Mean Indicator Values Research Articles

The need of data harmonization to derive robust empirical relationships between soil conditions and vegetation

AbstractQuestion:Is it possible to improve the general applicability and significance of empirical relationships between abiotic conditions and vegetation by harmonization of temporal data?Location:The Netherlands.Methods:Three datasets of vegetation, recorded after periods with different meteorological conditions, were used to analyze relationships between soil moisture regime (expressed by the mean spring groundwater level –MSLt calculated for different periods) and vegetation (expressed by the mean indicator value for moisture regime Fm). For each relevé, measured groundwater levels were interpolated and extrapolated to daily values for the period 1970–2000 by means of an impulse‐response model. Sigmoid regression lines between MSLt and Fm were determined for each of the three datasets and for the combined dataset.Results:A measurement period of three years resulted in significantly different relationships between Fm and MSLt for the three datasets (F‐test, p < 0.05). The three regression lines only coincided for the mean spring groundwater level computed over the period 1970–2000 (MSLclimate) and thus provided a general applicable relationship. Precipitation surplus prior to vegetation recordings strongly affected the relationships.Conclusions:Harmonization of time series data (1) eliminates biased measurements, (2) results in generally applicable relationships between abiotic and vegetation characteristics and (3) increases the goodness of fit of these relationships. The presented harmonization procedure can be used to optimize many relationships between soil and vegetation characteristics.

Adaptive solution of BVPs in singularly perturbed second-order ODEs, by the extended Numerov method combined with an iterative local grid h-refinement

The extended Numerov scheme of Chawla is one of high-order compact finite-difference discretisations applicable to BVPs in second-order ODEs, and to PDEs. The use of such discretisations in connection with adaptive grids remains largely unexplored. In order to make up this deficiency, we study the combination of the extended Numerov discretisation with the iterative local adaptive grid h-refinement previously tested by the present author at the assumption of the conventional finite-difference discretisation. A detailed formalism enabling a posteriori error analysis of the extended Numerov scheme is developed, together with several alternative choices for error estimators, grid refinement indicators, and regridding strategies. The adaptive algorithms obtained are examined in calculations on a collection of 15 examples of singularly perturbed ODEs, including equations relevant for electrochemistry. The most satisfactory algorithm involves: deferred approach to error estimation, based on virtual grid coarsening for truncation error evaluation; local discretisation errors as refinement indicators; and regridding strategy that uses the mean indicator values as the indicator thresholds for refinement. The extended Numerov scheme proves more efficient than the conventional discretisation, when used together with the iterative h-refinement.

Improving predictive mapping in Swiss mire ecosystems through re‐calibration of indicator values

AbstractQuestion: How may Landolt indicator values be re‐calibrated to improve the performance of predictive models?Location: Mires Gross Moos Schwändital (1250 m a.s.l.) in the Prealps, Burgmoos (465 m. a.s.l.) on the Central Plateau and La Burtignière (1000 m a.s.l.) in the Jura, Switzerland.Methods: Habitat distribution models based on high resolution remotely sensed data and vegetation field data are applied to monitor 130 mires. Instead of plant species or communities we used mean indicator values of vegetation records as response variables. To improve the differential power of indicator values for wetland habitat conditions, we calibrated these values using field data. Different methods were tested with our predictive models in three mires to see which calibration method is best in enhancing model performance. To assess the effect of the uneven distribution of vegetation records along environmental gradients, calibrations based on random and evenly distributed samples were compared. As a test of the predictive power of the models we used r2 between ground truth and model prediction.This approach is illustrated through an application with nutrient indicator values in the mire La Burtignière. Results: Model performances were not the same for the three mires. The predictive power was better for the nutrient values, soil reaction and humus values than for light and moisture values. 2000 records were sufficient as basis for re‐calibration. Models based on original Landolt indicator values were overall the weakest compared with re‐calibrated values. By comparing the predictive power of Models based on randomly or evenly selected records were about equally predictive.Conclusions: 1. Ahabitat‐specific re‐calibration of the Landolt indicator values enhances the predictive mapping of the Swiss mire ecosystems. 2. The re‐calibration based on weighted averaging gives a better performance than the one based on Gaussian logistic regression. 3. The uneven distribution of indicator values due to the over‐representation of mire habitats does not hamper model performance. 4. 2000 vegetation records are a sufficient basis for an optimal re‐calibration of the vegetation types. An illustration of the method is given by using the soil fertility pattern of the mire La Burtignière.

Experiments with a local adaptive grid h-refinement for the finite-difference solution of BVPs in singularly perturbed second-order ODEs

Despite many years of the development of adaptive grid methods, there is still a need for re-evaluation, tuning, and comparisons of the various approaches to grid adaptation. In this study we seek to identify the most reliable and computationally inexpensive variant of the iterative grid refinement/de-refinement algorithm for the finite-difference solution of singularly perturbed BVPs in second-order ODEs, that is also easily extendable to evolutionary PDEs. Conventional three-point discretisations of the first and second derivatives are used. Errors of the discrete solution and its first derivative are simultaneously controlled. The grid refinement is accomplished by adding nodes at inter-nodal positions, and de-refinement by removing previously added nodes in reverse order. Computational experiments are performed for 15 examples of ODEs, assuming several alternative choices of the a posteriori error estimators, grid refinement indicators, and regridding strategies. The most satisfactory results are obtained by combining: deferred approach to error estimation, based on higher-order reference derivative approximations for truncation error evaluation; interpolation-based indicator for grid refinement; and regridding strategy that uses the mean indicator value as the indicator threshold for refinement.

Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values

AbstractQuestion: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification?Location: The Netherlands.Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three‐dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations.Results and Conclusions: With indicator values, most reléves were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.

Improving predictive mapping in Swiss mire ecosystems through re-calibration of indicator values

ABSTRACT Question: How may Landolt indicator values be re-calibrated to improve the performance of predictive models? Location: Mires Gross Moos Schwandital (1250 m a.s.l.) in the Prealps, Burgmoos (465 m. a.s.l.) on the Central Plateau and La Burtigniere (1000 m a.s.l.) in the Jura, Switzerland. Methods: Habitat distribution models based on high resolution remotely sensed data and vegetation field data are applied to monitor 130 mires. Instead of plant species or communities we used mean indicator values of vegetation records as response variables. To improve the differential power of indicator values for wetland habitat conditions, we calibrated these values using field data. Different methods were tested with our predictive models in three mires to see which calibration method is best in enhancing model performance. To assess the effect of the uneven distribution of vegetation records along environmental gradients, calibrations based on random and evenly distributed samples were compared. As a test of ...

Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most reléves were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.

Imaging spectroscopy as a tool for mapping Ellenberg indicator values

Summary Maps of averaged plant indicator values can supply spatially explicit information about environmental gradients and are therefore tools of broad relevance in applied ecology. However, field‐based mapping demands extensive sampling and thus most assessments are at the level of plots rather than covering a whole area. To address these limitations, this study evaluated the potential of remote sensing to produce Ellenberg indicator maps. Ellenberg indicator values for water supply, soil pH and soil fertility were derived using species data from vegetation plots in montane rangeland. The extrapolation of these data was accomplished by partial least‐squares (PLS) regression between indicator values and plot reflectance values extracted from airborne ‘hyperspectral’ imagery. Applying the regression to the imagery resulted in three largely accurate maps giving the spatial distribution of certain soil attributes as indicated by Ellenberg values (R2 = 0·58–0·68 in cross‐validation). Synthesis and applications. Mean indicator values express floristic composition as a single, comparable, continuous and mappable variable. This makes them an appealing tool for vegetation monitoring. Imaging spectroscopy provides fast access to spatially contiguous and explicit information about soil conditions as indicated by plants. The technique allows the investigation to advance beyond plots and supplies indicator maps at a stand‐level resolution. The mapped gradients of environmental attributes are of direct relevance to plant growth. The maps depict life‐span growth conditions rather than ‘snap‐shots’ given by measurements that are often limited in space and time, cost intensive and difficult to implement.

