Current climate, isolation and history drive global patterns of tree phylogenetic endemism

AimWe mapped global patterns of tree phylogenetic endemism (PE) to identify hotspots and test hypotheses about possible drivers. Specifically, we tested hypotheses related to current climate, geographical characteristics and historical conditions and assessed their relative importance in shaping PE patterns.LocationGlobal.Time periodWe used the present distribution of trees, and predictors covering conditions from the mid‐Miocene to present.Major taxa studiedAll seed‐bearing trees.MethodsWe compiled distributions for 58,542 tree species across 463 regions worldwide, matched these to a recent phylogeny of seed plants and calculated PE for each region. We used a suite of predictor variables describing current climate (e.g., mean annual temperature), geographical characteristics (e.g., isolation) and historical conditions (e.g., tree cover at the Last Glacial Maximum) in a spatial regression model to explain variation in PE.ResultsTree PE was highest on islands, and was higher closer to the equator. All three groups of predictor variables contributed substantially to the PE pattern. Isolation and topographic heterogeneity promoted high PE, as did high current tree cover. Among mainland regions, temperature seasonality was strongly negatively related to PE, while mean annual temperature was positively related to PE on islands. Some relationships differed among the major floristic regions. For example, tree cover at the Last Glacial Maximum was a positive predictor of PE in the Palaeotropics, while tree cover at the Miocene was a negative predictor of PE in the Neotropics.Main conclusionsGlobally, PE can be explained by a combination of geographical, historical and current factors. Some geographical variables appear to be key predictors of PE. However, the impact of historic and current climate variables differs considerably among the major floristic regions, reflecting their unique histories. Hence, the current distribution of trees is the result of globally relevant geographical drivers and regional climatic histories.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153237/1/geb13001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153237/2/geb13001_am.pd

The Roman exoplanet Imaging data challenge: a major community engagement effort

Organized by the Turnbull Science Investigation Team (SIT), the 2019-2020 Roman Exoplanet Imaging Data Challenge (EIDC) launched in mid October 2019 and ran for eight months. This data challenge was a unique opportunity for exoplanet scientists of all backgrounds and experience levels to get acquainted with realistic Roman CGI (coronagraphic) simulated data with a new contrast regimes at 10-8 to 10-9 enabling to unveil planets down to the Neptune-mass in reflected light. Participating teams had to recover the astrometry of an exoplanetary system combining precursor radial velocity data (also simulated across 15 years) with two to six coronagraphic imaging epochs (HLC and Star Shade). They had to perform accurate orbital fitting and determine the mass of any planet hidden in the data. It involved PSF subtraction techniques, post-processing and other astrophysics hurdles to overcome such as contamination sources (stellar, extragalactic and exozodiacal light). We organized four tutorial "hack-a-thon" events to get as many people on-board. The EIDC proved to be an excellent way to engage with the intricacies of the first mission to perform wavefront control in space, as a pathfinder to future flagship missions. It also generated a lot of positive interactions between open source package owners and a whole new set of young exoplanet scientists running them. As a community we are a few steps closer to being ready to analyze real CGI data

Uncertainty in United States coastal wetland greenhouse gas inventorying

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Environmental Research Letters 13 (2018): 115005, doi:10.1088/1748-9326/aae157.Coastal wetlands store carbon dioxide (CO2) and emit CO2 and methane (CH4) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO2-equivalent (CO2e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO2e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO2e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH4 fluxes to CO2e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes. Social Media Abstract: US coastal wetlands were a recent and uncertain source of greenhouse gasses because of CH4 and erosion.Financial support was provided primarily by NASA Carbon Monitoring Systems (NNH14AY67I) and the USGS Land Carbon Program, with additional support from The Smithsonian Institution, The Coastal Carbon Research Coordination Network (DEB-1655622), and NOAA Grant: NA16NMF4630103

Comprehensive linkage and linkage heterogeneity analysis of 4344 sibling pairs affected with hypertension from the Family Blood Pressure Program

Linkage analyses of complex, multifactorial traits and diseases, such as essential hypertension, have been difficult to interpret and reconcile. Many published studies provide evidence suggesting that different genes and genomic regions influence hypertension, but knowing which of these studies reflect true positive results is challenging. The reasons for this include the diversity of analytical methods used across these studies, the different samples and sample sizes in each study, and the complicated biological underpinnings of hypertension. We have undertaken a comprehensive linkage analysis of 371 autosomal microsatellite markers genotyped on 4,334 sibling pairs affected with hypertension from five ethnic groups sampled from 13 different field centers associated with the Family Blood Pressure Program (FBPP). We used a single analytical technique known to be robust to interpretive problems associated with a lack of completely informative markers to assess evidence for linkage to hypertension both within and across the ethnic groups and field centers. We find evidence for linkage to a number of genomic regions, with the most compelling evidence from analyses that combine data across field center and ethnic groups (e.g., chromosomes 2 and 9). We also pursued linkage analyses that accommodate locus heterogeneity, which is known to plague the identification of disease susceptibility loci in linkage studies of complex diseases. We find evidence for linkage heterogeneity on chromosomes 2 and 17. Ultimately our results suggest that evidence for linkage heterogeneity can only be detected with large sample sizes, such as the FBPP, which is consistent with theoretical sample size calculations. Genet. Epidemiol . 2007. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56011/1/20202_ftp.pd

Proximity-Based Differential Single-Cell Analysis of the Niche to Identify Stem/Progenitor Cell Regulators

Physiological stem cell function is regulated by secreted factors produced by niche cells. In this study, we describe an unbiased approach based on differential single-cell gene expression analysis of mesenchymal osteolineage cells close to and further removed from hematopoietic stem/progenitor cells to identify candidate niche factors. Mesenchymal cells displayed distinct molecular profiles based on their relative location. Amongst the genes which were preferentially expressed in proximal cells, we functionally examined three secreted or cell surface molecules not previously connected to HSPC biology: the secreted RNase Angiogenin, the cytokine IL18 and the adhesion molecule Embigin and discovered that all of these factors are HSPC quiescence regulators. Our proximity-based differential single cell approach therefore reveals molecular heterogeneity within niche cells and can be used to identify novel extrinsic stem/progenitor cell regulators. Similar approaches could also be applied to other stem cell/niche pairs to advance understanding of microenvironmental regulation of stem cell function

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors