The Response Curve of Beech Tree (Fagus Orientalis Lipsky.) in Relation to Environmental Variables Using Generalized Additive Model in Khayroud Forest, Nowshahr

Document Type : Complete scientific research article

Authors

Abstract

The Response Curve of Beech Tree (Fagus Orientalis Lipsky.) in Relation to Environmental Variables Using Generalized Additive Model in Khayroud Forest, Nowshahr

Abstract
Background and objectives:
One of the main fields of interest in vegetation ecology is the analysis and understanding of vegetation-site relationships, particularly the response of species to underlying ecological gradients. Many attempts have been done in linking the performance of plant species to environmental variables since last decades. This study aims at investigating the response curve of beech tree to environmental variables using generalized additive model.
Materials and methods:
For this purpose, a stratified sampling method based on landform was used to locate 114 0.1 ha circular sample plots in beech dominated forests in experimental and educational forest of Kheyrud, Nowshar. The mean height of five largest diameter trees within each plot was considered as dominant height. Elevation above sea level, geographical aspect and slope of the ground were also recorded or measured. At the center of plot, soil samples from 0-10 cm depth were taken for analyzing soil texture, bulk density and saturation moisture, pH, lime (%), nitrogen (%), carbon and organic matter (%), potassium, calcium, magnesium and phosphorous. By using generalized additive model and mgcv package in R statistical software, the response curve of beech tree were individually and simultaneously analyzed. Due to the nature of response variable, Gaussian distribution and identity link function were selected for generalized additive model.
Results:
The comparison of response curves resulted from GAM for explanatory variables individually and simultaneously showed that there are considerable differences in the shape of response curve. There are also differences in significance of predictors among these two approaches. By using GAM for each explanatory variable individually indicated that altitude, slope, radiation index, clay, silts, sand, nitrogen, saturation moisture, carbon and organic matter, pH, phosphorous and potassium are significant (P < 0.05). Considering all non-collinear predictors showed that altitude, ration index, sand, bulk density, nitrogen, CN, pH and phosphorous are significant variables on beech dominant height in generalized additive model.
Conclusion:
The results of this research imply that if the study aims at investigating only the shape of response curve, considering simultaneously all predictors will present the precise description of species behavior to environmental variables. But if the researcher wants to extract ecological optimum and tolerance for the species besides the shape of response curve, the behavior of species to predictors individually would be better choice.

Keywords

References

1. Aertsen, W., Kint, V., Van Orshoven, J., Özkan, K., and Muys, B. 2010. Comparison and
ranking of different modelling techniques for prediction of site index in Mediterranean
mountain forests, Ecological Modeling. 221: 8. 1119-1130.
2. Ahmadi, K., Alavi, S.J., and Tabari Kouchaksaraei, M. 2015. Evaluation of oriental beech
(Fagus orientalis L.) site productivity using generalized additive model. Iranian Journal of
Forest. 7: 1. 17-32. (In Persian)
3. Alavi, S.J., Zahedi Amiri, Gh., Rahmani, R., Marvi Mohajer, M., Moyes, B., and Fathi, J.
2012. Extracting Ecological Optimum and Amplitude of Fagus orientalis along
environmental gradients in Kheyrud Forest, Nowshahr. Journal of Natural Environment
(Iranian Journal of Natural Resources). 64: 4. 399-415. (In Persian)
4. Alavi, S.J., Zahedi Amiri, Gh., Rahmani, R., Marvi Mohajer, M., Moyes, B., and Nouri, Z.
2013. Investigation on the response of Fagus orientalis Lipsky to some environmental
variables using beta function and its comparison with Gaussian function (Case study:
Kheyrud forest research station). Iranian Journal of Forest. 5: 2. 161-171. (In Persian)
5. Alder, D. 1980. Forest Volume Estimation and Yield Prediction, Food and Agriculture
Organization of the United Nations, Rome, 194p.
6. Bergès, L., Chevalier, R., Dumas, Y., Franc, A., and Gilbert, J. 2005. Sessile oak (Quercus
petraea Liebl.) site index variations in relation to climate, topography and soil in even-aged
high-forest stands in northern France, Annals of forest science. 62: 5. 391-402.
7. Bongers, F., Poorter, L., Rompaey, R.S.A.R., and Parren, M. 1999. Distribution of Twelve
Moist Forest Canopy Tree Species in Liberia and Côte d'Ivoire: Response Curves to a
Climatic Gradient. Journal of Vegetation Science. 10: 3. 371-382.
8. Comstock, J.P., and Ehleringer, J.R. 1992. Plant adaptation in the Great Basin and Colorado
plateau, Western North American Naturalist, 52(3): 195-215.
9. Cox, C.B., Ian, N.H., and Peter, D.M. 1973. Biogeography: An ecological and Publisher,
Alterra, 2007. 20 pages. Evolutionary approach. Blackwell Scientific Publication, 179p.
10. Elith, J., Leathwick, J.R., and Hastie, T. 2008. A working guide to boosted regression trees,
Journal of Animal Ecology. 77: 4. 802-813.
11. Franklin, J. 1998. Predicting the distribution of shrub species in southern California from
climate and terrain-derived variables. Journal of Vegetation Science. 9: 733-748.
12. Ghanbari, F., Shataee, Sh., Dehghani, A.A., and Ayoubi, Sh. 2009. Tree density estimation
of forests by terrain analysis and artificial neural network. Journal of Wood and Forest
Science and Technology. 16: 4. 25-42. (In Persian)
13. Guisan, A., Edwards, T.C., and Hastie, T. 2002. Generalized linear and generalized additive
models in studies of species distributions: setting the scene, Ecological Modeling. 157: 2. 89-
100.
14. Gulisashvili, V.Z., Makhatadze, L.B., and Prilipko, L.I. 1975. Vegetation of the
Caucasus. Moscow, 73-86.
15. Habibi Kaseb, H. 1992. Fundamentals of forest soil science. Tehran University. Press, 424p.
(In Persian)
16. Habibi, H. 1974. Investigation of influence of soil texture on Beech trees’ growth rate in
Iran. Journal of Iranian Natural Resources. 31: 61- 69. (In Persian)
17. Hastie, T., and Tibshirani, R. 1990. Non-parametric logistic and proportional odds
regression, Applied statistics. 260-276.
18. Heegaard, E. 2002. The outer border and central border for species–environmental
relationships estimated by non-parametric generalised additive models. Ecological
Modelling. 157: 2. 131-139.
19. Heikkinen, J., and Mäkipää, R. 2010. Testing hypotheses on shape and distribution of
ecological response curves. Journal of Ecological Modelling. 221: 3. 388-399.
20. Hogg, B.W., and Nester, M.R. 1991. Effect of stocking rate on predominant height of young
Caribbean pine plantations in coastal Queensland, Australian Forestry. 54: 3. 134-138.
21. Huisman, J., Olff, H., and Fresco, L.F.M. 1993. A hierarchical set of models for species
response analysis. Journal of Vegetation Science. 4: 1. 37-46.
22. Jafari Haghighi, M. 1382. Methods of soil analysis sampling and important physical. Nedaye
Zoha Press, 236p. (In Persian)
23. Jansen, F., and Oksanen, J. 2013. How to model species responses along ecological
gradients–Huisman–Olff–Fresco models revisited. Journal of Vegetation Science. 24: 6.
1108-1117.
24. Jongman, R.H.G., ter Braak, C.J.F., and van Tongeren, O.F.R. 1995. Data Analysis in
Community and Landscape Ecology. Cambridge University Press, 299p.
25. Kent, M. 2011. Vegetation description and data analysis: a practical approach. John Wiley
and Sons, 414p.
26. Lawesson, J.E., and Oksanen, J. 2002. Niche characteristics of Danish woody species as
derived from coenoclines. Journal of Vegetation Science. 13: 2. 279-290.
27. Marvi Mohajer, M. 2004. Silviculture. Tehran University. Press, 387p. (In Persian)
28. Marvi Mohajer, M. 1976. Evaluation of quality properties of the beech forests northern Iran.
Iranian Journal of Natural Resources. 34. 77-96. (In Persian)
29. Mataji, A. 2003. Site classification based on plant association, stand structure and soil
properties in natural forests (Case study in Khyroud forests of Iran). Ph.D. Thesis. Islamic
Azad University, 165p. (In Persian)
30. Miller, J., and Franklin, J. 2002. Modeling the distribution of four vegetation alliances using
generalized linear models and classification trees with spatial dependence. Ecological
Modelling. 157: 2. 227-247.
31. Oksanen, J., and Minchin, P.R. 2002. Continuum theory revisited: what shape are species
responses along ecological gradients? Ecological Modelling. 157: 2. 119-129.
32. R Development Core Team. 2014. R: A language and environment for statistical computing.
R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org
33. Sagheb Talebi, Kh., Sajedi, T., and Yazdian, F. 2014. Forests of Iran. Research Institute of
Forests and Rangelands Press, 28p. (In Persian)
34. Saie, K. 1948. Silviculture, volume 1. University of Tehran Press, 339p. (In Persian)
35. Sarmadian, F., and Jafari, M. 2001. Investigation of forest soil in Educational and
Experimental forest of Tehran University. Journal of Iranian Natural Resources, Special
Issue. 111p. (In Persian)
36. Shabani, S., Akbarinia, M., Jalali, S.G., and Aliarab, A.A. 2011. Relationship between Soil
Characteristics and Beech Regeneration Density in Canopy Gaps with Different Sizes.
Journal of Wood and Forest Science and Technology. 18: 3. 63-78. (In Persian)
37. Vetaas, O.R. 1993. Spatial and Temporal Vegetation Changes along Moisture Gradient in
Northeastern Sudan. Biotropica. 25: 2. 164-175.
38. Wang, G. 2000. Use of understory vegetation in classifying soil moisture and nutrient
regimes. Journal of Forest Ecology and Management. 129: 3. 93-100.
39. Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. Chapman and
Hall/CRC press, 384p.
40. Yee, T.W., and Mitchell, N.D. 1991. Generalized additive models in plant ecology, Journal
of Vegetation Science. 2: 5. 587-602.
Journal of Wood and Forest Science and Technology
Volume 24, Issue 1
June 2017
Pages 29-59

Files

Share

How to cite

Statistics