Multiple Pattern Analysis of a Woodland in South Central Queensland. I. The Original Trees and Shrubs

Abstract

Multivariate analyses of vegetation data are normally restricted to a single scale of sampling, but since the pattern of species populations may vary over a range of scales, restriction to a single scale can result in a loss of potentially useful information. It is possible to examine spatial variation for a single species or pairs of species by block size variance (or covariance) analysis, but this is a somewhat cumbersome procedure when more than a few species are involved. A method which combines block size variance analysis with a multivariate (principal components) analysis is described and applied to a woodland community in south central Queensland. Contiguous site data, recorded as density scores for all tree and shrub species along a transect 512 m by 20 m, were grouped into successively larger blocks. Variance covariance matrices at each block size were calculated and added to form a combined covariance matrix. This was subjected to a principal components analysis to obtain species and sites coordinates. Each characteristic root was subsequently partitioned into contributions from the various block sizes, and the partitioned roots plotted against block size as in conventional pattern analysis. The first two components represented macro-variation in the vegetation of the transect (at scales of 120-250 m) and separated three macrocommunities which were associated with soil types. Two subsequent components expressed compositional differences at smaller scales (30-60 m) within these macrocommunities.

Community pattern analysis: A method for quantifying community mosaic structure

“…Noy-Meir & Anderson (1971) proposed Multiple Pattern Analysis, based on PCA of covariance matrices constructed at different block sizes. There have been a number of applications of their method in Australia (Walker et al, 1972;Westman, 1975;Williams et al, 1978). Whittaker and colleagues (Whittaker & Naveh, 1979;Whittaker et al, 1979a, b: Shmida & Whittaker, 1981and also Bouxin & Gautier, 1982;Welden, 1985) were able to identify subjectively patterns from various shrub and grassland communities by plotting the first reciprocal averaging axis scores as ordinate scores against plot sequence for transect data.…”

The pattern of the woody species of the nylsvley savanna ecosystem project area

“…The scale of pattern in mature Burkea in regions A, C and D must be under some other environmental control. Kershaw (1959) suggests that varying scales of pattern are indicative of environmental control and Walker et al ( 1972) consider the macrovariation in the distribution of trees and shrubs corresponds to availability of water and nutrients in the soil. There is thus a need for the critical investigation of soil variability and moisture relations within the intensive study area at Nysvley, to determine which are the controlling factors of the pattern and how they affect the pattern of these plants.…”

“…It was thus necessary to separate the tree and shrub species populations into mature, intermediate and juvenile forms. Walker, NoyMeir, Anderson & Moore (1972) used the same method in E pattern analysis of a woodland in Australia. In the BraunBlanquet survey of the Nylsvley vegetation, Coetzee et al (1975) considered the stratification of the species at each relevé, but no standardised division of the species into classes was attempted.…”

Sampling grain influences trends in vegetation composition and diversity with time since fire in Australian heathland

“…Despite the importance of sampling grain, it is frequently chosen based on current practice or precedent, without considering the ecological processes or organisms being studied (MacNally & Quinn 1998). As there is no single 'correct' scale in which to investigate a given community (Palmer 1990;Levin 1992), a multi-scale approach may often yield more information and be more appropriate (Walker et al 1972;Levin 1988;Gardner 1998); however, such approaches have the disadvantage of being more timeconsuming to implement. However, the reason for choosing a particular sampling grain is seldom provided.…”