In this thesis I present and test a methodology for developing a stand scale index of structural complexity. If properly designed such an index can act as a summary variable for a larger set of stand structural attributes, providing a means of ranking stands in terms of their structural complexity, and by association, their biodiversity and vegetation condition. This type of index can also facilitate the use of alternative policy instruments for biodiversity conservation, such as mitigation banking, auctions and offsets, that rely on a common currency the index value that can be compared or traded between sites. My intention was to establish a clear and documentable methodology for developing a stand scale index of structural complexity, and to test this methodology using data from real stands.¶
As a starting point, I reviewed the literature concerning forest and woodland structure and found there was no clear definition of stand structural complexity, or definitive suite of structural attributes for characterising it. To address this issue, I defined stand structural complexity as a combined measure of the number of different structural attributes present in a stand, and the relative abundance of each of these attributes. This was analogous to approaches that have quantified diversity in terms of the abundance and richness of elements. It was also concluded from the review, that stand structural complexity should be viewed as a relative, rather than absolute concept, because the potential levels of different structural attributes are bound within certain limits determined by the inherent characteristics of the site in question, and the biota of the particular community will have evolved to reflect this range of variation. This implied that vegetation communities with naturally simple structures should have the potential to achieve high scores on an index of structural complexity.¶
I proposed the following five-stage methodology for developing an index of stand structural complexity:
1. Establish a comprehensive suite of stand structural attributes as a starting point for developing the index, by reviewing studies in which there is an established relationship between elements of biodiversity and structural attributes.
2. Develop a measurement system for quantifying the different attributes included in the comprehensive suite.
3. Use this measurement system to collect data from a representative set of stands across the range of vegetation condition (highly modified to unmodified) and developmental stages (regrowth to oldgrowth) occurring in the vegetation communities in which the index is intended to operate.
4. Identify a core set of structural attributes from an analysis of these data.
5. Combine the core attributes in a simple additive index, in which attributes are scored relative to their observed levels in each vegetation community.¶
Stage one of this methodology was addressed by reviewing a representative sample of the literature concerning fauna habitat relationships in temperate Australian forests and woodlands. This review identified fifty-five studies in south-east and south-west Australia, in which the presence or abundance of different fauna were significantly (p<0.05) associated with vegetation structural attributes. The majority of these studies concerned bird, arboreal mammal, and ground mammal habitat requirements, with relatively fewer studies addressing the habitat requirements of reptiles, invertebrates, bats or amphibians. Thirty four key structural attributes were identified from these fifty-five studies, by grouping similar attributes, and then representing each group with a single generic attribute. This set, in combination with structural attributes identified in the earlier review, provided the basis for developing an operational set of stand level attributes for the collection of data from study sites.¶
To address stages two and three of the methodology, data were collected from one woodland community Yellow Box-Red Gum (E. melliodora-E. Blakelyi ) and two dry sclerophyll forest communities Broadleaved Peppermint-Brittle Gum (E. dives-E. mannifera ), Scribbly Gum-Red Stringybark (E. rossii E. macrorhyncha ) in a 15,000 km2 study area in the South eastern Highlands Bioregion of Australia. A representative set of 48 sites was established within this study area, by identifying 24 strata, on the basis of the three vegetation communities, two catchments, two levels of rainfall and two levels of condition, and then locating two sites (replicates) within each stratum. At each site, three plots were systematically established, to provide an unbiased estimate of stand level means for 75 different structural attributes.¶
I applied a three-stage analysis to identify a core set of attributes from these data. The first stage a preliminary analysis indicated that the 48 study sites represented a broad range of condition, and that the two dry sclerophyll communities could be treated as a single community, which was structurally distinct from the woodland community. In the second stage of the analysis, thirteen core attributes were dentified using the criteria that a core attribute should:¶
1. Be either, evenly or approximately normally distributed amongst study sites;
2. Distinguish between woodland and dry sclerophyll communities;
3. Function as a surrogate for other attributes;
4. Be efficient to measure in the field.
The core attributes were: Vegetation cover <0.5m Vegetation cover 0.5-6.0m; Perennial species richness; Lifeform richness; Stand basal area of live trees; Quadratic mean diameter of live stems; ln(number of regenerating stems per ha+1); ln(number of hollow bearing trees per ha+1);ln(number of dead trees per ha+1);sqrt(number of live stems per ha >40cm dbh); sqrt(total log length per ha); sqrt(total largelog length per ha); Litter dry weight per ha. This analysis also demonstrated that the thirteen core attributes could be modelled as continuous variables, and that these variables were indicative of the scale at which the different attributes operated.¶
In the third and final stage of the analysis, Principal Components Analysis was used to test for redundancy amongst the core attributes. Although this analysis highlighted six groupings, within which attributes were correlated to some degree, these relationships were not considered sufficiently robust to justify reducing the number of core attributes.¶
The thirteen core attributes were combined in a simple additive index, in which, each attribute accounted for 10 points in a total index value of 130. Attributes were rescaled as a score from 0-10, using equations that modelled attribute score as a function of the raw attribute data. This maintained a high correlation (r > 0.97, p< 0.0001) between attribute scores and the original attribute data. Sensitivity analysis indicated that the index was not sensitive to attribute weightings, and on this basis attributes carried equal weight. In this form my index was straightforward to apply, and approximately normally distributed amongst study sites.¶
I demonstrated the practical application of the index in a user-friendly spreadsheet, designed to allow landowners and managers to assess the condition of their vegetation, and to identify management options. This spreadsheet calculated an index score from field data, and then used this score to rank the site relative to a set of reference sites. This added a regional context to the operation of the index, and is a potentially useful tool for identifying sites of high conservation value, or for identifying sites where management actions have maintained vegetation quality. The spreadsheet also incorporated the option of calculating an index score using a subset of attributes, and provided a measure of the uncertainty associated with this score.¶
I compared the proposed index with five prominent indices used to quantify vegetation condition or habitat value in temperate Australian ecosystems. These were: Newsome and Catlings (1979) Habitat Complexity Score, Watson et al.s (2001) Habitat Complexity Score, the Site Condition Score component of the Habitat Hectares Index of Parkes et al. (2003), the Vegetation Condition Score component of the Biodiversity Benefits Index of Oliver and Parkes (2003), and the Vegetation Condition Score component of the BioMetric Assessment Tool of Gibbons et al. (2004). I found that my index differentiated between study sites better than each of these indices. However, resource and time constraints precluded the use of a new and independent data set for this testing, so that the superior performance of my index must be interpreted cautiously.¶
As a group, the five indices I tested contained attributes describing compositional diversity, coarse woody debris, regeneration, large trees and hollow trees these were attributes that I also identified as core ones. However, unlike these indices, I quantified weeds indirectly through their effect on indigenous plant diversity, I included the contribution of non-indigenous species to vegetation cover and did not apply a discount to this contribution, I limited the direct assessment of regeneration to long-lived overstorey species, I used stand basal area as a surrogate for canopy cover, I quantified litter in terms of biomass (dry weight) rather than cover, and I included the additional attributes of quadratic mean diameter and the number of dead trees.¶
I also concluded that Parkes et al. (2003), Oliver and Parkes (2003), and Gibbons et al. (2004), misapplied the concept of benchmarking, by characterising attributes in terms of a benchmark range or average level. This ignored processes that underpin variation at the stand level, such as the increased development of some attributes at particular successional stages, and the fact that attributes can respond differently to disturbance agents. It also produced indices that were not particularly sensitive to the differences in attribute levels occurring between stands. I suggested that a more appropriate application of benchmarking would be at the overarching level of stand structural complexity, using a metric such as the index developed in this thesis. These benchmarks could reflect observed levels of structural complexity in unmodified natural stands at different successional stages, or thresholds for structural complexity at which a wide range of biota are present, and would define useful goals for guiding on-ground management.
| Identifer | oai:union.ndltd.org:ADTP/216820 |
| Date | January 2005 |
| Creators | McElhinny, Chris, chris.mcelhinny@anu.edu.au |
| Publisher | The Australian National University. Centre for Resource and Environmental Studies |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.anu.edu.au/legal/copyrit.html), Copyright Chris McElhinny |
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