Phenological and structural linkages to seasonality inform productivity relationships in the Amazon Rainforest

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149280/1/nph15783.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149280/2/nph15783_am.pd

THE USE OF A MULTI-OBJECTIVE GENETIC ALGORITHM FOR CALIBRATION OF WATER QUALITY NUMERICAL MODEL OF EAGLE CREEK RESERVOIR, IN

poster abstractWater quality models used for water resource management require large amounts of input parameters, whose values may or may not be readily available. The calibration of these models involves the adjustment of several input parameters. The credibility of calibrated models is judged based on their agreement with actual data. However, calibration of water quality numerical models can be an exceptionally computationally challenging process. In this research, the Environmental Fluid Dynamic Code’s (EFDC) HEM3D water quality model was developed for the Eagle Creek Reservoir in order to model three algal groups (cyanobacteria, diatoms, and greens) as well as reservoir nutrient dynamics. A multi-objective genetic algorithm was then used for calibration by adjusting predetermined input parameters within a certain range and based on the model’s agreement with observed data in the reservoir. The genetic algorithm was parallelized to work across a network of machines and on multiple threads. This presentation will demonstrate the advantages of using such a parallelized genetic algorithm for efficiently calibrating computationally expensive numerical models

Below-Ground Root Structure and Ecophysiological Controls of Plant Water Flux During Drought: From Individual to Ecosystem

Shifting patterns of precipitation and rising temperatures have highlighted forest vulnerability to heat- and drought-induced stress. For systems that face water-limitation, either from short-term, seasonal dry periods or longer-term droughts, plasticity of root system function establishes the ability of individuals to meet atmospheric demand and maintain physiological function. This functional plasticity is determined by an individual’s intrinsic properties and their interactions within the community and environment. Given limitations to in situ measurement, improved model representation of below-ground structural and functional complexity has provided means for exploring these ecophysiological feedbacks between drying soil and trees across biomes. This research addresses individual, ecosystem, and basin scale responses to water limitation by examining (i) the role of below-ground structure and ecophysiological controls on water uptake across functional gradients (i.e., low diversity vs. high diversity ecosystems); (ii) identifying and expanding the utility of novel proxies of hydraulic function; and (iii) exploring the feasibility of monitoring drought response at large scales using a parsimonious model of surface energy partitioning. Modeled root water uptake from both temperate and tropical systems highlight that independent of functional strategy, root lateral interactions at the tree scale directly impact the depth distribution of water uptake and plant hydraulic status. A newly developed index of root system interaction provides an amenable axis with which to explore the tradeoffs between structural investment and resource acquisition. Laboratory and field analysis show that conventional technologies used to measure sap flow velocity may contain hidden information regarding a tree’s hydraulic state. This low frequency signal may also serve well as a proxy for below-ground response to the drying soil, providing valuable validation for future modeling efforts. Finally, the feasibility of hourly, basin scale estimates of the land-surface energy budget partition are tested. The Maximum Entropy Production model is successfully applied to the Amazon River Basin, a highly complex region prone to strong seasonal droughts, elucidating avenues of future research needed to more fully link ecosystem and hydrologic processes. The methodologies developed and expanded in this work provide new avenues for assessing tree-scale water fluxes and hydraulic state, providing a means for observing and testing hypotheses related to ecophysiological response across spatiotemporal scales.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153402/1/lizagee_1.pd

Adding our leaves: A communityâ wide perspective on research directions in ecohydrology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/1/hyp13693.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/2/hyp13693_am.pd

Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure-volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions-which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts

Through a Glass, Darkly:The CIA and Oral History

This article broaches the thorny issue of how we may study the history of the CIA by utilizing oral history interviews. This article argues that while oral history interviews impose particular demands upon the researcher, they are particularly pronounced in relation to studying the history of intelligence services. This article, nevertheless, also argues that while intelligence history and oral history each harbour their own epistemological perils and biases, pitfalls which may in fact be pronounced when they are conjoined, the relationship between them may nevertheless be a productive one. Indeed, each field may enrich the other provided we have thought carefully about the linkages between them: this article's point of departure. The first part of this article outlines some of the problems encountered in studying the CIA by relating them to the author's own work. This involved researching the CIA's role in US foreign policy towards Afghanistan since a landmark year in the history of the late Cold War, 1979 (i.e. the year the Soviet Union invaded that country). The second part of this article then considers some of the issues historians must confront when applying oral history to the study of the CIA. To bring this within the sphere of cognition of the reader the author recounts some of his own experiences interviewing CIA officers in and around Washington DC. The third part then looks at some of the contributions oral history in particular can make towards a better understanding of the history of intelligence services and the CIA

Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis

Abstract: Uterine leiomyomata (UL) are the most common neoplasms of the female reproductive tract and primary cause for hysterectomy, leading to considerable morbidity and high economic burden. Here we conduct a GWAS meta-analysis in 35,474 cases and 267,505 female controls of European ancestry, identifying eight novel genome-wide significant (P < 5 × 10−8) loci, in addition to confirming 21 previously reported loci, including multiple independent signals at 10 loci. Phenotypic stratification of UL by heavy menstrual bleeding in 3409 cases and 199,171 female controls reveals genome-wide significant associations at three of the 29 UL loci: 5p15.33 (TERT), 5q35.2 (FGFR4) and 11q22.3 (ATM). Four loci identified in the meta-analysis are also associated with endometriosis risk; an epidemiological meta-analysis across 402,868 women suggests at least a doubling of risk for UL diagnosis among those with a history of endometriosis. These findings increase our understanding of genetic contribution and biology underlying UL development, and suggest overlapping genetic origins with endometriosis

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57