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.     

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.     

Apparently we do need phytosociological classes to calibrate Ellenberg indicator values!

Abstract. We stated (Wamelink et al. 2002) that mean Ellenberg indicator values are biased towards expectations of phytosociologists. Witte & von Asmuth (2003; this volume) have two major points of criticism: (1) the data we used would be systematically biased; (2) in calibrating Ellenberg indicator values for moisture availability against mean spring groundwater level we should have assumed a sigmoid response instead of a linear one. As to (1), a bias in the data would require that wet vegetation types were visited in wet years and dry vegetation types in dry years. We do not see any evidence for this. As to (2), our data do not provide strong evidence for a sigmoid relation instead of a linear one. Neither is there any indication that the bias in the Ellenberg indicator values would disappear when a sigmoid function would be fitted. We do agree with Witte & von Asmuth that it is preferable to characterize the species’ response by those variables to which they most directly respond.     

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 contribution of nitrogen deposition to the eutrophication signal in understorey plant communities of European forests

We evaluated effects of atmospheric deposition of nitrogen on the composition of forest understorey vegetation both in space and time, using repeated data from the European wide monitoring program ICP‐Forests, which focuses on normally managed forest. Our aim was to assess whether both spatial and temporal effects of deposition can be detected by a multiple regression approach using data from managed forests over a relatively short time interval, in which changes in the tree layer are limited. To characterize the vegetation, we used indicators derived from cover percentages per species using multivariate statistics and indicators derived from the presence/absence, that is, species numbers and Ellenberg's indicator values. As explanatory variables, we used climate, altitude, tree species, stand age, and soil chemistry, besides deposition of nitrate, ammonia and sulfate. We analyzed the effects of abiotic conditions at a single point in time by canonical correspondence analysis and multiple regression. The relation between the change in vegetation and abiotic conditions was analyzed using redundancy analysis and multiple regression, for a subset of the plots that had both abiotic data and enough species to compute a mean Ellenberg N value per plot using a minimum of three species. Results showed that the spatial variation in the vegetation is mainly due to “traditional” factors such as soil type and climate, but a statistically significant part of the variation could be ascribed to atmospheric deposition of nitrate. The change in the vegetation over the past c. 10 years was also significantly correlated to nitrate deposition. Although the effect of deposition on the individual species could not be clearly defined, the effect on the vegetation as a whole was a shift toward nitrophytic species as witnessed by an increase in mean Ellenberg's indicator value.     

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.     

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.     

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.     

Trends in species diversity and composition of urban vegetation over three decades

Question: What was the change in diversity of urban synantropic vegetation in a medium‐sized Central European city during the period of increasing urbanization (1960s‐1990s)? Location: The city of Plzeň, an industrial centre of the western part of the Czech Republic. Methods: Sampling of various types of synanthropic vegetation, conducted in the 1960s, was repeated by using the same methods in the 1990s. This yielded 959 relevés, of which 623 were made in the 1960s and 336 in the 1990s. The relevés were assigned to the following phytosociological classes: Chenopodietea, Artemisietea vulgaris, Galio‐Urticetea, Agropyretea repentis and Plantaginetea majoris. Total number of vascular plant species, evenness index J, number of alien species (classified into archaeophytes and neophytes), and mean Ellenberg indicator values for light, temperature, continentality, moisture, soil reaction, and nutrients were obtained for each relevé. Results: From 1960s to 1990s, there was a significant decrease of species richness and diversity in synanthropic vegetation. The proportion of archaeophytes decreased in most vegetation types, indicating the contribution of this group of species, often confined to specific rural‐like habitats, to the observed impoverishment of ruderal vegetation. The proportion of neophytes did not change between the two periods. Comparison between 1960s and 1990s indicated a decrease in light, temperature, moisture, soil reaction and nutrient indicator values in some vegetation types. In both periods, Artemisieta, Galio‐Urticetea and Chenopodietea formed a distinct group harbouring more species than Agropyretea and Plantaginetea. Neophytes, i.e. recently introduced species, were most represented in the early successional annual vegetation of Chenopodietea, rather than in perennial vegetation of the other classes. Conclusions: Synanthropic vegetation of Plzeň exhibited a general trend of decrease in species diversity.     

Imputation of environmental variables for vegetation plots based on compositional similarity

Question: Large databases contain many plots, but few subsets with measured environmental data. To obtain broader datasets, researchers use expert‐based indicator values as surrogates; alternatively, these can be estimated by imputation. Does imputation provide more exact approximations than indicator values? Location: West Carpathians (Slovakia, Poland, Czech Republic) and Bulgaria. Methods: We developed a simple imputation method based on plot similarity that estimates missing environmental variables for plots –MOSS (mean of similar samples). This was tested for pH and conductivity, important environmental factors influencing vegetation composition and structure within wetlands, on two datasets of 485 (West Carpathians) and 118 (Bulgaria) plots for which directly measured values were available. The West Carpathian dataset was used for calibration. Imputation was based on calculating mean of the measured factor from a group of most similar plots. Using pre‐defined similarity criteria, we selected subsets of both datasets and compared estimated and measured values. Using root mean‐squared error of prediction, we compared predictive power of MOSS with Ellenberg indicator values and other recent methods. Results: Within one study region, MOSS predicts sample pH and conductivity more precisely than Ellenberg and similar calibration methods. Predictive power slightly decreased when MOSS was transferred to a distant region. Conclusions: Imputation using MOSS appears to accurately predict pH and conductivity from existing composition data within a single geographical region, and thus increases number of replicates. MOSS does not require expert‐based indicator values, which may be imprecise. We provide examples where MOSS can be utilised without risk of circular reasoning or introducing pseudo‐replications.     

Prediction of herbage yield in grassland: How well do Ellenberg N‐values perform?

Question: How useful are Ellenberg N‐values for predicting the herbage yield of Central European grasslands in comparison to approaches based on ordination scores of plant species composition or on soil parameters? Location: Central Germany (11°00′‐11°37’E, 50°21‐50°34’N, 500–840 m a.s.l.). Methods: Based on data from a field survey in 2001, the following models were constructed for predicting herbage yield in montane Central European grasslands: (1) Linear regression of mean Ellenberg N‐, R‐ and F‐values; (2) Linear regression of ordination scores derived from Non‐metric Multidimensional Scaling (NMDS) of vegetation data; and (3) Multiple linear regression (MLR) of soil variables. Models were evaluated by cross‐validation and validation with additional data collected in 2002. Results: Best predictions were obtained with models based on species composition. Ellenberg N‐values and NMDS scores performed equally well and better than models based on Ellenberg R‐ or F‐values. Predictions based on soil variables were least accurate. When tested with data from 2002, models based on Ellenberg N‐values or on NMDS scores accurately predicted productivity rank order of sites, but not the actual herbage yield of particular sites. Conclusions: Mean Ellenberg N‐values, which are easy to calculate, are as accurate as ordination scores in predicting herbage yield from plant species composition. In contrast, models based on soil variables may be useful for generating hypotheses about the factors limiting herbage yield, but not for prediction. We support the view that Ellenberg N‐values should be called productivity values rather than nitrogen values.     

Ecological preferences of alien plant species in North-Eastern Germany

The large, comprehensive vegetation database of Mecklenburg-Vorpommern/NE Germany with 51,328 relevés allowed us to study an entire regional flora of 133 non-native plants (NNP, immigration after 1492 AD) with regard to their preferences to all kinds of habitats and along different ecological gradients. For each relevé, we computed average Ellenberg indicator values (EIV) for temperature, light, moisture, reaction, nutrients and salt as well as plant strategy type weights. We partitioned the dataset into relevés with and without occurrences of NNP and compared them with respect to the relative frequencies of EIVs and strategy type weights. We identified deviations from random differences by testing against permuted indicator values. To account for bias in EIV between community types, NNP preferences were differentiated for 34 phytosociological classes. We tested significance of preferences for the group of NNP as a whole, as well as for single NNP species within the entire dataset, as well as differentiated by phytosociological classes and formations. NNP as a group prefer communities with high EIVs for temperature and nutrients and low EIVs for moisture. They avoid communities with low EIV for reaction and high EIV for salt. NNP prefer communities with high proportions of ruderal and low proportion of stress strategists. The differentiation by phytosociological classes reinforces the general trends for temperature, nutrients, moisture, R and S strategy types. Nevertheless, preferences of single species reveal that NNP are not a congruent group but show individualistic ecological preferences.     

