Shifts in the ecological behaviour of plant species between two distant regions: evidence from the base richness gradient in mires

Aim Water pH and conductivity are known to be major environmental factors controlling the species composition of nutrient‐poor wetlands. Based on the analysis of two large data sets of species co‐occurrence, sampled along the entire pH/calcium gradient, we explored whether species exhibit similar or different ecological behaviour in the two regions. Location West Carpathians (central Europe) and Bulgaria (south‐eastern Europe), situated 800 km apart. Bulgaria represents a range margin for many mire species. Methods The probability of occurrence of the 41 most common species along the pH and conductivity gradients was assessed using logistic regression fitted by means of generalized additive models. The species optimum and amplitude were determined. To check the possible effect of competitive release, we estimated where the potential maximum number of species (maximum overlap in realized niches) occurs along the base richness gradient. Results Most of the 41 frequently occurring species showed a significant response to water pH and ln‐transformed conductivity (approximating total mineral richness) in both regions. Eight species showed a shift in pH optimum greater than one unit, while 12 species showed the same or a larger shift along the conductivity gradient. Nearly all these striking shifts were connected to an extension of species tolerance towards mineral‐poor acid habitats in Bulgaria, which causes links between species and measured factors to be conspicuously weaker in Bulgaria than in the West Carpathians. Regarding ecological amplitude, 24 species exhibited a wider tolerance to water conductivity in the West Carpathians, whereas 17 species exhibited a wider tolerance in Bulgaria. Main conclusions A distinctive variation in the realized niche was observed in a large portion of the species examined. Niche shifts between local populations of the same species were similar to those of closely related vicariant species. Ecotypic adaptation within species is a possible explanation for this pattern. Other possible explanations (competitive release, specific habitat conditions, compensation for climate) seem to be less justified. The local populations of rich‐fen species may have adapted to mineral‐poor acid conditions in the high crystalline mountains of Bulgaria during dry periods of pleniglacials. Nomenclature Marhold & Hindák (1998) ; for Balkan elements not included in this source, Andreev et al. (1992) .     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Reliability of Ellenberg indicator values for moisture,nitrogen and soil reaction: a comparison with field measurements

Abstract. Ellenberg indicator values for moisture, nitrogen and soil reaction were correlated with measured soil and vegetation parameters. Relationships were studied through between‐species and between‐site comparisons, using data from 74 roadside plots in 14 different plant communities in The Netherlands forming a wide range. Ellenberg moisture values correlated best with the average lowest moisture contents in summer. Correlations with the annual average groundwater level and the average spring level were also good. Ellenberg N‐values appeared to be only weakly correlated with soil parameters, including N‐mineralization and available mineral N. Instead, there was a strong relation with biomass production. We therefore endorse Hill & Carey's (1997) suggestion that the term N‐values be replaced by ‘productivity values'. For soil reaction, many species values appeared to need regional adjustment. The relationship with soil pH was unsatisfactory; mean indicator values were similar for all sites at pH > 4.75 because of wide species tolerances for intermediate pH levels. Site mean reaction values correlated best (r up to 0.92) with the total amount of calcium (exchangeable Ca²⁺ plus Ca from carbonates). It is therefore suggested that reaction values are better referred to as ‘calcium values'. Using abundance values as weights when calculating mean indicator values generally improved the results, but, over the wide range of conditions studied, differences were small. Indicator values for bryophytes appeared well in line with those for vascular plants. It was noted that the frequency distributions of indicator values are quite uneven. This creates a tendency for site mean values to converge to the value most common in the regional species pool. Although the effect on overall correlations is small, relationships tended to be less linear. Uneven distributions also cause the site mean indicator values at which species have their optimum to deviate from the actual Ellenberg values of these species. Suggestions for improvements are made. It is concluded that the Ellenberg indicator system provides a very valuable tool for habitat calibration, provided the appropriate parameters are considered.     

Empirical realised niche models for British higher and lower plants – development and preliminary testing

Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables? Location: Great Britain. Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi‐natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover‐weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity. Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables. Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.     

