© 2016 The Author(s). Larvae of intertidal species develop at sea and must return to adult habitats to replenish populations. Similarly, nutrients, detritus and plankton provide important subsidies spurring growth and reproduction of macroalgae and filter-feeding invertebrates that form the foundation of intertidal communities. Together, these factors determine the density and intensity of interactions among community members. We hypothesized that spatial variation in surfzone hydrodynamics affects the delivery of plankton subsidies. We compared entire zooplankton communities inside and outside the surf zone daily while monitoring physical conditions for one month each at two shores with different surfzone characteristics. Opposite cross-shore distributions of larvae and other zooplankters occurred at the two sites: zooplankton was much more abundant inside the mildly sloping dissipative surf zone (DSZ) with rip currents and was more abundant outside the steep reflective surf zone (RSZ). Biophysical numerical simulations demonstrated that zooplankters were concentrated in rip channels of the DSZ and were mostly unable to enter the RSZ, indicating the hydrodynamic processes behind the observed spatial variation of zooplankters in the surf zone. Differences in the concentration of larvae and other zooplankters between the inner shelf and surf zone may be an underappreciated, key determinant of spatial variation in inshore communities.

Larvae of many intertidal species develop offshore and must cross the surf zone to complete their onshore migration to adult habitats. Depending on hydrodynamics, the surf zone may limit this migration, especially on reflective rocky shores. As a logistically tractable analog of a rocky shore environment, we carried out a comprehensive biological and physical study of the hydrodynamics of a steep reflective sandy beach. Holoplankton and precompetent larval invertebrates were much less abundant within the surf zone than offshore, and their concentrations inside and outside the surf zone were not significantly correlated, suggesting that they were not entering the surf zone. Persistent offshore flow throughout the water column at the outer edge of the surf zone may prevent these organisms from entering the surf zone. In contrast, the concentrations of detritus and a competent larval invertebrate (i.e. cyprids), while also not significantly correlated with concentrations offshore, were frequently more concentrated in the surf zone than offshore. Within the surf zone, the concentration of detritus was significantly correlated with concentrations of competent larval invertebrates (barnacles, gastropods, polychaetes, and bopyrid amphipod) and organisms that may be associated with detritus (amphipods and harpacticoid copepods). These concentrations were significantly negatively correlated with average daily wave height. We hypothesize that detritus and larvae enter the surf zone near the bottom during calm wave conditions by a process of near-bottom streaming. Near-bottom streaming is associated with all surf zones and may be a general mechanism for onshore transport of larvae close to the coast.

Identifying biophysical mechanisms of larval transport is essential to understanding the delivery of larvae to adult habitats. In addition, harmful algal blooms (HABs) can be transported onshore from populations that form offshore. In summer 2011, we measured sea surface and bottom temperatures and daily phytoplankton abundance and intertidal cyprid (barnacle post larvae) settlement at Carmel River State Beach, California, USA. Using time-series analysis, we compared the abundance of Pseudo-nitzschia spp. and daily cyprid settlement to physical forcing mechanisms (e.g. internal tides and upwelling-relaxation events) that could generate onshore delivery. Minimum bottom water temperature was significantly cross-correlated with the spring-neap tidal cycle; minimum temperatures occurred between neap and spring tides, and maximum temperatures were recorded around neap tides. When the temperature data were transformed to remove the relationship between tides and temperature, we found significantly higher maximum sea surface temperatures during upwelling-relaxation events. We observed 4 pulses in Pseudo-nitzschia spp. abundance. Pseudo-nitzschia spp. chains were longest at the start of pulses and then decreased, suggesting that they had been transported to shore from a more productive site offshore, likely the upwelling front. Pulses occurred during periods of maximum sea surface temperature associated with upwelling-relaxation events. In contrast, cyprid settlement was significantly cross-correlated with the spring-neap tidal cycle, with settlement peaks occurring during fortnightly periods of cold bottom temperatures; onshore transport of cyprids appears to have been due to the internal tides. © Inter-Research 2014.

Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r-band observations of 40 deg2 to 10 yr LSST depth. We present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N-body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star-galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2-6) in the estimated power spectra at small scales (> 1200), highlighting the impact of blending in studies at small angular scales in LSST.

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalogue used in this analysis contains 3697 members with mean redshift of z = 0.45. We detect lensing of the CMB by the galaxy clusters at 8.1s significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly 17 per cent precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentring.

Clusters of galaxies trace the most non-linear peaks in the cosmic density field. The weak gravitational lensing of background galaxies by clusters can allow us to infer their masses. However, galaxies associated with the local environment of the cluster can also be intrinsically aligned due to the local tidal gradient, contaminating any cosmology derived from the lensing signal. We measure this intrinsic alignment in Dark Energy Survey (DES) Year 1 REDMAPPER clusters. We find evidence of a non-zero mean radial alignment of galaxies within clusters between redshifts 0.1–0.7. We find a significant systematic in the measured ellipticities of cluster satellite galaxies that we attribute to the central galaxy flux and other intracluster light. We attempt to correct this signal, and fit a simple model for intrinsic alignment amplitude (AIA) to the measurement, finding AIA = 0.15 ± 0.04, when excluding data near the edge of the cluster. We find a significantly stronger alignment of the central galaxy with the cluster dark matter halo at low redshift and with higher richness and central galaxy absolute magnitude (proxies for cluster mass). This is an important demonstration of the ability of large photometric data sets like DES to provide direct constraints on the intrinsic alignment of galaxies within clusters. These measurements can inform improvements to small-scale modelling and simulation of the intrinsic alignment of galaxies to help improve the separation of the intrinsic alignment signal in weak lensing studies.

Lensing without borders is a cross-survey collaboration created to assess the consistency of galaxy-galaxy lensing signals (Δς) across different data sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of Δς using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3σ in four lens bins and three radial ranges. For lenses with zL > 0.43 and considering statistical errors, we detect a 3-4σ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognized galaxy blends on shear calibration and imperfections in photometric redshift calibration. At zL > 0.54, amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets that are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15 per cent (25 per cent) ruled out in three lens bins at 68 per cent (95 per cent) confidence at z < 0.54. Differences with respect to predictions based on clustering are observed to be at the 20-30 per cent level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the 'lensing is low' effect at z < 0.54. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.

We combine Dark Energy Survey Year 1 clustering and weak lensing data with baryon acoustic oscillations and Big Bang nucleosynthesis experiments to constrain the Hubble constant. Assuming a flat ΛCDM model with minimal neutrino mass (Σmυ = 0.06 eV), we find H0 = 67.4 -1.2+1.1 km s-1 Mpc-1 (68 per cent CL). This result is completely independent of Hubble constant measurements based on the distance ladder, cosmic microwave background anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: (a) have no shared observational systematics; and (b) each constrains the Hubble constant with fractional uncertainty at the few-per cent level. We compare these five independent estimates, and find that, as a set, the differences between them are significant at the 2.5σ level (χ2/dof = 24/11, probability to exceed = 1.1 per cent). Having set the threshold for consistency at 3σ, we combine all five data sets to arrive at H0 = 69.3-0.6+0.4 km s-1 Mpc-1.

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample.We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1 Mpc)-3, and a median photo-z bias (zspec - zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.