Submitted to: International Rangeland Congress
Publication Type: Abstract only
Publication Acceptance Date: 8/16/2002
Publication Date: 1/1/2003
Citation: Northup, B.K., Brown, J.R., Ash, A.J. 2003. Using variability to identify potential state and transition stages of a tropical tallgrass site in northeast Australia [abstract]. International Rangeland Congress. v. 20. Paper No. B1.25. Interpretive Summary: ABSTRACT ONLY
Technical Abstract: Conceptual state [S(i)] and transition models are used to help define landscape condition in northeast Australia, but there are no standards for amount(s) of indicators that define state conditions. Endemic variability in the distribution of vegetation also makes describing condition changes difficult, but could serve as a useful monitoring tool. This study tested whether within-unit (paddock) variability in herbaceous vegetation might serve as a better indicator of state condition than among-unit responses. Data were collected during 1993, 1995, and 1998 from experimental paddocks, under different grazing pressures, on a tropical tallgrass site. Forage produced by indicator species, and basal area of perennial grasses, were ascertained by BOTANAL procedures on 100, 0.5 m^2 quadrats along fixed transects and standardized across all observations (n=8000). Means from paddocks degrading under heavy grazing were analyzed by univariate statistics, and line diagrams of standardized variation were constructed. Paddock-scale means were ineffective at identifying changes in indicators due to high variability (c.v. 50-140%), and large sample numbers (n>1000) were required. Line diagrams identified changes in amount and distribution of indicators, including: basal area and Bothriochloa ewartiana (S1 indicators, 1995 and 1998); annual grasses and forbs (S2 indicators, 1995); and Bothriochloa pertusa (S3 indicator, 1995 and 1998). Tropical tallgrass sites can rapidly respond to disturbances. Monitoring systems that fail to identify early stages of disturbance will contribute to the damage. Spatially oriented measures of variability can allow more accurate identification of changes in condition than paddock-scale means, and help limit landscape degradation.