:: Volume 8, Issue 17 (2-2021) ::
PEC 2021, 8(17): 123-138 Back to browse issues page
Study of the effect of forest stand spatial pattern on results of different estimators of the nearest individual distance method (Case study: in Forests of Chaharzbar Olia village in Kermanshah province)
Atefe sadat Haghani1 , Reza Hosein Heidary1 , Soheila Aghabeigi Ami *
1- Department of Natural Resources, Faculty of Agriculture, Razi University, Kermanshah, Iran
Razi university, Department of Natural Resources, Faculty of Agriculture, Razi University, Kermanshah, Iran , saghabeigi@yahoo.com
Abstract:   (2267 Views)
Estimators of distance sampling methods based on random spatial pattern of trees are bias in non-random spatial pattern. The purpose of this study was to evaluate the effect of spatial pattern of oak trees on the results of quantitative characteristics estimation of three different stands by nearest-individual method estimators in Chaharzabar  forests in Islamabad, west of Kermanshah province. For this purpose, three stands were selected in the area and within each stand half one hectare plot was identified. After full callipering of trees in the stands, according to completely randomized design, 30 samples with  nearest-individual method in each stand carried out. Then, spatial pattern, density, canopy cover and tree height were calculated with nearest-individual sampling method. Results showed that from the three studied stands, two stands had random spatial pattern and one had clumped pattern. Of  five estimators of nearest –individual method based on acceptable accuracy criteria (range ± 10%) for estimate density of trees, the formula proposed by Byth and Ripley in the random spatial pattern and the formula suggested by Batcheler and Bell in aggregate pattern were the most appropriate estimators. In order to estimate trees canopy cover percentage, the formulas suggested by Byth and Ripley, Cottam et al. and Morisita in the random spatial pattern were appropriate. However,  none of them were appropriate to estimate the canopy percentage of trees in clumped spatial pattern. To estimate the height of the trees, since the height was calculated independently of the estimators, it was found that estimation of the tree height by this sampling method yielded good results in both random and clumped patterns. Finally, it can be concluded that the spatial pattern of the trees was effective on the estimators of the nearest-individual distance sampling method.

 
Keywords: Spatial pattern, Canopy, Density, Zagros forests, nearest-individual method
Full-Text [PDF 200 kb]   (439 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/10/7 | Accepted: 2020/08/6 | Published: 2021/03/12
References
1. Batcheler, C.L., & D.J. Bell, 1970. Experiments in estimating density from joint-point and nearest neighbour distances, Proceedings of the New Zealand Ecological Society, 17:111-117.
2. Byth, K. &Ripley, B.D., 1980 On sampling spatial patterns by distance methods. Biometrics, 36, 279-84.
3. Cottam, G., J.T. Curtis & B. wild Hale, 1953. Some sampling characteristics of a population of randomly dispersed individuals, Ecology, 34(4): 741-757.
4. Engerman, R.M., Sugihara, R.T., Pank, L.F. and Dusenberry, W.E., 1994. A comparison of plotless density estimators using Monte Carlo simulation. Ecology, 75(6): 1769-1779.
5. Ghalandarayeshi, S., Nord-Larsen, T., Johannsen, V. K., & Larsen, J. B. (2017). Spatial patterns of tree species in Suserup Skov – a semi-natural forest in Denmark. Forest Ecology and Management, 406, 391-401.
6. Kissa, D.O. and Sheil, D., 2012. Visual detection based distance sampling offers efficient density estimation for distinctive low abundance tropical forest tree species in complex terrain. Forest Ecology and Management, 263: 114-121.
7. Krebs, C.J. 1989. Ecological Methodology, Harper Collins: New York, 653 pp.
8. Krebs, C.J., 1999. Ecological Methodology. Second Edition. Addison Welsey Educational Publisher Inc., Benjamin/Cummings imprint, 581 p.
9. Laycock, W.A. & C.L. Batcheler, 1975. Comparison of distance measurement techniques for sampling Tussock grassland species in New Zealand, J. Range Manage, 28(1): 235-239.
10. Morisita, M. 1953. Estimation of population density by spacing method. Memoirs of the Faculty of Science KyushuUniversity, Series E, Biology 1:187-197.
11. Morisita, M., 1957. A new method for the estimation of density by the spacing method applicable to non randomly distributed populations, Physoil Ecology, 7(2): 134-144.
12. Roger B. Johnson and William J. Zimmer 1985. A More Powerful Test for Dispersion Using Distance Measurements, Ecology, 66(5): 1669 – 1675.
13. Wong, D.W.S. and Lee, J., 2005. Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS. John Wiley and Sins. 463 p.
14. Ludwig,J.A. and Reynolds J.F.,1988. Statistical Ecology, A primer in methods and computing. John Wiley and sons.337pp


XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 8, Issue 17 (2-2021) Back to browse issues page