Measurement errors and regression to the mean cannot explain bias in average Ellenberg indicator values

Smart & Scott (2004, this is sue) criticized our paper (Wamelink et al. 2002) about the bias in average Ellenberg indicator values. Their main criticism concerns the method we used, regression analysis. They state the bias can be mimicked by the construction of an artificial data set and that regression analysis is not a suited tool to investigate underlying phenomena. Moreover they claim that the present bias is caused by the distribution of Ellenberg indicator values between syntaxa, instead of a bias in average Ellenberg indicator values per species. We show that their criticism of the use of regression analysis does not hold. We selected average Ellenberg values per vegetation group for several pH classes and applied an F‐test to determine whether or not the vegetation groups within each pH class differed significantly from each other. This was the case for all tested classes (P < 0.001). Moreover we simulated an artificial data set, of which the F‐test for varying measurement error could not explain the magnitude of the F‐value we found earlier. This indicates that the bias we found in average Ellenberg indicator values cannot be explained by measurement errors or by regression to the mean. In the end, Smart & Scott, as we did, come to the conclusion that there is a bias present and that separate regression lines per vegetation type are necessary, but the debate remains open on whether or not this is caused by the bias in Ellenberg indicator values per species.     

Prediction of yield in the Rothamsted Park Grass Experiment by Ellenberg indicator values

Abstract. The Rothamsted Park Grass Experiment was established in 1856, with experimental plots subjected to annual applications of fertilizer and twice-yearly cutting of hay. There were two major responses to fertilizer, one reflecting high ammonium-nitrogen and increased acidity and the other reflecting high herbage yield without increased acidity. We calculated mean Ellenberg indicator values for N (nitrogen) and R (soil reaction) for the hay harvested between 1948 and 1975, using both unweighted and abundance-weighted means. Plot Ellenberg values were compared with herbage yield and with fertilizer application rates and published soil data. Annual yield of hay varied from 1.5 to 7.4 t/ha and was well predicted by the unweighted mean Ellenberg N-values (r = 0.91). Relatively large negative residuals from the relationship were found in plots whose soil combined low K and low pH. Soil pH was poorly predicted by the unweighted mean R-value, but showed a moderately good relation with weighted mean R (r = 0.73). The fact that Ellenberg N-values correlated better with yield than with applied nitrogen suggests that they might rather be called productivity values.     

Prediction of occurrence of vascular plants in deciduous forests of South Sweden by means of Ellenberg indicator values

Abstract. In this study we present a new method for predicting the occurrences of species using data from deciduous forests in South Sweden. Complete species lists of vascular plants were compiled from 101 stands and from representative sample plots inside the stands. Soil samples from each stand were collected for determination of pH and nitrogen mineralization. Presence-absence data for species were fitted to the values of four environmental variables - soil moisture, soil reaction (pH), soil nitrogen and light - by means of Linear (Multiple) Logistic Regression (LLR), and Gaussian (Multiple) Logistic Regression (GLR). First, these values were estimated by calculating the weighted averages of Ellenberg indicator values. Second, the estimates for reaction and nitrogen were substituted by the real measurements of pH and mineralized NH₄⁺, keeping the Ellenberg estimates for light and moisture. The models were validated by an independent test data set. In general, the models had high predictive abilities. GLR fitted the species occurrences better to the environmental variables than LLR, but had a lower accuracy of prediction of species occurrence in the stands. The use of soil measurements instead of Ellenberg indicator values did not improve the predictive abilities of the models. The environmental conditions in the stand test set were successfully estimated by using species data from the plots. When using the species lists of the stands instead of plot data, a slightly better predictive ability was obtained. The collection of plot data, however, is easier and less time-consuming. The accuracy of prediction differed considerably between species.     

