Indicator species analysis by considering all possible combinations of site groups

Document Type : Research Paper

Authors

1 Ph.D. Student, Dept. of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran.

2 Associate Prof., Dep. of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran.

10.22034/ijf.2023.363038.1892

Abstract

Introduction: In this research, the capability of indicator species analysis with the emphasis of the group combination concept for determining the pre-classified plant communities was investigated.
Material and Methods: For this purpose, the yew ecological groups of sites in the Jahan Nama Protected Area (JNPA) yew forests were firstly classified by TWINSPAN method based of vegetation abundance data and then the indicator species of each ecological groups were derived by using group-equalized indicator value analysis as well as the groups combination concept. The indicator value of the species-group was analyzed by ‘multipatt’ function and its statistical significance was evaluated using the permutation test in the ‘indicspecies’ package. Finally, the importance of indicator species analysis results by considering all possible combinations of JNPA yew groups of sites in order to assigning each ecological groups to pre- classified yew communities in the Braun-Blanquet hierarchical synoptic table were illustrated.
Findings: Combining groups of sites in the indicator species analysis not only provide indicator species in target combined groups but also presenting the zero fidelity of those species to non-target groups of sites and allows the differentiation and distinction of plant communities/groups of sites based on both features of prescence and absent of indicator species. While, in the typical indicator special analysis only species-target groups of sites association is taken into account. Results also showed that indicator species analysis by combining groups of sites not only provided the pre- classified JNPA yew communities which were determined by Braun-Blanquet synoptic table but also provides their hierarchical order and also higher syntaxa at the next hierarchical rank in Braun-Blanquet synoptic tables as well as dendrogram.
Conclusion: Consequently, the results of this research reiterate that indicator species analysis by combining groups of sites compared with individual ones provides the more reliable indicator species lists for characterizing plant communities in phytosociological studies. So, we believe that applying indicator species analysis by combining groups of sites approach could be useful in the automatic (expert system) classification of plant communities which is inevitable while an extensive database of vegetation composition is available. Consideration of combining groups of sites in indicator species analysis due to providing common occurrence of indicator species at different hierarchical plant community classification, will improve the capability of expert system method in characterizing of the next higher syntaxa than association as the basic unit in the braun-blanquet synoptic table method.