Do we really need phytosociological classes to calibrate Ellenberg indicator values?

Abstract. Wamelink et al. (2002) calibrated Ellenberg indicator values for acidity and water availability against measured soil pH and measured mean spring groundwater level (MSL), respectively. Linear regression between indicator value and measured value of all the observations gave a poor fit. Regression lines per phytosociological vegetation class, on the other hand, generally described the observations well. In this article we demonstrate that this result is, at least partly, an artefact. First, because the data utilized are likely to contain systematic errors, and second, because a wrong regression model was applied. A sigmoid function for the relation between the indicator value for water availability and MSL gives a far better fit than a linear function does.‘Vegetation class’ is not an obvious choice as an extra explanatory variable for the regression, as it is only a convenient label for vegetation and should not be used as if it were a real independent environmental variable. In general, indicator values of plant species should be calibrated against environmental variables with great care. This implies that researchers should have knowledge about the ecological demands plants make on their environment, as well as about the spatial and temporal variability of this environment.     

Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps – from Averages to Multivariate Spectra

We present an approach to produce maps of Ellenberg values for soil reaction (R-value) in the Bavarian Alps. Eleven meaningful environmental predictors covering GIS-derived information on climatic, topographic and soil conditions were used to predict R-values. As dependent variables, Ellenberg indicator values for soil reaction were queried from plot records in the vegetation database WINALPecobase. We used an additive georegression model, which combines complex prediction models and the increased prediction accuracy of a boosting algorithm. In addition to environmental predictors we included spatial effects into the model to account for spatial autocorrelation. As we were particularly interested in the usefulness of averaged R-values for spatial prediction, we applied two different models: (1) a geo-additive regression model that estimates mean R-values and (2) a proportional odds model predicting the probability distribution over R-values 1 to 9. We found meaningful dependencies between the R-value and our predictors. Both models produced the same spatial pattern of predictions. Spatial effects had an impact only in the first model. The main drawback of mean R-values is the oversimplification of complex conditions of soil reaction, which is entailed by averaging and regression to mean values. Therefore, regionalized average indicator values provide only limited information on site-ecological characteristics. Model 1 failed to predict the range and shapes of original indicator spectra precisely. In contrast, the second model provided a more sophisticated picture of soil reaction. To make the multivariate output of model 2 comparable to that of model 1, we propose to plot the distribution in a three-dimensional color-space. In addition, comparison of both models based on a multiple linear regression model resulted in a R ² of 0.93. The proportional odds model is a promising approach also for other indicator values and different regions as well as for other ordinal-scaled ecological parameters.     

Calibration of Ellenberg indicator values for the Faroe Islands

Abstract. Elenberg's bio‐indication system for soil moisture (F), soil nitrogen (N) and soil reaction (R) was examined, based on 559 vegetation samples and environmental characteristics (vegetation cover, soil depth, soil moisture, chemical soil properties) from four Faroe islands. The original indicator values from central Europe were used for the calculation of weighted community indicator values of F, N and R. These were regressed with respect to environmental data, applying standard curvilinear regression and generalized linear modelling (GLM) and new predicted values of community indicator values were obtained from the best model. Faroe species optima values of 162 taxa for one or more of the three EUenberg scales were derived from fitting Huisman‐Olff‐Fresco (HOF) models of species abundance with respect to predicted community indicator values and are proposed as new EUenberg species indicator values to be used in the Faroe Islands. F was best correlated with a GLM model containing soil moisture, organic soil fraction, soil depth and total vegetation cover, R with a GLM model containing pH and calcium in % organic soil fraction, N with total phosphorus in % organic soil fraction. The calibrated species indicator scales are much truncated, as compared with the original values, resulting in significantly different overall distributions of the original and new species indicator values. The recalculated community indicator values are much better correlated to environmental measurements. Several species do not have clear optima, but linear or monotone relationships to the examined indicator scales. This probably indicates that the occurrence of some species in the Faroe Islands are either determined by factors other than moisture, pH or soil nutrient status or, given the young age and environmental instability of the islands, are governed by stochastic mechanisms. Extension of Ellenberg indicator values outside central Europe should always be carefully calibrated by means of adequate environmental data and adequate statistical models, such as HOF models, should be applied.     

