[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 9, Issue 18 (10-2021) ::
PEC 2021, 9(18): 69-92 Back to browse issues page
Assessment of plotless plant density methods of Astragalus albispinus Sirj & Bornm in various plant density intensities and distribution patterns
Hamid Jamali1 , Elham Ghehsareh Ardestani * , Ataollah Ebrahimi1 , Fatemeh Pordel1
1- Department of Natural Resources and Earth Sciences, Shahrekord University
Shahrekord University, Department of Natural Resources and Earth Sciences, Shahrekord University , elham.ghehsareh@nres.sku.ac.ir
Abstract:   (2257 Views)
Quantitative methods to the evaluation of vegetation are the basis for describing and analyzing the plant communities and density as one of the main characteristics plays an important role in evaluating the performance (short and long effects) of rangeland ecosystems and management decisions. Therefore, the efficiency of plotless plant density methods in different density intensities and distribution patterns have been examined using a simulated population based on the real density of Astragalus albispinus (control method) in a semi-arid region of Marjan, Boroujen. Simulation schemes in the three density intensities and three distribution patterns within eight 40×100 m sampling units were designed. The predictive precision and accuracy of seven distance-based methods in various density intensities and distribution patterns were investigated comparing with the control method and the ideal point error index. Overall, angel order method in regular distribution pattern and point centered quarter method in random distribution pattern were the best plotless density methods of plant populations in low density. Third closest individual method was the most accurate and precise method of density among the studied methods in other different density intensities and distribution patterns. Therefore, in selecting the most appropriate distance method for estimating the density, the type of distribution pattern and in some cases the intensity of plant species density should be considered to achieve an unbiased estimation of plant populationʼs density, accordingly, the different habitats of this plant species can be monitored and evaluated in terms of management and utilization.
Keywords: Vegetation, Distance methods of density estimation, Distribution pattern, Simulation, Ideal point error
Full-Text [PDF 966 kb]   (501 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/09/21 | Accepted: 2021/01/20 | Published: 2021/09/28
References
1. Bonham, C.D. 2013. Measurements for terrestrial vegetation. 2nd edition, John Wiley & Sons, UK
2. Byth, K., Ripley, B.D. 1980. On sampling spatial patterns by distance methods. Biometrics, 36: 279-284. https://doi.org/10.2307/2529979.
3. Cottam, G., Curtis, J.T. 1956. The use of distance measures in phytosociological sampling. Ecology, 37(3): 451-460. https://doi.org/10.2307/1930167.
4. Dobrowski, S.Z., Murphy, S.K. 2006. A practical look at the variable area transect. Ecology, 87: 1856-1860.
5. Elshorbagy, A., Corzo, G., Srinivasulu, S., Solomatine, D.P. 2010. Experimental investigation of the predictive capabilities of data-driven modeling techniques in hydrology - Part 2: Application. Hydrology and Earth System Sciences, 14(10): 1943-1961. https://doi.org/10.5194/hess-14-1943-2010.
6. Elzinga, C.L., Salzer, D.W., Willoughby, J.W. 1998. Measuring and monitoring plant population. Bureau of Land Management National Business Center, Denver, Colorado.
7. Engeman, R.M., Nielson, R.M., Sugihara, R.T. 2005. Evaluation of optimized variable area transect sampling using totally enumerated data sets. Envaiormentrics, 16(7): 767-772. https://doi.org/10.1002/env.736.
8. Engeman, R.M., Sugihara, R.T. 1998. Optimization of variable area transect sampling using Monte Carlo simulation. Ecology, 79(4): 1425-1434. https://doi.org/10.1890/0012-9658(1998)079[1425:OOVATS]2.0.CO;2.
9. Engeman, R.M., Sugihara, R.T., Pank, L.F., Dusenberry, W.E. 1994. A comparison of plotless density estimators using Monte Carlo simulation. Ecology, 75: 1769-1779. https://doi.org/10.2307/1939636.
10. Ghaderi, S., Fathipour, Y., Asgari, S. 2018. Population density and spatial distribution pattern of Tuta absoluta (Lepidoptera: Gelechiidae) on different tomato cultivars. Journal of Agricultural Science and Technology, 20(3): 543-556.
11. Hijbeek, R., Koedam, N., Islam Khan, M.N., Kairo, J.G., Schoukens, J., Dahdouh-Guebas, F. 2013. An evaluation of plotless sampling using vegetation simulations and field data from a Mangrove Forest. Plos One, 8(6): e67201-e67201. https://doi.org/:10.1371/journal.pone.0067201.
12. Islam Khan, M.N.I., Hijbeek, R., Berger, U., Koedam, N. 2016. An evaluation of the plant density estimator the Point-Center Quarter Method (PCQM) using Monte Carlo simulation. Plos One, 11(6): e0157985- e0157985. https://doi.org/10.1371/journal.pone.0157985.
13. Jamali, H., Ghehsareh Ardestani, E., Ebrahimi, A., Pordel, F. 2020. Comparing distance-based methods of measuring plant density in an arid sparse scrubland: testing field and simulated sampling. Environ Monit Assess 192, 343. https://doi.org/10.1007/s10661-020-08329-8.
14. Krebs, C.J. 2014. Ecological methodology, University of British Columbia, New York.
15. Maassoumi, A.A. 2000. Important notes on Astragalus subgenus Tragacantha Bunge in Iran. Journal of Botany, 8(2): 309-326.
16. Mitchell, K.W. 2007. Quantitative analysis by the point-centered quarter Method.Hobart and William Smith Colleges, Geneva.
17. Mulyana, B., Rohman, R., Purwanto, R.H. 2018. Application of point sampling method in estimation of stand basal area in community forest. Journal of Sylva Indonesiana, 1(1): 45-54.
18. Nath, V.D., Pelissier, R., Garcia, C. 2010. Comparative efficiency and accuracy of variable area transects versus square plot for sampling tree diversity and density. Agroforest Systems, 79: 223-236. https://doi.org/10.1007/s10457-009-9255-5.
19. Silva, L.B., Alves, M., Elias, R.B., Silva, L. 2017. Comparison of T-square, point centered quarter, and N-tree sampling methods in Pittosporum undulatum invaded woodlands. International Journal of Forestry Research, 2017: 1-13. https://doi.org/10.1155/2017/2818132.
20. White, N.A., Engeman, R.M., Sugihara, R.T., Krupa, H.W. 2008. A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated fild data set. BMC Ecology, 8(6): 1-11. https://doi.org/10.1186/1472-6785-8-6
21. Zhu, X., Zhang, J. 2009. Quartered neighbor method: A new distance method for density estimation. Frontiers of Biology in China, 4: 574-578. https://doi.org/10.1007/s11515-009-0039-0.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Jamali H, Ghehsareh Ardestani E, Ebrahimi A, Pordel F. Assessment of plotless plant density methods of Astragalus albispinus Sirj & Bornm in various plant density intensities and distribution patterns. PEC 2021; 9 (18) :69-92
URL: http://pec.gonbad.ac.ir/article-1-726-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 9, Issue 18 (10-2021) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
Persian site map - English site map - Created in 0.05 seconds with 37 queries by YEKTAWEB 4645