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2011 | Vol. 59, nr 4 | 823-828
Tytuł artykułu

Optimal method to determine density of the scarce vegetation in dry desert : Variable Area Transect (VAT) applied to saxaul (Haloxylon ammodenderon) community

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The assessment of the density and cover of very scarce vegetation in dry habitats may create methodological problems. The variable area transect method (VAT) is a potential labour-saving sampling method and an alternative to plot (quadrate) method. It allows for density estimation without the time-consuming studies associated with other plot-less density estimators. We used the method in a natural shrubland of Saxaul (Haloxylon ammodenderon C.A.M) to define optimum parameters include transect width and individual.s number to which, distance is measured. Three transect widths were chosen, 10-m, 15-m and 20-m and distances to the 3rd, 4th and 5th individual. Transect width affected the estimation, a 20-m width transect had the least relative bias (-0.5%), and a 10-m width sampling had the greatest bias (-20%). However, all methods underestimated the plant density. The most accurate estimation was with the 3rd plant distance and 20-m transect. As the VAT method is more efficient per unit effort in the field than the quadrate methods, it can be recommended for rapid assessment of desert communities density (like saxaul) especially when plants are dispersed at random.
Wydawca

Rocznik
Strony
823-828
Opis fizyczny
Bibliogr. 20 poz.,Fot., rys., tab.,
Twórcy
autor
autor
autor
autor
  • Department of Forest Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran, masoudtabari@yahoo.com
Bibliografia
  • 1. Dobrowski S.Z, Murphy S.H. 2006 – A practical look at the variable area transect – Ecology, 87: 1856–1860.
  • 2. Engeman R.M., Nielson R.M., Sugihara R.T 2005 – Evaluations of optimized variable area transect sampling using totally enumerated data sets – Environmentrics, 16: 767–772.
  • 3. Engeman R.M., Sterner R.T 2002 – A comparison of potential labor-saving sampling methods for assessing large mammal damage in corn – Crop Protection, 21: 101–105.
  • 4. Engeman R.M., Sugihara R.T .1998 – Optimizations of variable area transect sampling using Monte Carlo simulation – Ecology, 79: 1425–1434.
  • 5. Engeman R.M., Sugihara R.T., Pank L.F., Dusenberr y W.E .1994 – A comparison of Plotless density estimators using Monte Carlo simulation – Ecology, 75: 1769–1779.
  • 6. Fidelbis M.W., Mac Aller T.F 1993 – Methods for plant sampling – Department of Biology, San Diego State University, 6 pp.
  • 7. Foster R.B., Hernandez N.C., Kakudidi E.K., Burnham R.J 1998 – Rapid assessment of tropical plant communities using variable transects: an informal and practical guide – Dept. Botany, The Field Museum, Chicago, 13 pp.
  • 8. Iran Nejad Parizi M.H., Sarhangzadeh J., Azimzadeh H., Elmi M., Hosseini S.Z., Hazeri F 2006 – Capabilities and difficulties of Siahkooh protected area Yazd – Iranian J. Environ. Sc. 39: 89–100.
  • 9. Kinzie R.A. 2005 – Techniques and practice of ecological sampling – Dept. Zoology, Hawaii University, http://www.hawaii.edu/~kinzie/documents/439L/lab2.doc
  • 10. Krebs C.H.J. 1999 – Ecological Methodology – University of Columbia, pp. 20.
  • 11. Mark A.F., Essler A.E 1970 – An assessment of the point-centered quarter method of Plotless sampling in some New Zealand forests – Proceedings of the New Zealand Ecological Society, 17: 106–110.
  • 12. Nath C.H.D., Pelissier R., Garcia C. 2009 – Comparative efficiency and accuracy of variable area transects versus square plots for sampling tree diversity and density – Agroforest Syst, Springer publications, pp. 12.
  • 13. Parker K.R. 1979 – Density estimation by variable area transect – J. Wildlife Manag. 43: 484–492.
  • 14. Picard N., Bar-Hen A. 2007 – Estimation of density of a clustered point pattern using a distance method – Environ Ecol Stat. 14: 341–353.
  • 15. Sabeti K. 1994 – Forests, trees and shrubs of Iran – Yazd University Press, pp. 383–387.
  • 16. Scott C.T., Gove G.H. 2002 – Forest inventory (In: Encyclopedia of environmentrics, Ed: A.H. El-Shaarawi) – Piegorsch, Wiley, Chichester, pp. 814–820.
  • 17. Sheng Y., Zheng W., Pei K., Ma K. 2005 – Genetic variation within and among populations of dominant desert tree Haloxylon ammodenderon in China – Annals of Botany, 96: 245–252.
  • 18. Thomas L., Buckland S.T., Burnham K.P., Anderson D.R., Laake J.L., Borchers D.L., Strindberg S. 2002 – Distance sampling (In: Encyclopedia of environmentrics, Ed: A.H. El-Shaarawi) – Piegorsch, Wiley, Chichester, pp. 544–552.
  • 19. 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 data sets – BMC Ecology, 8: pp. 11.
  • 20. Zobeiri M. 1994 – Forest inventory – Tehran University Press, 400 pp.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BGPK-3625-4006
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