A Trophic Analysis of Three Species of Elmidae from Polecat and Riley Creeks, Coles County, Ill.

The food habits of Stenelmis sexlineata, Stenelmis vittipennis, Stenelmis crenata, and larval Stenelmis were studied in Polecat and Riley Creeks, Coles County, Illinois. Rock Scrapings were taken and compared with the gut analyses of the larval and adult beetles. These beetles were found to be scraper/collectors and detritivore/herbivores, generally scraping surfaces of rocks. Detritus, green algae, and diatoms were found to be the major categories present in both the rock scrapings and the gut analyses. The cellular contents of diatoms (chloroplasts and other cytoplasmic inclusions) were digested by the beetles. No conclusions regarding feeding preferences are made, although it was noted that green algae make up a disproportionately large percentage of the diet. This may indicate some selective feeding or that microhabitats in which the green algae are abundant are preferred. No evidence of resource partitioning was found on the basis of food type. However, resource partitioning may be accomplished by other means, many of which are enumerated by Seagle (1979)

A Trophic Analysis of Three Species of Elmidae from Polecat and Riley Creeks, Coles County, Ill.

The food habits of Stenelmis sexlineata, Stenelmis vittipennis, Stenelmis crenata, and larval Stenelmis were studied in Polecat and Riley Creeks, Coles County, Illinois. Rock Scrapings were taken and compared with the gut analyses of the larval and adult beetles. These beetles were found to be scraper/collectors and detritivore/herbivores, generally scraping surfaces of rocks. Detritus, green algae, and diatoms were found to be the major categories present in both the rock scrapings and the gut analyses. The cellular contents of diatoms (chloroplasts and other cytoplasmic inclusions) were digested by the beetles. No conclusions regarding feeding preferences are made, although it was noted that green algae make up a disproportionately large percentage of the diet. This may indicate some selective feeding or that microhabitats in which the green algae are abundant are preferred. No evidence of resource partitioning was found on the basis of food type. However, resource partitioning may be accomplished by other means, many of which are enumerated by Seagle (1979)

Microwave Spectroscopy

Contains reports on nine research projects.Contract DA36-039-sc-7301

Microwave Spectroscopy

Contains reports on seven research projects.United States Army Signal Corps (Contract DA36-039-sc-74895

Microwave Spectroscopy

Contains research objectives and reports on four research projects.Contract DA36-039 sc-73014Department of the Arm

Ozone depletion, greenhouse gases, and climate change

This symposium was organized to study the unusual convergence of a number of observations, both short and long term that defy an integrated explanation. Of particular importance are surface temperature observations and observations of upper atmospheric temperatures, which have declined significantly in parts of the stratosphere. There has also been a dramatic decline in ozone concentration over Antarctica that was not predicted. Significant changes in precipitation that seem to be latitude dependent have occurred. There has been a threefold increase in methane in the last 100 years; this is a problem because a source does not appear to exist for methane of the right isotopic composition to explain the increase. These and other meteorological global climate changes are examined in detail

Microwave Spectroscopy

Contains research objectives and reports on five research projects.Signal Corps Contract DA36-039-sc-73014Signal Corps Contract DA36-039-sc-7489

High-Redshift Quasars Found in Sloan Digital Sky Survey Commissioning Data IV: Luminosity Function from the Fall Equatorial Stripe Sampl

This is the fourth paper in a series aimed at finding high-redshift quasars from five-color imaging data taken along the Celestial Equator by the SDSS. during its commissioning phase. In this paper, we use the color-selected sample of 39 luminous high-redshift quasars presented in Paper III to derive the evolution of the quasar luminosity function over the range of 3.6<z<5.0, and -27.5<M_1450<-25.5 (Omega=1, H_0=50 km s^-1 Mpc^-1). We use the selection function derived in Paper III to correct for sample incompleteness. The luminosity function is estimated using three different methods: (1) the 1/V_a estimator; (2) a maximum likelihood solution, assuming that the density of quasars depends exponentially on redshift and as a power law in luminosity and (3) Lynden-Bell's non-parametric C^- estimator. All three methods give consistent results. The luminous quasar density decreases by a factor of ~ 6 from z=3.5 to z=5.0, consistent with the decline seen from several previous optical surveys at z<4.5. The luminosity function follows psi(L) ~ L^{-2.5} for z~4 at the bright end, significantly flatter than the bright end luminosity function psi(L) \propto L^{-3.5} found in previous studies for z<3, suggesting that the shape of the quasar luminosity function evolves with redshift as well, and that the quasar evolution from z=2 to 5 cannot be described as pure luminosity evolution. Possible selection biases and the effect of dust extinction on the redshift evolution of the quasar density are also discussed.Comment: AJ accepted, with minor change

Transgenic miR156 Switchgrass in the Field: Growth, Recalcitrance and Rust Susceptibility

Sustainable utilization of lignocellulosic perennial grass feedstocks will be enabled by high biomass production and optimized cell wall chemistry for efficient conversion into biofuels. MicroRNAs are regulatory elements that modulate the expression of genes involved in various biological functions in plants, including growth and development. In greenhouse studies, overexpressing a microRNA (miR156) gene in switchgrass had dramatic effects on plant architecture and flowering, which appeared to be driven by transgene expression levels. Highexpressing lines were extremely dwarfed, whereas low and moderate-expressing lines had higher biomass yields, improved sugar release and delayed flowering. Four lines with moderate or low miR156 overexpression from the prior greenhouse study were selected for a field experiment to assess the relationship between miR156 expression and biomass production over three years. We also analysed important bioenergy feedstock traits such as flowering, disease resistance, cell wall chemistry and biofuel production. Phenotypes of the transgenic lines were inconsistent between the greenhouse and the field as well as among different field growing seasons. One low expressing transgenic line consistently produced more biomass (25%–56%) than the control across all three seasons, which translated to the production of 30% more biofuel per plant during the final season. The other three transgenic lines produced less biomass than the control by the final season, and the two lines with moderate expression levels also exhibited altered disease susceptibilities. Results of this study emphasize the importance of performing multiyear field studies for plants with altered regulatory transgenes that target plant growth and development

From Meters to Kilometers: A Look at Ocean-Color Scales of Variability, Spatial Coherence, and the Need for Fine-Scale Remote Sensing in Coastal Ocean Optics

The physical, biological, chemical, and optical processes of the ocean operate on a wide variety of spatial and temporal scales, from seconds to decades and from micrometers to thousands of kilometers (Dickey et al., this issue; Dickey, 1991). These processes drive the accumulation and loss of living and nonliving mass constituents in the water column (e.g., nutrients, phytoplankton, detritus, sediments). These mass constituents frequently have unique optical characteristics that alter the clarity and color of the water column (e.g., Preisendorfer, 1976). This alteration of the ocean color, or more specifically the change in the spectral “water-leaving radiance,” L(λ), has led to the development of optical techniques to sample and study the change in biological and chemical constituents (Schofield et al., this issue). Thus, these optical techniques provide a mechanism to study the effects of underlying biogeochemical processes. In addition, because time- and space-dependent changes in L(λ) may be measured remotely, optical oceanography provides a way to sample ecological interactions over a wide range of spatial and temporal scales. The question often posed by scientists trying to resolve problems involving the temporal and spatial variation of oceanic properties is: “What is the optimal time/ space sampling frequency?” The obvious answer is that the sampling frequency should be one half the frequency of the variation (i.e., Nyquist frequency) of the property of interest. However, therein lies the rub for the oceanographer: the range of the relevant scales is large, and the range of available resources and/or actual engineering capabilities to sample all relevant scales is often small. Hence, the decisions affecting resource allocation become critical in order to maximize the total data information in both quantity and quality. While these scientific resource decisions are rarely discussed in explicit terms of cost-benefit analysis, such discussions should be integral parts of the scientific design of instruments, platforms, and experiments aimed at resolving oceanic processes. The practical examples of this problem in remote sensing include: “What is the optimal repeat coverage frequency?” and “What is the optimal Ground Sample Distance (GSD) or pixel size of the data?” For the optical oceanographer, there is also the issue of optimal spectral coverage needed to resolve the optical constituents of interest (Chang et al., this issue). The sum of these considerations feed into the sensor, deployment platform, and deployment schedule decisions. For polarorbiting and geo-stationary satellites that cost hundreds of millions of dollars, as well as airborne sensors that have smaller upfront costs but higher deployment costs, the decision of sampling frequency directly impacts the scientific use of the data stream, and what processes may be addressed with data streams collected by these sensors. These scientific cost-benefit analyses extend beyond the cost in dollars because the typical lifetime and replacement cycle of these sensors is on the order of years to decades, and a poorly designed sensor package is very difficult to replace. In 2001, the Office of Naval Research (ONR) sponsored the Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) (Dickey et al., this issue), which presented the opportunity to study the question of scales of variability in remote-sensing data. Hyperspectral airborne sensors were deployed on several platforms at various altitudes. This coverage was supplemented by numerous space-borne, remote-sensing satellites. The airborne instruments included two versions of the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS 1 and PHILLS 2) (Davis et al., 2002) operating at an altitude of less than 10,000 feet and 30,000 feet, respectively, as well as the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor operating at 60,000 feet. These sensors provided hyperspectral data at 2 m, 9 m, and 20 m GSDs, respectively. The satellite data collected included the multi-spectral images from Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Fengyun 1 C (FY1-C), Oceansat as well as the multispectral polarimeter Multiangle Imaging SpectroRadiometer (MISR) sensor and sea surface temperature (SST) sensor Advanced Very High Resolution Radiometer (AVHRR). These collections provided a wealth of remote- sensing and field data during a spatially and temporally intense oceanographic field campaign, and they offered the ability to begin to address the issue of optimal sampling scales for the coastal ocean. The use of these multiple remote-sensing data streams requires the calibration, validation, and atmospheric correction of the sensor signals to retrieve estimates of L(λ), or “remote sensing reflectance,” Rᵣₛ(λ), a normalized measure of the L(λ). Our goals in this paper are to illuminate some of the issues of remote sensing spatial scaling in the nearshore environment and attempt to derive some understanding of appropriate sampling scales in the nearshore environment. We will focus on the data collected by a single sensor (PHILLS 2) to reduce uncertainties in the analysis that may result from the different data processing techniques applied to each of the individual sensors’ data