Ecological Indicators 14 (2012) 82–86
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NIRS meets Ellenberg’s indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics Valentin H. Klaus a,∗ , Till Kleinebecker a , Steffen Boch b , Jörg Müller c , Stephanie A. Socher b , Daniel Prati b , Markus Fischer b,c , Norbert Hölzel a a
University of Münster, Institute of Landscape Ecology, Robert-Koch-Str. 28, D-48149 Münster, Germany University of Bern, Institute of Plant Sciences, Altenbergrain 21, CH-3013 Bern, Switzerland c University of Potsdam, Institute of Biochemistry and Biology, Maulbeerallee 1, D-14469 Potsdam, Germany b
a r t i c l e
i n f o
Article history: Received 10 January 2011 Received in revised form 2 May 2011 Accepted 18 July 2011 Keywords: Biodiversity exploratories Cover-weighting Near-infrared reflectance spectroscopy (NIRS) Nitrogen Nutrient concentrations Phosphorus
a b s t r a c t 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. © 2011 Elsevier Ltd. All rights reserved.
1. Introduction Ellenberg indicator values (Ellenberg, 1974; Ellenberg et al., 2001) are one of the most commonly used ecological indicators in Central Europe. Calculating an average indicator value of a vegetation record provides helpful environmental site information, which can replace time-consuming and costly measurements (Schaffers and Sykora, 2000; Diekmann, 2003; Kollmann and Fischer, 2003). Although ordinally scaled, Ellenberg indicator values have been commonly treated as quasi metric, obtaining reasonable results when averaged (Käfer and Witte, 2004). They have been widely extended and modified (e.g. Thompson et al., 1993; Diekmann, 1995; Hill et al., 1999, 2000; Lawesson et al., 2003) and are now used all over Europe as an effective tool to assess
∗ Corresponding author. Tel.: +49 251 8339770; fax: +49 251 8338338. E-mail address:
[email protected] (V.H. Klaus). 1470-160X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.07.016
vegetation-environment relationships, for risk assessment and for conservation evaluation in ecology, agriculture and forestry (e.g. Cornwell and Grubb, 2003; Fanelli et al., 2006; Odland, 2009; Duprè et al., 2010; Sullivan et al., 2010). However, Ellenberg values are subject of permanent discussion concerning reliability, extension, statistical approaches and field methodologies (e.g. Schaffers and ´ Sykora, 2000; Käfer and Witte, 2004; Wagner et al., 2007; Otypková, 2009). Mean Ellenberg indicator values, particularly for mean “moisture” (mF) and “nitrogen” (mN), were well related to environmental variables, such as groundwater level and soil moisture tension (mF) or to soil nitrogen concentrations and annual yield (mN) (Schaffers and Sykora, 2000; Wamelink et al., 2002; Diekmann, 2003). Moreover, Schmidtlein (2005) predicted mean Ellenberg indicator values on the landscape level from vegetation reflectance extracted from airborne hyperspectral imagery. Thus, although mean Ellenberg indicator values are deduced from the floristic composition of the vegetation, they
V.H. Klaus et al. / Ecological Indicators 14 (2012) 82–86
incorporate ecological information on site characteristics and possibly on the biomass of the vegetation itself. We tested this hypothesis for mN and mF values in grassland biomass with means of near-infrared reflectance spectroscopy (NIRS). NIRS is a fast and economic technique to analyse the chemical composition of organic material. It is a standard method in agriculture, food and pharmaceutical industry and now increasingly applied to master large sample sizes typical for ecological studies (e.g. Chodak, 2008). By using NIR-radiation, C–H, N–H and O–H bonds, as the main constituents of organic material, are induced to vibrate. Because the chemical composition of the organic material determines the constitution and number of bonds, the resulting NIR-spectrum contains information on the chemical composition and can thus be used for qualitative and quantitative analyses (Burns and Ciurczak, 2008). NIRS measurements allow predicting the composition of unknown samples with high accuracy based on multivariate regression models and were successfully