Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

MODELING FUNCTIONAL DATA WITH SPATIALLY HETEROGENEOUS SHAPE CHARACTERISTICS

We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.

Endothelial survival factors and spatial completion, but not pericyte coverage of retinal capillaries determine vessel plasticity

Pericyte loss and capillary regression are characteristic for incipient diabetic retinopathy. Pericyte recruitment is involved in vessel maturation, and ligand-receptor systems contributing to pericyte recruitment are survival factors for endothelial cells in pericyte-free in vitro systems. We studied pericyte recruitment in relation to the susceptibility toward hyperoxia-induced vascular remodeling using the pericyte reporter X-LacZ mouse and the mouse model of retinopathy of prematurity (ROP). Pericytes were found in close proximity to vessels, both during formation of the superficial and the deep capillary layers. When exposure of mice to the ROP was delayed by 24 h, i.e., after the deep retinal layer had formed [at postnatal (p) day 8], preretinal neovascularizations were substantially diminished at p18. Mice with a delayed ROP exposure had 50% reduced avascular zones. Formation of the deep capillary layers at p8 was associated with a combined up-regulation of angiopoietin-1 and PDGF-B, while VEGF was almost unchanged during the transition from a susceptible to a resistant capillary network. Inhibition of Tie-2 function either by soluble Tie-2 or by a sulindac analog, an inhibitor of Tie-2 phosphorylation, resensitized retinal vessels to neovascularizations due to a reduction of the deep capillary network. Inhibition of Tie-2 function had no effect on pericyte recruitment. Our data indicate that the final maturation of the retinal vasculature and its resistance to regressive signals such as hyperoxia depend on the completion of the multilayer structure, in particular the deep capillary layers, and are independent of the coverage by pericytes.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Lava discharge rate estimates from thermal infrared satellite data at Pacaya Volcano, Guatemala

Time-averaged discharge rates (TADR) were calculated for five lava flows at Pacaya Volcano (Guatemala), using an adapted version of a previously developed satellite-based model. Imagery acquired during periods of effusive activity between the years 2000 and 2010 were obtained from two sensors of differing temporal and spatial resolutions; the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Geostationary Operational Environmental Satellites (GOES) Imager. A total of 2873 MODIS and 2642 GOES images were searched manually for volcanic “hot spots”. It was found that MODIS imagery, with superior spatial resolution, produced better results than GOES imagery, so only MODIS data were used for quantitative analyses. Spectral radiances were transformed into TADR via two methods; first, by best-fitting some of the parameters (i.e. density, vesicularity, crystal content, temperature change) of the TADR estimation model to match flow volumes previously estimated from ground surveys and aerial photographs, and second by measuring those parameters from lava samples to make independent estimates. A relatively stable relationship was defined using the second method, which suggests the possibility of estimating lava discharge rates in near-real-time during future volcanic crises at Pacaya.

Spatial and temporal analysis of land cover and landscape structure change in Zagros Forests

The Zagros oak forests in Western Iran are critically important to the sustainability of the region. These forests have undergone dramatic declines in recent decades. We evaluated the utility of the non-parametric Random Forest classification algorithm for land cover classification of Zagros landscapes, and selected the best spatial and spectral predictive variables. The algorithm resulted in high overall classification accuracies (>85%) and also equivalent classification accuracies for the datasets from the three different sensors. We evaluated the associations between trends in forest area and structure with trends in socioeconomic and climatic conditions, to identify the most likely driving forces creating deforestation and landscape structure change. We used available socioeconomic (urban and rural population, and rural income), and climatic (mean annual rainfall and mean annual temperature) data for two provinces in northern Zagros. The most correlated driving force of forest area loss was urban population, and climatic variables to a lesser extent. Landscape structure changes were more closely associated with rural population. We examined the effects of scale changes on the results from spatial pattern analysis. We assessed the impacts of eight years of protection in a protected area in northern Zagros at two different scales (both grain and extent). The effects of protection on the amount and structure of forests was scale dependent. We evaluated the nature and magnitude of changes in forest area and structure over the entire Zagros region from 1972 to 2009. We divided the Zagros region in 167 Landscape Units and developed two measures— Deforestation Sensitivity (DS) and Connectivity Sensitivity (CS) — for each landscape unit as the percent of the time steps that forest area and ECA experienced a decrease of greater than 10% in either measure. A considerable loss in forest area and connectivity was detected, but no sudden (nonlinear) changes were detected at the spatial and temporal scale of the study. Connectivity loss occurred more rapidly than forest loss due to the loss of connecting patches. More connectivity was lost in southern Zagros due to climatic differences and different forms of traditional land use.

