Edge-Interior Gradient Effects on the Understorey Bird Community in an Isolated Ayer Hitam Forest Reserve, Malaysia

The fact that the world is losing its biodiversity due to human activities, particularly around the tropical forest region, has been widely known. One of the biggest threats to biodiversity is the edge effects, especially in isolated and fragmented habitats. Thus, to investigate the edge effects on the community of understorey birds, an isolated tropical rain forest of Malaysia was chosen. The objectives of this study were: (1) to examine the species composition, richness, abundance, and density changes across edge-interior gradient; (2) to detect any distinct bird communities associated with certain habitat types and the factors affecting the association (3) to distinguish the interior and edge specialist species and guilds. The point-count sampling method was used in a 1248-ha lowland rain forest patch of Ayer Hitam Forest Reserve to carry out a survey on the individual understorey bird and species, at each of the 93 survey points, between December 2006 and July 2008 Birds and environmental variables were recorded within a 25 m radius of each point. A total of 2263 observations, 72 species, representing 19 families were recorded in this study. The species composition, density, abundance, and diversity of birds showed some significant differences across the edge-interior gradient at the guild and species levels. Based on the bird-habitat association, along the edge-interior gradient, two groups were distinguished. These were the edge-specialist group which was positively correlated with ground cover, light intensity, shrub cover, temperature, and percentage of shrub cover between 0.5 and 2 m in height; meanwhile the interior-specialist group was highly sensitive to the forest edge and could indicate good habitat quality of forest interior with high humidity, dense canopy cover, high number of dead trees, high percentage of litter cover, and deep litter layer. At the guild level, the results showed that the terrestrial insectivores and sallying insectivores are sensitive to edge and have positive correlation with distance from the edge, leaf litter depth, canopy cover, and the number of tall trees (>10 m). The presence of some species such as the Yellow-vented Bulbul, Cream-vented Bulbul, and Plaintive Cuckoo was associated with high light intensity and shrub cover, which are the best indicators of the edge. Meanwhile, the presence of Short-tailed Babbler, Moustached Babbler, and Black-caped Babbler was associated with high relative humidity and leaf litter cover, which are the best indicators of forest interior. Changes in the micro-environment at the edge are a key factor to indicate the understorey avian responses to the edge-interior gradient. As edge specialists can be widely found in the matrix surrounding the patch, they require less conservation against being declined or endangered; i.e. they can be well managed in the matrix surrounding the forest patches. Interior-specialists, on the other hand, especially terrestrial insectivores, should be given the most attention in conservation of forest areas. From the conservation viewpoint, the forest remnants in the lowlands of Peninsular Malaysia are of considerable concern. Due to the characteristics including thick leaf litter layer, dense canopy cover, high number of dead trees, and high relative humidity, these remnants have the capability of supporting the understorey bird species sensitive to edge effects

Dimension Reduction and Variable Selection

High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. in recent years. In this dissertation, we worked on three projects. The first one combines local modal regression and Minimum Average Variance Estimation (MAVE) to introduce a robust dimension reduction approach. In addition to being robust to outliers or heavy-tailed distribution, our proposed method has the same convergence rate as the original MAVE. Furthermore, we combine local modal base MAVE with a $L_1$ penalty to select informative covariates in a regression setting. This new approach can exhaustively estimate directions in the regression mean function and select informative covariates simultaneously, while being robust to the existence of possible outliers in the dependent variable. The second project develops sparse adaptive MAVE (saMAVE). SaMAVE has advantages over adaptive LASSO because it extends adaptive LASSO to multi-dimensional and nonlinear settings, without any model assumption, and has advantages over sparse inverse dimension reduction methods in that it does not require any particular probability distribution on \textbf{X}. In addition, saMAVE can exhaustively estimate the dimensions in the conditional mean function. The third project extends the envelope method to multivariate spatial data. The envelope technique is a new version of the classical multivariate linear model. The estimator from envelope asymptotically has less variation compare to the Maximum Likelihood Estimator (MLE). The current envelope methodology is for independent observations. While the assumption of independence is convenient, this does not address the additional complication associated with a spatial correlation. This work extends the idea of the envelope method to cases where independence is an unreasonable assumption, specifically multivariate data from spatially correlated process. This novel approach provides estimates for the parameters of interest with smaller variance compared to maximum likelihood estimator while still being able to capture the spatial structure in the data