Modeling fine woody debris volume stock using logistic analyses in the Hyrcanian Kheiroudkenar research forest

Document Type : Scientific article

Authors

1 Assistant Professor, Forests and Rangelands Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, AREEO, Sari, I. R. Iran

2 Professor, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Mazandaran, Sari, I. R. Iran

Abstract

Background and objectives: Due to ecological and biological importance of fine woody debris (FWDs) in forest ecosystems, it is essential to be clarified influencing factors on the FWDs volume stock through appropriate model. One of the models can be decisively efficient for optimal management and control of the FWDs volume stock is the ordinal logistic model. It is expected that the FWDs have a significant contribution in the Hyrcanian forests where different types of plant communities and trees are widely distributed across altitude gradients. Thus, the aim of this research is to use rank logistic analysis to examine the impact of various factors on the volume accumulation of the FWDs in relation to the altitude gradient of the Khairudkanar research forest.
Methodology: The current study was conducted in the Kheiroud forest using the cluster sample plots implemented along the altitude (100- 1800 m). A total of 12 samples were arranged in an altitude gradient from sea level. These samples consisted of three circular plots, each with a radius of 7.32 meters. The circular plots were positioned in the form of a triangle, with azimuth angles of 0, 120, and 240 degrees. Additionally, another sample with the same area was placed at the center of this design. The distance between each of the circular plots in the cluster was 36.6 meters. In this study, the placement of sample plots was conducted randomly within the specified altitude range, with an interval of 150 meters. A total of 36 clusters were established, comprising 144 samples, within the research forest. These samples were categorized into three diameter classes for the FWDs: 1-2.5 cm, 2.5-4.5 cm, and 4.5-7.5 cm. To measure the diameter of these small dry trees, a linear transect was executed from the center of each circular sample plot. The transect had a fixed azimuth of 150 degrees. Specifically: The diameter layer within the range of 1-2.5 cm was measured at a distance of 4.27-1.6 meters, and For other diameter layers, the measurements were taken at distances ranging from 7.32 to 7 meters. Ultimately, a total of 4.27 meters were harvested during this investigation. During the evaluation process, a transect line was used to measure the FWDs diameter whose central axis was obstructed by the line. Additionally, the type of species, the decay or lack of decay of the FWDs were also recorded. However, the length of these FWDs was not measured in the field due to the evaluation method used. In the analysis, we employed Spearman’s test and calculated Spearman’s correlation coefficient. Additionally, we utilized Chi-square statistics and the dimensions of the consensus table to evaluate the significance of correlation intensity between variables. To model this relationship, we included all uncorrelated variables as input variables, irrespective of any co-linear relationships among the factor variables. Nominal and rank variables were introduced as factors with fixed effects, while quantitative variables served as side variables during the implementation of rank logistic analysis. To validate each model, we assessed various variables using the model fit index. Additionally, we considered the pseudo-explanation coefficients. The significance of the model and its calculation coefficients was examined using the Wald statistic. Furthermore, we evaluated the model’s accuracy in relation to estimated values and observations using Pearson and standard deviation tests.
Results: The research findings indicated that the average volume of the FWDs per hectare in the studied forest was 2.14, 6.01, and 16.23 cubic meters for the first, second, and third diameter classes, respectively. The results of logistic analyses based on the log-likelihood test (X2= 0.06; P > 0.05) and pseudo-R2 (Pesudo-R2 = 0.001) showed that the altitude gradient has no significant effect on the volume accumulations of the FWDs in different diameter classes. According to the Cramer's V and Spearman correlation tests, the results demonstrated that altitude gradient significantly correlated with the stand types, aspect, species and decay of the FWDs in the study forest. Moreover, the results obtained using Kramer's correlation coefficient to eliminate the effects of collinearity between input variables indicated that there is no significant relationship between the aspects and the species, and decay classes. Also, there were no significant correlations between the stand type and decay, and the FWDs species. Taking into account the non-collinearity and variance inflation factor (VIF), the final results of modeling showed that the FWDs species and decay have only significant effects on variations of the first diameter class of FWDs volume stock (Pesudo-R2= 0.24; P < 0.05; X2= 22.61).
Conclusion: On the basis of the Wald test, effects of the FWDs species were similarly significant, and the FWDs decay was inversely significant in association with the FWDs first diameter class response (P < 0.01). It appears that the amount of volume accumulation of the FWDs in thicker diameters was not affected by the factors that were introduced. This implies that many unforeseen events, such as natural disturbances of varying scales and intensity, as well as the inherent nature of forestry - including developmental stages - may be the foremost factors that significantly impact the volume inventory of the FWDs in the thicker diameters.

Highlights

Agresti, A., Analysis of ordinal categorical data. 1th ed.; Hoboken, N.J: Wiley 2010, ISBN 978-0470082898. p 424.

Bardelli, T.; Gómez-Brandón, M.; Ascher-Jenull, J.; Fornasier, F.; Arfaioli, P.; Francioli, D.; Egli, M.; Sartori, G.; Insam, H.; Pietramellara, G., Effects of slope exposure on soil physico-chemical and microbiological properties along an altitudinal climosequence in the Italian Alps. Science of the Total Environment 2017, 1(575), 1041-55.

Bihamta, M.R.; Zare Chahouki, M.A., Principles of statistics for the natural resources science. University of Tehran Press 2010, Tehran, Iran, pp. 320. (In Persian).

Delcourt, C.J.F.; Veraverbeke, S., Allometric equations and wood density parameters for estimating aboveground and woody debris biomass in Cajander larch (Larix cajanderi) forests of northeast Siberia. Biogeosciences 2022, 19 (18), 4499–4520.

Fagerland, M.W., How to test for goodness of fit in ordinal logistic regression models. The Stata Journal 2022, 17(3), 668-686.

Gómez-Brandón, M.; Ascher-Jenull, J.; Bardelli, T.; Fornasier, F.; Fravolini, G.;
Arfaioli, P.; Ceccherini, M.T.; Pietramellara, G.; Lamorski, K.; Slawiński,
C.; Bertoldi, D.; Egli, M.; Cherubini, P., Physicochemical and microbiological evidence of exposure effects on Picea abies – coarse woody debris at different stages of decay. Forest Ecology and Management 2017, 391, 376–389.

Harmon, M.E.; Woodall, C.W.; Fasth, B.; Sexton, J., Woody Detritus Density and Density Reduction Factors for Tree Species in the United States: A Synthesis.  Northern Research Station 2007, 84, pp 29.

Korboulewsky, N.; Bilger, I.; Bessad, A., How to Evaluate Downed Fine Woody Debris ncluding Logging Residues?. Forests 2021, 12 (7), 1-20.

Lelisho, M.E.; Wogi, A.A.; Tareke, S.A., Ordinal Logistic Regression Analysis in Determining Factors Associated with Socioeconomic Status of Household in Tepi Town, Southwest Ethiopia. The Scientific World Journal 2022, 1(1),1- 9. https://doi.org/10.1155/2022/2415692.

Marshall, P.L.; Davis, G.; LeMay, V.M. Unsing Line Intersect Sampling for Coarse Woody Debris; Technical Report. Forest Research B.C.: Nanaimo, BC, Canada, 2000; pp. 34.

Marvie-Mohadjer, M.R., Silviculture and forest tending. University of Tehran Press 2005, Tehran, Iran, pp. 378. (In Persian).

Masrouri, E.; Shataei, S.H.; Moayeri, M.H; Soosani, J.; Bagheri, R., Modeling of forest degradation extend using using physiographic and socio-economic variables (case study: a part of kaka-reza district in Khoram-Abad). Ecology of Iranian Forests 2015, 3 (5), 20-30. (In Persian) 

Moridi, M.; Malakshahi, M.; Etemad, V.; Sefidi, K., Accumulation of fine woody debris in the stem exclusion phase in mixed beech (Fagus orientlais Lipsky) stands. Forest Research and Development 2016, 1 (4), 351-361 (In Persian).

Poorbabaei, H.; Poorrostam, A.; Salehi, A., Modeling the Degradation of Hyrcanian Forests Using Logestic Regression Method (Case Study: Shenrood Forests, Guilan). Iranian Journal of Applied Ecology 2022, 11 (3), 37-46 (In Persian).

Rondeux, J.; Bertini, R.; Bastrup-Birk, A.; Corona, P.; Latte, N.; McRoberts, R.E.; Ståhl, G.; Winter, S.; Chirici, G., Assessing Deadwood Using Harmonized National Forest Inventory Data. Forest Science 2012, 58 (3), 269–283.

Sefidi, K.; Marviemohajer, M.R.; Etemad, V., Coarse and fine woody debris accumulation in mixed beech stands, Case study Gorazbon forests. Journal of Forest Sustainable development 2014, 1(2), 137-149 (In Persian).

Sefidi, K.; Mohadjer, M. M., Characteristics of coarse woody debris in successional stages of natural beech (Fagus orientalis) forests of Northern Iran. Journal of forest Science 2010, 56(1), 7-17.

Shadmani, S.; Ghodskhah daryaei, M.; Ghajar, I.; Heidari Safari Koichi, A., Modeling the Forest Degradation Degrees of Masal Watershed NO: 12 in Guilan Province, Using Logistic Regression. Journal of Natural Environment 2020, 73 (1), 49-61 (In Persian).

Teissier Du Cros, R.; Lopez, S., Preliminary study on the assessment of deadwood volume by the French national forest inventory. Annals of Forest Science 2009, 66, 302.

Van Wagner, CE., The line intersect method in forest fuel sampling. Forest Science 1968, 14(1), 20-26.

Woodall, C.; Williams, M.S., Sampling Protocol Estimation, and Analysis Procedures for the Down Woody Materials Indicator of the FIA Progam. North Central Research Station Forest Service U.S. Department of Agriculture 2005, pp. 47.

Woodall, C.W.; Walters, B.F.; Oswalt, S.N.; Domke, G.M.; Toney, C.; Gray, A.N., Biomass and carbon attributes of downed woody materials in forests of the United States. Forest Ecology and Management 2013, 305, 48–59.

Woodall, CW.; Liknes, GC., Climatic regions as an indicator of forest coarse and fine woody debris carbon stocks in the United States. Carbon balance and Management 2008, 3 (5), 1-8.

Zobeiri, M., Forest Biometry. Tehran University Press 2002, 2561, 411 pp (In Persian). 