Geospatial analysis of western juniper (Juniperus occidentalis) encroachment utilizing remotely sensed data

Utilizing remote sensing methods to assess landscape-scale ecological change are rapidly becoming a dominant force in the natural sciences. Powerful and robust non-parametric statistical methods are also actively being developed to compliment the unique characteristics of remotely sensed data. The focus of this research is to utilize these powerful, robust remote sensing and statistical approaches to shed light on woody plant encroachment into native grasslands--a troubling ecological phenomenon occurring throughout the world. Specifically, this research investigates western juniper encroachment within the sage-steppe ecosystem of the western USA. Western juniper trees are native to the intermountain west and are ecologically important by means of providing structural diversity and habitat for many species. However, after nearly 150 years of post-European settlement changes to this threatened ecosystem, natural ecological processes such as fire regimes no longer limit the range of western juniper to rocky refugia and other areas protected from short fire return intervals that are historically common to the region. Consequently, sage-steppe communities with high juniper densities exhibit negative impacts, such as reduced structural diversity, degraded wildlife habitat and ultimately the loss of biodiversity. Much of today's sage-steppe ecosystem is transitioning to juniper woodlands. Additionally, the majority of western juniper woodlands have not reached their full potential in both range and density. The first section of this research investigates the biophysical drivers responsible for juniper expansion patterns observed in the sage-steppe ecosystem. The second section is a comprehensive accuracy assessment of classification methods used to identify juniper tree cover from multispectral 1 m spatial resolution aerial imagery.

Spatial and Temporal Evolution of the Volcanics and Sediments of the Kenya Rift

New volumetric and mass flux estimates have been calculated for the Kenya Rift. Spatial and temporal histories for volcanic eruptions, lacustrine deposition, and hominin fossil sites are presented, aided by the compilation of a new digital geologic map. Distribution of volcanism over time indicates several periods of southward expansion followed by relative positional stasis. Volcanism occurs throughout the activated rift length, with no obvious abandonment as the rift system migrated. The main exception is a period of volcanic concentration around 10 Ma, when activity was constrained within 2° of the equator. Volumes derived from seismic data indicate a total volume of c. 310,000 km3 (2.47 x 1010 kg/yr ), which is significantly more than the map-derived volumes found here or published previously. Map-based estimates are likely affected by a bias against recognizing small volume events in the older record. Such events are, however, the main driver of erupted volume over the last 5 Ma. A technique developed here to counter this bias results in convergence of the two volume estimation techniques. Relative erupted composition over time is variable. Overall, the erupted material has a mafic to silicic ratio of 0.9:1. Basalts are distinctly more common in the Turkana region, which previously experienced Mesozoic rifting. Despite the near equal ratio of mafic to silicic products, the Kenya Rift otherwise fits the definition of a SLIP. It is proposed that the compositions would better fit the published definition if the Turkana region was not twice-rifted. Lacustrine sedimentation post-dates initial volcanism by about 5 million years, and follows the same volcanic trends, showing south and eastward migration over time. This sedimentation delay is likely related to timing of fault displacements. Evidence of hominin habitation is distinctly abundant in the northern and southern sections of the Kenya Rift, but there is an observed gap in the equatorial rift between 4 and 0.5 million years ago. After 0.5 Ma, sites appear to progress towards the equator. The pattern and timing of hominid site distributions suggests that the equatorial gap in habitation may be the result of active volcanic avoidance.

Spatial and temporal patterns in ungulate-ecosystem interactions

Ungulates are important components of a variety of ecosystems worldwide. This dissertation integrates aspects of ungulate and forest ecology to increase our understanding of how they work together in ways that are of interest to natural resource managers, educators, and those who are simply curious about nature. Although animal ecology and ecosystem ecology are often studied separately, one of the general goals of this dissertation is to examine how they interact across spatial and temporal scales. Forest ecosystems are heterogeneous across a range of scales. Spatial and temporal habitat use patterns of forest ungulates tend to be congregated in patches where food and/or cover are readily available. Ungulates interact with ecosystem processes by selectively foraging on plants and excreting waste products in concentrated patches. Positive feedbacks may develop where these activities increase the value of habitat through soil fertilization or the alteration of plant chemistry and architecture. Heterogeneity in ecosystem processes and plant community structure, observed at both stand and local scales, may be the integrated outcome of feedbacks between ungulate behavior and abiotic resource gradients. The first chapter of this dissertation briefly discusses pertinent background information on ungulate ecology, with a focus on white-tailed deer (Odocoileus virginianus) in the Upper Great Lakes region and moose (Alces acles) in Isle Royale National Park, Michigan, USA. The second chapter demonstrates why ecological context is important for studying ungulate ecology in forest ecosystems. Excluding deer from eastern hemlock (Tsuga canadensis) stands, which deer use primarily as winter cover, resulted in less spatial complexity in soil reactive nitrogen and greater complexity in diffuse light compared to unfenced stands. The spatial patterning of herbaceous-layer cover was more similar to nitrogen where deer were present, and was a combination of nitrogen and light within deer exclosures. This relationship depends on the seasonal timing of deer habitat use because deer fertilize the soil during winter, but leave during the growing season. The third chapter draws upon an eight-year, 39-stand data set of deer fecal pellet counts in hemlock stands to estimate the amount of nitrogen that deer are depositing in hemlock stands each winter. In stands of high winter deer use, deer-excreted nitrogen inputs consistently exceeded those of atmospheric deposition at the stand scale. At the neighborhood scale, deer-excreted nitrogen was often in excess of atmospheric deposition due to the patchy distribution of deer habitat use. Spatial patterns in habitat use were consistent over the eight-year study at both stand and neighborhood scales. The fourth chapter explores how foraging selectivity by moose interacts with an abiotic resource gradient to influence forest structure and composition. Soil depth on Isle Royale varies from east to west according to glacial history. Fir saplings growing in deeper soils on the west side are generally more palatable forage for moose (lower foliar C:N) than those growing in shallower soils on the east side. Therefore, saplings growing in better conditions are less likely to reach the canopy due to moose browsing, and fir is a smaller overstory component on the west side. Lastly, chapter five focuses on issues surrounding eastern hemlock regeneration failure, which is a habitat type that is important to many wildlife species. Increasing hemlock on the landscape is complicated by several factors including disturbance regime and climate change, in addition to the influence of deer.