applied to a multiplicity of compounds in organic material, including nitrogen and carbon concentrations, complex organic substances and organic bound minerals such as phosphorus, potassium, magnesium and calcium (e.g. Norris et al., 1976; Clark et al., 1987; Bolster et al., 1996; Gillon et al., 1999; Petisco et al., 2006; Stolter et al., 2006). As shown by Kleinebecker et al. (2009), it is even possible to predict the carbon and nitrogen isotopic composition of plant tissue. In this study, we applied the NIRS technique to aboveground grassland biomass from the Biodiversity Exploratories project (Fischer et al., 2010). Spectral and chemical properties of the biomass samples were related to floristic characteristics of mean Ellenberg indicator values for “moisture” (mF) and “nitrogen” (mN). The goal is not to substitute vegetation records, but rather to predict Ellenberg indicator values from aboveground community biomass and to deepen our insight in how far Ellenberg N and F values are directly related to properties of the vegetation. Previous studies already showed that annual yield (total biomass) is well correlated with the Ellenberg mN and partly with mF (Diekmann, 2003). We therefore concentrated on measurements of the chemical composition of the biomass to compare their relationship with Ellenberg indicator values that were calculated from vegetation records or inferred from NIRS. In particular, we addressed the following questions: - Is it possible to predict Ellenberg mN and mF values of grasslands based on the near-infrared spectral characteristics of biomass samples? - Do weighted or un-weighted mean Ellenberg values give better predictions? - How well are Ellenberg indicator values related to directly measured nutrient concentrations or fibre fractions in the biomass, thereby providing a possible explanation of NIRS calibration models? 2. Material and methods 2.1. Vegetation and biomass sampling Sampling took place in agricultural grasslands in three regions in Germany belonging to the Biodiversity Exploratories (Fischer et al., 2010; www.biodiversity-exploratories.de): the UNESCO Biosphere Reserve Schorfheide-Chorin in the north east, the National Park Hainich with surroundings in the centre and the UNESCO Biosphere Reserve Schwäbische Alb with surroundings in the south west of Germany. Plots were selected along a gradient of land-use intensities, covering a broad range
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of grassland productivity and being representative for large parts of the Central European grassland vegetation (Fischer et al., 2010). Most of the studied grasslands belong to Arrhenatherion elatioris W. Koch 1926 and Cynosurion cristati Tx. 1947 communities, with transitions to Polygono-Trisetion Br.Bl. et Tx. ex Marschall 1947 nom. invers. propos., Calthion Tx. 1937 and Magnocaricion elatae W. Koch 1926 communities depending on altitude and soil moisture. Additionally, Bromion erecti Koch 1926-communities occurred on shallow calcareous soils. From mid of May to mid of June 2009 we recorded vascular plant species on 2 m × 1.5 m plots on 45–49 grasslands in each of the three study regions. Additionally we harvested the aboveground biomass as mixed samples of four randomly placed quadrates of 0.25 m2 each. Although all grasslands were regularly managed by resident farmers, temporary fences ensured that biomass could be harvested prior to mowing or grazing. Biomass samples were dried immediately after harvesting for 48 h at 80 ◦ C and grounded to pass a 0.5 mm-screen. 