The Contribution of Late-type/Irregulars to the Faint Galaxy Counts from HST Medium Deep Survey Images

We present a complete morphologically classified sample of 144 faint field galaxies from the HST Medium Deep Survey with 20.0 < I <22.0 mag. We compare the global properties of the ellipticals, early and late-type spirals, and find a non-negligible fraction (13/144) of compact blue [(V-I) < 1.0 mag] systems with $r^{1/4}$-profiles. We give the differential galaxy number counts for ellipticals and early-type spirals independently, and find that the data are consistent with no-evolution predictions based on conventional flat Schechter luminosity functions (LF's) and a standard cosmology. Conversely, late-type/Irregulars show a steeply rising differential number count with slope $(\frac{\delta log N}{\delta m}) = 0.64\pm 0.1$. No-evolution models based on the Loveday et al. (1992) and Marzke et al. (1994b) {\it local} luminosity functions under-predict the late-type/Irregular counts by 1.0 and 0.5 dex, respectively, at I = 21.75 mag. Examination of the Irregulars alone shows that $\sim 50$% appear inert and the remainder have multiple cores. If the inert galaxies represent a non-evolving late-type population, then a Loveday-like LF ($\alpha\simeq -1.0$) is ruled out for these types, and a LF with a steep faint-end ($\alpha\simeq -1.5$) is suggested. If multiple core structure indicates recent star-formation, then the observed excess of faint blue field galaxies is likely due to {\it evolutionary} processes acting on a {\it steep} field LF for late-type/Irregulars. The evolutionary mechanism is unclear, but 60% of the multiple-core Irregulars show close companions. To reconcile a Marzke-like LF with the faint redshift surveys, this evolution must be preferentially occurring in the brightest late-type galaxies with z > 0.5 at I = 21.75 mag.Comment: 29 pages, 1 catalog and 10 figures. The figures and catalog can be found at http://www.phys.unsw.edu.au/~spd/bib.htm

A note on the Hybrid Soil Moisture Deficit Model v2.0

peer-reviewedThe Hybrid Soil Moisture Deficit (HSMD) model has been used for a wide range of applications, including modelling of grassland productivity and utilisation, assessment of agricultural management opportunities such as slurry spreading, predicting nutrient emissions to the environment and risks of pathogen transfer to water. In the decade since its publication, various ad hoc modifications have been developed and the recent publication of the Irish Soil Information System has facilitated improved assessment of the spatial soil moisture dynamics. In this short note, we formally present a new version of the model (HSMD2.0), which includes two new soil drainage classes, as well as an optional module to account for the topographic wetness index at any location. In addition, we present a new Indicative Soil Drainage Map for Ireland, based on the Irish Soil Classification system, developed as part of the Irish Soil Information System

Predicting soil moisture conditions for arable free draining soils in Ireland under spring cereal crop production

peer-reviewedTemporal prediction of soil moisture and evapotranspiration has a crucial role in agricultural and environmental management. A lack of Irish models for predicting evapotranspiration and soil moisture conditions for arable soils still represents a knowledge gap in this particular area of Irish agro-climatic modelling. The soil moisture deficit (SMD) crop model presented in this paper is based on the SMD hybrid model for Irish grassland (Schulte et al., 2005). Crop and site specific components (free-draining soil) have been integrated in the new model, which was calibrated and tested using soil tension measurements from two experimental sites located on a well-drained soil under spring barley cultivation in south-eastern Ireland. Calibration of the model gave an R2 of 0.71 for the relationship between predicted SMD and measured soil tension, while model testing yielded R2 values of 0.67 and 0.65 (two sites). The crop model presented here is designed to predict soil moisture conditions and effective drainage (i.e., leaching events). The model provided reasonable predictions of soil moisture conditions and effective drainage within its boundaries, i.e., free-draining land used for spring cereal production under Irish conditions. In general, the model is simple and practical due to the small number of required input parameters, and due to model outputs that have good practical applicability, such as for computing the cumulative amount of watersoluble nutrients leached from arable land under spring cereals in free-draining soils

Agriculture, meteorology and water quality in Ireland: a regional evaluation of pressures and pathways of nutrient loss to water

peer-reviewedThe main environmental impact of Irish agriculture on surface and ground water quality is the potential transfer of nutrients to water. Soil water dynamics mediate the transport of nutrients to water, and these dynamics in turn depend on agro-meteorological conditions, which show large variations between regions, seasons and years. In this paper we quantify and map the spatio-temporal variability of agro-meteorological factors that control nutrient pressures and pathways of nutrient loss. Subsequently, we evaluate their impact on the water quality of Irish rivers. For nitrogen, pressure and pathways factors coincide in eastern and southern areas, which is reflected in higher nitrate levels of the rivers in these regions. For phosphorus, pathway factors are most pronounced in north-western parts of the country. In south-eastern parts, high pressure factors result in reduced biological water quality. These regional differences require that farm practices be customised to reflect the local risk of nutrient loss to water. Where pathways for phosphorus loss are present almost year-round—as is the case in most of the north-western part of the country—build-up of pressures should be prevented, or ameliorated where already high. In south-eastern areas, spatio-temporal coincidence of nutrient pressures and pathways should be prevented, which poses challenges to grassland management

Hubble Space Telescope Imaging of the Ultracompact Blue Dwarf Galaxy HS 0822+3542: An Assembling Galaxy in a Local Void?

We present deep U, narrow-V, and I-band images of the ultracompact blue dwarf galaxy HS 0822+3542, obtained with the Advanced Camera for Surveys / High Resolution Channel of the Hubble Space Telescope. This object is extremely metal-poor (12 + log(O/H) = 7.45) and resides in a nearby void. The images resolve it into two physically separate components that were previously described as star clusters in a single galaxy. The primary component is only \~100 pc in maximum extent, and consists of starburst region surrounded by a ring-like structure of relatively redder stars. The secondary component is ~50 pc in size and lies at a projected distance of ~80 pc away from the primary, and is also actively star-forming. We estimate masses ~10^7 M(sol) and ~10^6 M(sol) for the two components, based on their luminosities, with an associated dynamical timescale for the system of a few Myr. This timescale and the structure of the components suggests that a collision between them triggered their starbursts. The spectral energy distributions of both components can be fitted by the combination of recent (few Myr old) starburst and an evolved (several Gyr old) underlying stellar population, similar to larger blue compact dwarf galaxies. This indicates that despite its metal deficiency the object is not forming its first generation of stars. However, the small sizes and masses of the two components suggests that HS 0822+3542 represents a dwarf galaxy in the process of assembling from clumps of stars intermediate in size between globular clusters and objects previously classified as galaxies. Its relatively high ratio of neutral gas mass to stellar mass (~1) and high specific star formation rate, log(SFR/M(sol) = -9.2, suggests that it is still converting much of its gas to stars.Comment: 11 pages, 2 figures, accepted for publication in Astrophysical Journal Letter

MODISTools - downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools)