Common Raven Impacts on the Productivity of a Small Breeding Population of Snowy Plovers

Common ravens (ravens; Corvus corax), an adaptable, synanthropic generalist, have thrived coincident with increasing human landscape modifications and fragmentation, consequently affecting their prey, which are often sensitive native and protected species. Ravens are a conservation concern for the protected western snowy plover (plover; Charadrius nivosus nivosus), causing low nest and chick survival in some breeding areas along the Pacific coast of North America. We used a long-term dataset from a breeding snowy plover monitoring program in Point Reyes National Seashore (PRNS) to investigate potential impacts of ravens on snowy plover nest and fledging success. Between 2002 and 2020, ravens accounted for 33.7% of all plover nest failures and 40.8% of unexclosed plover nest failures. Raven activity varied by plover breeding site, with more ravens observed per survey hour at Kehoe Beach and the Abbotts Lagoon restoration area, sites that had lower fledge success than other breeding areas. Binomial generalized linear mixed models found that plover nest success was best explained by raven activity (negative relationship) and use of nest exclosures (positive relationship). Our model results on snowy plover fledge success were less apparent, resulting in difficult management planning for this vital rate when using exclosures. Furthermore, nest exclosures were effective in increasing long-term snowy plover nest success in an ecosystem inundated by high raven activity. Evidence from PRNS and other plover breeding sites along the Pacific coast point to long-term negative impacts from ravens

The Aemulus Project III: Emulation of the Galaxy Correlation Function

Using the N-body simulations of the AEMULUS Project, we construct an emulator for the non-linear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for possible velocity bias. The model includes 15 parameters, including both cosmological and galaxy bias parameters. We demonstrate that our emulator achieves ~ 1% precision at the scales of interest, 0.1<r<10 h^{-1} Mpc, and recovers the true cosmology when tested against independent simulations. Our primary parameters of interest are related to the growth rate of structure, f, and its degenerate combination fsigma_8. Using this emulator, we show that the constraining power on these parameters monotonically increases as smaller scales are included in the analysis, all the way down to 0.1 h^{-1} Mpc. For a BOSS-like survey, the constraints on fsigma_8 from r<30 h^{-1} Mpc scales alone are more than a factor of two tighter than those from the fiducial BOSS analysis of redshift-space clustering using perturbation theory at larger scales. The combination of real- and redshift-space clustering allows us to break the degeneracy between f and sigma_8, yielding a 9% constraint on f alone for a BOSS-like analysis. The current AEMULUS simulations limit this model to surveys of massive galaxies. Future simulations will allow this framework to be extended to all galaxy target types, including emission-line galaxies.Comment: 14 pages, 8 figures, 1 table; submitted to ApJ; the project webpage is available at https://aemulusproject.github.io ; typo in Figure 7 and caption updated, results unchange

A High Throughput Workflow Environment for Cosmological Simulations

The next generation of wide-area sky surveys offer the power to place extremely precise constraints on cosmological parameters and to test the source of cosmic acceleration. These observational programs will employ multiple techniques based on a variety of statistical signatures of galaxies and large-scale structure. These techniques have sources of systematic error that need to be understood at the percent-level in order to fully leverage the power of next-generation catalogs. Simulations of large-scale structure provide the means to characterize these uncertainties. We are using XSEDE resources to produce multiple synthetic sky surveys of galaxies and large-scale structure in support of science analysis for the Dark Energy Survey. In order to scale up our production to the level of fifty 10^10-particle simulations, we are working to embed production control within the Apache Airavata workflow environment. We explain our methods and report how the workflow has reduced production time by 40% compared to manual management.Comment: 8 pages, 5 figures. V2 corrects an error in figure

Cross-correlation Weak Lensing of SDSS galaxy Clusters II: Cluster Density Profiles and the Mass--Richness Relation

We interpret and model the statistical weak lensing measurements around 130,000 groups and clusters of galaxies in the Sloan Digital Sky Survey presented by Sheldon et al. 2007 (Paper I). We present non-parametric inversions of the 2D shear profiles to the mean 3D cluster density and mass profiles in bins of both optical richness and cluster i-band luminosity. We correct the inferred 3D profiles for systematic effects, including non-linear shear and the fact that cluster halos are not all precisely centered on their brightest galaxies. We also model the measured cluster shear profile as a sum of contributions from the brightest central galaxy, the cluster dark matter halo, and neighboring halos. We infer the relations between mean cluster virial mass and optical richness and luminosity over two orders of magnitude in cluster mass; the virial mass at fixed richness or luminosity is determined with a precision of 13% including both statistical and systematic errors. We also constrain the halo concentration parameter and halo bias as a function of cluster mass; both are in good agreement with predictions of LCDM models. The methods employed here will be applicable to deeper, wide-area optical surveys that aim to constrain the nature of the dark energy, such as the Dark Energy Survey, the Large Synoptic Survey Telescope and space-based surveys

NUTRITONAL CONDITION OF ADULT FEMALE SHIRAS MOOSE IN NORTHWEST WYOMING

The "animal indicator concept" assumes that because an animal is a product of its en&shy;vironment, it likely reflects the quality of its environment. Although this concept has been applied to assess population condition and habitat quality for Alaskan moose (Alces alces gigas), to our knowledge this is the first time it has been used to assess the nutritional status of a Shiras moose (A.a. shirasi) population. We investigated the physical condition and nutritional status of adult (&ge; 2 years) female Shiras moose captured in northwest Wyoming during the winters of 2005-2007. Rump fat depth was measured via ultrasonography and biological samples were collected and analyzed for hematology, serum chemistry, micro- and macronutrients, endo- and ectoparasites, and bacterial and viral serology. Five blood parameters believed to be important predictors of moose condition (packed cell volume, total serum protein, hemoglobin [Hb], calcium [Ca], and phosphorous [P]) were compared to data from Alaskan moose considered to be in average-above average condition. Micro- and macronutrient values were evaluated based on published deficiency levels for domestic herbivores. We conducted a correlation analysis to determine if a significant relationship existed between hematological and serum chemical parameters and rump fat depth. Mean rump fat depth did not differ among years and was greater than reported values for Alaskan moose. However, a high proportion of sampled moose had Hb, Ca, and P values lower than Alaskan moose that were considered to be in average condition. Hair and serum micro- and macronutrient analyses indicated a high proportion of moose were potentially deficient in copper, zinc, manganese, and P. We observed a marginally significant relationship between depth of rump fat and two serum chemical parameters (aspartate amimotransferase and lactate dehydrogenase). The results are suggestive of a Shiras moose population in marginal physical condition that is probably related to less than optimal habitat quality. These findings should assist managers in evaluating the health of Shiras moose populations throughout their range