2.2. Recording NIR-spectra and calibration development Ground samples were scanned with a Spectra Star 2400 (Unity Scientific, Columbia, MD, USA) at 1 nm intervals over the range 1250–2350 nm. Each sample scan consisted of 24 single measurements, which were mediated to the resulting spectrum. Spectral data were recorded as log 1/R (where R is reflectance) and the first and second derivatives of log 1/R. We used standard normal variate prior to the calibration to clear data from spectral differences due to particle size and structure of samples that are unrelated to chemical composition (Foley et al., 1998). We divided the data set into a calibration set with 113 spectra and a validation set with 28 spectra by arranging the respective parameters according to size and taking each fifth sample for validation. This ensured that both calibration and validation data set represented the range of observed values. Additionally, a cross-validation procedure with 50 randomized runs was performed for internal validation of calibration models. Calibrations were calculated by partial least-squares regressions (PLSR) using the software package SL Calibration Workshop (Senso-Logic Software GmbH, Norderstedt, Germany). The standard error of cross-validation (SECV) is a measure of the difference between the actual and predicted property values calculated over the cross-validation. The SECV was used to determine the number of factors used for calibration. Usually, this was the number of factors at which the first minimum of SECV occurred. While calculating calibrations for each parameter, several samples were identified as outliers. To improve calibrations a maximum of four outliers was excluded from the calibration. Optimal calibrations were selected based on a large coefficient of multiple determinations (R), a low SECV and a low standard error of calibration (SEC). The SEC is exclusively based on spectra used for calibration and indicates the theoretical accuracy when using the calibration to predict unknown spectra. To prove the reliability of obtained NIRS calibrations for the prediction of unknown samples, an external validation procedure was carried out with the validation data set (Burns and Ciurczak, 2008). According to the SEC, the calculation of the SEP (standard error of prediction) is performed, but with validation samples only. The ratio of the standard deviation (SD) of a data to the SECV of the respective calibration, called RSC, was used to evaluate the goodness of NIRS calibrations for a particular parameter. Predictions are regarded as good for RSC > 2.0, acceptable for RSC 1.4 ≤ RSC ≥ 2.0, and inappropriate for RSC < 1.4 (Chang et al., 2001). For further methodological details and ecological applications of the NIRS methodology, see Foley et al. (1998) and Chodak (2008) and references therein.
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A
8
B
6.0
Predicted Ellenberg mF
Predicted Ellenberg mN
7
6
5
4
3
2
6.5
3
4
5
6
7
5.0
4.5
4.0
Predicted Nu y = 1.2161+0.787*x R2 = 0.80
2
5.5
8
3.5 3.5
Predicted Fu y = 1.4678+0.717*x R2 = 0.69
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Observed Ellenberg mF
Observed Ellenberg mN
Fig. 1. Observed vs. NIRS predicted values of best calibrations for (A) mean Ellenberg “nitrogen” indicator value (calibration Nu) and (B) mean Ellenberg “moisture” indicator value (calibration Fu) with u = un-weighted values. Filled quadrates = samples used for calibration with cross-validation; open circles = samples of the additional external validation.
2.3. Determination of nutrient and fibre concentrations in biomass Total concentrations of nitrogen (N) in biomass samples were determined by using an element auto analyzer (NA 1500, Carlo Erba, Milan, Italy). For the analyses of phosphorus (P) and potassium (K) samples were digested in a microwave (MLS Start, Milestone, Bergamo, Italy) with concentrated nitric acid (65%) and hydrogen peroxide (30%). After digestion, concentrations were determined by inductively coupled plasma optical emission spectrometry (ICPOES analyses) (Vista-PRO Axial, Varian, Palo Alto, USA). Neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) were measured gravimetrically according to Naumann and Bassler (1976) (Fibertec 2010, Foss, Höganäs, Sweden). 2.4. Calculation of Ellenberg indicator values and further statistics Mean Ellenberg N and F values calculated without and with were cover-weighting, using mE = xi × Ai/ Ai, with mE = mean Ellenberg value, xi = Ellenberg value of species i and Ai = cover of species i (Dierschke, 1994). We also calculated Ellenberg reaction values, but discarded calibrating them due to a too narrow range (low SD). All statistical tests were performed using SPSS 17.0. As the Pearson correlations yielded similar results compared to nonparametric rank correlations we therefore present parametric tests. 