The LABOCA Survey of the Extended Chandra Deep Field-South: Clustering of Submillimetre Galaxies

We present a measurement of the spatial clustering of submillimetre galaxies (SMGs) at z = 1–3. Using data from the 870 μm LABOCA submillimetre survey of the Extended Chandra Deep Field South, we employ a novel technique to measure the cross-correlation between SMGs and galaxies, accounting for the full probability distributions for photometric redshifts of the galaxies. From the observed projected two-point cross-correlation function we derive the linear bias and characteristic dark matter halo masses for the SMGs. We detect clustering in the cross-correlation between SMGs and galaxies at the \u3e 4σ level. Accounting for the clustering of galaxies from their autocorrelation function, we estimate an autocorrelation length for SMGs of r0 = 7.7 +1.8 −2.3 h −1 Mpc assuming a power-law slope γ = 1.8, and derive a corresponding dark matter halo mass of log(Mhalo[h M⊙]) = 12.8 +0.3 −0.5. Based on the evolution of dark matter haloes derived from simulations, we show that that the z = 0 descendants of SMGs are typically massive (∼ 2–3 L) elliptical galaxies residing in moderateto high-mass groups (log(Mhalo[h M⊙]) = 13.3 +0.3 −0.5). From the observed clustering we estimate an SMG lifetime of ∼100 Myr, consistent with lifetimes derived from gas consumption times and star-formation timescales, although with considerable uncertainties. The clustering of SMGs at z ∼ 2 is consistent with measurements for optically-selected quasi-stellar objects (QSOs), supporting evolutionary scenarios in which powerful starbursts and QSOs occur in the same systems. Given that SMGs reside in haloes of characteristic mass ∼ 6× 10 h M⊙, we demonstrate that the redshift distribution of SMGs can be described remarkably well by the combination of two effects: the cosmological growth of structure and the evolution of the molecular gas fraction in galaxies. We conclude that the powerful starbursts in SMGs likely represent a short-lived but universal phase in massive galaxy evolution, associated with the transition between cold gas-rich, star-forming galaxies and passively evolving systems

Vegetation Outlook (VegOut): Predicting Remote Sensing–Based Seasonal Greenness

Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing–based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006), which in turn may help to address agricultural development and food security issues, as well as improve early warning systems. Many studies have demonstrated the value of Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), calculated from satellite observations for assessing vegetation cover and conditions (Tucker et al., 1985; Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005), and such data have become a common source of information for vegetation monitoring. The term vegetation condition in this chapter refers to vegetation greenness or vegetation health, as inferred from canopy reflectance values measured by satellite observations (Mennis, 2001; Anyamba and Tucker, 2005). The vegetation greenness metric is commonly calculated from time-series NDVI (Reed et al., 1994) and represents the seasonal, time-integrated NDVI at a specific date, which has been shown to be representative of indicators of general vegetation health including net primary production (NPP) and green biomass (Tucker et al., 1985; Reed et al., 1996; Yang et al., 1998; Eklundh and Olsson, 2003; Hill and Donald, 2003). As a result, VIs and VI derivatives such as time-integrated VI can be used to characterize the temporal and spatial relationships between climate and vegetation and improve our understanding of the lagged relationship between climate (e.g., precipitation and temperature) and vegetation response (Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005; Camberlin et al., 2007; Groeneveld and Baugh, 2007). Quantitative descriptions of climate-vegetation response lags can then be used to identify and predict vegetation stress during drought

Using MODIS to detect cropping frequency variation in mechanized agriculture in Amazonia.

Policy makers concerned with managing rapidly developing agriculture on the Amazon frontier currently have no Basin-wide spatial and temporal information on exactly when and how soubean and other mechanized annual cropping have developed in the region. To address this, we present a reliminary evaluation of the use of moderate resolution Imaging Spectroradiometer (MODIS) 250 m vegetation index (VI) time-series data to detect croppping frequency in two municipalities, Vilhena, Rondônia, and Santarém, Pará

Effect of exercise-induced muscle damage on vascular function and skeletal muscle microvascular deoxygenation

Citation: Caldwell, J. T., Wardlow, G. C., Branch, P. A., Ramos, M., Black, C. D., & Ade, C. J. (2016). Effect of exercise-induced muscle damage on vascular function and skeletal muscle microvascular deoxygenation. Physiological Reports, 4(22), 12. doi:10.14814/phy2.13032This paper investigated the effects of unaccustomed eccentric exercise-induced muscle damage (EIMD) on macro-and microvascular function. We tested the hypotheses that resting local and systemic endothelial-dependent flow-mediated dilation (FMD) and microvascular reactivity would decrease, (V) over dotO(2max) would be altered, and that during ramp exercise, peripheral O-2 extraction, evaluated via near-infrared-derived spectroscopy (NIRS) derived deoxygenated hemoglobin + myoglobin ([HHb]), would be distorted following EIMD. In 13 participants, measurements were performed prior to (Pre) and 48 h after a bout of knee extensor eccentric exercise designed to elicit localized muscle damage (Post). Flow-mediated dilation and postocclusive reactive hyperemic responses measured in the superficial femoral artery served as a measurement of local vascular function relative to the damaged tissue, while the brachial artery served as an index of nonlocal, systemic, vascular function. During ramp-incremental exercise on a cycle ergometer, [HHb] and tissue saturation (TSI%) in the m. vastus lateralis were measured. Superficial femoral artery FMD significantly decreased following EIMD (pre 6.75 +/- 3.89%; post 4.01 +/- 2.90%; P 0.05). At each progressive increase in workload (i.e., 0-100% peak), the [HHb] and TOI% responses were similar pre-and 48 h post-EIMD (P > 0.05). Additionally, (V) over dotO(2max) was similar at pre-(3.0 +/- 0.67 L min(-1)) to 48 h post (2.96 +/- 0.60 L min(-1))-EIMD (P > 0.05). Results suggest that moderate eccentric muscle damage leads to impaired local, but not systemic, macrovascular dysfunction

Using MODIS to detect cropping frequency variation in mechanized agriculture in Amazonia.

Policy makers concerned with managing rapidly developing agriculture on the Amazon frontier currently have no Basin-wide spatial and temporal information on exactly when and how soubean and other mechanized annual cropping have developed in the region. To address this, we present a reliminary evaluation of the use of moderate resolution Imaging Spectroradiometer (MODIS) 250 m vegetation index (VI) time-series data to detect croppping frequency in two municipalities, Vilhena, Rondônia, and Santarém, Pará

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regional level vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions

HerMES: A Statistical Measurement of the Redshift Distribution of Herschel-SPIRE Sources Using the Cross-correlation Technique

The wide-area imaging surveys with the Herschel Space Observatory at submillimeter (sub-mm) wavelengths have now resulted in catalogs of the order of one-hundred-thousand dusty, starburst galaxies. These galaxies capture an important phase of galaxy formation and evolution, but, unfortunately, the redshift distribution of these galaxies, N(z), is still mostly uncertain due to limitations associated with counterpart identification at optical wavelengths and spectroscopic follow-up. We make a statistical estimate of N(z) using a clustering analysis of sub-mm galaxies detected at each of 250, 350 and 500 μm from the Herschel Multi-tiered Extragalactic Survey centered on the Boötes field. We cross-correlate Herschel galaxies against galaxy samples at optical and near-IR wavelengths from the Sloan Digital Sky Survey, the NOAO Deep Wide Field Survey, and the Spitzer Deep Wide Field Survey. We create optical and near-IR galaxy samples based on their photometric or spectroscopic redshift distributions and test the accuracy of those redshift distributions with similar galaxy samples defined with catalogs from the Cosmological Evolution Survey (COSMOS), which has superior spectroscopic coverage. We model the clustering auto- and cross-correlations of Herschel and optical/IR galaxy samples to estimate N(z) and clustering bias factors. The S_(350) > 20 mJy galaxies have a bias factor varying with redshift as b(z) = 1.0^(+1.0)_(–0.5)(1 + z)^1.2^(+0.3)_(–0.7). This bias and the redshift dependence is broadly in agreement with galaxies that occupy dark matter halos of mass in the range of 1012 to 10^(13) M_☉. We find that galaxy selections in all three Spectral and Photometric Imaging Receiver (SPIRE) bands share a similar average redshift, with = 1.8 ± 0.2 for 250 μm selected samples, and = 1.9 ± 0.2 for both 350 and 500 μm samples, while their distributions behave differently. For 250 μm selected galaxies we find the a larger number of sources with z ≤ 1 when compared with the subsequent two SPIRE bands, with 350 and 500 μm selected SPIRE samples having peaks in N(z) at progressively higher redshifts. We compare our clustering-based N(z) results to sub-mm galaxy model predictions in the literature, and with an estimate of N(z) using a stacking analysis of COSMOS 24 μm detections

Coupling dendroecological and remote sensing techniques to assess the biophysical traits of \u3ci\u3eJuniperus virginiana\u3c/i\u3e and \u3ci\u3ePinus ponderosa\u3c/i\u3e within the Semi-Arid grasslands of the Nebraska Sandhills

Woody species encroachment is occurring within the semi-arid grasslands of the Nebraska Sandhills U.S., primarily driven by native Juniperus virginiana and Pinus ponderosa, altering ecosystems and the services they provide. Effective, low cost, and cross-scale monitoring of woody species growth and performance is necessary for integrated grassland and forest management in the face of climate variability and change. In this study, we sought to establish a relationship between remote sensing-derived vegetation indices (VIs), tree dendrochronological (raw and standardized tree ring width) measurements, and the abiotic environment [(precipitation, temperature, Palmer Drought Severity Index (PDSI), and soil water content (0–300 cm depth)], over a 30-year period (1984–2013), to assess the performance of encroaching woody J. virginiana and P. ponderosa within the Nebraska Sandhills. We also investigated whether VIs can be used as an effective alternative tool to replace or complement ground measurements. Our results indicate that precipitation, temperature, and PDSI were significant (p \u3c 0.05) predictors of J. virginiana and P. ponderosa growth based on dendrochronological measurements and VIs, while soil water content from 40 to 300 cm depth was a significant predictor of J. virginiana performance. Out of the six VIs that were investigated, four were significant predictors of tree ring growth. R2 values between grassland VIs and growing season climate were greater than those of J. virginiana or P. ponderosa, while grassland performance was decoupled from soil water content. Additionally, climatic conditions in the previous year were significant determinants of current year growth of tree species but did not affect current year grassland performance. This study provides evidence for the efficacy of remote sensing-based VIs in monitoring interannual variation in the growth of woody species, while determining abiotic factors impacting the growth of grassland vegetation, J. virginiana, and P. ponderosa in the Nebraska Sandhills