Keywords

Main Subjects


 
Asadi, H., Esmailzadeh, O., De Cáceres, M., & Hosseini, S.M. (2021). The assignment of relevés to pre-existing vegetation units: a comparison of approaches using species fidelity. Annals of Forest Science, 78(1), 1-23. https://doi.org/10.1007/s13595-020-01017-0
Asadi, H., Esmailzadeh, O., Hosseini, S.M., Asri, Y., & Zare, H. (2016). Application of Cocktail method in vegetation classification. Taxonomy and Biosystematics, 8(28), 21-38. (In persian)
Barkman, J. (1989). Fidelity and character-species, a critical evaluation. Vegetatio, 85(1), 105-116. https://doi.org/10.1007/BF00042260
Burger, J. (2006). Bioindicators: types, development, and use in ecological assessment and research. Environmental Bioindicators, 1(1), 22-39. https://doi.org/10.1080/15555270590966483
Cáceres, M.D., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566-3574. https://doi.org/10.1890/08-1823.1
Chytrý, M., Tichý, L., Holt, J., & Botta‐Dukát, Z. (2002). Determination of diagnostic species with statistical fidelity measures. Journal of Vegetation science, 13(1), 79-90. https://doi.org/10.1111/j.1654-1103.2002.tb02025.x
De Cáceres, M. (2013). How to use the indicspecies package (ver. 1.7. 1). R Proj, 29.
De Caceres, M., Jansen, F., & De Caceres, M.M. (2016). Package ‘indicspecies’. indicators, 8, 1.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674-1684. https://doi.org/10.1111/j.1600-0706.2010.18334.x
Dufrêne, M., & Legendre, P. (1997). Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological monographs, 67(3), 345-366. https://doi.org/10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2
Esmailzadeh, O., & Asadi, H. (2014). Total phi fidelity index (TPFI) as a new algorithm in plant communities analysis. Iranian journal of forest, 6(2), 215-232.‌ (In persian)
Esmailzadeh, O., & Soofi, M. (2022). Syntaxonomy and gradient analysis of common yew (Taxus baccata L.) communities in eastern Hyrcanian forests, northern Iran. Ecological Research, 37(3), 325-343. https://doi.org/10.1111/1440-1703.12291
Gholizadeh, H., Naqinezhad, A., & Chytrý, M. (2020). Classification of the Hyrcanian forest vegetation, Northern Iran. Applied Vegetation Science, 23(1), 107-126. https://doi.org/10.1111/avsc.12469
Hamzeh'ee, B., Naqinezhad, A., Attar, F., Ghahreman, A., Assadi, M., & Prieditis, N.. (2008). Phytosociological survey of remnant Alnus glutinosa ssp. barbata communities in the lowland Caspian forests of northern Iran. Phytocoenologia, 117-132. https://doi.org/10.1127/0340-269X/2008/0038-0117
Hill, M.O. (1979). A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. TWINSPAN.
Karami‑Kordalivand, P., & Esmailzadeh, O. (2021). Application of expert systems in vegetation classification. Iranian Journal of Forest and Poplar Research, 29(3), 214-229. (In persian)
Khabazi, F., Esmailzadeh, O., & Najafi, A. (2019). Supervised classification of Buxus hyrcana plant communities using artificial neural network. Iranian Journal of Forest, 11(3), 387-400. (In persian)
Landres, P.B., Verner, J. & Thomas, J.W. (1988). Ecological uses of vertebrate indicator species: a critique. Conservation biology, 2(4), 316-328. https://doi.org/10.1111/j.1523-1739.1988.tb00195.x
Landucci, F., Tichý, L., Šumberová, K., & Chytrý, M. (2015). Formalized classification of species‐poor vegetation: a proposal of a consistent protocol for aquatic vegetation. Journal of Vegetation Science, 26(4), 791-803. https://doi.org/10.1111/jvs.12277
Legendre, P., & Legendre, L. (2012). Numerical ecology. Elsevier.
McGeoch, M.A., & Chown, S.L. (1998). Scaling up the value of bioindicators. Trends in Ecology & Evolution, 13(2), 46-47. https://doi.org/10.1016/S0169-5347(97)01279-2
Mucina, L. (1997). Classification of vegetation: Past, present and future. Journal of Vegetation Science, 8(6), 751-76. https://doi.org/10.2307/3237019
Niemi, G.J., & McDonald, M.E. (2004). Application of ecological indicators. Annual Review of Ecology, Evolution, and Systematics, 35, 89-111. https://doi.org/10.1146/annurev.ecolsys.35.112202.130132
Ricotta, C., Acosta, A.T., Caccianiga, M., Cerabolini, B.E., Godefroid, S., & Carboni, M. (2020). From abundance-based to functional-based indicator species. Ecological Indicators, 118, 106761. https://doi.org/10.1016/j.ecolind.2020.106761
Ricotta, C., Pavoine, S., Cerabolini, B.E., & Pillar, V.D. (2021). A new method for indicator species analysis in the framework of multivariate analysis of variance. Journal of Vegetation Science, 32(2), e13013. https://doi.org/10.1111/jvs.13013
Saberi, B.G., Esmailzadeh, O., & Asadi, H. (2020). Evaluating the different indicator species analysis in the classification of plant communities. Iranian Journal of Forest, 12(4), 541-555. (In persian)
Siddig, A.A., Ellison, A.M., Ochs, A., Villar-Leeman, C., & Lau, M.K. (2016). How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecological Indicators, 60, 223-230. https://doi.org/10.1016/j.ecolind.2015.06.036
Thuiller, W., Lavorel, S., Midgley, G., Lavergne, S., & Rebelo, T. (2004). Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron taxa. Ecology, 85(6), 1688-1699. https://doi.org/10.1890/03-0148
Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation science, 17(6), 809-818. https://doi.org/10.1111/j.1654-1103.2006.tb02504.x
Tichý, L., Chytrý, M., & Landucci, F. (2019). GRIMP: A machine‐learning method for improving groups of discriminating species in expert systems for vegetation classification. Journal of Vegetation Science, 30(1), 5-17. https://doi.org/10.1111/jvs.12696
Tsiripidis, I., Bergmeier, E., Fotiadis, G., & Dimopoulos, P. (2009). A new algorithm for the determination of differential taxa. Journal of Vegetation Science, 20(2), 233-240. https://doi.org/10.1111/j.1654-1103.2009.05273.x