Multispecies, Multi-patterns At Multiple Scales And Their Scaling

We investigated the distributions and habitats of Ceratoides ewersmanniana, Haloxylon ammodendron, Rerosimonia soongorica and Calligonum leucocladum in two plots (200m×200m, the distance between the plots is 15 km) at Mosuowan desert. We analyzed their intraspecific clumpiness (caespitose) and periodicity, interspecific interaction, edge effect, and their relationship with habitats'geomorphology and soil physical and chemical properties.Wavelet analysis was used to detect edges from east to west, explain its existence, and quantify its location; Furthermore, we combined Wavelet Analysis and Point Pattern Analysis to detect and quantify the scales and periods of clumpiness. Finally, using Wavelet Analysis, Point Pattern Analysis and Canonical Correspondence Analysis, we studies the importance of habitats'geomorphology and soil physical or chemical properties to biological patterns.We got some important finding: First of all, the distributions of C.ewersmanniana and H.ammodendron were non-continuous, rather showing aggregated and periodic distribution patterns (115-125m for C.ewersmanniana, and 130-140m for H.ammodendron), which were caused by the geomorphologic oscillation period at 110m. P.soongorica and C.leucocladum showed aggregated distribution patterns but no period.Second, detected edge effect demonstrated that the east-west edges corresponding to the influences of their geomorphology, in the contrary, the south-north edges corresponded to the influences of incoming solar radiation, shad effect and surface characteristics.Third, the result of interspecific interactions indicated that aggregation of desert plants was correlated with habitat conditions. Desert plants could form a micro-habitat by aggregation to reduce evaportranspiration stress and serve as an important adaptive strategy at the community level.Finally, we also identified signal catastrophe points with wavelets and verified heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns at large scale into small sampling plot, and the position with the largest variance also had strongest heterogeneity. In a word, wavelet analysis method could scale-up spatial distribution patterns and habit heterogeneity. With this method and other method derived from this one, such as, wavelet scale, wavelet variance, position variance, and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.