SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Methyl arsenic adsorption and desorption behavior on iron oxides

Arsenic is a toxic element that is widely distributed throughout the earth??s crust as a result of both natural geologic processes and anthropogenic activities. In virtually all environments, methylated forms of arsenic can be found. Because of the widespread distribution and toxicity of arsenic and methyl-arsenic, their adsorption behavior on soil minerals is of great interest. Although considerable attention has been given to the behavior of inorganic arsenic on mineral surfaces, little research has been conducted regarding interactions of the methyl-arsenic forms. The objective of this study was to compare the adsorption and desorption behavior of methylarsonate (MMAsV), methylarsonous acid (MMAsIII), dimethylarsinate (DMAsV), dimethylarsinous acid (DMAsIII), arsenate (iAsV), and arsenite (iAsIII) on iron oxide minerals (goethite and ferrihydrite) by means of adsorption isotherms and adsorption envelopes. Additionally, desorption envelopes were obtained using sulfate and phosphate as competitive ligands. Arsenic was measured by FI-HG-AAS. MMAsV and iAsV were adsorbed in higher amounts than DMAsV on goethite and ferrihydrite at all pH values studied. Although MMAsV and iAsV were adsorbed quantitatively at lower concentrations on goethite and ferrihydrite, as arsenic concentration was increased MMAsV was adsorbed in slightly lower quantities than iAsV. DMAsV was not quantitatively adsorbed at any concentration on goethite or ferrihydrite. MMAsV and iAsV exhibited high adsorption affinities on both goethite and ferrihydrite at pH values below 9 and showed decreasing adsorption above this point (more rapidly for MMAsV). DMAsV was adsorbed only at pH values below 8 on ferrihydrite and below 7 on goethite. MMAsV, iAsV, and DMAsV each exhibited adsorption characteristics suggesting specific adsorption on both goethite and ferrihydrite. Increased methyl substitution resulted in increased ease of arsenic release from the iron oxide surface. MMAsIII and DMAsIII exhibited no evidence for any type of specific adsorption under the conditions studied. Phosphate was a more effective desorbing ion than sulfate, but neither desorbed all arsenic species quantitatively

HIGH RESOLUTION FT-IR SPECTROSCOPY OF TRANS-1.2-DIFLUOROETHYLENE

$^{1}$ N. C. Craig and E. A. Entemann. J. Am. Chem. Soc. 83. 3047(1961): N. C. Craig and J. Overend. J. Chem Phys. 51 . 1127 (1969) $^{2}$ S. Saebo. and H. Sellers. J. Phys. Chem.92. 4266(1988). $^{3}$ V. W. Laurie and D. T. Pence. J. Chem. Phys. 38. 2693(1963) $^{4}$ J. L. Carios. R. R. Karl, and S. H. Bauer. J. Chem. Soc. Faraday Trans 2. 177(1974); E. J. M. van Schaick. F. C. Mijlbolt. G. Renes, and H. J. Goise. J. Mol. Struct. 21. 17(1974).Author Institution: Department of Chemistry, Oberlin College; Molecular Physics Division, National Institute of Standards and TechnologyContrary to qualitative notions, the cis Isomer of 1,2-difluoroethylene has a lower electronic energy than the trans $Isomer.^{1}$ Consequently. calculating the energy difference and the subtle structural differences has attracted the attention of ab initlo $theorists. ^{2}$ A complete, microwave-derived structure for the cis Isomer is $available. ^{3}$ but only a partial, electron diffraction-derived structure exists for the non-polar trans $Isomer.^{4}$ With the goal of obtaining a full structure of the trans Isomer, we have begun an Investigation of the high resolution FT. IR spectra of $trans-^{1.2}$ difuoroethylene and its $-d_{1}$ and $-d_{2}$ modifications on a Bomem DA3 FT-IR spectrometer at a resolution of $0.004 cm^{-1}$. This Isomer is a near-symmetric protate rotor with $\kappa= -0,9898$. Since this molecule has $C_{2h}$ symmetry. bands are either pure type-C or hybrid type-A/B in shape. The dominant type-A component of the $\nu_{10}(b_{u})$ fundamental centered at $1274cm^{-1}$ and the type-C $v_{6}(A_{u})$ fundamental centered at $874 cm^{-1}$ have been analyzed in detail. Part of the type-B component of the hybrid band of the $v_{6} + v_{8} (Au)$ combination lone centered at $1656 cm^{-1}$ has also been analyzed. Eleven rolational parameters have been fit to 1107 ground state combination differences, derived from the three bands, with a $0.00060 cm^{-1}$. These ground state rotational parameters include $A = 1.8934058(25), B = 0.1345413(11), C = 0.1255427(10) cm^{-1}$. The significance of these results for the structure of the trans isomer will be discussed. For the type-A band, which is unperturbed, nine upper state rotational constants were list to 1052 transitions with $\sigma = 0.00060 cm^{-1}$. For this upper state, $A = 1.8926002(29), B = 0.13464887(17), C = 0.12546217(19) cm^{-1}$. The type-B and type-C bands are perturbed. A partial analysis of the latter will be discussed

Arsenite modifies structure of soil microbial communities and arsenite oxidization potential

International audienceThe influence of arsenite [As(III)] on natural microbial communities and the capacity of exposed communities to oxidize As(III) has not been well explored. In this study, we conducted soil column experiments with a natural microbial community exposed to different carbon conditions and a continuous flow of As(III). We measured the oxidation rates of As(III) to As(V), and the composition of the bacterial community was monitored by 454 pyrosequencing of 16S rRNA genes. The diversity of As(III)-oxidizing bacteria was examined with the aox gene, which encodes the enzyme involved in As(III) oxidation. Arsenite oxidation was high in the live soil regardless of the carbon source and below detection in sterilized soil. In columns amended with 200 lmol kg À1 of As (III), As(V) concentrations reached 158 lmol kg À1 in the column effluent, while As(III) decreased to unmeasurable levels. Although the number of bacterial taxa decreased by as much as twofold in treatments amended with As(III), some As(III)-oxidizing bacterial groups increased up to 20-fold. Collectively, the data show the large effect of As(III) on bacterial diversity, and the capacity of natural communities from a soil with low initial As contamination to oxidize large inputs of As(III)

Electron Energy-Loss Safe-Dose Limits for Manganese Valence Measurements in Environmentally Relevant Manganese Oxides

Manganese (Mn) oxides are among the strongest mineral oxidants in the environment and impose significant influence on mobility and bioavailability of redox-active substances, such as arsenic, chromium, and pharmaceutical products, through oxidation processes. Oxidizing potentials of Mn oxides are determined by Mn valence states (2+, 3+, 4+). In this study, the effects of beam damage during electron energy-loss spectroscopy (EELS) in the transmission electron microscope have been investigated to determine the “safe dose” of electrons. Time series analyses determined the safe dose fluence (electrons/nm²) for todorokite (10⁶ e/nm²), acid birnessite (10⁵), triclinic birnessite (10⁴), randomly stacked birnessite (10³), and δ-MnO₂ (<10³) at 200 kV. The results show that meaningful estimates of the mean Mn valence can be acquired by EELS if proper care is taken