Iranian Journal of Forest

Iranian Journal of Forest

The Effect of Data Transformation on Detrended Correspondence Analysis in Vegetation Studies (Case study: Kermanshah oak forests)

Document Type : Research Paper

Authors
1 Ph.D in Forestry, Faculty of Natural Resources, Urmia University, Urmia, I.R. Iran
2 Prof. of Forestry, Faculty of Natural Resources, Urmia University, Urmia, I.R. Iran
3 Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran.
4 Associate Prof. of Forestry, Faculty of Natural Resources, Tarbiat Modarres University, Noor, I.R. Iran
Abstract
Vegetation sampling is a fundamental practice in ecological research, often quantified by estimating species cover. This process typically involves transforming the data to account for the presence of common and rare species, as well as zeros in the dataset, which can significantly influence the analysis results. The subsequent analysis of these datasets aims to elucidate community patterns and dynamics within various ecosystems. However, the optimal transformation method for multivariate analysis remains a subject of ongoing debate among ecologists, as different transformation techniques can yield varying interpretations of ecological data. In this study, we aimed to evaluate the effects of different data transformations on the results of detrended correspondence analysis (DCA), specifically within oak forests (Quercus brantii Lindl.) located in the Zagros region of Iran. This region is characterized by its unique biodiversity and ecological significance, making it an ideal setting for such investigations. To achieve our objectives, we selected three distinct forest patches characterized by similar slopes and altitudes, ensuring that environmental variables were controlled. Vegetation sampling was conducted at five specific distances—0, 25, 50, 100, and 150 meters—along three transects that were spaced 200 meters apart from each other. This systematic approach allowed us to obtain a comprehensive representation of species distribution across the forest patches. We utilized the Braun-Blanquet cover percentage and the van der Maarel scale to prepare our datasets, ensuring consistency and reliability in our measurements. Each dataset underwent various transformations, including log, square root, and general relativization transformations. Subsequently, we applied DCA to each transformed dataset and compared the resulting ordination outcomes through Procrustes analysis, a method that quantifies the similarity between two datasets. The findings revealed that both log-transformed and square-root transformed datasets significantly enhanced the DCA results by effectively decreasing the variation present in the dataset. Procrustes analysis demonstrated that the concordance between the log-transformed and square-root transformed datasets and the raw data was significantly higher than that of the other transformations evaluated. Importantly, our results indicated that general relativization transformations were unsuitable for DCA analysis, as they did not adequately represent the underlying ecological relationships. Consequently, we recommend performing data transformations, particularly log or square root transformations, prior to conducting ordination analyses. This approach will not only enhance the reliability of the results but also facilitate more accurate ecological interpretations, ultimately contributing to a deeper understanding of community dynamics in forest ecosystems.
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Ahmad, S., & Yasmin, T. (2011). Vegetation classification along Hanna Lake, Baluchistan using ordination techniques. Pakistan Journal of Botany, 43(2), 863-872.
Chahouki, M.A.Z. (2013). Classification and ordination methods as a tool for analyzing of plant communities. Multivariate analysis in management, engineering and the sciences, 221p.
Diekmann, M., Eilertsen, O., Fremstad, E., Lawessonal, J., & Aude, E. (1999). Beech forest community in Nordic countries – a multivariate analysis. Plant Ecology, 140(2), 203-220. https://doi.org/10.1023/A:1009768225798.
Dooley, K., & Collins, S. (1984). Ordination and classification of Western Oak Forests in Oklahoma. American Journal of Botany, 71, 1221-122. https://doi.org/10.1002/j.1537-2197.1984.tb11977.
Eshaghi Rad, J., Soleimani, F., & Khodakarami, Y. (2017). Comparison of flora at the edge and within Oak forests in southern slopes of Kermanshah forests. Applied Biology, 30(1), 19-36. https://doi.org/10.22051/jab.2017.2986. (In Persian)
Froniter, S. (1976). Study of the decrosisaca of valcure pro pers in principal components analysis. Biology and Ecology, 25, 67-75.
García-Mijangos, I., Berastegi, A., Biurrun, I., Dembicz, I., JaniŠová, M., Kuzemko, A., ... & Dengler, J. (2021). Grasslands of Navarre (Spain), focusing on the Festuco-Brometea: classification, hierarchical expert system and characterisation. Vegetation Classification and Survey2, 195. https://doi.org/10.1002/10.3897/VCS/2021/69614
Gartner, S., & Reif, A. (2004). The impact of forest transformation on stand structure and ground vegetation in the southern Black Forest, Germany. Plant and Soil, 264, 35-51. https://doi.org/ 10.1023/B:PLSO.0000047751.25915.77
Gartner, S., & Reif, A. (2004). The response of ground vegetation to structural change during forest conversion in the Southern Black Forest. European Journal of Forest Research, 124, 221-231. https://doi.org/ 10.1007/s10342-005-0065-7
Gauch, H.G. (1982). Multivariate Analysis in Community Ecology. Cambridge: Cambridge University Press https://doi.org/10.1017/CBO9780511623332
Gulshan, A., Salma, S., Malik, M., & Saeed, S. (2014). Probe the community structure of vegetation from Piedmont downhill to alluvial plains of Dera Ghazi Khan. International Journal of Agriculture and Crop Sciences, 7, 91-99.
Gower, J.C. (1971). Statistical methods for comparing different multivariate analyses of the same data. In: Mathematics in the Archaeological & Historical Sciences (Eds F.R. Hodson, D.G., Kendall & P., Tautu). NewYork: University Press
Guttman, L. (1954). Some necessary conditions for common factor analysis. Psychometrika, 19,149-161. https://doi.org/10.1007/BF02289162
Al Harthy, L., & Grenyer, R. (2019). Classification and ordination of the main plant communities of the Eastern Hajar Mountains, Oman. Journal of Arid Environments, 169, 1-18. https://doi.org/10.1016/j.jaridenv.2019.05.017
Hill, M., & Gauch, O. (1980). Detrended Correspondence Analysis: an improved ordination technique. Vegetatio, 42, 1-3,47-58. https://doi.org/10.1007/BF00048870
Hardtle, W., Redecker, B., Assmann, T.h., & Meyer, H. (2006). Vegetation responses to environmental conditions in floodplain grasslands: Prerequisites for preserving plant species diversity. Basic and Applied Ecology, 7, 280-288. https://doi.org/10.1016/J.BAAE.2005.09.003
Hardtle, W., VON OHIMB, G., & Westphal, C. (2005). Relationships between the vegetation and soil conditions in beech and beech-oak forests of northern Germany. Plant Ecology, 177(1),113–124. https://doi.org/10.1007/s11258-005-2187-x
Husain, Z., & Malk R. N. (2006).Classification and ordination of vegetation communities of the Lohibehr reserve forest and its surrounding areas, Rawlpindi, Pakistan. Pakistan Journal of Botany, 38, 3: 543-558.
Jackson, D.A., & Somers, K.M. (1991). Putting things in order: the ups and downs of detrended correspondence analysis. American Naturalist, 137, 74-712. https://doi.org/10.2307/2462603
Jackson, D.A. (1993). Multivariate analysis of benthic invertebrate communities: the implication of choosing particular data standardization, measures of association, and ordination methods. Hydrobiologia, 268, (1), 9-26. https://doi.org/10.1007/BF00005737
Jackson, D.A. (1995).  PROTEST: a PROcrustean randomization TEST of community environment concordance. Ecoscience, 2, 297–303. https://doi.org/10.1080/11956860.1995.11682297
Kent, M., (2012). In: Vegetation Description and Data Analysis: A Practical Approach, 2 ed.Wiley-Blackwell, Chichester, UK, and Hoboken, NJ.
Khan, M., & Hussain, F. (2013). Classification and ordination of vegetation in Tehsil Takht-e-Nasrati District Karak, Khyber Pakhtunkhawa, Pakistan. Journal of Ecology and the Natural Environment, 5, 3, 30-39.
Lengyel, A., Roberts, D.W., & Botta-Dukát, Z. (2019). Comparison of silhouette-based reallocation methods for vegetation classification. Journal of Applied Statistics, 32(1), 1-10. https://doi.org/10.1101/630384
Lengyel, A., Landucci, F., Mucina, L., Tsakalos, J.L., & Botta-Dukát,Z.(2018) Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications. Journalo f Vegetation Science, 29(2), 336–347.https://doi.org/10.1111/jvs.12604
Lengyel, A., & Podani, J. (2015). Assessing the relative importance of methodological decisions in classifications of vegetation data, Journal of Vegetation Science, 26(4), 804-15. https://doi.org/10.1111/jvs.12268
Legendre, P., & Borcard, D. (2018). Box–Cox‐chord transformations for community composition data prior to beta diversity analysis. Ecography, 41(11), 1820-1824. https://doi.org/ 10.1111/ecog.03498.
Legendre, P., & Legendre, L. (1998). Numerical Ecology (2nd English ed.) Amsterdam: Elsevier.
