Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel

Citation
Y. Carmel et R. Kadmon, Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel, PLANT ECOL, 145(2), 1999, pp. 243-254
Citations number
53
Categorie Soggetti
Environment/Ecology
Journal title
PLANT ECOLOGY
ISSN journal
13850237 → ACNP
Volume
145
Issue
2
Year of publication
1999
Pages
243 - 254
Database
ISI
SICI code
1385-0237(199912)145:2<243:EOGATO>2.0.ZU;2-C
Abstract
The dynamics of Mediterranean vegetation over 28 years was studied in the N orthern Galilee Mountains, Israel, in order to identify and quantify the ma jor factors affecting it at the landscape scale. Image analysis of historic al and current aerial photographs was used to produce high resolution digit al vegetation maps (pixel size = 30 cm) for an area of 4 km(2) in the Galil ee Mountains, northern Israel. GIS tools were used to produce corresponding maps of grazing regime, topographic indices and other relevant environment al factors. The effects of those factors were quantified using a multiple r egression analyses. Major changes in the vegetation occurred during the per iod studied (1964-1992); tree cover increased from 2% in 1964 to 41% in 199 2, while herbaceous vegetation cover decreased from 56% in 1964 to 24% in 1 992. Grazing, topography and initial vegetation cover were found to signifi cantly affect present vegetation patterns. Both cattle grazing and goat gra zing reduced the rate of increase in tree cover, yet even intensive grazing did not halt the process. Grazing affected also the woody-herbaceous veget ation dynamics, reducing the expansion of woody vegetation. Slope, aspect, and the interaction term between these two factors, significantly affected vegetation pattern. Altogether, 56% and 72% of the variability in herbaceou s and tree cover, respectively, was explained by the regression models. Thi s study indicates that spatially explicit Mediterranean vegetation dynamics can be predicted with fair accuracy using few biologically important envir onmental variables.