Spatial isolation and genetic differentiation in naturally fragmented plant populations of the Swiss Alps

Aims The effect Of anthropogenic landscape fragmentation on the genetic diversity and adaptive potential of plant populations is a major issue in conservation biology. However, little is known about the partitioning of genetic diversity in alpine species, which occur in naturally fragmented habitats. Here, we, investigate molecular patterns of three alpine plants (Epilobium fleischeri, Geum reptans and Campanula thyrsoides) across Switzerland and ask whether Spatial isolation has led to high levels of populations differentiation, increasing over distance, and a decrease of within-population variability. We further hypothesize that file contrasting potential for long-distance dispersal (LDD) of Seed in these Species will considerably influence and explain diversity partitioning. Methods For each study species, we Sampled 20-23 individuals from each of 20-32 populations across entire Switzerland. We applied Random Amplified Polymorphic Dimorphism markers to assess genetic diversity within (Nei's expected heterozygosity, H-e; percentage of polymorphic hands, P-P) and among (analysis of molecular variance, Phi(st)) populations and correlated population size and altitude with within-populalion diversity. Spatial patterns of genetic relatedness were investigated using Mantel tests and standardized major axis regression as well as unweighted pair group method with arithmetic mean cluster analyses and Monmonier's algorithm. To avoid known biases, We standardized the numbers of populations, individuals and markers using multiple random reductions. We modelled LDD with a high alpine wind data set using the terminal velocity and height of seed release as key parameters. Additionally, we assessed a number of important life-history traits and factors that potentially influence genetic diversity partitioning (e.g. breeding system, longevity and population size). Important findings For all three species, We found a significant isolation-by-distance relationship but only a moderately high differentiation among populations (Phi(st): 22.7, 48 and 16.8%, for E. fleischeri, G. reptans and C. thyrsoides, respectively). Within-population diversity (H-c: 0.19-0.21, P-p: 62-75%) was not reduced in comparison to known results from lowland species and even small populations with < 50 reproductive individuals contained high levels of genetic diversity. We further found no indication that a high long-distance seed dispersal potential enhances genetic connectivity among populations. Gene flow seems to have a strong stochastic component causing large dissimilarity between population pairs irrespective of the spatial distance. Our results suggest that other life-history traits, especially the breeding System, may play an important role in genetic diversity partitioning. We conclude that spatial isolation in the alpine environment has a strong influence on population relatedness but that a number of factors can considerably influence the strength of this relationship.

The annexins: spatial and temporal coordination of signaling events during cellular stress

Annexins are a family of structurally related, Ca2+-sensitive proteins that bind to negatively charged phospholipids and establish specific interactions with other lipids and lipid microdomains. They are present in all eukaryotic cells and share a common folding motif, the "annexin core", which incorporates Ca2+- and membrane-binding sites. Annexins participate in a variety of intracellular processes, ranging from the regulation of membrane dynamics to cell migration, proliferation, and apoptosis. Here we focus on the role of annexins in cellular signaling during stress. A chronic stress response triggers the activation of different intracellular pathways, resulting in profound changes in Ca2+ and pH homeostasis and the production of lipid second messengers. We review the latest data on how these changes are sensed by the annexins, which have the ability to simultaneously interact with specific lipid and protein moieties at the plasma membrane, contributing to stress adaptation via regulation of various signaling pathways.