3. Results and discussion Mean, maximum and minimum of Ellenberg indicator values of grassland stands showed minor variation for cover-weighted or un-weighted data (Table 1). Variation of mean “moisture” (mF) values was lower than of mean “nitrogen” (mN) values, which may have negatively influenced calibrations for mF or at least have led to a lower RSC (Chang et al., 2001). Although Ellenberg indicator values are floristic and not strictly environmental attributes, NIRS models could predict both parameters satisfactorily (Fig. 1). Calibration correlation coefficients ranged between 0.90 and 0.79 and internal as well as external validation proved the models to predict Ellenberg mN and mF accurately (Table 2). Calibrations with high quality for quantitative predictions (RSC > 2) could only be developed for mN, whereas calibration quality for mF was lower but still
Table 1 Descriptive statistics of mean Ellenberg indicator values for “nitrogen” (mN) and “moisture” (mF); w = cover-weighted, u = un-weighted, SD = standard deviation. Abbreviation
Cover-weighting
Mean
SD
Min
Max
Ellenberg mN Nu Nw
Un-weighted Weighted
5.7 6.0
1.0 1.1
2.5 2.3
7.5 7.6
Ellenberg mF Fu Fw
Un-weighted Weighted
5.1 5.3
0.5 0.7
3.7 3.5
6.4 6.9
acceptable (1.4 ≤ RSC ≥ 2) (Table 2). Among the different data transformation tested, the best results were obtained using absorbance spectral data for all mN calibrations and the first derivative for mF calibrations. Schmitdlein and Sassin (2004) and Schmidtlein (2005) already predicted mean Ellenberg indicator vales from hyperspectral data by remote sensing. Our data show, to our knowledge for the first time that mean Ellenberg indicator values can also be predicted by NIR spectra of the vegetation biomass and its chemical composition. Thus, our data represents something like a basic validity and ground truth for mean Ellenberg indicator values. Table 2 Calibration and validation statistics of mean Ellenberg N and F (data set of 113 spectra for calibration, 28 spectra for validation), only best calibrations shown; w = cover-weighted; u = un-weighted. Transformations (mathematic data pretreatment): abs = absorbance + standard normal variate, 1d = absorbance + standard normal variate + first derivative. R: multiple correlation coefficient of calibration data; SEC: standard error of calibration; SECV: standard error of cross-validation; RSC: ratio of standard deviate of reference values to SECV; SEP: standard error of prediction; r = multiple correlation coefficient of validation data. Nu
Nw
Fu
Fw
Calibration statistics Transformation R No. of factors Removed outliers SEC SECV RSC
abs 0.90 7 3 0.41 0.45 2.2
abs 0.90 8 4 0.49 0.55 2.1
1d 0.84 5 4 0.28 0.32 1.7
1d 0.79 5 4 0.41 0.48 1.4
Validation statistics SEP r
0.47 0.84
0.45 0.91
0.29 0.81
0.40 0.73
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Table 3 Pearson correlations coefficient between cover-weighted (w) and un-weighted (u) mean Ellenberg N and F values and nutrient concentrations and fibre fractions in aboveground grassland biomass (in % DW) (n = 141; NDF: neutral detergent fibre; ADF: acid detergent fibre). N
P
Ellenberg mN Nu Nw
0.37** 0.22**
0.59** 0.59**
Ellenberg mF Fu Fw
0.44** 0.37**
0.32** 0.29**
* **
K 0.21* 0.28** −0.08 −0.04
Ca
Mg
NDF
ADF
Lignin
−0.02 −0.09
0.27** 0.13
−0.03 −0.12
−0.15 −0.24**
−0.31** −0.35* *
−0.01 −0.04
0.39** 0.33**
0.06 0.07
−0.15 −0.10
−0.37** −0.29**
p < 0.05. p < 0.01.
Table 4 Pearson correlation coefficient between observed and NIRS-predicted mean Ellenberg N and F values and nutrient concentrations in biomass (in %). Predictions are based on the best calibration models applied to the external validation set (n = 28). Predicted values
N
P
K
Ellenberg mN Observed values Nu Predicted values Nu
0.84** 1
0.13 0.09
0.57** 0.59**
Ellenberg mF Observed values Fu Predicted values Fu
0.81** 1
0.44* 0.56**
0.27 0.37
* **
Ca
Mg
0.30 0.08
−0.23 −0.31
0.23 0.25
−0.14 −0.20
0.12 0.24
0.60** 0.64**
NDF
ADF
Lignin
0.00 0.01
−0.23 −0.36
−0.25 −0.39*
−0.01 −0.18
−0.18 −0.25
−0.20 −0.20
p < 0.05. p < 0.01.
