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.     

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.     

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.     

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.     

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.     

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.     

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.     

Interpretation of altitudinal gradients in South Central Norway based on vascular plants as environmental indicators

The main aim of this study is to quantify environmental differences along altitudinal gradients on the basis of different sets of plant indicator systems and recorded vascular plants within 100-m altitudinal bands. Two areas are included in the study, and in total they include an altitudinal span from sea-level up to 2400 m a.s.l. The applied indicator systems are the six values defined by Ellenberg et al. [Ellenberg, H., Weber, H.E., Düll, R., Wirth, V., Werner, W., Paulissen, D., 1991. Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica 18, 1–248], eight Raunkiaer life forms, the respiration values of Dahl [Dahl, E., 1998. The phytography of Northern Europe. Cambridge University Press], and the snow indicator values of Odland and Munkejord [Odland, A., Munkejord, H.K., 2008. Plants as indicators of snow layer duration in southern Norwegian mountains. Ecological Indicators 8, 57–68]. Mean indicator values are calculated for each altitudinal band, and they show significant linear increasing or decreasing trends, significant unimodal (quadratic) trends or no significant altitudinal trend. The altitudinal variation in mean indicator values is discussed in relation to general environmental conditions such as bedrock, soil characteristics, topography, summer temperature, light, snow and distribution of major vegetation types. The main differences between the altitudinal bands as shown by a PCA analysis indicate that the highest bands, representing the high alpine zone (more than 1800 m a.s.l.) strongly differ from the others. The altitudinal gradient is mainly associated with variation in temperature, nitrogen indicators and some of the Raunkiaer life forms (primarily the Phanerophytes, Nanophanerophytes, Geophytes and Therophytes). The two tested temperature indicator systems tested were highly linearly correlated, but the altitudinal variation in respiration value followed the general temperature lapse rate better than the Ellenberg temperature indicator value. Indicators of high soil moisture have their optima at intermediate altitudes. The uppermost bands in Aurland show lower mean temperature indicator values and a higher snow indicator value compared to bands at the same altitude in Jotunheimen. This may partly be explained as a result of the Massenerhebung effect. Relationships between altitudinal distribution patterns of some vascular plants in Aurland and their Ellenberg temperature indicator value indicate that the values should be revised to better suit Norwegian conditions.     

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.     

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.     

Are Plant Censuses Carried Out on Small Quadrats More Reliable than on Larger Ones?

Plant censuses are known to be significantly affected by observers’ biases. In this study, we checked whether the magnitude of observer effects (defined as the % of total variance) varied with quadrat size: we expected the census repeatability (% of the total variance that is not due to measurement errors) to be higher for small quadrats than for larger ones. Variations according to quadrat size of the repeatability of species richness, Simpson equitability and reciprocal diversity indices, Ellenberg indicator values, plant cover and plant frequency were assessed using 359 censuses of vascular plants. These were carried out independently by four professional botanists during spring 2002 on the same 18 forest plots, each comprising one 400-m² quadrat, four 4-m² and four 2-m² quadrats. Time expenditure was controlled for. General Linear Models using random effects only were applied to the ecological indices to estimate variance components and magnitude of the following effects (if possible): plot, quadrat, observer, plant species and two-way interactions. High repeatability was obtained for species richness and Ellenberg indicator values. Species richness and Ellenberg indicator values were generally more accurate but also more biased in large quadrats. Simpson reciprocal diversity and equitability indices were poorly repeatable (especially equitability) probably because plant cover estimates varied widely among observers, irrespective of quadrat size. Grouping small quadrats usually increased the repeatability of the variable considered (e.g. species richness, Simpson diversity, plant cover) but the number of plant species found on those pooled 16 m² was much lower than if large plots were sampled. We therefore recommend to use large, single quadrats for forest vegetation monitoring.     

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.     

An application case of ecological indicator values (Zeigerwerte) calculated with a simple algorithmic approach

Abstract

The vegetation of the study site near Rome (Castelporziano Estate), where different woodland types occur, was analysed on the basis of ecological indicator values (Zeigerwerte) for light, temperature, continentality of climate, soil moisture, soil pH and nitrogen. Indicator values were estimated with Hill's reprediction algorithm for the flora of Central-Southern Italy relying on a database of 4,207 original relevés representing a balanced survey of the vegetation of this and surrounding areas. It was possible to obtain indicator values for an important fraction of the Italian Mediterranean flora. Results are ecologically reasonable, and it was possible to find strong correlation between the recalculated values and a few environmental variables. These correlations were not significant in an analogous test with subjectively derived scores of Ellenberg indicator values.     

Can vegetation indicate landfill cover features?

Generally, great efforts are made in measuring features of landfill covers. However, conventional physical or chemical parameters reach their limits in indicating the small scale changes of the habitats. Bio-indication is a proven tool to assess habitat conditions. The advantages of vegetation monitoring are obvious: cheap, easy, and integrating over time and space. Our study displays, how vegetation can indicate landfill cover features by adapting some common evaluation methods. Ellenberg's ecological indicator values were used, but ubiquitous species were excluded from multivariate data analysis of the Ellenberg values. Four groups of habitats were distinguished according to their cover material: (i) loamy substrates; (ii) wet hollows and areas with mature compost; (iii) fresh compost and mechanically biologically treated waste; (iv) slag from municipal solid waste incineration and leachate-influenced areas with fresh untreated waste or sewage sludge. The differences were assessed by ecological indices. The results give a promising impression of the potential vegetation monitoring has in the indication of landfill cover features.     

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.     

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.     

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.     

Regionalizing Nutrient Values of Vegetation to Assess Site Fertility of Mountain Forests in the Bavarian Alps

Height growth and productivity of forests depend on temperature, water and nutrients. Ellenberg indicator values that summarize vegetation response to growth factors are suited to predict site fertility. Macronutrients (NPK), as represented by N-values, are a crucial component of site fertility and susceptibility towards biomass extraction. Based on 1,500 vegetation plots from an inventory stratified over all important forest types of the Bavarian Alps we regionalized Ellenberg N-values against area-wide soil, climate and relief predictors including a spatial effect at a scale of 1 : 25,000, resulting in a general additive model (GAM) with eight predictors and an explained deviance of 53 % on test data. The N-value layer was combined with other regionalized indicator values (temperature, reaction, moisture) to predict height of Norway spruce at reference age (site index) of an independent forest inventory data set, resulting in a GAM with an explained deviance of 35 %. After temperature the nutrient value was the second most important predictor of site index and clearly superior to soil reaction. It can be concluded that forest growth is sensitive to reductions of NPK-availability through whole tree harvesting and that maps of N-values deliver important information for planning sustainable harvesting.     

Application of the Ancient Forest Concept to Potential Natural Vegetation Mapping in Flanders, A Strongly Altered Landscape in Northern Belgium

Construction of potential natural vegetation (PNV) poses particular challenges in landscapes heavily altered by human activity and must be based on transparent, repeatable methods. We integrated the concept of ancient forest (AF) and ancient forest species (AFS) into a four-step procedure of PNV mapping: 1) classification of forest vegetation relevés; 2) selection of those vegetation types that can serve as PNV units, based on AF and AFS; 3) merging of selected vegetation types into five PNV units that can be predicted from a digital morphogenetic soil map; 4) mapping of three additional PNV units based on additional environmental data. The second step, concerning the selection of reference forest vegetation, is of particular interest for PNV construction in Flanders (northern Belgium), where forest cover has been subject to temporal disruption and spatial fragmentation. Among the variety of extant forest recovery states, we chose as PNV units those vegetation types for which a high proportion of relevés had been located in AF and that contained many AFS. As the frequency of AFS depends on site conditions, we only compared and selected vegetation types that are found on similar sites according to average Ellenberg indicator values. While succession is irrelevant for the definition of PNV, colonization rates of AFS can be used to estimate the time required for PNV to be restored in a site.