Keywords

Main Subjects


Agresti, A., Analysis of ordinal categorical data. 1th ed.; Hoboken, N.J: Wiley 2010, ISBN 978-0470082898. p 424.
Bardelli, T.; Gómez-Brandón, M.; Ascher-Jenull, J.; Fornasier, F.; Arfaioli, P.; Francioli, D.; Egli, M.; Sartori, G.; Insam, H.; Pietramellara, G., Effects of slope exposure on soil physico-chemical and microbiological properties along an altitudinal climosequence in the Italian Alps. Science of the Total Environment 2017, 1(575), 1041-55.
Bihamta, M.R.; Zare Chahouki, M.A., Principles of statistics for the natural resources science. University of Tehran Press 2010, Tehran, Iran, pp. 320. (In Persian).
Delcourt, C.J.F.; Veraverbeke, S., Allometric equations and wood density parameters for estimating aboveground and woody debris biomass in Cajander larch (Larix cajanderi) forests of northeast Siberia. Biogeosciences 2022, 19 (18), 4499–4520.
Fagerland, M.W., How to test for goodness of fit in ordinal logistic regression models. The Stata Journal 2022, 17(3), 668-686.
Gómez-Brandón, M.; Ascher-Jenull, J.; Bardelli, T.; Fornasier, F.; Fravolini, G.;
Arfaioli, P.; Ceccherini, M.T.; Pietramellara, G.; Lamorski, K.; Slawiński,
C.; Bertoldi, D.; Egli, M.; Cherubini, P., Physicochemical and microbiological evidence of exposure effects on Picea abies – coarse woody debris at different stages of decay. Forest Ecology and Management 2017, 391, 376–389.
Harmon, M.E.; Woodall, C.W.; Fasth, B.; Sexton, J., Woody Detritus Density and Density Reduction Factors for Tree Species in the United States: A Synthesis.  Northern Research Station 2007, 84, pp 29.
Korboulewsky, N.; Bilger, I.; Bessad, A., How to Evaluate Downed Fine Woody Debris ncluding Logging Residues?. Forests 2021, 12 (7), 1-20.
Lelisho, M.E.; Wogi, A.A.; Tareke, S.A., Ordinal Logistic Regression Analysis in Determining Factors Associated with Socioeconomic Status of Household in Tepi Town, Southwest Ethiopia. The Scientific World Journal 2022, 1(1),1- 9. https://doi.org/10.1155/2022/2415692.
Marshall, P.L.; Davis, G.; LeMay, V.M. Unsing Line Intersect Sampling for Coarse Woody Debris; Technical Report. Forest Research B.C.: Nanaimo, BC, Canada, 2000; pp. 34.
Marvie-Mohadjer, M.R., Silviculture and forest tending. University of Tehran Press 2005, Tehran, Iran, pp. 378. (In Persian).
Masrouri, E.; Shataei, S.H.; Moayeri, M.H; Soosani, J.; Bagheri, R., Modeling of forest degradation extend using using physiographic and socio-economic variables (case study: a part of kaka-reza district in Khoram-Abad). Ecology of Iranian Forests 2015, 3 (5), 20-30. (In Persian) 
Moridi, M.; Malakshahi, M.; Etemad, V.; Sefidi, K., Accumulation of fine woody debris in the stem exclusion phase in mixed beech (Fagus orientlais Lipsky) stands. Forest Research and Development 2016, 1 (4), 351-361 (In Persian).
Poorbabaei, H.; Poorrostam, A.; Salehi, A., Modeling the Degradation of Hyrcanian Forests Using Logestic Regression Method (Case Study: Shenrood Forests, Guilan). Iranian Journal of Applied Ecology 2022, 11 (3), 37-46 (In Persian).
Rondeux, J.; Bertini, R.; Bastrup-Birk, A.; Corona, P.; Latte, N.; McRoberts, R.E.; Ståhl, G.; Winter, S.; Chirici, G., Assessing Deadwood Using Harmonized National Forest Inventory Data. Forest Science 2012, 58 (3), 269–283.
Sefidi, K.; Marviemohajer, M.R.; Etemad, V., Coarse and fine woody debris accumulation in mixed beech stands, Case study Gorazbon forests. Journal of Forest Sustainable development 2014, 1(2), 137-149 (In Persian).
Sefidi, K.; Mohadjer, M. M., Characteristics of coarse woody debris in successional stages of natural beech (Fagus orientalis) forests of Northern Iran. Journal of forest Science 2010, 56(1), 7-17.
Shadmani, S.; Ghodskhah daryaei, M.; Ghajar, I.; Heidari Safari Koichi, A., Modeling the Forest Degradation Degrees of Masal Watershed NO: 12 in Guilan Province, Using Logistic Regression. Journal of Natural Environment 2020, 73 (1), 49-61 (In Persian).
Teissier Du Cros, R.; Lopez, S., Preliminary study on the assessment of deadwood volume by the French national forest inventory. Annals of Forest Science 2009, 66, 302.
Van Wagner, CE., The line intersect method in forest fuel sampling. Forest Science 1968, 14(1), 20-26.
Woodall, C.; Williams, M.S., Sampling Protocol Estimation, and Analysis Procedures for the Down Woody Materials Indicator of the FIA Progam. North Central Research Station Forest Service U.S. Department of Agriculture 2005, pp. 47.
Woodall, C.W.; Walters, B.F.; Oswalt, S.N.; Domke, G.M.; Toney, C.; Gray, A.N., Biomass and carbon attributes of downed woody materials in forests of the United States. Forest Ecology and Management 2013, 305, 48–59.
Woodall, CW.; Liknes, GC., Climatic regions as an indicator of forest coarse and fine woody debris carbon stocks in the United States. Carbon balance and Management 2008, 3 (5), 1-8.
Zobeiri, M., Forest Biometry. Tehran University Press 2002, 2561, 411 pp (In Persian).