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.     

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.     

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

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

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.     

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.     

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.     

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

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

Deviances from expected Ellenberg indicator values for nitrogen are related to N throughfall deposition in forests

The indication of environmental changes and their impacts on various compartments of ecosystems are high on the political and scientific agenda. Spatially and temporally different inputs of eutrophying nitrogen compounds into European forest ecosystems and their effects are still of concern. Tending floristic changes and respective changes of nitrogen indicator values are one of the suspected effects. Those can, however, not easily be discovered within any cross-sectional short-term approach. Since continuous long-term observations with a sufficiently large sample on an adequate geographical scale are not available, the investigation of deviations from a rather balanced and scientifically settled relationship between the soil acidity status and N mineralization rate is used to indicate additional nitrogen supply from atmospheric inputs. The suspected deviances show a significant statistical relationship with total N throughfall deposition (to a lesser degree also with oxidised and reduced nitrogen compounds), measured at the same locations. This suggests a higher N availability at sites with greater N deposition rates, causing a disproportion between site-specific mineralization rates and the effective amount of plant available nitrogen. In spite of some minor methodological restrictions, the approach might be an appropriate means to localise and regionalise eutrophying effects from atmospheric deposition of nitrogen on larger scales.     

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.     

pH optima for Danish forest species compared with Ellenberg reaction values

Species distribution depends on the physiological and ecological niche where a species can exist and regenerate in resource competition with other species (niche limitation). The realized niche is influenced by local biotic processes that influence species behaviour and the shape of the response curves relative to environmental gradients. Processes on larger scales also influence the species niche through source-sink mechanisms (dispersal limitation) and the species richness of an area (pool limitation). Despite the growing evidence of skewed or irregular species response curves along gradients, many ecologists still assume symmetric, unimodal response curves along gradients in ecological interpretation. Ellenberg’s indicator system is probably the most common example. However, the assumption is not ecologically or statistically valid, due to the many different processes affecting the distribution of plant species. Here I present the results of Huisman-Olff-Fresco (HOF) regressions for 209 Danish forest species. HOF modelling is chosen to avoid the classical drawbacks of assuming symmetric, unimodal response patterns. I calculate the optima for all species with unimodal responses to soil pH and compare these with the Ellenberg indicator values for reaction (R), which are often used as a substitute for soil pH measurements. I demonstrate that the assumption of symmetric, unimodal species behaviour is violated in 54% of the cases and that pH optima and R indicator values for species are not always compatible. Ellenberg reaction scale has been used byEwald (Folia Geobot. 38: 357–366, 2003) as an indicator of which species are calcicole, i.e., whether they can grow and reproduce on calcareous soils. Such affinities of species, however, are related to both local niche properties and processes on large scales and cannot be generalized from a single empirical variable such as pH, nor from Ellenberg semi-ordinal indicator scale. I conclude that while the determination of whether species are calcicole or calcifuge requires more research, it is evident that Denmark contains a fairly balanced number of calciphytic and acidophytic species. This is probably due to the nearly equal areas with acidic and alkaline soils in Denmark, which also contribute to the high species richness of more than 500 vascular plant species in Danish forests.     

Changes in vegetation types and Ellenberg indicator values after 65 years of fertilizer application in the Rengen Grassland Experiment,Germany

Question: How does semi-natural grassland diversify after 65 years of differential application of Ca, N, P, and K fertilizers? Is fertilizer application adequately reflected by the Ellenberg indicator values (EIVs)? Location: Eifel Mountains, West Germany. Methods: The Rengen Grassland Experiment (RGE) was established in an oligotrophic grassland in 1941. Six fertilizer treatments (Ca, CaN, CaNP, CaNP-KCl, CaNP-K₂SO₄, and unfertilized control) were applied annually in five complete randomized blocks. Species composition of experimental plots was sampled in 2006 and compared with constancy tables representing grassland types in a phytosociological monograph of a wider area. Each plot was matched to the most similar community type using the Associa method. Mean EIVs were calculated for each treatment. Results: The control plots supported oligotrophic Nardus grassland of the Polygalo-Nardetum association (Violion caninae alliance). Vegetation in the Ca and CaN treatments mostly resembled montane meadow of Geranio-Trisetetum (Polygono-Trisetion). Transitional types between Poo-Trisetetum and Arrhenatheretum (both from the Arrhenatherion alliance) developed in the CaNP treatment. In the CaNP-KCl and CaNP-K₂SO₄ treatments, vegetation corresponded to the mesotrophic Arrhenatheretum meadow. Major discontinuity in species composition was found between control, Ca, and CaN treatments, and all treatments with P application. EIVs for both nutrients and soil reaction were considerably higher in P treatments than in Ca and CaN treatments. Surprisingly, the control plots had the lowest EIVs for continentality and moisture, although these factors had not been manipulated in the experiment. Conclusions: Long-term fertilizer application can create different plant communities belonging to different phytosociological alliances and classes, even within a distance of a few meters. Due to their correlated nature, EIVs can erroneously indicate changes in factors that actually did not change, but co-varied with factors that did change. In P-limited ecosystems, EIVs for nutrients may indicate availability of P rather than N.     

On the relationship between sample plot size and beta diversity in boreal coniferous forests

Nested sample plots of three sizes (16, 1, and 1/16 sq. m) from three different studies of Norwegian coniferous forests have been subjected to DCA ordination using the same choice of options. At each sample plot size, species quantities are recorded as frequency in 16 subplots. Beta diversity, measured as length of the first DCA axis, invariably increased upon lowering of sample plot size. The same applied to the eigenvalues of the axes. This is explained as a consequence of the weakening of structure in the data matrices when the fine-grained patterns of the vegetation are emphasized.     

Effects of plot size on the ordination of vegetation samples

Questions: Do ordination patterns differ when based on vegetation samples recorded in plots of different size? If so, how large is the effect of plot size relative to the effects of data set heterogeneity and of using presence/absence or cover‐abundance data? Can we combine plots of different size in a single ordination? Methods: Two homogeneous and two heterogeneous data sets were sampled in Czech forests and grasslands. Cover‐abundances of plant species were recorded in series of five or six nested quadrats of increasing size (forest 49‐961 m2; grassland 1‐49 m²). Separate ordinations were computed for plots of each size for each data set, using either species presences/absences or cover‐abundances recorded on an ordinal scale. Ordination patterns were compared with Procrustean analysis. Also, ordinations of data sets jointly containing plots of different size were calculated; effects of plot size were evaluated using a Monte Carlo test in constrained ordination. Results: The results were consistent between forest and grassland data sets. In homogeneous data sets, the effect of presence/absence vs. cover‐abundance was similar to, or larger than, the effect of plot size; for presence/absence data the differences between ordinations of differently sized plots were smaller than for cover‐abundance data. In heterogeneous data sets, the effect of plot size was larger than the effect of presence‐absence vs. cover‐abundance. The plots of smaller size (= 100 m2 in forests, = 4 m² in grasslands) yielded the most deviating ordination patterns. Joint ordinations of differently sized plots mostly did not yield patterns that would be artifacts of different plot size, except for plots from the homogeneous data sets that differed in size by a factor of four or higher. Conclusions: Variation in plot size does influence ordination patterns. Smaller plots tend to produce less stable ordination patterns, especially in data sets with low ß‐diversity and species cover‐abundances. Data sets containing samples from plots of different sizes can be used for ordination if they represent vegetation with large ß‐diversity. However, if data sets are homogeneous, i.e. with low ß‐diversity, the differences in plot sizes should not be very large, in order to avoid the danger of plot size differences distorting the real vegetation differentiation in ordination patterns.