Legendre, P., & Gallagher, E. (2001). Ecologically meaningful transformation for ordination of species data. Oecologia, 129(2), 271-280. https://doi.org/10.1007/s004420100716
Leps, J., & Smilauer, P. (2003). Multivariate Analysis of Ecological Data using Canoco. Cambridge: University Press, https://doi.org/10.1017/CBO9780511615146
Levis, E., Cakiroglu, A., Bucak, T., Odgaard, B., & Davidson, T. (2014). Similarity between contemporary vegetation and plant remains in the surface sediments in Mediterranean lakes. Freshwater Biology, 59(4), 724-736. https://doi.org/10.1111/fwb.12299
McCune, B., & Grace, G. (2002). Analysis of Ecological Communities. Oregon: MjM Software Design.
McCune, B., & Mefford, M.J. (1999). PCORD. Multivariate Analysis of Ecological Data, Version 4. Oregon:MjM Software.
Minchin, P.R. (1987). An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio, 69(1-3), 89-107. https://doi.org/10.1007/BF00038690
Maindonald, J., & Braun, W.J. (2007). Data analysis and graphics using R: An example-based approach (2nd Eds.). Cambridge University Press.
Molder, A., Bernhardt, M., & Schmidt, R. (2008). Herb-layer diversity in deciduous forests: Raised by tree richness or beaten by beech?. Forest Ecology and Management, 256, 272–281. https://doi.org/10.1016/j.foreco.2008.04.012
Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., & Minchin, P.R. (2013). vegan: Community Ecology Package. R package version, 2(0-2), 1-295. (http://vegan.r-forge.r-project.org/)
Peres-Neto, P.R., & Jackson, D.A. (2001). How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia, 129, 2, 169–178. https://doi.org/10.1007/s004420100720
Pakgohar, N., Eshaghi Rad, J., Gholami, G.H., Alijanpour, A., & Roberts, D.W.A. (2021). Comparative Study of Hard Clustering Algorithms for Vegetation Data. Journal of Vegetation Science, e13042. https://doi.org/10.1111/jvs.13042
Ter Braak, C.J., & Prentice, I. C. (1988). A theory of gradient analysis. In Advances in Ecological Research Academic Press.
Roberts, D.W. (2015) Vegetation classification by two new iterative reallocation optimization algorithms. Plant Ecology, 216(5), 741-58. https://doi.org/10.1007/s11258-014-0403-2
Ruokolainen, L., & Salo, K. (2006).  Differences in performance of four ordination methods on a complex vegetation dataset. Annales Botanici Fennici, 43(4), 269-275.
Roberts, S. (2008). Transform your data. Nutrition, 24(5), 492-494. https://doi.org/10.1016/j.nut2008.001.004
Schuman, M., White, A., & Withman, W. (2003). The effect of harvest – Created gaps on plant species diversity composition and abundance in a Maine oak-pine forest. Forest Ecology and Management, 176(1-3), 543-561. https://doi.org/10.1016/S0378-1127(02)00233-5
Super, L., Vellend, M., & Bradfield, G. (2013). Urban ecology in action: vegetation changes in Pacific Spirit Regional Park, Vancouer, BC Canada. Davidsonia, 23(1), 21-31.
O'Hara, R., & Kotze, J. (2010). Do not log-transform count data. Nature Precedings, 1-1. https://doi.org/10.1038/npre.2010.4136.1
Tukey, J.W. (1977). Exploratory Data Analysis. London, Amsterdam, Don Mills, Ontario, Sydney:Addison-Wesley https://doi.org/10.1002/bimj.4710230408
Tabachnick, B.G., Linda, S.,  & Fidell. (2007). Using Multivariate Statistics (5th ed.). Boston: Pearson.  
Tichý, L., Hennekens, S.M., Novák, P., Rodwell, J.S., Schaminée, J.H., & Chytrý, M. (2020). Optimal transformation of species cover for vegetation classification. Applied Vegetation Science23(4), 710-717. https://doi.org/10.1111/avsc.12510
Uotila, A., & Kouki, J. (2005). Understorey vegetation in spruce-dominated forests in eastern Finland and Russian Karelia: Successional patterns after anthropogenic and natural disturbances. Forest Ecology and Management, 215, 113–137. https://doi.org/10.1016/j.foreco.2005.05.008
Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.
Xianping, Z., Mengben, W., Bo, S.h., & Yang, X. (2006). Quantitative classification and ordination of forest communities in Pangquangou National Nature Reserve. Acta Ecologica Sinica, 26(3), 754−761. https://doi.org/10.1016/S1872-2032(06)60013-9
Van der Maarel, E. (1979). Transformation of cover-abundance values in phytosociology and its effect on community similarity. Vegetatio, 39(2), 97–114. https://doi.org/10.1007/BF00052021
Zuur, A.F., Ieno, E., & Meesters, E. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1, 3-14. https://doi.org/10.1111/j.2041-210X.2009.00001.x
Volume 16, Issue 5
Winter 2025
Pages 45-55

  • Receive Date 10 May 2022
  • Revise Date 12 August 2024
  • Accept Date 13 October 2024