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.     

Exploring large vegetation databases to detect temporal trends in species occurrences

Question: Can vegetation relevé databases be used to analyse species losses and gains in specific vegetation types in Germany over time? Does the type of response (increase or decline in relative frequency) conform to observed large‐scale environmental trends in the last decades? Location: Germany. Exploring the German Vegetation Reference Database Halle (GVRD) that was established for forest and grassland vegetation within the framework of German Biodiversity Exploratories. Methods: Use of generalized linear models (GLMs) for testing changes in temporal frequency of plant taxa in a semi‐dry grassland data set (Mesobromion) and a beech forest data set (Fagion). Data were either aggregated by year, decade or by a balanced re‐sampling approach. Interpretation of the observed changes was based on species traits. Results: In both data sets significant temporal changes were observed, although the frequency of the majority of species remained unchanged. In both data sets, species with a temporal increase in frequency had higher Ellenberg N and F indicator values, compared to species that decreased, thus indicating effects of widespread atmospheric nitrogen deposition. In the forest data set, the observed increase in recruitment of deciduous trees pointed to a change in management, while trends in the grassland data set suggested use abandonment, as seen in an increased frequency of woody species. Conclusion: We demonstrate that vegetation databases represent very valuable resources for analysis of temporal changes in species frequencies. GLMs proved their value in detecting these trends, as also shown by the interpretability of model results with species traits. In contrast, the method of aggregation or re‐sampling had little influence on the general outcome of analyses.     

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.     

Using topographic wetness index in vegetation ecology: does the algorithm matter?

Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.     

Soil phosphorus as a control of productivity and openness in temperate interglacial forest ecosystems

Aim Observations of long chronosequences in forest ecosystems show that, after some millennia of build‐up, biomass declines in relation to the slow depletion of soil phosphorus. Plants that dominate during this period of soil impoverishment have specialized strategies for P acquisition, including ectomycorrhiza or root clusters. We use quantitative, pollen‐based reconstructions of regional vegetation in four Quaternary warm stages (Holocene, Eemian, Holsteinian, Harreskovian) to test whether inferred forest cover and productivity changes are consistent with long‐term modification of soil nutrient pools. Location Southern Scandinavia (Denmark, southern Sweden). Methods The REVEALS model was used to estimate regional vegetation abundances of 25 pollen‐type‐equivalent taxa from pollen records of large sedimentary basins in southernmost Scandinavia. Based on the estimated regional vegetation, we then calculated time‐series of Ellenberg indicator values for L (light), R (soil reaction) and N (a productivity proxy). We classified the vegetation records into distinct phases and compared these phases and the samples using hierarchical clustering and ordination. Results All three interglacials developed coniferous or mixed forests. However, pure deciduous forests were never reached during the Holsteinian, while pure coniferous forests never developed in the Holocene. Above‐ground productivity was inferred to be low initially, peaking in the first third of the warm stages and then slowly declining (except during the Holocene). Dominant trees of the post‐peak phases all had ectomycorrhiza as a strategy for P acquisition, indicating that easily accessible P pools had become depleted. Increases in fire regimes may have amplified the inferred final drop in productivity. Mid/late Holocene productivity changes were much influenced by agricultural activities. Main conclusions REVEALS vegetation estimates combined with Ellenberg indicator values suggest a consistent pattern in warm stages of initially rising productivity, followed by a long and slow decline. The P‐acquisition strategies of dominant trees indicate that the decline reflects increasing P depletion of soils.