Benthic assemblages are more predictable than fish assemblages at an island scale

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sandin, S. A., Alcantar, E., Clark, R., de Leon, R., Dilrosun, F., Edwards, C. B., Estep, A. J., Eynaud, Y., French, B. J., Fox, M. D., Grenda, D., Hamilton, S. L., Kramp, H., Marhaver, K. L., Miller, S. D., Roach, T. N. F., Seferina, G., Silveira, C. B., Smith, J. E., Zgliczynski, B. J., & Vermeij, M. J. A. Benthic assemblages are more predictable than fish assemblages at an island scale. Coral Reefs, 41, (2022.): 1031–1043, https://doi.org/10.1007/s00338-022-02272-5.Decades of research have revealed relationships between the abundance of coral reef taxa and local conditions, especially at small scales. However, a rigorous test of covariation requires a robust dataset collected across wide environmental or experimental gradients. Here, we surveyed spatial variability in the densities of major coral reef functional groups at 122 sites along a 70 km expanse of the leeward, forereef habitat of Curaçao in the southern Caribbean. These data were used to test the degree to which spatial variability in community composition could be predicted based on assumed functional relationships and site-specific anthropogenic, physical, and ecological conditions. In general, models revealed less power to describe the spatial variability of fish biomass than cover of reef builders (R2 of best-fit models: 0.25 [fish] and 0.64 [reef builders]). The variability in total benthic cover of reef builders was best described by physical (wave exposure and reef relief) and ecological (turf algal height and coral recruit density) predictors. No metric of anthropogenic pressure was related to spatial variation in reef builder cover. In contrast, total fish biomass showed a consistent (albeit weak) association with anthropogenic predictors (fishing and diving pressure). As is typical of most environmental gradients, the spatial patterns of both fish biomass density and reef builder cover were spatially autocorrelated. Residuals from the best-fit model for fish biomass retained a signature of spatial autocorrelation while the best-fit model for reef builder cover removed spatial autocorrelation, thus reinforcing our finding that environmental predictors were better able to describe the spatial variability of reef builders than that of fish biomass. As we seek to understand spatial variability of coral reef communities at the scale of most management units (i.e., at kilometer- to island-scales), distinct and scale-dependent perspectives will be needed when considering different functional groups.This research and the larger efforts of Blue Halo Curacao were supported by funding from the Waitt Institute and with permissions from the Government of Curacao, Ministry of Health, Environment, and Nature. Field logistics were further supported by the Waitt Institute vessel crew, CARMABI Foundation, The Dive Shop Curacao, and Dive Charter Curacao

Big issues for small feet : developmental, biomechanical and clinical narratives on children's footwear

The effects of footwear on the development of children's feet has been debated for many years and recent work from the developmental and biomechanical literature has challenged long-held views about footwear and the impact on foot development. This narrative review draws upon existing studies from developmental, biomechanical and clinical literature to explore the effects of footwear on the development of the foot. The emerging findings from this support the need for progress in [children's] footwear science and advance understanding of the interaction between the foot and shoe. Ensuring clear and credible messages inform practice requires a progressive evidence base but this remains big issue in children's footwear research

Atomically-thin micas as proton conducting membranes

Monolayers of graphene and hexagonal boron nitride (hBN) are highly permeable to thermal protons. For thicker two-dimensional (2D) materials, proton conductivity diminishes exponentially so that, for example, monolayer MoS2 that is just three atoms thick is completely impermeable to protons. This seemed to suggest that only one-atom-thick crystals could be used as proton conducting membranes. Here we show that few-layer micas that are rather thick on the atomic scale become excellent proton conductors if native cations are ion-exchanged for protons. Their areal conductivity exceeds that of graphene and hBN by one-two orders of magnitude. Importantly, ion-exchanged 2D micas exhibit this high conductivity inside the infamous gap for proton-conducting materials, which extends from 100 C to 500 C. Areal conductivity of proton-exchanged monolayer micas can reach above 100 S cm-2 at 500 C, well above the current requirements for the industry roadmap. We attribute the fast proton permeation to 5 A-wide tubular channels that perforate micas' crystal structure which, after ion exchange, contain only hydroxyl groups inside. Our work indicates that there could be other 2D crystals with similar nm-scale channels, which could help close the materials gap in proton-conducting applications

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly α Systems

Abstract We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel−1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N &gt; 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.</jats:p

The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument

In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies, luminous red galaxies, and quasars. In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey and the Milky Way Survey. DESI also observes a selection of “secondary” targets for bespoke science goals. This paper gives an overview of the publicly available pipeline (desitarget) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID (TARGETID) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe “supporting” DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target-selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data

