Species composition and vegetation structure of an upper montane forest at the summit of Mt. Doi Inthanon, Thailand

Upper montane forest (UMF) within Doi Inthanon National Park, Northern Thailand, was investigated by means of fifty, 40 × 40 m stratified random plots situated between 2080 and 2565 m altitude. The aim was to address a number of community ecological questions concerning woody species composition and structural heterogeneity of the forest. A total of 7474 individuals of trees and woody climbers ≥ 15 cm gbh (girth at breast height) were included in the study and these were identified to 47 species, 39 genera and 26 families. The average density was 934 individuals/ha and the average stem basal area was 71.8 m²/ha. The most important species were: Quercus eumorpha, Sjzygium angkae, Litsea martabanica, Helicia nilagirica, Lindera caudata, Schima wallichii, Osmanthus fiagrans, Eurya acuminata, Myrsine semiserrata and Ilex umbellulata and the most important families were Fagaceae, Lauraceae, Theaceae and Myrtaceae. Altitude was the most important environmental variable explaining species composition and vegetation structure. Most of the calculated vegetation variables showed significant correlation with altitude: species richness, family richness, diversity, density and crown cover declined with altitude, average tree height was uncorrelated with altitude and basal area increased with altitude. An analysis of size class distributions indicated good forest conditions and reverse-J-shaped age class distribution of most species.     

Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain

Background

Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model.

Results

In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border.

Conclusion

We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches.