogen-rich soil have a better water-use efficiency (Ellenberg 1996).

We did not expect any correlation between phenological development and species trends, because the scores reflect the sequence of flowering rather than the growth of roots, shoots and leaves. However, there seems to be a positive correlation between the seasonal development of different parts of the plant, and there was a trend (albeit with low r2) for late-developing species to increase in abundance. One reason for this may be that phenological development and plant height are highly positively correlated (r2 = 0.368, P < 0.001, n = 130).

The results of our study allow us to define a so–called attribute syndrome, i.e. a set of co-occurring attributes (Lavorel et al. 1999). We predict that species that are found in sites with a high ratio of percentage minNO3 to pH or have tall stature, hydro- to helomorph anatomy, high leaf nitrogen concentration and late phenological development, will increase in abundance in response to enhanced nitrogen levels. Athyrium filix-femina, Epilobium angustifolium and Urtica dioica are examples where these attributes co-occur, but Calamagrostis arundinacea and Pteridium aquilinum, two tall and late-developing species, have negative Ndev values and, partly, negative trends, and Moehringia trinervia, a small, rather early developing species with positive Ndev values, appears to increase in abundance. Other, less well-known attributes of forest vascular plants therefore appear to affect the species’ responses to nitrogen enrichment, such as morphological characteristics of the plant or of the plant-mycorrhizal root system, the nutrient (other than nitrogen) content of the plant, etc. We need to gain a better knowledge of the basic life history of species in order to be able to make more accurate predictions of their responses to atmospheric deposition. Another interesting task would be to study whether relevant life history traits differ in importance between deciduous forest sites with different soil acidity and nutrient regimes, or between deciduous forests and other habitat types suffering from airborne nitrogen deposition, such as grasslands, heaths and wetlands.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We thank Germund Tyler and Åke Rühling for kindly allowing us to use their sample plots from Skåne and Småland. We are grateful to Anita Balogh who was responsible for most of the soil analyses. Two anonymous referees, John Lee and Lindsay Haddon made many valuable comments on earlier drafts of the paper. The study was made possible by research grants from MISTRA.

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  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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