3D Correlations in the Lyman-α\alpha Forest from Early DESI Data

We present the first measurements of Lyman-$\alpha$ (Ly$\alpha$) forest correlations using early data from the Dark Energy Spectroscopic Instrument (DESI). We measure the auto-correlation of Ly$\alpha$ absorption using 88,509 quasars at $z>2$, and its cross-correlation with quasars using a further 147,899 tracer quasars at $z\gtrsim1.77$. Then, we fit these correlations using a 13-parameter model based on linear perturbation theory and find that it provides a good description of the data across a broad range of scales. We detect the BAO peak with a signal-to-noise ratio of $3.8\sigma$, and show that our measurements of the auto- and cross-correlations are fully-consistent with previous measurements by the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). Even though we only use here a small fraction of the final DESI dataset, our uncertainties are only a factor of 1.7 larger than those from the final eBOSS measurement. We validate the existing analysis methods of Ly$\alpha$ correlations in preparation for making a robust measurement of the BAO scale with the first year of DESI data

Target Selection and Validation of DESI Quasars

The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.9 2.1. We present several methods to select candidate quasars for DESI, using input photometric imaging in three optical bands (g, r, z) from the DESI Legacy Imaging Surveys and two infrared bands (W1, W2) from the Wide-field Infrared Survey Explorer. These methods were extensively tested during the Survey Validation of DESI. In this paper, we report on the results obtained with the different methods and present the selection we optimized for the DESI main survey. The final quasar target selection is based on a random forest algorithm and selects quasars in the magnitude range of 16.5 2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions

Influence of Molecular Dipole Orientations on Long-Range Exponential Interaction Forces at Hydrophobic Contacts in Aqueous Solutions

Strong and particularly long ranged (>100 nm) interaction forces between apposing hydrophobic lipid monolayers are now well understood in terms of a partial turnover of mobile lipid patches, giving rise to a correlated long-range electrostatic attraction. Here we describe similarly strong long-ranged attractive forces between self-assembled monolayers of carboranethiols, with dipole moments aligned either parallel or perpendicular to the surface, and hydrophobic lipid monolayers deposited on mica. We compare the interaction forces measured at very different length scales using atomic force microscope and surface forces apparatus measurements. Both systems gave a long-ranged exponential attraction with a decay length of 2.0 +/- 0.2 nm for dipole alignments perpendicular to the surface. The effect of dipole alignment parallel to the surface is larger than for perpendicular dipoles, likely due to greater lateral correlation of in-plane surface dipoles. The magnitudes and range of the measured interaction forces also depend on the surface area of the probe used: At extended surfaces, dipole alignment parallel to the surface leads to a stronger attraction due to electrostatic correlations of freely rotating surface dipoles and charge patches on the apposing surfaces. In contrast, perpendicular dipoles at extended surfaces, where molecular rotation cannot lead to large dipole correlations, do not depend on the scale of the probe used. Our results may be important to a range of scale-dependent interaction phenomena related to solvent/water structuring on dipolar and hydrophobic surfaces at interfaces

The Lyman-α\alpha forest catalog from the Dark Energy Spectroscopic Instrument Early Data Release

We present and validate the catalog of Lyman-$\alpha$ forest fluctuations for 3D analyses using the Early Data Release (EDR) from the Dark Energy Spectroscopic Instrument (DESI) survey. We used 96,317 quasars collected from DESI Survey Validation (SV) data and the first two months of the main survey (M2). We present several improvements to the method used to extract the Lyman-$\alpha$ absorption fluctuations performed in previous analyses from the Sloan Digital Sky Survey (SDSS). In particular, we modify the weighting scheme and show that it can improve the precision of the correlation function measurement by more than 20%. This catalog can be downloaded from https://data.desi.lbl.gov/public/edr/vac/edr/lya/fuji/v0.3 and it will be used in the near future for the first DESI measurements of the 3D correlations in the Lyman-$\alpha$ forest

The Dark Energy Spectroscopic Instrument: one-dimensional power spectrum from first Ly α forest samples with Fast Fourier Transform

We present the one-dimensional Ly α forest power spectrum measurement using the first data provided by the Dark Energy Spectroscopic Instrument (DESI). The data sample comprises 26 330 quasar spectra, at redshift z > 2.1, contained in the DESI Early Data Release and the first 2 months of the main survey. We employ a Fast Fourier Transform (FFT) estimator and compare the resulting power spectrum to an alternative likelihood-based method in a companion paper. We investigate methodological and instrumental contaminants associated with the new DESI instrument, applying techniques similar to previous Sloan Digital Sky Survey (SDSS) measurements. We use synthetic data based on lognormal approximation to validate and correct our measurement. We compare our resulting power spectrum with previous SDSS and high-resolution measurements. With relatively small number statistics, we successfully perform the FFT measurement, which is already competitive in terms of the scale range. At the end of the DESI survey, we expect a five times larger Ly α forest sample than SDSS, providing an unprecedented precise one-dimensional power spectrum measurement