Ecological factors controlling the abundance of non-native invasive black cherry ( Prunus serotina) in deciduous forest understory in Belgium

The relation between invasion success of Prunus serotina and type of recipient habitat was studied in a large forest in central Belgium. The major emphasis in this study was the determination of factors controlling the abundance of P. serotina in understory strata. Environmental variables measured in 34 sample plots were slope, aspect, litter depth, soil type, pH, soil compaction, soil moisture, air humidity, soil temperature and light intensity in spring and late summer. Site conditions were also expressed indirectly for 210 sample plots using Ellenberg indicator values (soil nutrients, acidity, moisture, light conditions). Plots with P. serotina had lower mean indicator values for soil moisture, reaction (pH) and nitrogen, compared to plots without P. serotina. Twenty indicator species were identified for plots in which P. serotina occurs. The species richness of the herb layer was negatively correlated with the percentage cover of black cherry in the shrub layer. The percentage cover of P. serotina saplings in different overstory types was ranked as follows: Quercus > Pinus > Fagus > logging areas. Only three variables explained significant amounts of variation in Prunus abundance: slope, light intensity at 120 cm in April and light intensity at ground level in September. We found a positive response of black cherry seedlings to light intensity between 58 and 80% of full light (April measurements at 120 cm), while saplings showed a negative response within this range. Between 21 and 47% of full light (April measurements at 120 cm), seedlings were poorly represented whereas saplings showed a quite high cover. Between 0.3 and 1.8% light (September measurements at ground level), seedlings were almost absent while saplings maintained a high abundance. The results suggest that P. serotina shows a differential response to light intensity in relation to its development stage, i.e. the species is heliophilous at the seedling stage and becomes a shade plant at the sapling stage.

The influence of the spatial pattern of trees on forest floor vegetation and silver fir ( Abies alba Mill.) regeneration in uneven-aged forests

In the Western Carpathians (of southern Poland), in three representative multilayered fir stands, all trees above 6.9 cm in d 1.3 were mapped. Subsequently, forest floor vegetation cover, vascular plant species, and fir seedlings (below 0.5 m in height) were systematically sampled on small circular plots in a 5 m × 5 m grid. On the basis of location in the stand and indicator values of plant species registered within the plot, an index of influence and a mean indicator value of soil moisture, soil trophy, and soil pH was assigned to every plot. The mean values of these variables were compared in plots with abundant and scarce fir regeneration. Spatial correlation methods were used to test the relationships between the tree pattern and fir seedling density, the vegetation cover, and the species composition of the forest floor. In the studied stands, despite relatively homogeneous spatial structure, both species composition of the forest floor vegetation and vegetation cover, as well as fir seedling density, exhibited a patchy spatial variation. Patches with a higher proportion of larger trees were characterised by less vegetation cover, more frequent occurrence of plant species associated with mesic sites, and more abundant fir regeneration. In gaps, canopy openings, and fragments with relatively small trees, fir regeneration was frequently absent. Occurrence of extremely dense forest patches, creating very unfavourable niches for plants in general, and competition of the forest floor vegetation could be acknowledged as the prime reasons for the lack of fir regeneration only in 30–50% of the area of the stands. It is suggested that adequately explaining the spatial variation of regeneration occurrence would require the inclusion of a complex of factors that potentially limit the survival of juvenile seedlings in fertile microhabitats. If occurrence of fir regeneration on fertile sites is controlled in larger measure by factors associated with edaphic variability than by light conditions, then cuttings aimed at prompting regeneration may be successful only where a regeneration bank has earlier been established. Otherwise, such removal of trees may cause an increase in percent of coverage by forest floor vegetation and, through unfavourable eutrophisation of initially acidified microhabitats, additionally hinder the establishment of fir seedlings.

Selecting target species to evaluate the success of wet grassland restoration

The evaluation of restoration success needs the definition of goals and the set up of evaluation criteria. This will be done for wet grassland restoration on fen soils in NW Germany. These ecosystems lost their habitat function for co-adapted plant species due to melioration and intensive farming since the 1950s. The recovery of this function is a major concept deduced from environmental policies. In order to evaluate the success of restoration measures in wet grassland restoration target plant species are identified. Doing this, sets of required abiotic conditions are deduced and translated into calibrated mean indicator values for plant communities [Scripta Geobot. 18 (1992) 248]. These are arranged in a successional series which is expected to occur on fens when restoration measures are realised. Thresholds of mean indicator values are used in order to select “target systems”. Four hundred and seventy three plant species that occur in the successional fen series are reduced to 136 target species using different selection criteria. For the evaluation of restoration success it is necessary to establish assemblages of target species with different indication power, invasion probability and frequency. As an indicator integrating these demands, the potential of species to occur during different temporal stages (within successional lines) on fen soils and different habitats is classified. A further ranking of these target species therefore is based on their different niche breadths and temporal persistence during secondary succession. Six vulnerability classes are established which represent species groups with gradually changing vulnerability for intensification or abandonment, respectively. Species with a narrow niche and short persistence during succession are strongly endangered by supposed changes in land management. It is clear from restoration experiments in NW Germany and The Netherlands however that these species are hardly capable of re-establishing during restoration. Higher frequencies in intensive grasslands enhance the re-invasion into recovered grassland swards. Considering this low re-colonisation rate it has to be a priority concept to protect those relic habitats still existing, especially remnants of small sedge-, wet grassland- and stream valley communities.

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.

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

Abstract The relationship between mean Ellenberg indicator values (IV) per vegetation releve 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 releves and measured soil pH (3631 records) or mean spring groundwater level (MSL, 1600 records). The releves 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 slop...

Assessment of light and soil conditions in ancient and recent woodlands by Ellenberg indicator values

Summary The system of Ellenberg indicator species is widely accepted and used to characterize climate, light and soil conditions in central Europe and beyond. The aim of this study was to examine the extent to which Ellenberg indicator values can be used to characterize environmental conditions in ancient and recent woodlands. The study was based on data from 264 plots in ancient deciduous woodlands and adjacent 70‐year‐old recent woodlands located on the same soils in the Carpathian foothills. Vegetation data were related to information on light intensity and a range of soil variables. Despite a similar number of species on the plots in the ancient and recent woodlands, the correlations between the mean indicator values for light (mL), reaction (mR) and nitrogen (mN) and measured light intensity, soil pH and cation exchange capacity, respectively, were significantly lower in the recent woodlands than in the ancient woodlands. Due to a very slow and differentiated regeneration rate of the field layer, the species with the lowest indicator values for reaction and nitrogen were significantly less abundant in the recent woodlands than in the ancient woodlands, while the species with higher values of these indicators were more frequent. The change of the reaction of many species to light conditions was the main reason for poor prediction of light intensity in the recent woodlands by the mean indicator values for light. The mean Ellenberg indicator values for light, reaction and nitrogen were relatively good predictors of the environmental conditions in the ancient woodlands, with stabilized species composition determined mainly by the environmental regime. They were, however, much weaker indicators in the recent woodlands. This implies that the mean indicator values for secondary woodlands should be interpreted with caution and verified by measurements of environmental variables.

The use of ground vegetation and humus type as indicators of soil nutrient regime for an ecological site classification of British forests

In many countries ground vegetation and humus type are used as indicators of forest soil quality, especially nutrient regime. This paper reports the development of such methods for use in British forests, within a new Ecological Site Classification combining climate, soil moisture regime and soil nutrient regime. To develop a field assessment method for soil nutrient regime, a study was made of soil chemistry, humus type and ground vegetation in British forests. Sites were selected in both mature plantations and semi-natural woodlands. Soil and humus profiles were described and the soil was sampled volumetrically for later chemical analysis. Vascular ground vegetation was recorded in quadrats by species cover fraction, and classified according to the existing British National Vegetation Classification. Soils were analyzed for a number of chemical variables. Vegetation data were treated by application of the species indicator values for soil reaction ( R) and soil nitrogen ( N), as proposed by Ellenberg [Vegetation Ecology of Central Europe, 4th Edition. Cambridge University Press, Cambridge]. Site mean indicator values mR and mN (weighted by species cover fraction) were then calculated. Multivariate statistical analysis techniques were applied to both the soil chemical and the vegetation sample data. Soil nutrient regime was shown to be a composite gradient of several soil chemical variables, of which the pH value and the availability of mineral (especially nitrate) nitrogen, and of calcium, were of particular importance. The species composition of the ground vegetation was related to position on this soil nutrient gradient. The vegetation: soil nutrient correlation using the site mean Ellenberg values was satisfactory ( r=0.89), but was improved by using indicator values generated from within the present data. The occurrence of the major humus types (mor, moder and mull) is broadly related to soil nutrient regime defined in this way. Both ground vegetation and humus type can therefore be used as soil nutrient indicators in British forests. A division of the soil nutrient gradient into five classes (Very Poor, Poor, Medium, Rich and Very Rich) is proposed. Future sampling work may lead to the definition of an additional class of soils with carbonate nutrient regimes. The Ecological Site Classification will provide forest managers in Great Britain with an improved basis for the selection of tree species for planting, and the adoption of silvicultural methods best suited to the site.

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.

The relationship between butterflies and environmental indicator values: a tool for conservation in a changing landscape

We examined relationships between Dutch butterfly species and the Ellenberg indicator values for nutrients, acidity and moisture. Presence/absence data on butterflies were obtained from monitoring transects of the Dutch Butterfly Monitoring Scheme. Mean indicator values were calculated from vegetation samples of a selection of transect sections. Single and multiple logistic regression models were used to analyse the relationships. Except for the moisture value, the vegetation samples covered the Ellenberg scales quite well. Significant correlations between moisture and acidity (−) and nutrients and acidity (+) were observed. Sites that were both acid and nutrient-rich were not observed. Most of the observed significant relationships were unimodal (Gaussian), in which species show a clear optimum indicator value. Other species showed a sigmoidal (linear) response to one or more of the ecological indicator values. Several species were significantly correlated with all three indicator values. For a small group this was also the case in the multiple regression model. This was probably caused by multicollinearity of the indicator values, leading to some spurious significant single regression models. We discuss methodical problems and possibilities for refinements of the models. The observed relationships between butterflies and indicator values agreed well with available ecological information.The observed regression models can be used to (a) predict the effects of environmental factors on the butterfly fauna, (b) use changes in the abundances of certain species as indicators of ecological processes and (c) determine the sensitivity of butterflies for eutrophication, acidification and ground-water draining. In conclusion, the models provide a powerful aid in the conservation of butterflies in a changing environment.

Using Ellenberg Indicator Values to Assess Soil Quality in British Forests from Ground Vegetation: A Pilot Study

1. As a pilot study, descriptions of the vegetation and soil sampling were carried out on 20 forest sites to investigate the soil factors likely to be of importance in assessing soil quality for silvicultural/ecological purposes, and to see how useful indicator species were in predicting soil factors. 2. Soil analytical properties, mostly expressed in units of kg ha -1 , were examined statistically by principal components analysis. 3. The vegetation descriptions were treated numerically by assigning to each plant species Ellenberg indicator values for F (soil moisture), R (soil reaction) and N (soil nitrogen). The indicator values were weighted either using the Domin scores for abundance or the frequency of each species in the vegetation. 4. Measures of nitrogen availability (especially mineralizable NO 3 and NH 4 ) and pH were the most important variables loading Principal Component 1 (PC1). 5. The site mean indicator values for R (mR) and N (mN) were combined (mR + mN) to provide a simple proxy for 'soil nutrient regime', and the site mean indicator value for F (mF) was used as a proxy for 'soil moisture regime'. 6. The scores for each site on PC1 were related to the values of mR + mN. 7. A grid formed from mF and (mR + mN) was used to ordinate the sites in terms of 'soil quality' (soil nutrient regime and soil moisture regime). 8. Further work along similar lines is proposed to enable local (British) ecological indicator values for plant species to be developed.

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.

Analyse der Artenzusammensetzung des Hordeetum murini in sieben europäischen Großstädten entlang eines West-Ost-Transektes: Ein Beitrag zur Charakterisierung der Stadtflora

In each of seven European metropolises 50 relevees of the Hordeetum murini were taken. The flora lists of the Hordeetum of these towns were analysed regarding species composition, mean indicator values, and spectra of indicator values, life forms, persistence of leaves, anatomical structure, phytosociological behaviour of the species. This analysis presents the Hordeetum showing urbain traits more intensely than the average urban flora does. There are some correlations between the geographical situation of the towns and the attributes of the flora of these towns' Hordeetum murini, especially obvious when looking at some indicator values (temperature, continentalitt, and moisture) and the anatomical structure