Species richness in deciduous forests: Effects of species pools and environmental variables

Abstract. The study was conducted in deciduous forests of two Swedish regions, Öland and Uppland. It had two objectives: to (1) test the species pool hypothesis by examining if differences in small‐scale species richness are related to differences in large‐scale species richness and the size of the regional species pool, and (2) to examine the relationship between species richness and productivity and its scale‐dependence. The first data set comprised 36 sites of moderate to high productivity. In each site, we recorded the presence of vascular plant species in nested plots ranging from 0.001 to 1000 m² and measured several environmental variables. Soil pH and Ellenberg site indicator scores for nitrogen were used as estimators of productivity. The second data set included 24 transects (each with 20 1‐m² plots) on Öland in sites with low to high productivity. Species number, soil pH and relative light intensity were determined in each plot. The forest sites on Öland were more species‐rich than the Uppland sites on all spatial scales, although environmental conditions were similar. Small‐scale and large‐scale species richness were positively correlated. The results present evidence in favour of the species pool hypothesis. In the nested‐plots data set, species number was negatively correlated with pH and nitrogen indicator scores, whereas a unimodal relationship between species number and pH was found for the transect data set. These results, as well as previously published data, support the hump‐shaped relationship between species richness and productivity in Swedish deciduous forests. Two explanations for the higher species richness of the sites with moderate productivity are given: first, these sites have a higher environmental heterogeneity and second, they have a larger ‘habitat‐specific’ species pool.     

Testing the species pool hypothesis for mire vegetation: exploring the influence of pH specialists and habitat history

The Evolutionary species pool hypothesis (ESPH) predicts that historically more common habitats will be richer in species because they have had greater opportunity for the evolution of suitably adapted species. We explored the relationship between mire species richness and pH, an important environmental variable in mires, in two regions that differ in habitat pH distribution: the West Carpathians and Bulgaria. Mire habitats in both the West Carpathians and Bulgaria demonstrate support for the ESPH prediction that habitats with more common pH values host more species. We also explored the influence of habitat history by examining the distribution of generalists and specialists along gradients of habitat pH, using extensive community-level vegetation data from European mires in these two regions. We found a striking pattern with the distribution of pH-specialists having three distinct peaks in both regions, whereas the total species pool peaked in near neutral pH habitats in both regions. Because the peaks in specialist richness do not correspond to regional pH distribution patterns, we hypothesize that historical explanations may be important, and that habitats currently rich in pH-specialists may have historically acted as pleniglacial refugia for many mire species. Our findings support the general predictions of the ESPH, but further suggest that historical processes such as patterns of glacial refugia, may significantly influence contemporary species distributions and the diversity of plant species in mire habitats.     

Long‐term impacts of nitrogen and sulphur deposition on forest floor vegetation in the Northern limestone Alps,Austria

Question: Are there effects of long‐term deposition of airborne nitrogen and sulphur on the forest floor vegetation from permanent plots collected in 1993 compared to 2005. Location: Northern limestone Alps in Austria. Methods: Single species responses were analysed by correlating trends in cover‐abundance values, as derived from marginal models, with Ellenberg indicator values. Changes in the species composition of plots were analysed by correlating changes in mean Ellenberg indicator values with the displacement of plots within a multidimensional scaling ordination. Results: Trends in single species abundance were positively correlated with indicator values of soil pH but were independent of nutrient availability. A general trend towards the homogenisation of vegetation, due to convergent time vectors of the relevés, became obvious. Oligotrophic sites previously situated at the distal ends of ordination axes shifted towards the centre since they were enriched by species preferring mesotrophic conditions. The bulk of plots with intermediate site conditions hardly showed any trends. A concomitant analysis demonstrated that temporal changes in species composition exceed the variation in cover abundance estimates among different field botanists. Conclusions: N deposition can lead to a homogenisation of forest floor vegetation. Larger limestone areas with diverse soil conditions, such as the Northern limestone Alps in Austria, as a whole are thus negatively affected by airborne N deposition. Nevertheless, the vegetation was at least as strongly affected by an increase of basiphilous species as a result of decreasing S deposition.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Epigeic bryophytes do not improve bioindication by Ellenberg values in mountain forests

