Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as taxonomy, biology, pharmacy,finance, agriculture, ecology, health and architecture

Inferring the occurrence of regime shifts in a shallow lake during the last 250 years based on multiple indicators

Regime shifts are ecosystem-scale phenomena. In lake studies, most supporting evidence is frequently based on a single state variable. We examined the sediment record of the shallow lake Blanca Chica (Argentina) to explore the response of multiple proxies belonging to different trophic levels (nutrients, chlorophyll and carotenoid pigments, diatoms, Cladocera remains, and Rotifera resting eggs) over the last 250 yr. We explored different ecological indicators to assess changes consistent with regime shifts. To do so, first we identified the timing of transitional periods on multiple-proxies. Then, we explored (1) the nature of the change (linear versus non-linear dynamics), (2) different indicators of a shift across the food web: multimodality and resilience indicators (standard deviation and autocorrelation), and (3) examined the synchronicity of the detected indicators at multiple-trophic levels. Generalized additive models fitted to the ordination scores of the assemblages analyzed revealed two transitions: ca. 1860–1900, and 1915–1990. Ecological indicators of regime shifts revealed that the first transition is consistent with a threshold state response (change in the ecosystem state manifest as a jump when the driver exceeds a state threshold), and the second one with a critical transition (hysteretic transition in which the system change to an alternate stable state). After the first transition lake structure shifted from littoral to pelagic species dominance (evidenced by Cladocera and diatom assemblages), and turbidity increased, indicating a rise in lake water level. This transition was non-linear, showed multimodality, and is most likely driven by an increase in precipitation registered in the region since 1870. During the second transition, nutrient levels rose, all indicators showed multimodality, non-linear dynamics and an increase in standard deviation prior to the regime shift. These dynamics are consistent with a critical transition in response to eutrophication, and coincides with a post-1920 change in land use. Our results show that several ecological indicators of regime shifts need to be examined to perform an accurate diagnosis. We highlight the relevance of a multi-proxy approach including multiple-trophic level responses as the appropriate scale of analysis to determine the occurrence, type and dynamics of regime shifts. We also show that resilience indicators and critical transitions can be detectable in the whole food web and that shallow lakes can undergo different types of regime shifts.Fil: Gonzalez Sagrario, Maria de Los Angeles. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Musazzi, Simona. Consiglio Nazionale delle Ricerche; ItaliaFil: Cordoba, Francisco Elizalde. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mendiolar, Manuela. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Lami, Andrea. Consiglio Nazionale delle Ricerche; Itali

Use of Whole Genome Shotgun Sequencing for the Analysis of Microbial Communities in Arabidopsis thaliana Leaves

Microorganisms, such as all Bacteria, Archaeae, and some Eukaryotes, inhabit all imaginable habitats in the planet, from water vents in the deep ocean to extreme environments of high temperature and salinity. Microbes also constitute the most diverse group of organisms in terms if genetic information, metabolic function, and taxonomy. Furthermore, many of these microbes establish complex interactions with each others and with many other multicellular organisms. The collection of microbes that share a body space with a plant or animal is called the microbiota, and their genetic information is called the microbiome. The microbiota has emerged as a crucial determinant of a host’s overall health and understanding it has become crucial in many biological fields. In mammals, the gut microbiota has been linked to important diseases such as diabetes, inflammatory bowel disease, and dementia. In plants, the microbiota can provide protection against certain pathogens or confer resistance against harsh environmental conditions such as drought. Furthermore, the leaves of plants represent one of the largest surface areas that can potentially be colonized by microbes. The advent of sequencing technologies has let researchers to study microbial communities at unprecedented resolution and scale. By targeting individual loci such as the 16S rDNA locus in bacteria, many species can be studied simultaneously, as well as their properties such as relative abundance without the need of individual isolation of target taxa. Decreasing costs of DNA sequencing has also led to whole shotgun sequencing where instead of targeting a single or a number of loci, random fragments of DNA are sequenced. This effectively renders the entire microbiome accessible to study, referred to as metagenomics. Consequently many more areas of investigation are open, such as the exploration of within host genetic diversity, functional analysis, or assembly of individual genomes from metagenomes. In this study, I described the analysis of metagenomic sequencing data from microbial 11 communities in leaves of wild Arabidopsis thaliana individuals from southwest Germany. As a model organisms, A. thaliana not only is accessible in the wild but also has a rich body of previous research in plant-microbe interactions. In the first section, I describe how whole shotgun sequencing of leaf DNA extracts can be used to accurately describe the taxonomic composition of the microbial community of individual hosts. The nature of whole shotgun sequencing is used to estimate true microbial abundances which can not be done with amplicons sequencing. I show how this community varies across hosts, but some trends are seen, such as the dominance of the bacterial genera Pseudomonas and Sphingomonas . Moreover, even though there is variation between individuals, I explore the influence of site of origin and host genotype. Finally, metagenomic assembly is applied to individual samples, showing the limitations of WGS in plant leaves. In the second section, I explore the genomic diversity of the most abundant genera: Pseudomonas and Sphingomonas . I use a core genome approach where a set of common genes is obtained from previously sequenced and assembled genomes. Thereafter, the gene sequences of the core genome is used as a reference for short genome mapping. Based on these mappings, individual strain mixtures are inferred based on the frequency distribution of non reference bases at each detected single nucleotide polymorphism (SNP). Finally, SNP’s are then used to derive population structure of strain mixtures across samples and with known reference genomes. In conclusion, this thesis provides insights into the use of metagenomic sequencing to study microbial populations in wild plants. I identify the strengths and weaknesses of using whole genome sequencing for this purpose. As well as a way to study strain level dynamics of prevalent taxa within a single host