For both Ellenberg mN and mF, cover-weighting did not improve calibrations and the RSC was better using un-weighted means (Table 2). These findings are in line with Käfer and Witte (2004) who showed that cover-weighting does not improve correlations between Ellenberg mF and ground water levels. As expected, Ellenberg mN was strongly related to nutrient concentrations in the biomass (Table 3), which may explain the good NIRS calibrations. Moreover, in our study Ellenberg mF was related positively with N, Mg and P concentrations and negatively with lignin (Table 3). This can be possibly explained by higher nutrient availability in moderately wet soils in agricultural grasslands (Misra and Tyler, 2000), leading to moisture-related differences in the chemical biomass composition, especially for N and Mg. In the external validation data set (n = 28), NIRS-predicted mF values were similarly related to nutrient concentration as the observed values (Table 4). As there were no further correlations with other chemical compounds, NIRS predictions of Ellenberg mF could be predominantly a result of inter-correlations with the N and Mg concentration in the biomass. Both nutrients are known to be well predicted by means of NIRS (Bolster et al., 1996; Petisco et al., 2006). Against expectations, Ellenberg mN was more strongly related to P concentrations in biomass than to N or K concentrations (Table 3). Up to now, relationships between mN and nutrient concentrations in biomass were only reported for N (Schaffers and Sykora, 2000; Diekmann, 2003). Furthermore, mN values slightly increased with Mg concentrations and decreased with fibre fractions hard to digest (Table 3). Thus, our findings underlined that Ellenberg N values primarily indicate nutrient supply or productivity and should not be interpreted as a “nitrogen value” (Hill and Carey, 1997; Ertsen et al., 1998; Schaffers and Sykora, 2000), at least in agricultural grasslands. The observed strong relationship between P concentrations in community biomass and Ellenberg mN is of particular relevance, as biodiversity and conservation value of grasslands were found to be strongly associated not only with nitrogen but also with phosphorus deficiency (Wassen et al., 2005; Klaus et al., 2011). Similarly, NIRS predicted mN values of the validation data set were more strongly related
to P concentrations and to a lesser extent to lignin in biomass (Table 4). 4. Conclusions We presented calibration models for Ellenberg mean nitrogen and mean moisture indicator values of grassland vegetation and showed that both indicator values can be accurately predicted by means of NIRS analysis of aboveground biomass. As the applicability of NIRS for the prediction of Ellenberg values is suggested to be caused by inter-correlations with the chemical composition of biomass, our findings clearly showed that Ellenberg mN and mF indicator values are directly related to the vegetation biomass. In line with former studies, our results implied that cover-weighted Ellenberg indicator values did not improve the description of environmental characteristics compared with un-weighted values (Käfer and Witte, 2004). While Ellenberg mF was rather associated with N concentrations in biomass, Ellenberg mN was strongly related to P concentrations, suggesting the term Ellenberg “nitrogen” value should be replaced by nutrient or productivity value. Acknowledgements We thank the managers of the three exploratories, Swen Renner, Sonja Gockel, Andreas Hemp and Martin Gorke and Simone Pfeiffer for their work in maintaining the plot and project infrastructure, and Dominik Hessenmöller, Elisabeth Kalko, Eduard Linsenmair, Jens Nieschulze, Ingo Schöning, Ernst-Detlef Schulze, Wolfgang W. Weisser for setting up the exploratory project. Furthermore, we thank Frederike Velbert and Annika Brinkert for assistance and Verena Möllenbeck for commenting a former version of this article. The work has been funded by the DFG Priority Program 1374 “Infrastructure-Biodiversity-Exploratories” (grants HO 3830/2-1, FI 1246/6-1, FI 1246/9-1). Fieldwork permits were given by the responsible state environmental offices of BadenWürttemberg, Thüringen, and Brandenburg (according to § 72 BbgNatSchG).
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References Bolster, K.L., Martin, M.E., Aber, J.D., 1996. Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: a comparison of statistical methods. Can. J. For. Res. 26, 590–600. Burns, D.A., Ciurczak, E.W. (Eds.), 2008. Handbook of Near-Infrared Analysis. Practical Spectroscopy Series, vol. 35, third ed. CRC Press, Boca Raton. Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., 2001. Near-infrared reflectance spectroscopy—principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 65, 480–490. Chodak, M., 2008. Application of near infrared spectroscopy for analysis of soils, litter and plant materials. Polish J. Environ. Stud. 17, 631–642. Clark, D.H., Mayland, H.F., Lamb, R.C., 1987. Mineral analysis of forages with near infrared reflectance spectroscopy. Agron 79, 485–490. Cornwell, W.K., Grubb, P.J., 2003. Regional and local patterns in plant species richness with respect to resource availability. Oikos 100, 417–428. Diekmann, M., 1995. Use and improvement of Ellenberg’s indicator values in deciduous forests of the Boreo-nemoral zone in Sweden. Ecography 18, 178–189. Diekmann, M., 2003. Species indicator values as an important tool in applied plant ecology—a review. Basic Appl. Ecol. 4, 493–506. Dierschke, H., 1994. Pflanzensoziologie. Eugen Ulmer, Stuttgart. Duprè, C., Stevens, C.J., Ranke, T., Bleekers, A., Peppler-Lisbach, C., Gowing, D.J.G., Diese, N.B., Dorland, E., Bobbink, R., Diekmann, M., 2010. Changes in species richness and composition in European acidic grasslands over the past 70 years: the contribution of cumulative atmospheric nitrogen deposition. Global Change Biol. 16, 344–357. Ellenberg, H., 1974. Zeigerwerte der Gefäßpflanzen Mitteleuropas. Scr. Geobot. 9, 1–97. Ellenberg, H., Weber, H.E., Düll, R., Wirth, V., Werner, W., Paulissen, D., 2001. Zeigerwerte von Pflanzen in Mitteleuropa, third ed. Scr. Geobot. 18, 1–248. Ertsen, A.C.D., Alkmade, J.R.M., Wassen, M.J., 1998. Calibrating Ellenberg indicator values for moisture, acidity, nutrient availability and salinity in the Netherlands. Plant Ecol. 135, 113–124. Fanelli, G., Tescarello, P., Testi, A., 2006. Ecological indicators applied to urban and suburban floras. Ecol. Indic. 6, 444–457. Fischer, M., Bossdorf, O., Gockel, S., Hänsel, F., Hemp, A., Hessenmöller, D., Korte, G., Nieschulze, J., Pfeiffer, S., Prati, D., Renner, S., Schöning, I., Schumacher, U., Wells, K., Buscot, F., Kalko, E.K.V., Linsenmair, K.E., Schulze, E.-D., Weisser, W.W., 2010. Implementing large-scale and long-term functional biodiversity research: the biodiversity exploratories. Basic Appl. Ecol. 11, 473–485. Foley, W.J., McIlwee, A., Lawler, I., Aragones, L., Woolnough, A.P., Berding, N., 1998. Ecological applications of near infrared reflectance spectroscopy—a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116, 293–305. Gillon, D., Houssard, C., Joffre, R., 1999. Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material. Oecologia 118, 173–182. Hill, M.O., Carey, P.D., 1997. Prediction of yield in the Rothamsted Park Grass Experiment by Ellenberg indicator values. J. Veg. Sci. 8, 579–586. Hill, M.O., Mountford, J.O., Roy, D.B., Bunce, R.G.H., 1999. Ellenberg’s Indicator Values for British plants. Institute of Terrestrial Ecology, Huntingdon, UK. Hill, M.O., Roy, D.B., Mountford, J.O., Bunce, R.G.H., 2000. Extending Ellenberg’s indicator values to a new area: an algorithmic approach. J. Appl. Ecol. 37, 3–15.
Käfer, J., Witte, J-P.M., 2004. Cover-weighted averaging of indicator values in vegetation analyses. J. Veg. Sci. 15, 647–652. Klaus, V.H., Kleinebecker, T., Hölzel, N., Boch, S., Müller, J., Socher, S.A., Prati, D., Fischer, M., 2011. Nutrient concentrations and fibre contents of plant community biomass reflect species richness patterns along a broad range of land-use intensities among agricultural grasslands. Perspect. Plant Ecol. Evol. Syst., doi:10.1016/j.ppees.2011.07.001, in press. Kleinebecker, T., Schmidt, S.R., Fritz, C., Smolders, A.J.P., Hölzel, N., 2009. Prediction of ␦13 C and ␦15 N in plant tissues with near-infrared reflectance spectroscopy. New Phytol. 184, 732–739. Kollmann, J., Fischer, A., 2003. Vegetation as indicator for habitat quality. Basic Appl. Ecol. 4, 489–491. Lawesson, J.E., Fosaa, A.M., Olsen, E., 2003. Calibration of Ellenberg indicator values for the Faroe Islands. Appl. Veg. Sci. 6, 53–62. Misra, A., Tyler, G., 2000. Effect of wet and dry cycles in calcareous soil on mineral nutrient uptake of two grasses, Agrostis stolonifers L. and Festuca ovina L. Plant Soil 224, 297–303. Naumann, C., Bassler, H., 1976. VDLUFA-Methodenbuch, Vol. III. Die chemische Untersuchung von Futtermitteln mit Ergänzungen von 1983, 1988, 1993, 1997, 2004 und 2006. VDLUFA, Darmstadt. Norris, K.H., Barnes, R.F., Moore, J.E., Shenk, J.S., 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43, 889–897. Odland, A., 2009. Interpretation of altitudinal gradients in South Central Norway based on vascular plants as environmental indicators. Ecol. Indic. 9, 409–421. ´ Otypková, Z., 2009. The influence of sample plot size on evaluations with Ellenberg indicator values. Biologia 64 (6), 1123–1128. Petisco, C., García-Criado, B., Mediavilla, S., de Aldana, B.R.V., Zabalgogeazcoa, I., García-Ciudad, A., 2006. Near infrared reflectance spectroscopy as a fast and nondestructive tool to predict foliar organic constituents of several woody species. Anal. Bioanal. Chem. 386, 1823–1833. Schaffers, A.P., Sykora, K.V., 2000. Reliability of Ellenberg indicator values for moisture, nitrogen and soil reaction: a comparison with field measurements. J. Veg. Sci. 11, 225–244. Schmidtlein, S., 2005. Imaging spectroscopy as a tool for mapping Ellenberg indicator values. J. Appl. Ecol. 42, 966–974. Schmitdlein, S., Sassin, J., 2004. Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sens. Environ. 92, 126–138. Stolter, C., Julkunen-Tiitto, R., Ganzhorn, J.U., 2006. Application of near infrared reflectance spectroscopy (NIRS) to assess some properties of a sub-arctic ecosystem. Basic Appl. Ecol. 7, 167–187. Sullivan, C.A., Sheehy Skeffington, M., Gormally, M.J., Finn, J.A., 2010. The ecological status of grasslands on lowland farmlands in western Ireland and implications for grassland classification and nature value assessment. Biol. Conserv. 143, 1529–1539. Thompson, K., Hodgson, J.G., Grime, J.P., Rorison, I.H., Band, S.R., Spencer, R.E., 1993. Ellenberg numbers revisited. Phytocoenologia 23, 277–289. Wagner, M., Kahmen, A., Schlumprecht, H., Audorff, V., Perner, J., Buchmann, N., Weisser, W.W., 2007. Prediction of herbage yield in grassland: how well do Ellenberg N-values perform? Appl. Veg. Sci. 10, 15–24. Wamelink, G.W.W., Joosten, V., van Dobben, H.F., Berendse, F., 2002. Validity of Ellenberg indicator values judged from physico-chemical field measurements. J. Veg. Sci. 13, 269–278. Wassen, M.J., Olde Venterink, H., Lapshina, E.D., Tanneberger, F., 2005. Endangered plants persist under phosphorus limitation. Nature 437, 547–550.