Based on a stratified random sample of 93 vegetation plots and coincident measurements of ecological conditions in mountain forests of the Bavarian Alps, the degree to which species composition and Ellenberg indicator values derived thereof were related to measured environmental variables was assessed for vascular understorey plants and epigeic bryophytes. According to Mantel tests vascular composition contained ca. 30% more ecological information than bryophyte composition. When expressed as average Ellenberg or Düll values, vascular plant-based values reflected 60% more of measured variables than bryophyte-based values. The differences remained after rarefaction of the vascular matrix to the gamma diversity of bryophytes, showing that indication is not a function of indicator richness. Analysing vascular plants and bryophytes combined yielded very similar, or even slightly less stringent relationships with the environment than using vascular plants only.Bivariate relationships of indicator values with corresponding ecological measurements confirmed the specific potential of the values to estimate ecological factors from both plant groups, but vascular plants performed better for all factors. Bryophyte indication was particularly poor for light, temperature and base saturation. Bryophyte-based indicator values did not significantly predict the residuals of measured ecological variables against vascular plant-based Ellenberg values.For the study region, it is concluded that indicator values of vascular forest understorey should be used without consideration of Düll's indicator values for epigeic bryopyhtes. There appears to be potential to improve bioindication by recalibrating indicator values of epigeic bryophytes based on ecological measurements and vascular plant indicator values.     

Validity of Ellenberg indicator values judged from physico‐chemical field measurements

Abstract. The relationship between mean Ellenberg indicator values (IV) per vegetation relevé and environmental parameters measured in the field usually shows a large variation. We tested the hypothesis that this variation is caused by bias dependent on the phytosociological class. For this purpose we collected data containing vegetation relevés and measured soil pH (3631 records) or mean spring groundwater level (MSL, 1600 records). The relevés were assigned to vegetation types by an automated procedure. Regression of the mean indicator values for acidity on soil pH and the mean indicator values for moisture on MSL gave percentages explained variance similar to values that were reported earlier in literature. When the phytosociological class was added as an explanatory factor the explained variance increased considerably. Regression lines per vegetation type were estimated, many of which were significantly different from each other. In most cases the intercepts were different, but in some cases their slopes differed as well. The results show that Ellenberg indicator values for acidity and moisture appear to be biased towards the values that experts expect for the various phytosociological classes. On the basis of the results, we advise to use Ellenberg IVs only for comparison within the same vegetation type.     

Impact of tree species on nutrient and light availability: evidence from a permanent plot study of old-field succession

This paper compares vegetation composition, light availability, carbon and nutrient pools and Ellenberg indicator values among four old-field successional permanent plots that have received an initial treatment (ploughing, herbicide or sterilisation) prior to being left undisturbed in 1969, a second set of six plots received additional treatments (continued ploughing or mulching until 1982). On all plots species rich pioneer forests developed. Vegetation still varies among plots with different initial treatments: Sterilised plots can be distinguished from the others by dominance of Betula pendula, ploughed plots by Fraxinus excelsior, whereas herbicide-treated plots are intermediate with proportions of both species. By affecting light availability at the ground, tree species in turn influences ground vegetation and soil properties. Light availability turned out to be the dominant factor determining the composition of the vegetation in old-field succession.     

Species Richness, Community Specialization and Soil-Vegetation Relationships of Managed Grasslands in a Geologically Heterogeneous Landscape

The increasing importance of the conservation value of managed grasslands has led to many studies exploring edaphic determinants of grassland biodiversity. Most studies, however, come either from very large areas, where biogeographical factors such as dispersal limitation may play a role, or from small, but ecologically rather uniform, regions. In addition, few studies further distinguish between plant specialists and generalists in the interpretation of the observed patterns. Here we studied species richness in semi-natural, managed grasslands in the Strá?ovské vrchy Mountains in the West Carpathians, Slovakia, where there is a matrix of different bedrocks (crystalline, sandstone, claystone, limestone) on a steep altitudinal gradient. In 89 vegetation plots we sampled the species composition of vascular plants and bryophytes and measured soil chemistry, slope angle, heat index, altitude and soil depth. We further applied Ellenberg indicator values and classified species into community specialists or generalists based on the analysis of a large phytosociological database. Using cluster analysis, we delimited five vegetation types that clearly differed in response to soil characteristics. Species richness varied between 19 and 64 species per 16?m². The main compositional gradient correlated with measured soil pH and calcium, but species richness was not significantly correlated with these factors. Soil available phosphorus was not associated with species composition as has been found elsewhere, but it did correlate negatively with species richness and the richness of specialists. Overall, species richness was largely driven by the number of specialists in the plot and particular vegetation types differed conspicuously in their number. We further found significant effects of iron, potassium and sodium on species richness, species composition and the representation of specialists and generalists. Our results provide new insights into the determinants of diversity in managed grasslands as well as to the theoretical species pool concept, explaining species richness variation along a pH gradient.     

Bias in Ellenberg indicator values — problems with detection of the effect of vegetation type

A recent analysis published in this journal found different relationships between mean Ellenberg indicator values and environmental measurements in different vegetation types. The cause was stated as bias in mean Ellenberg values between relevés which in turn suggested to reflect a bias in individual Ellenberg values. We discuss two phenomena that could explain these results without the need to invoke bias in either individual or mean Ellenberg values. Firstly, slopes of linear regression lines underestimate true relationships when analyses involve explanatory variables measured with error. Secondly, syntaxon‐specific distributions of Ellenberg values follow from the floristic definition of phytosociological units. Mean Ellenberg values per relevé therefore carry the stamp of their associated syntaxon even though associated abiotic conditions may vary between relevés. This will lead to variation in slopes and intercepts between vegetation types not because of bias in individual Ellenberg values but because of prescribed bias in the distribution of Ellenberg values between syntaxa. The residual variation in calibrations carried out across vegetation types is undoubtedly reduced by introducing vegetation type as a factor. However users should note that this is unlikely to reflect bias in individual Ellenberg values but is more likely to reflect error in environmental measurements as well the constraint imposed by phytosociological classification.     

Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip

Background

Imputation of genotypes from low-density to higher density chips is a cost-effective method to obtain high-density genotypes for many animals, based on genotypes of only a relatively small subset of animals (reference population) on the high-density chip. Several factors influence the accuracy of imputation and our objective was to investigate the effects of the size of the reference population used for imputation and of the imputation method used and its parameters. Imputation of genotypes was carried out from 50 000 (moderate-density) to 777 000 (high-density) SNPs (single nucleotide polymorphisms).

Methods

The effect of reference population size was studied in two datasets: one with 548 and one with 1289 Holstein animals, genotyped with the Illumina BovineHD chip (777 k SNPs). A third dataset included the 548 animals genotyped with the 777 k SNP chip and 2200 animals genotyped with the Illumina BovineSNP50 chip. In each dataset, 60 animals were chosen as validation animals, for which all high-density genotypes were masked, except for the Illumina BovineSNP50 markers. Imputation was studied in a subset of six chromosomes, using the imputation software programs Beagle and DAGPHASE.

Results

Imputation with DAGPHASE and Beagle resulted in 1.91% and 0.87% allelic imputation error rates in the dataset with 548 high-density genotypes, when scale and shift parameters were 2.0 and 0.1, and 1.0 and 0.0, respectively. When Beagle was used alone, the imputation error rate was 0.67%. If the information obtained by Beagle was subsequently used in DAGPHASE, imputation error rates were slightly higher (0.71%). When 2200 moderate-density genotypes were added and Beagle was used alone, imputation error rates were slightly lower (0.64%). The least imputation errors were obtained with Beagle in the reference set with 1289 high-density genotypes (0.41%).

Conclusions

For imputation of genotypes from the 50 k to the 777 k SNP chip, Beagle gave the lowest allelic imputation error rates. Imputation error rates decreased with increasing size of the reference population. For applications for which computing time is limiting, DAGPHASE using information from Beagle can be considered as an alternative, since it reduces computation time and increases imputation error rates only slightly.     

NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics

Ellenberg indicator values are widely used ecological tools to elucidate relationships between vegetation and environment in ecological research and environmental planning. However, they are mainly deduced from expert knowledge on plant species and are thus subject of ongoing discussion. We researched if Ellenberg indicator values can be directly extracted from the vegetation biomass itself. Mean Ellenberg “moisture” (mF) and “nitrogen” (mN) values of 141 grassland plots were related to nutrient concentrations, fibre fractions and spectral information of the aboveground biomass. We developed calibration models for the prediction of mF and mN using spectral characteristics of biomass samples with near-infrared reflectance spectroscopy (NIRS). Prediction goodness was evaluated with internal cross-validations and with an external validation data set. NIRS could accurately predict Ellenberg mN, and with less accuracy Ellenberg mF. Predictions were not more precise for cover-weighted Ellenberg values compared with un-weighted values. Both Ellenberg mN and mF showed significant and strong correlations with some of the nutrient and fibre concentrations in the biomass. Against expectations, Ellenberg mN was more closely related to phosphorus than to nitrogen concentrations, suggesting that this value rather indicates productivity than solely nitrogen. To our knowledge we showed for the first time that mean Ellenberg indicator values could be directly predicted from the aboveground biomass, which underlines the usefulness of the NIRS technology for ecological studies, especially in grasslands ecosystems.     

On the statistical analysis of vegetation change: a wetland affected by water extraction and soil acidification

Abstract. A case study is presented on the statistical analysis and interpretation of vegetation change in a wetland subjected to water extraction and acidification, without precise information on the environmental changes. The vegetation is a Junco-Molinion grassland and the changes in vegetation are evaluated on the basis of relevés in 1977 and 1988 of 20 plots in a small nature reserve on moist oligotrophic, Pleistocene sands in the Netherlands. The changes are attributed to water extraction (since 1972) and soil acidification and the effect of the environmental changes on the vegetation is inferred from data on water depth and acidity collected in 1988. Many species typical of wetlands decreased in abundance, including rare species such as Parnassia palustris, Selinum carvifolia and Ophioglossum vulgatum. Some species increased, notably Anthoxanthum odoratum, Holcus lanatus and Plantago lanceolata. A significant decrease was found in the mean Ellenberg indicator values for moisture and acidity. The mean indicator value for nutrients did not change significantly. Multivariate analysis of the species data by Redundancy Analysis demonstrated the overall significance of the change in species composition between 1977 and 1988 (P < 0.01, Monte Carlo permutation). The spatial and temporal variation in the species data was displayed in ordination diagrams and interpreted in terms of water depth and pH. A simple model is developed to infer the change in water depth and pH from the relevé data and recent data on water depth and pH. Because the correlation between water depth and pH made a joint estimation of the changes useless, the change in pH was estimated for a series of likely changes in water depth. For the most likely change in water depth, significant acidification was inferred from the change in vegetation. The model is more generally applicable as a constrained calibration method.     

Calibrating Ellenberg indicator values for moisture,acidity, nutrient availability and salinity in the Netherlands

A general calibration of Ellenberg indicator values for moisture, acidity, nutrient availability and salinity was carried out on a large database of relevées and environmental variables from a variety of ecosystems in the Netherlands.Satisfying relationships with Ellenberg indicator values for moisture, acidity and salinity were found for mean groundwater level in spring time, soil pH and chloride concentration in groundwater. For mean groundwater level in spring and chloride concentration in groundwater subdivision of the database led to clearer relationships with indicator values. For the Ellenberg indicator value for nutrient availability satisfying calibration results were only achieved with data on standing crops and N stock in standing crop. The relationship with soil chemical variables was less clear.Although the correlation between indicator and measured values is obvious, the variation around the regression lines is considerable. However, because of the size and composition of the database, it is unlikely that our calibration results can be much improved by adding more (Dutch) data.The calibration results will be applied in the multi-stress model SMART-MOVE, developed to predict changes in species composition due to acidification, eutrophication and the effects of lowering groundwater.