A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology

In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.Comment: Revision accepted for publication by the Journal of Agricultural, Biological, and Environmental Statistic

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning Researc

The roles of divergence and hybridization in shaping patterns of genetic and phenotypic variation across the evolutionary continuum in Juniperus and Piper

Genetic and phenotypic variation across populations, species, and radiations mediates the form and outcome of biotic and abiotic interactions and represents a major axis of biodiversity. Resolving patterns of variation across shallow and deep evolutionary divergences can provide key insights into the processes that generate and maintain this variation over micro- and macroevolutionary timescales. Additionally, variation in functional traits that interface with the biotic and abiotic environments plays an important role in adaptive evolution, and can shed light on the drivers of differentiation and diversification. Here, I analyzed genome-scale variation spanning individuals, populations, and species to 1) resolve complex diversification histories, 2) characterize landscape patterns of hybrid admixture and plant secondary chemistry, and 3) characterize macroevolutionary patterns of plant secondary chemistry. First, I reconstructed the evolutionary history of the serrate juniper clade of North America (Juniperus) as it diversified into arid habitats of the western United States and Mexico. Second, I examined how admixture across the species boundary influences patterns of genetic and phytochemical variation following secondary contact among three serrate juniper species. Finally, I resolve the timing and tempo of diversification in the Radula clade of Piper to understand how secondary chemistry evolves within a diverse tropical plant radiation. My work demonstrates the importance of evolutionary processes occurring along the evolutionary continuum for generating contemporary patterns of variation and diversity

Quantitative electron microscopy for microstructural characterisation

Development of materials for high-performance applications requires accurate and useful analysis tools. In parallel with advances in electron microscopy hardware, we require analysis approaches to better understand microstructural behaviour. Such improvements in characterisation capability permit informed alloy design. New approaches to the characterisation of metallic materials are presented, primarily using signals collected from electron microscopy experiments. Electron backscatter diffraction is regularly used to investigate crystallography in the scanning electron microscope, and combined with energy-dispersive X-ray spectroscopy to simultaneusly investigate chemistry. New algorithms and analysis pipelines are developed to permit accurate and routine microstructural evaluation, leveraging a variety of machine learning approaches. This thesis investigates the structure and behaviour of Co/Ni-base superalloys, derived from V208C. Use of the presently developed techniques permits informed development of a new generation of advanced gas turbine engine materials.Open Acces

New computational methods and plant models for evolutionary genomics

This thesis is in the service of a greater understanding of the genetic basis of adaptive traits. Chapter 1 introduces background literature relevant to this thesis. Chapters 2, 3, and 4 develop novel methods and software for the analysis of genetic sequencing data. Chapter 5 details a large collaborative project to establish genetic resources in the model cereal Brachypodium, and perform a genome-wide association study for several agriculturally-relevant traits under two climate change scenarios. Chapter 6 investigates the spatial genetic patterns in two species of woodland eucalypt, and determines the landscape process that could be driving these patterns. Finally, Chapter 7 summarises these works, and proposes some areas of further study. In Chapters 2 and 3, I develop methods that enable analysis of Genotyping-by-sequencing analysis. Axe, a short read sequence demultiplexer, demultiplexes samples from multiplexed GBS sequencing datasets. I show Axe has high accuracy, and outperforms previously published software. Axe also tolerates complex indexing schemes such as the variable-length combinatorial indexes used in GBS data. Trimit and libqcpp (Chapter 3) implements several low-level sequence read quality assessment and control methods as a C++ library, and as a command line tool. Both these works have been published in peer-reviewed journals, and are used by numerous groups internationally. In Chapter 4, I develop kWIP, a de novo estimator of genetic distance. kWIP enables rapid estimation of genetic distances directly from sequence reads. We first show kWIP outperforms a competing method at low coverage using simulations that mimic a population resequencing experiment. We propose and demonstrate several use cases for kWIP, including population resequencing, initial assessment of sample identity, and estimating metagenomic similarity. kWIP was published in PLoS Computational Biology. In Chapter 5, I present the results of a large, collaborative project which surveys the global genetic diversity of the model cereal Brachypodium. We amass a collection of over 2000 accessions from the Brachypodium species complex. Using GBS and whole genome sequencing we identify around 800 accessions of the diploid Brachypodium distachyon, within which we find extensive population structure and clonal families. Through population restructuring we create a core collection of 74 accessions containing the majority of genetic diversity in the "A genome" sub-population. Using this core collection, we assay several phenotypes of agricultural interest including early vigour, harvest index and energy use efficiency under two climates, and dissect the genetic basis of these traits using a genome-wide association study (GWAS). This work has been accepted for publication at Genetics; I am co-first author with Pip Wilson and Jared Streich, having lead many genomic analyses. In Chapter 6, I perform a study of landscape genomic variation in two woodland eucalypt species. Using whole genome sequencing of around 200 individuals from around 20 localities of both E. albens and E. sideroxylon, I find incredible genetic diversity and low genome-wide inter-species differentiation.I find no support for strong discrete population structure, but strong support for isolation by (geographic) distance (IBD). Using generalised dissimilarity modelling, I further examine the pattern of IBD, and establish additional isolation by environment (IBE). E. albens shows moderately strong IBD, explaining 26% of deviance in genetic distance using geographic distance, and an additional 6% deviance explained by incorporating environmental predictors (IBE). E. sideroxylon shows much stronger IBD, with 78% of deviance explained by geography, and stronger IBE (12% additional deviance explained). This work will soon be submitted for publication

A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

This is a post-peer-review, pre-copyedit version of an article published in Journal of Agricultural, Biological and Environmental Statistics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s13253-019-00367-1In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.Icelandic Centre for Research (152457).Peer reviewe

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed