Ten year change in forest succession and composition measured by remote sensing

Vegetation dynamics and changes in ecological patterns were measured by remote sensing over a 10 year period (1973 to 1983) for 148,406 landscape elements, covering more than 500 sq km in a protected forested wilderness. Quantitative measurements were made possible by methods to detect ecologically meaningful landscape units; these allowed measurement of ecological transition frequencies and calculation of expected recurrence times. Measured ecological transition frequencies reveal boreal forest wilderness as spatially heterogeneous and highly dynamic, with one-sixth of the area in clearings and early successional stages, consistent with recent postulates about the spatial and temporal patterns of natural ecosystems. Differences between managed forest areas and a protected wilderness allow assessment of different management regimes

A Hybrid Dynamical–Statistical Downscaling Technique. Part I: Development and Validation of the Technique

Abstract In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical–statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region's warming patterns, enhancing the credibility of the final results

Variation in Loblolly Pine Cross-Sectional Microfibril Angle With Tree Height and Physiographic Region

The effect of height and physiographic region on whole disk cross-sectional microfibril angle (CSMFA) in loblolly pine (Pinus taeda L.) in the southern United States was evaluated. Whole disk CSMFA was determined at 1.4, 4.6, 7.6, 10.7, and 13.7 m up the stem of 59 trees, representing five physiographic regions. A mixed-effects analysis of variance was performed to test the significance of height, region, and the height by region interaction on CSMFA. Height, region, and the height by region interaction terms were all found to be significant at the 0.10 level. Significant differences were found in CSMFA between 1.4 m and all other height levels in all regions. However, there was no difference between CSMFA at 1.4 m and 13.7 m in the Gulf Coastal Plain. No significant difference was found in CSMFA between 4.5, 7.6, and 10.7 meter-height levels in all regions. CSMFA was found to be significantly larger in the north Atlantic and Piedmont regions compared to the south Atlantic, Gulf, and Hilly regions at all heights. The analysis of variance also indicated that significant variation exists among trees within stands and across stands within regions. This is an indicator that aside from the distinct patterns of CSMFA within trees, other factors including site quality, length of growing season, rainfall, and genetics could possibly play a key role in CSMFA development

Gemini-South + FLAMINGOS Demonstration Science: Near-Infrared Spectroscopy of the z=5.77 Quasar SDSS J083643.85+005453.3

We report an infrared 1-1.8 micron (J+H-bands), low-resolution (R=450) spectrogram of the highest-redshift radio-loud quasar currently known, SDSS J083643.85+005453.3, obtained during the spectroscopic commissioning run of the FLAMINGOS multi-object, near-infrared spectrograph at the 8m Gemini-South Observatory. These data show broad emission from both CIV 1549 and CIII] 1909, with strengths comparable to lower-redshift quasar composite spectra. The implication is that there is substantial enrichment of the quasar environment, even at times less than a billion years after the Big Bang. The redshift derived from these features is z = 5.774 +/- 0.003, more accurate and slightly lower than the z = 5.82 reported in the discovery paper based on the partially-absorbed Lyman-alpha emission line. The infrared continuum is significantly redder than lower-redshift quasar composites. Fitting the spectrum from 1.0 to 1.7 microns with a power law f(nu) ~ nu^(-alpha), the derived power law index is alpha = 1.55 compared to the average continuum spectral index = 0.44 derived from the first SDSS composite quasar. Assuming an SMC-like extinction curve, we infer a color excess of E(B-V) = 0.09 +/- 0.01 at the quasar redshift. Only approximately 6% of quasars in the optically-selected Sloan Digital Sky Survey show comparable levels of dust reddening.Comment: 10 pages, 1 figure; to appear in the Astrophysical Journal Letter

Two-component mixtures of generalized linear mixed effects models for cluster correlated data

Abstract: Finite mixtures of generalized linear mixed effect models are presented to handle situations where within-cluster correlation and heterogeneity (subpopulations) exist simultaneously. For this class of model, we consider maximum likelihood (ML) as our main approach to estimation. Owing to the complexity of the marginal loglikelihood of this model, the EM algorithm is employed to facilitate computation. The major obstacle in this procedure is to integrate over the random effects' distribution to evaluate the expectation in the E step. When assuming normally distributed random effects, we consider adaptive Gaussian quadrature to perform this integration numerically. We also discuss nonparametric ML estimation under a relaxation of the normality assumption on the random effects. Two real data sets are analysed to compare our proposed model with other existing models and illustrate our estimation methods

Robust estimation for zero-inflated Poisson regression

ABSTRACT. The zero-inflated Poisson regression model is a special case of finite mixture models that is useful for count data containing many zeros. Typically, maximum likelihood (ML) estimation is used for fitting such models. However, it is well known that the ML estimator is highly sensitive to the presence of outliers and can become unstable when mixture components are poorly separated. In this paper, we propose an alternative robust estimation approach, robust expectation-solution (RES) estimation. We compare the RES approach with an existing robust approach, minimum Hellinger distance (MHD) estimation. Simulation results indicate that both methods improve on ML when outliers are present and/or when the mixture components are poorly separated. However, the RES approach is more efficient in all the scenarios we considered. In addition, the RES method is shown to yield consistent and asymptotically normal estimators and, in contrast to MHD, can be applied quite generally

Long short-term memory language models with additive morphological features for automatic speech recognition

Abstract Models of morphologically rich languages suffer from data sparsity when words are treated as atomic units. Word-based lan-guage models cannot transfer knowledge from common word forms to rarer variant forms. Learning a continuous vector representa-tion of each morpheme allows a compositional model to represent a word as the sum of its constituent morphemes ’ vectors. Rare and unknown words containing common morphemes can thus be repre-sented with greater fidelity despite their sparsity. Our novel neural network language model integrates this additive morphological rep-resentation into a long short-term memory architecture, improving Russian speech recognition word error rates by 0.9 absolute, 4.4% relative, compared to a robust n–gram baseline model. Index Terms — language modeling, neural networks, long short-term memory, compositional morpholog