Chapter 5: a Method for Collecting Structural Attribute Data in a Representative Sample of Stands
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Chapter 5: A method for collecting structural attribute data Chapter 5: A method for collecting structural attribute data in a representative sample of stands Chapter summary This chapter describes the method used to collect data for quantifying a comprehensive set of structural attributes in 144 plots, at 48 study sites located within a 15,000 km2 study area in the South-eastern Highlands Bioregion of Australia. To identify a representative set of sites, the study area was stratified by the factors considered most likely to contribute to variation at the landscape scale. This produced 24 strata on the basis of three vegetation communities, two catchments, two levels of rainfall and two levels of condition. Two sites (replicates) were measured within each of these 24 strata. All sites were clearly defined stands located within an agricultural matrix, or within a larger tract of native vegetation. At the site scale, three plots were systematically established, from a random starting point, to provide an unbiased sample of the target stand. At the plot level, measurements incorporated three different sampling methods - 50mx20m quadrat, 20mx20m quadrat and 50m transect - so that a comparison could be made between the precision and efficiency of the different methods. 5.1 Introduction In this chapter I address stages two and three of the methodology for developing an index of structural complexity, which was outlined in Chapter 3. To do this, I describe a method for collecting data based on the comprehensive set of structural attributes identified in the previous chapter, and the sampling approach used to apply this method to a representative set of study sites. The purpose of collecting these data was to identify which were the most important, or core, attributes in the comprehensive set. This was done by identifying those attributes that best distinguished between study sites, and were most efficient to measure in the field, and by reducing the number of attributes on the basis of correlations between attributes. This analysis is presented in the next chapter. 78 Chapter 5: A method for collecting structural attribute data 5.2 Study area The study was located within the woodlands and dry sclerophyll forests of south-eastern Australia. These communities occur largely on private or leasehold land and have been extensively cleared or modified in the past. They now exist as remnants and patches of regrowth, in a fragmented landscape dominated by agriculture (National Forest Inventory, 1998). A metric for ranking stands in terms of their condition and potential contribution to biodiversity, such as a structural complexity index, is particularly relevant to these communities. If properly designed, it could provide an objective basis for assessing applications to clear native vegetation (e.g. Gibbons et al., 2004), and for allocating financial incentives to improve or maintain the condition of remnants on private land (e.g. Parkes et al., 2003). It could also form the basis for developing a practical tool for guiding and improving management within the context of private ownership (e.g. Goldney and Wakefield, 1997). The study area was located within the South-eastern Highlands Bioregion of Australia (Thackway and Creswell, 1995), and is shown in Figure 3. The area covered 15,000 km2, and ranged in latitude from -350 38' 46" to -340 17' 8", and in longitude from 1480 35' 28" to 1490 45' 6". It ranged in altitude from 500m to 1100m, and included sections of the Murrumbidgee and Lachlan river catchments. The woodlands and dry sclerophyll forests of south-eastern Australia occur in the relatively dry environments of the tablelands and western slopes of New South Wales where annual rainfall is between 400-800mm. The vegetation typically comprises a single canopy layer dominated by Eucalyptus and Corymbia species, over an understorey of forbs, perennial grasses and sclerophyllous shrubs (McIntyre et al., 2002). In woodlands, overstorey trees are often more widely spaced than in dry sclerophyll forests, so that woodland projective foliage cover is usually less than 30% whereas in dry sclerophyll forest it ranges from 30-70% (Specht, 1970). 79 Figure 3: The study area covered 15,000 km2, and ranged in latitude from -350 38' 46" to -340 17' 8", and in longitude from 1480 35' 28" to 1490 45' 6". It ranged in altitude from 500m to 1100m, and included sections of the Murrumbidgee and Lachlan river catchments. A total of 144 plots in 48 sites were used to sample variation across the study area. Chapter 5: A method for collecting structural attribute data Three communities, one woodland and two dry sclerophyll forest, were chosen as the focus for the study (Table 13). These communities were defined in terms of their dominant overstorey species, and were selected because they often intergrade to form a topographic sequence from ridge top to lower slope and valley floor (Costin, 1954). Table 13: The three vegetation communities selected for this study. Communities were defined in terms of their dominant overstorey species, and were chosen because they intergrade to form a topographic sequence from ridge top to lower slope and valley floor. These communities incorporate one or more of the ecosystem types classified by Thomas et al., (2000)A, and now exist as small remnants and patches of regrowth in a fragmented landscape dominated by agricultureB. Vegetation Overstorey Topographic Ecosystem % pre –1750 community species position typesA area clearedB CRA 161 97.8% E. melliodora Yellow Box-Red Gum Lower slopes CRA 160 96.1% E. blakelyi grassy woodland and valley floors CRA 159 96.6% E. bridgesiana CRA 154 95.2% CRA 111 89.6% E. dives Broadleaved Peppermint- CRA 109 54.8% E. mannifera Middle and Brittle Gum CRA 108 40.0% E. macrorhyncha lower slopes dry sclerophyll forest CRA 80 47.5% E. dalrympleana CRA 81 37.0% Scribbly Gum-Red E. rossii Upper slopes Stringybark CRA 114 52.8% and ridges dry sclerophyll forest E. macrorhyncha 5.3 Data collection 5.2.1 Sampling design Identifying study sites To collect a representative data set from the 15,000 km2 study area, a sampling design was employed which balanced the allocation of resources between effort spent in sampling between site variation, and effort spent in sampling within site variation. To sample between site variation, the study area was stratified by the following 4 variables, which were considered most likely to contribute to 81 Chapter 5: A method for collecting structural attribute data variation at the landscape scale: 1. Vegetation community; 3 groups based on overstorey species composition. a) Scribbly Gum - Red Stringybark community b) Broadleaved Peppermint - Brittle Gum community c) Yellow Box - Red Gum community. 2. Catchment; 2 groups based on location within either the Lachlan river catchment or the Murrumbidgee river catchment. A shortage of suitable sites in the Lachlan catchment meant that 6 study sites were located in the adjoining Shoalhaven and Wollondilly catchments. 3. Rainfall; 2 groups based on whether mean annual rainfall was high (700- 800mm/yr), or low (600-700mm/yr). Rainfall was predicted using ESOCLIM (Hutchinson, 1989) and a 250m Digital Elevation Model. 4. Condition; 2 groups indicating the degree of modification since European settlement. Sites were classified as either unmodified or modified. Unmodified sites were ones in which the effects of any modification since European settlement appeared negligible. These sites contained little evidence of past clearing or logging in the form of stumps, and were typically dominated by senescing trees although a regrowth component could also be present. Modified sites were either regrowth stands on land formerly cleared for agriculture, or sites in which a large proportion of the original overstorey had been removed as evidenced by the presence of stumps, parts of felled trees, and a much reduced canopy cover. For the Yellow Box-Red Gum community there were no unmodified stands. For this community “unmodified” indicated stands whose structure and composition were relatively less modified than other stands. These 4 variables produced 3x2x2x2 = 24 strata. Within each stratum two study sites (replicates) were subjectively identified with the aid of a New South Wales National Parks and Wildlife Service database of high quality sites (Rehwinkle, 2002), a recently completed forest classification and mapping GIS layer (Thomas et al., 2000), considerable local knowledge and expert advice from 82 Chapter 5: A method for collecting structural attribute data Nick Webb (Environment ACT) and Jacqui Stol (CSIRO Sustainable Ecosystems), and my own field reconnaissance observations. In total 48 sites were used to sample variation across the study area (Figure 3 and Table 14). Sites existed either as an isolated stand within an agricultural matrix, or as a clearly defined stand within a larger tract of native vegetation. Patch size was not included in the stratification design, because reconnaissance surveys indicated that this was not a good predictor of site condition, an observation supported by Prober and Thiele (1995). For example, modified stands could occur as large or small regrowth patches on land which had been cleared for pasture and then abandoned, and unmodified stands could occur as small isolated remnants apparently buffered against invasion by exotic vegetation by a relatively infertile environment, or as large intact patches protected within a reserve system. Establishing plots at each site At the site scale, data for a range of structural attributes were obtained by establishing three measurement plots within the target stand at each site. A pilot study indicated that three plots was an efficient number for estimating means and variances for a range of stand variables. To capture within stand variation, plots were located on a stand transect running diagonally across the direction of the main environmental gradient (assumed to be topography), and passing through the centre of the stand.