Volume 10, Issue 20 (11-2022)
ifej 2022, 10(20): 23-32 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
soliemannezhad S, Es-hagh Nimvari M, safavi S R, Kazemnezhad F, Sheykholeslami A. (2022). Modeling of Corticolouslichen Spatial diversity in Forest Stands using Geographically Weighted Regression. ifej. 10(20), 23-32. doi:10.52547/ifej.10.20.23
URL: http://ifej.sanru.ac.ir/article-1-422-en.html
URL: http://ifej.sanru.ac.ir/article-1-422-en.html
Shabnam Soliemannezhad 
, Majid Es-hagh Nimvari , Seyed Reza Safavi , Farid Kazemnezhad , Ali Sheykholeslami
, Majid Es-hagh Nimvari , Seyed Reza Safavi , Farid Kazemnezhad , Ali Sheykholeslami
Department of Forestry, Faculty of Natural Resources, Islamic Azad University, Chalus Branch, Chalus, Iran
Abstract: (1893 Views)
Extended Abstract
Introduction and Objective: Biodiversity has a very important role in the sustainability and self-regulation of ecosystems and is used as an indicator to compare the ecological status of forest ecosystems. Corticolous lichen are one of the most common components of biodiversity in the forest community. The high diversity of Corticolous lichenes in an area indicates the biodiversity and sustainability of an ecosystem. One of the most important approaches to interpreting and tracking spatial variations of biodiversity is to use a regression model. The aim of this study is to model the diversity of Corticolous lichen species.
Material and Methods: This research was carried out in section 2 of Shurab of Golband forestry projects in Noshahr city (Mazandaran province). Firstly, 54 samples were collected using rotating forest and selective sampling method. Then the Corticolous lichenes species in the parts were identified. Spatial location of all sample plots was recorded using GPS. All skin lichens were collected in each sample plot. Collected specimens were identified using valid lithological sources as well as laboratory methods. In this study, to determine the biodiversity in the next step, the values of Shannon Wiener and N1-Hill diversity indices and J-Pilo uniformity index were calculated for each of the sample plots. Then, a map of geographical and topographic factors affecting diversity including distance from road and distance from waterway and slope, height, wetting index, flow strength index and erodibility factor was prepared. weighted geographical regression and Ordinary Least Squares for modeling were used.
Results: In this study, 17 species of lichens belonging to 14 genera and 11 families were identified. The results showed that the weighted geographical regression for Shannon Wiener, N1 Hill and J Pilo indices based on the coefficients of explanation coefficient and the modified Akaike information criterion had better results than the Ordinary least squares regression. The amount of lichen diversity based on Shannon-Wiener and N1 Hill indices was calculated with a range from 1.24 to 2.98 and 2.06 to 6.99, respectively, and the amount of J Pilo uniformity index was 0.205 to 0.830. Also, the results of Moran I index showed that the spatial correlation in dermatological lichens is significant and their distribution pattern is clustered.
Conclusion: In general, the results of this study showed that the geographical weighted regression method has a relatively good capability in modeling the spatial diversity of bark lichen species in forest stands. This regression model can be used to model lichen diversity.
Introduction and Objective: Biodiversity has a very important role in the sustainability and self-regulation of ecosystems and is used as an indicator to compare the ecological status of forest ecosystems. Corticolous lichen are one of the most common components of biodiversity in the forest community. The high diversity of Corticolous lichenes in an area indicates the biodiversity and sustainability of an ecosystem. One of the most important approaches to interpreting and tracking spatial variations of biodiversity is to use a regression model. The aim of this study is to model the diversity of Corticolous lichen species.
Material and Methods: This research was carried out in section 2 of Shurab of Golband forestry projects in Noshahr city (Mazandaran province). Firstly, 54 samples were collected using rotating forest and selective sampling method. Then the Corticolous lichenes species in the parts were identified. Spatial location of all sample plots was recorded using GPS. All skin lichens were collected in each sample plot. Collected specimens were identified using valid lithological sources as well as laboratory methods. In this study, to determine the biodiversity in the next step, the values of Shannon Wiener and N1-Hill diversity indices and J-Pilo uniformity index were calculated for each of the sample plots. Then, a map of geographical and topographic factors affecting diversity including distance from road and distance from waterway and slope, height, wetting index, flow strength index and erodibility factor was prepared. weighted geographical regression and Ordinary Least Squares for modeling were used.
Results: In this study, 17 species of lichens belonging to 14 genera and 11 families were identified. The results showed that the weighted geographical regression for Shannon Wiener, N1 Hill and J Pilo indices based on the coefficients of explanation coefficient and the modified Akaike information criterion had better results than the Ordinary least squares regression. The amount of lichen diversity based on Shannon-Wiener and N1 Hill indices was calculated with a range from 1.24 to 2.98 and 2.06 to 6.99, respectively, and the amount of J Pilo uniformity index was 0.205 to 0.830. Also, the results of Moran I index showed that the spatial correlation in dermatological lichens is significant and their distribution pattern is clustered.
Conclusion: In general, the results of this study showed that the geographical weighted regression method has a relatively good capability in modeling the spatial diversity of bark lichen species in forest stands. This regression model can be used to model lichen diversity.
Keywords: Biodiversity, Geographical weighted regression, Ordinary least squares, Moran I index, Shorab
Type of Study: Research |
Subject:
اکولوژی جنگل
Received: 2021/02/11 | Accepted: 2021/06/15 | Published: 2022/10/23
Received: 2021/02/11 | Accepted: 2021/06/15 | Published: 2022/10/23
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