The strengths and weaknesses of species distribution models in biome delimitation

This is the final published version, also available from Frontiers via the DOI in this record.Aim: The aim was to test whether species distribution models (SDMs) can reproduce major macroecological patterns in a species-rich, tropical region and provide recommendations for using SDMs in areas with sparse biotic inventory data. Location: North-east Brazil, including Minas Gerais. Time period: Present. Major taxa studied: Flowering plants. Methods: Species composition estimates derived from stacked SDMs (s-SDMs) were compared with data from 1,506 inventories of 933 woody plant species from north-east Brazil. Both datasets were used in hierarchical clustering analyses to delimit floristic units that correspond to biomes. The ability of s-SDMs to predict the identity, functional composition and floristic composition of biomes was compared across geographical and environmental space. Results: The s-SDMs and inventory data both resolved four major biomes that largely corresponded in terms of their distribution, floristics and function. The s-SDMs proved excellent at identifying broad-scale biomes and their function, but misassigned many individual sites in complex savanna–forest mosaics. Main conclusions: Our results show that s-SDMs have a unique role to play in describing macroecological patterns in areas lacking inventory data and for poorly known taxa. s-SDMs accurately predict floristic and functional macroecological patterns but struggle in areas where non-climatic factors, such as fire or soil, play key roles in governing distributions.Natural Environment Research Council (NERC)Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorConselho Nacional de Desenvolvimento Científico e TecnológicoRoyal Societ

An exploration of materials taxonomies to support streamlined life cycle assessment

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 130-134).As life cycle assessment (LCA) gains prominence as a reliable method of environmental evaluation, concerns about data availability and quality have become more important. LCA is a resource intensive methodology, and thus data gaps pose a frequent challenge, motivating the need for robust streamlining approaches. Existing methods for filling data gaps often ignore the effects of the uncertainty inherent in estimated data. Under-specification, or using structured data to provide less information in product characterization, is one option to incorporate uncertainty, and has been shown to be a viable method both for streamlining and decision-making under uncertainty. However, previous work did not emphasize developing robust data structures intended to balance trade-offs between effectiveness and efficiency in streamlining methods. Furthermore, there was little consideration given to analyzing the environmental profile (multiple impacts) of a process, rather than a single impact. This thesis explores how data mining techniques can be used to quantitatively develop data structures to enable streamlined assessment. The use of clustering and principal component analysis is explored in an effort to identify potential material classifications, and other statistical methods further assess the classifications. These insights are used to create hierarchical taxonomies that are evaluated in terms of effectiveness and efficiency. The method is applied to life cycle inventory process datasets for three material types (metals, polymers, and precious metals). Four environmental midpoints from the TRACI 2.0 impact assessment method are used to illustrate the uncertainty reduction enabled by classification. It was found that the most useful classification method for both metals and polymers was based on price, and for precious metals, material type and recycled content. In general, the method was able to select efficient groupings that accounted for a large percentage of the overall variation in the data. With one additional level in the taxonomy, the overall median percent error rates were approximately 30- 40% for all impacts except non carcinogenicity, which was 65-80%. This is compared to initial error rates that were on average twice as high for the metals and precious metals datasets. Case studies demonstrated how the analysis and structure provided by this methodology can be useful in comparative decision-making, to reduce the number of elements prioritized for detailed data collection in triage methods, and for developing models to predict materials' impacts. This work serves as a framework for structuring data to enable streamlined LCA as well as provides guidance for predictive model development. By showing the feasibility of developing effective and efficient taxonomies, the work demonstrates a method to reduce the amount of information required to characterize a product while achieving relatively low uncertainty in the final product impact.by Lynn Reis.S.M. in Technology and Polic

Biogeochemistry of marine phanerogams soils

Marine phanerogams have belowground organs owed to their past as terrestrial plants. Traditionally, its substrata have been considered sediments. However, given enough time, they would promote the formation of a soil. There are two distinct biogeochemical compartments in seagrass soils, the rhizosphere and the subsoil. The main processes found in this thesis were somewhat related to organic matter accumulation and mineralization. Changes in plant physiology, affect the rhizosphere biogeochemistry, but not the subsoil