Abstract
We investigated the question whether plant species can be used to indicate quantitative chemical properties of soil in physical units. The investigation is based on the data of the second National Forest Soil Inventory (NFSI II) in Germany. Generalized linear models were used to calibrate species response curves for soil parameters that significantly affect vegetation. These response curves are used to indicate the most probable value of the soil parameters based on Bayes’ formula. For the major soil habitat factors [soil acidity, nutrient stocks (e.g. potassium)], an indicator system was developed that can predict the values of these habitat factors in physical units based on the vegetation present. The indicator system was validated with an independent dataset. At present, the indicator system is valid for Germany only, but the concept could be extended to other regions and other habitat factors as well. The indicator system is built on intensive quantitative soil analyses and vegetation surveys. The values of habitat conditions can be predicted based only on the plant species occurring in a site. This way, a rapid assessment of major habitat factors is possible. This is helpful when soil analyses are lacking.
You have full access to this open access chapter, Download chapter PDF
Similar content being viewed by others
10.1 Introduction
It has long been known that the distribution of plant species in space depends on site factors. Humboldt and Bonpland (1807) described the dependence of plant species on altitude in tropical countries, i.e. across a temperature gradient . The year 2013 was the centenary anniversary of the publication of Braun-Blanquet’s first paper in the field of vegetation science (Braun-Blanquet 1913), in which he asserted that chemical properties of soils, specifically acidity and content of nutrients, are the prime factors that determine the distribution patterns of plant species. Based on these findings, Ellenberg (1950) developed a system to indicate soil conditions on agricultural fields based on the weed species growing between the crops. He assigned ordinal values ranging from 1 (little) to 5 (much) for weed species for five main site factor groups: soil acidity (pH), nitrogen (N) availability, water supply, temperature and continentality. The arithmetic mean of the indicator values for all weed species at a given place was used as an indication of site condition.
Later, Ellenberg extended the concept to all Central European vegetation types and plant species, respectively, and based the system on a 9-point scale (Ellenberg 1974). This concept of indicator values has been improved in recent years (Ellenberg et al. 2001, 2003) and has been adapted for other countries, such as Switzerland (Landolt 1977; Landolt et al. 2010 ), Great Britain (Hill et al. 1999), Hungary (Zólyomi et al. 1967; Soó 1980; Borhidi 1995) and Poland (Zarzycki et al. 2002).
Today, indicator values remain a very popular tool for the integrated assessment of site conditions. For example, Persson (1981) used indictor values to support the interpretation of ordination diagrams.
Without detracting the credit these works deserve, it should be noted that the underlying concept suffers from several deficits:
-
1.
The assignment of indicator values to plant species is primarily based on the personal experience of the investigators. They “reflect the opinion of a (few) experts” (Wildi 2016) rather than being a result of the analysis of measured habitat conditions. Direct measurements have been used only marginally to define the indicator values, as systematic measurements of site conditions in phytosociological relevés were very rare. The comparison of mean indicator values with measurements of certain factors has been applied a posteriori only by way of example (Ellenberg et al. 2001).
-
2.
The calculation of arithmetic means of indicator values is problematic, as ordinal variables should not be averaged. Möller (1992) suggests the use of medians instead of arithmetic means. Averaging the indicator values of the species occurring on a given plot is permissible only under the (implicit) auxiliary hypothesis that Ellenberg’s indicator value classes have (at least approximately) equal class widths.
-
3.
Furthermore, there is debate as to whether either averages weighted or unweighted by cover should be used (Rodenkirchen 1982). The idea is that a species is to be expected to have a higher cover value when growing close to its ecological optimum . But cover also has a strong species-specific component: some species—orchids, for example—never occur at high cover, while others often do. The difference between averages weighted and unweighted by cover, however, depends on the evenness of the vegetation of the plot: for plots with high evenness, the difference is small because all species have more or less the same weight. Relevant differences are expected only in plots with a pronounced dominance structure. However, using cover as a weight for a weighted average is problematic in any case: measurement of the specific response of a certain species to the site factor under discussion would be necessary to determine the width of the ecological amplitude (Peppler-Lisbach 2008). Species with very narrow ecological amplitude should receive a higher weight than species that occur over a wider range of ecological conditions. But such a measure is completely lacking in all indicator systems to date. The cover-weighted average is only a substitute for this shortcoming.
-
4.
Mean indicator values have also been used for environmental monitoring . Ewald et al. (2013) showed an increase in mean N values in forests in Central Europe over a period of nearly one century. Ewald and Ziche (2017), however, showed that Ellenberg’s mean N values are positively correlated with several nutrients but not with N concentration in topsoil . So the question arises: which measurable environment factor caused the increase of the N values?
Several attempts have already made to overcome these shortcomings: a posteriori calibration tries to justify the use of indicator values (Ellenberg et al. 2001). In most cases, the expected correlation is significant even though there is a low coefficient of determination. In some cases, however, the sign of the regression coefficient is even opposite to expected (Wamelink et al. 2002).
Hill et al. (1999), for example, recalibrated Ellenberg’s indicator values based on the national database of British vegetation samples. Ewald and Ziche (2017) analysed which physical units correspond to the mean indicator value for nutrients.
Nevertheless, none of these approaches addresses the major concern: the indicator system should indicate measurable properties of the soil (and other habitat variables) directly. Fischer (1986) used ordination methods to predict soil properties (pH) directly from vegetation relevés. However, prior to the second National Forest Soil Inventory (NFSI II) in Germany, data required to follow this approach was limited to small datasets covering only small spatial ranges and few vegetation types.
The goal of this paper is to create a species-based indicator system to assess soil properties . The indicator system is based on intensive soil analyses and vegetation surveys that yielded the German NFSI II data.
10.2 Climate, Soil, and Vegetation Data
The NFSI II recorded soil data as well as vegetation relevés on a regular grid of 8 km grid size at 1838 sampling points (BMELV 2006; Wellbrock et al. 2016). Sampling was conducted between 2006 and 2008. The vegetation was sampled on an area of 400 m2 within a 30 m circle around the central soil pits of the plots. On the NFSI plots, 819 vascular plant species were recorded overall. 425 plant species occurred on more than 4 plots. Nomenclature of species follows Jansen and Dengler (2008).
Soil variables that were accounted for in our work included pH (H2O), pH (KCl), pH(CaCl2), cation exchange capacity and the stocks of total nitrogen, total phosphorous (aqua regia extracts), organic and inorganic carbon and the cations Ca , Mg , K, Na, Al, Fe , Mn and H. Furthermore, the available field capacity was estimated based on texture analyses. Details on analytical methods can be found in HFA (GAFA 2014). Initial studies have shown that correlation between vegetation and soil is closest for the top 10 cm of soil (Ziche et al. 2016). Consequently, the first two depths (0–5 and 5–10 cm) were used for further analyses. Additionally, climate information was attributed to the NFSI plots using geostatistical methods such as ordinary kriging (precipitation ) or regression kriging (temperature, Ziche and Seidling 2010). Complete datasets with all recognized variables were available for 1619 sampling plots.
Within the framework of the intensive (Level II) monitoring of forest ecosystems in Germany,Footnote 1 another independent dataset was compiled that comprises soil data as well as vegetation relevés (Seidling 2005). This second dataset with 48 sampling points was used to evaluate our indicator system. All soil analyses were performed after 2005 and at depth intervals of 0–5, 5–10, 10–20, 20–40 and 40–80 cm.
Correspondence analysis (CA) using the programme CANOCO was employed to identify outliers (ter Braak and Šmilauer 2002). Species’ cover values were transformed according to Pudlatz (1975) to compensate the extremely skewed distribution of cover values . Rare species with a frequency of less than 5% were omitted from the analysis because they do not contribute to the general floristic structure of the vegetation. Only 20% of the omitted species have their main occurrence in forests (Schmidt et al. 2011). Forty-five percent are regarded as species of forests as well as open land. They are less typical for forests. Thirty-four percent of the species are not mentioned at all in the list of Schmidt et al. (2011). These are either typical species of open habitats that were geminated in the forest only by chance or taxa that could not be determined to species level (e.g. Quercus sp.). The CA indicates that no plots with outlying species composition were present. After omitting rare species, one relevé contained no other species and hence was omitted.
10.3 Environmental Impact on Species Composition
With the remaining 1618 relevés, forward selection of environmental variables using a Monte Carlo significance test with a canonical correspondence analysis (CCA) was applied to select soil variables that most affect the species composition of the vegetation . Forward selection of environmental variables in CCA is a stepwise regression approach: a variable is selected only if it contributes additionally to explaining species pattern compared to the variables already selected. The variable that explained the most and hence was selected first is base saturation (BS). C/N ratio and pH also show high predictive power. All variables appeared to significantly affect vegetation structure. This may be due to the large sample size. Moreover, significant relationships may not be relevant. For the purpose of indicators, the focus should be on the variables with the highest F value (Table 10.1).
Organic carbon is very highly correlated with total N. It was excluded from the analysis to avoid problems with collinearity.
The new indicator system is described below using base saturation as an example. Nevertheless, the model was developed for all variables listed in Table 10.1. However, the best results are expected for the first few variables in the list.
Finally, CCA was performed to identify the major gradients in vegetation . A biplot of the CCA with relevés and soil factors as environmental variables (Fig. 10.1) revealed a soil acidity gradient along the first axis with base saturation (BS) and pH increasing to the left and C/N ratio (CN) to the right. The second axis displays a climatic gradient with a positive correlation with altitude above sea level (Alti) and precipitation (Prec) and a negative correlation with temperature (Temp). The floristic structure of Germany’s forests is primarily determined by a soil acidity gradient and secondarily by a temperature gradient . Hence, soil properties associated with acidity (base saturation, pH, C/N ratio) can be expected to be good indicated in a species-based indicator system. The significant effect of temperature on forest vegetation demonstrates the high sensitivity of forests to the expected climate changes.
10.4 Modelling Species Response to Soil Properties
We fitted generalized linear models (GLM), applying second-order polynomial logistic regression (McCullagh and Nelder 1989). Soil variables serve as independent variables to the presence/absence data of the species, the dependent variable. These models describe the conditional probability of the occurrence of the species depending on the soil conditions:
where p(V|x) is the conditional probability for finding species V at soil condition x and b 0, b 1 and b 2 are the coefficients of the regression equation. This approach is equivalent to fitting a symmetric, unimodal response curve to the occurrences of the species (Jongman et al. 1995). This is a parametric approach. Parametric methods have the advantage that the species response curve can be described using terms such as optimum and tolerance (Jongman et al. 1995). These values for base saturation are given in the appendix.
Unimodal response curves are the underlying principle of the commonly used CCA (ter Braak 1987; ter Braak and Šmilauer 2002). However, it remains unclear whether the unimodal model is appropriate to describe species response curves (Austin and Meyers 1996). While some authors state that it is “unlikely ecologically” that species have a multimodal response to a single variable (Roberts n.d.), others favour bimodal and skewed response curves (Zelený and Tichý n.d.). Therefore, we also applied generalized additive models (GAM), which allow any kind of response curve, to test for the occurrence of species with bimodal or skewed response curves. For this, we compared 95% confidence bands of the GLM with the GAM curves. If the GAM curve lies within the confidence band of the GLM, the latter is obviously an adequate description of the species response, and there is no reason to consider the more complicated GAM. Fitting of GLM and GAM was performed using the software package R (R Core Team 2016).
In our dataset, most species fit very well to the unimodal model. This was expected as the unimodal model is the basis for the widely used correspondence analysis. If a majority of species violates the basic assumption of this method, it would not perform as well as it does. Frequent and successful application of this method also suggests that the unimodal model is appropriate for most species. We were able to show that bimodal response curves are the exception rather than the rule.
Figure 10.2 shows some examples: Circaea lutetiana is a species with a clear, bell-shaped distribution within the base saturation gradient with an ecological optimum at 0.7, whereas beech (Fagus sylvatica) is an example of a species with wide ecological amplitude with respect to base saturation. Deschampsia flexuosa and Mercurialis perennis are species clearly preferring the lower or upper end of the gradient, respectively, with an optimum at the lower or upper end of the range, respectively.
Nevertheless, some species cannot be described adequately with a unimodal response curve. One example is Acer pseudoplatanus (Fig. 10.2e), which has one optimum around 30% base saturation and a second one at 100%. The GAM of this species lies outside the 95% confidence bands of the GLM. Such species cannot serve as indicator species in any indicator system as they do not have a clear optimum. Therefore, bimodal species were excluded from our indicator system. Fortunately, these cases are very rare, and most species in the dataset fit to the unimodal model.
In a few cases, the response curve was U-shaped instead of bell-shaped. This is the case if the coefficient b 2 in the logistic regression is significantly greater than 0. In fact, this is a special case of a bimodal response curve. Such species also do not qualify as indicator species and were excluded from the indicator system. An example is Picea abies (Fig. 10.2f).
10.5 Predicting Soil Properties by Species Composition
In the next step, we needed to derive the conditional probabilities of different values of the soil factor to be indicated \( p\left(x|\overrightarrow{V}\right) \) depending on the species combination \( \overrightarrow{V} \) found in the forest stand. This is performed by means of the Bayes’ formula (Fischer 1990, 1994):
The multivariate conditional probability of finding the species combination \( \overrightarrow{V} \) at soil condition x is estimated from the univariate response curves assuming independence of the effects:
The prior probabilities of the soil conditions p(x) correspond to the frequency distribution of x in the area of investigation. Kernel density estimation with R function “density” was used to determine p(x). The prior probability of the species composition \( p\left(\overrightarrow{V}\right) \) is a constant at a given point in the landscape and hence does not need to be considered. The value of the soil condition x with the highest conditional probability \( p\left(x|\overrightarrow{V}\right) \) is then taken as the result of the indication.
The results of the application of the indicator model are curves describing the probability of different values of the soil factor on certain relevés based on the species growing at those points . Four examples from different regions in Germany are shown in Fig. 10.3 for the variable base saturation. In most cases, these profiles display a clear peak. The base saturation with the highest probability is the indicated base saturation value. Only in very few cases is the profile a flat curve indicating uncertain indication. An example is given in Fig. 10.3d.
Comparison of the indicated and the measured base saturation in the independent reference dataset yields a quite high coefficient of determination of R 2 = 0.6 (Fig. 10.4). The second important variable according to forward selection of environmental variables (Table 10.1) is the ratio of organic carbon and total N (C/N ratio). With an R 2 of 0.38, nearly 40% of the variability of the C/N ratio can be predicted with the indicator system. With pH, the third variable in the ranking of the forward selection of environmental variables (Table 10.1), the coefficient of determination (R 2) was 0.40.
10.6 The WeiWIS Indicator System
We implemented the algorithm described above in a MS-ACCESS application called “Weihenstephan Wood Indicator System” or WeiWIS. WeiWIS combines vegetation relevés with a species’ calibration data and generates indicated values for habitat factors of all relevés and habitat factor profiles for selected plots (Fig. 10.5).
With WeiWIS as a new indicator tool, many unresolved problems with existing indicator systems become obsolete. The question as to whether the arithmetic mean or the median should be used to characterize a site does not apply to our approach, because the response curves themselves are considered, not only the maxima. This is also the reason why the question as to whether weighted or unweighted means leads to better result is not relevant here. In fact, species are weighted implicitly with the width of the ecological amplitude: species with a narrow and high peak have a higher weight in the system compared to species with a wider, flatter curve.
Furthermore, the resulting probability profile for each relevé gives an indication of the reliability of the result. A wide and flat profile suggests a less reliable result, probably due to a smaller number of species or a species combination that includes contradictory species.
10.7 Discussion
Although the application of indicator values is very popular, comparisons with measured values are quite rare in the literature. Michler (1994) found significant correlations between measured soil variables and mean indicator values with an R 2 of up to 0.2 (pH with mean R value, K+, Zn2+ and phosphate with mean N value). Total N was not significantly correlated with the N value. Ewald and Ziche (2017) found base saturation was the best variable to predict Ellenberg’s indicator value for nutrients (mN). They found a correlation of −0.55 between C/N ratio and mN, corresponding to an R 2 of 0.30.
Our indicator system, however, shows a significant correlation between measured and indicated base saturation, with R 2 = 0.6. This is a remarkable improvement compared to Ewald and Ziche (2017), especially since they used the same BZE II dataset as our study.
A major advantage of our indicator system is that it indicates soil properties (such as base saturation or C/N ratio ) directly and not via a dimensionless value (mean indicator value) that needs to be correlated a posteriori with the desired measurable parameter.
Several attempts have already been made to define indicator values for plant species on the basis of measurements. Wamelink et al. (2005) used the mean of pH values for all relevés where a species is present as an indicator value. However, this concept is biased by the extent of the range of the observed gradient. If the calibration dataset does not cover the entire range of habitat conditions that a species can occupy, the mean cannot correctly reflect the species’ optimum. Furthermore, the mean is influenced by the frequency distribution of the habitat factor. If some habitat conditions occur more frequently in the calibration dataset than others, these conditions will receive a higher weight for the definition of the indicator values. Gégout et al. (2003) overcame this problem by using GLM to define the ecological optimum . The value of the habitat factor with the highest probability is taken as the indicator value for the species. However, both approaches have their indicator based on the arithmetic mean of the indicator values of the species present in a plot.
A very different approach is the imputation method of Tichý et al. (2010). Here, the vegetation relevé for which environmental variables should be indicated is compared with the relevés of a reference dataset for which environmental measurements are available. The mean of the values of the measured environmental variables of floristically similar relevés in the reference dataset is used for indication. This approach requires a huge reference dataset where all vegetation types are represented with statically representative sample sizes. Otherwise, indicator values cannot be identified for all relevés. In contrast to our approach, Tichý’s method produces only one indicator value but provides no measure of the reliability of the value. With his recommended parameter (maximum distance = 0.5), only 22% of our relevés could be assigned a value, showing that our reference dataset with 1619 relevés is still too small for Tichý’s approach. With a maximum distance of 1, all relevés were assigned. But the variance of the estimated values was significantly smaller (p = 0.09) than the measured ones. Consequently, the imputed values are clearly biased.
Peppler-Lisbach (2008) based his indicator concept on niche overlap: the indicated value is the centre of the overlap of the niches of all species in a plot. Obviously, this concept can only be applied if all species in a plot have overlapping niches.
10.8 Conclusions
To date, all species-based systems to indicate site conditions, such as the famous Ellenberg indicator values, reflect “expert opinions only” (Wildi 2016), rather than an outcome of sound statistical analysis of measured data. With WeiWIS, we have developed an indicator system for soil properties that is based on a large, statistically representative sample of measurements. We have undertaken the step away from systems developed during the last half century that are primarily subjective towards a quantitative system that is based on a systematic sampling design and which follows a sound statistical approach.
Species-based indicator systems that indicate complex factors, like Ellenberg’s system, should not be a substitute for ecological measurements in environmental monitoring (Michler 1994), because changes, for example, in the mean N value, cannot be attributed to a concrete, measurable variable. However, this step is necessary to set up effective, sustainable planning in the management of habitats and to avoid misinterpretations. Our system avoids these problems because it directly indicates measurable values. The method can be applied to all environmental variables that effect species distribution.
Moreover, our indicator systems can help to estimate the conditions in the past, for which no measurements are available, and then compare them to the current situation within the same system.
References
Austin MP, Meyers JA (1996) Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity. For Ecol Manag 85(1–3):95–106. https://doi.org/10.1016/s0378-1127(96)03753-x
BMELV (2006) Arbeitsanleitung für die zweite bundesweite Bodenzustandserhebung im Wald (BZE II). Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz, Berlin
Borhidi A (1995) Social behaviour types, the naturalness and relative ecological indicator values of the higher plants in the Hungarian flora. Acta Bot Hungar 39(1–2):97–181
Braun-Blanquet J (1913) Die Vegetationsverhältnisse der Schneestufe in den Rätisch-Lepontischen Alpen: Ein Bild des Pflanzenlebens an seinen äussersten Grenzen. Schweizerische Naturforschende Gesellschaft, Zurich
Ellenberg H (1950) Unkrautgemeinschaften als Zeiger für Klima und Boden. Eugen Ulmer, Stuttgart
Ellenberg H (1974) Zeigerwerte der Gefässpflanzen Mitteleuropas. Scripta Geobotanica IX. Erich Goltze, Göttingen
Ellenberg H, Weber HE, Düll R, Wirth V, Werner W (2001) Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica XVIII, 3rd edn. Erich Goltze, Göttingen
Ellenberg H, Weber HE, Düll R, Wirth V, Werner W (2003) Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica XVIII, Datenbank. Erich Goltze, Göttingen
Ewald J, Hennekens S, Conrad S, Wohlgemuth T, Jansen F, Jenssen M, Cornelis J, Michiels HG, Kayser J, Chytry M, Gegout JC, Breuer M, Abs C, Walentowski H, Starlinger F, Godefroid S (2013) Spatial and temporal patterns of Ellenberg nutrient values in forests of Germany and adjacent regions—a survey based on phytosociological databases. Tuexenia 33:93–109
Ewald J, Ziche D (2017) Giving meaning to Ellenberg nutrient values: National Forest Soil Inventory yields frequency-based scaling. Appl Veg Sci 20(1):115–123. https://doi.org/10.1111/avsc.12278
Fischer H (1986) Zur Vorhersage ökologischer Parameter aufgrund der floristischen Struktur der Vegetation. Tuexenia 6:405–414
Fischer HS (1990) Simulating the distribution of plant communities in an alpine landscape. Coenoses 5:37–43
Fischer HS (1994) Simulation der räumlichen Verteilung von Pflanzengesellschaften auf der Basis von Standortskarten. Dargestellt am Beispiel des MaB-Testgebiets Davos = Simulation of the spatial distribution of plant communities based on the maps of site factors. Veröffentlichungen des Geobotanischen Instituts der Eidgenössischen Technischen Hochschule, Stiftung Rübel, vol 122. ETH Zürich, Zurich
GAFA (ed) (2014) Handbuch Forstliche Analytik (HFA). Grundwerk und 1. – 5. Ergänzung des Gutachterausschuss Forstliche Analytik (GAFA). Federal Ministry of Food, Agriculture and Consumer Protection, Northwest German Forest Research Institute, Bonn
Gégout J-C, Hervé J-C, Houllier F, Pierrat J-C (2003) Prediction of forest soil nutrient status using vegetation. J Veg Sci 14(1):55–62
Hill MO, Mountford J, Roy D, Bunce RGH (1999) Ellenberg’s indicator values for British plants. Ecofact Research Report Series 2b. Institute of Terrestrial Ecology, Huntingdon
Humboldt A, Bonpland A (1807) Ideen zu einer Geographie der Pflanzen nebst einem Naturgemälde der Tropenländer. Cotta, Tübingen
Jansen F, Dengler J (2008) GermanSL -Eine universelle taxonomische Referenzliste für Vegetationsdatenbanken in Deutschland. Tuexenia 28:239–253
Jongman RHG, ter Braak CJF, van Tongeren OFR (1995) Data analysis in community and landscape ecology. Cambridge University Press, Cambridge
Landolt E (1977) Ökologische Zeigerwerte zur Schweizer Flora. Veröffentlichungen des Geobotanischen Instituts der Eidgenössischen Technischen Hochschule, Stiftung Rübel, vol 64. ETH Zürich, Zurich
Landolt E, Bäumler B, Erhardt A, Hegg O, Klötzli F, Lämmler W, Nobis M, Rudmann-Maurer K, Schweingruber F, Theurillat J (2010) Flora indicativa: Ökologische Zeigerwerte und biologische Kennzeichen zur Flora der Schweiz und Alpen, 2nd edn. Haupt Verlag, Bern
McCullagh P, Nelder JA (1989) Generalized linear models. Monographs on statistics and applied probablilty, vol 37, 2nd edn. Chapman and Hall/CRC, London, New York
Michler B (1994) Vegetationskundliche, bodenkundliche, phänologische und morphologische Untersuchungen zur Variabilität sekundärer Pflanzeninhaltsstoffe am Beispiel der Pyrrolizidinalkaloide in Symphytum officinale agg. Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen
Möller H (1992) Zur Verwendung des Medians bei Zeigerwertberechnungen nach Ellenberg. Tuexenia 12:25–28
Peppler-Lisbach C (2008) Using species-environmental amplitudes to predict pH values from vegetation. J Veg Sci 19(4):437–U434. https://doi.org/10.3170/2008-8-18394
Persson S (1981) Ecological indicator values as an aid in the interpretation of ordination diagrams. J Ecol 69(1):71–84
Pudlatz H (1975) Zur Transformation der Variablen bei mangelnder Normalverteilung. Gießener Geogr Schriften 32:29–33
R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Roberts D (n.d.) R labs for vegetation ecologists. LAB 5—Modeling species/environment relations with generalized additive models. http://ecology.msu.montana.edu/labdsv/R/labs/lab5/lab5.html
Rodenkirchen H (1982) Wirkungen von Meliorationsmaßnahmen auf die Bodenvegetation eines ehemals streugenutzten Kiefernstandortes in der Oberpfalz. Forstliche Forschungsberichte München, vol 53. Chair for soil sience at University Munich, Munich
Schmidt M, Kriebitzsch W-U, Ewald J (2011) Waldartenlisten der Farn-und Blütenpflanzen, Moose und Flechten Deutschlands. BfN, Bonn
Seidling W (2005) Ground floor vegetation assessment within the intensive (Level II) monitoring of forest ecosystems in Germany: chances and challenges. Eur J For Res 124(4):301–312. https://doi.org/10.1007/s10342-005-0087-1
Soó R (1980) Synopsis systematico-geobotanica florae vegetationisque Hungariae VI. Akadémiai Kiadó, Budapest
ter Braak CJF (1987) Unimodal models to relate species to environment. University Wageningen, Wageningen
ter Braak CJF, Šmilauer P (2002) CANOCO reference manual and CanoDraw for Windows user’s guide: software for canonical community ordination (version 4.5). Biometris, Wageningen
Tichý L, Hájek M, Zelený D (2010) Imputation of environmental variables for vegetation plots based on compositional similarity. J Veg Sci 21(1):88–95. https://doi.org/10.1111/j.1654-1103.2009.01126.x
Wamelink GWW, Goedhart PW, van Dobben HF, Berendse F (2005) Plant species as predictors of soil pH: replacing expert judgement with measurements. J Veg Sci 16(4):461–470. https://doi.org/10.1111/j.1654-1103.2005.tb02386.x
Wamelink GWW, Joosten V, van Dobben HF, Berendse F (2002) Validity of Ellenberg indicator values judged from physico-chemical field measurements. J Veg Sci 13(2):269–278. https://doi.org/10.1658/1100-9233(2002)013[0269:voeivj]2.0.co;2
Wellbrock N, Bolte A, Flessa H (eds) (2016) Dynamik und räumliche Muster forstlicher Standorte in Deutschland: Ergebnisse der Bodenzustandserhebung im Wald 2006 bis 2008. Thünen Report, vol 43. Johann Heinrich von Thünen Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries, Braunschweig
Wildi O (2016) Why mean indicator values are not biased. J Veg Sci 27(1):40–49
Zarzycki K, Trzcińska-Tacik H, Różański W, Szeląg Z, Wołek J, Korzeniak U (2002) Ecological indicator values of vascular plants of Poland. Biodiversity of Poland, vol 2. W. Szafer Institute of Botany, Polish Academy of Science, Krakow
Zelený D, Tichý L (n.d.) Species response curves in JUICE. http://www.sci.muni.cz/botany/zeleny/hof.php
Ziche D, Michler B, Fischer HS, Kompa T, Höhle J, Hilbrig L, Ewald J (2016) Boden als Grundlage biologischer Vielfalt. In: Wellbrock N, Bolte A, Flessa H (eds) Dynamik und räumliche Muster forstlicher Standorte in Deutschland. Ergebnisse der Bodenzustandserhebung im Wald 2006 bis 2008. Thünen Report, vol 43. Johann Heinrich von Thünen Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries, Braunschweig, pp 292–342
Ziche D, Seidling W (2010) Homogenisation of climate time series from ICP Forests Level II monitoring sites in Germany based on interpolated climate data. Ann For Sci 67(8):804. https://doi.org/10.1051/forest/2010051
Zólyomi B, Baráth Z, Fekete G, Jakucs P, Kárpáti I, Kárpáti V, Kovács M, Máté I (1967) Einreihung von 1400 Arten der ungarischen Flora in ökologische Gruppen nach TWR-Zahlen. Fragm Bot Mus Hist Nat Hung 4:101–142
Acknowledgements
The National Forest Soil Inventory is a joint project of the federal government and the German states. We thank the individual federal states for providing the datasets, as well as the Federal Ministry of Food and Agriculture (BMEL) for facilitating the data analysis. We acknowledge in particular all site investigators and the persons in charge of the individual federal research stations (http://www.blumwald.de/bze/ansprechpartner-bze) for their assistance and support. This work was supported by the Thünen Institute, the German Federal Research Institute for Rural Areas, Forestry and Fisheries. A previous version that was limited to Bavaria was supported by project F49 of the board of trustees of the Bavarian Agency for Forest and Forestry and the Bavarian State Ministry for Agriculture and Forestry.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2019 The Author(s)
About this chapter
Cite this chapter
Fischer, H.S., Michler, B., Ziche, D., Fischer, A. (2019). Plants as Indicators of Soil Chemical Properties. In: Wellbrock, N., Bolte, A. (eds) Status and Dynamics of Forests in Germany . Ecological Studies, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-030-15734-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-15734-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15732-6
Online ISBN: 978-3-030-15734-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)