An evaluation of woodland reclamation on strip-mined lands in east Texas

We compared the composition and structural characteristics of reclaimed and native woody plant communities near Fairfield, Texas, to evaluate the effectiveness of woodland reclamation 3–11 years since establishment. Species composition, foliage density, canopy cover, and woody plant densities were recorded in plots randomly placed along transects bisecting blocks of reclaimed and native woodlands. During summer, vertical foliage densities at heights ≤2 m were similar among native and reclaimed areas. Foliage density and canopy cover declined in reclaimed blocks during winter, but remained relatively constant in native woodlands, where evergreens and vines were more common. Canopy cover was absent in reclaimed woodlands <6 years old but increased with age in 6 to 11-year-old blocks. These data indicated that approximately 27 years will be needed before trees in reclaimed blocks will achieve the stature of canopy trees in native woodlands. Reclaimed woodlands contained different woody plant species and had lower woody stem densities compared to native woodlands. On average, stem densities in reclaimed blocks were six times lower than densities in native woodlands. Comparisons with planting records indicate that survival of most commonly planted woody species was low. Only green ash(Fraxinus pennsylvanica), Russian oliver(Elaeagnus commutata), smooth sumac(Rhus glabra), and redbud(Cercis canadensis) had estimated survival rates >50%. Reclamation procedures used at Big Brown Mine (BBM) during 1981–1988 have not produced woodland habitats with vegetative characteristics comparable to premined woodlands and may not be providing the cover needed to encourage use by certain wildlife species. Procedures for improving woodland reclamation are recommended.     

Assessing coastal vulnerability to climate change: comparing segmentation at global and regional scales

Recent concerns about potential climate-change effects on coastal systems require the application of vulnerability assessment tools in order to define suitable adaptation strategies and improve coastal zone management effectiveness. In fact, while various research efforts were devoted to evaluate coastal vulnerability to climate change on a national to global level, fewer applications were carried out so far to develop more comprehensive and site-specific vulnerability assessments suitable to plan possible adaptation measures at the regional scale. In this respect, specific indicators are needed to address climate-change-related issues for coastal zones and to identify vulnerable areas at the regional level. Two sets of coastal vulnerability indicators were selected, one for regional and one for global studies, respectively, concerning the same features of coastal systems, including topography and slope, geomorphological characteristics, presence and distribution of wetlands and vegetation cover, density of coastal population and number of coastal inhabitants. The proposed set of indicators for the regional scale was chosen taking into account the availability of environmental and territorial data for the whole coastal area of the Veneto region and was based on site-specific datasets characterized by a spatial resolution appropriate for a regional analysis. Moreover, a GIS-based segmentation procedure was applied to divide the coastline into linear segments, homogeneous in terms of vulnerability to climate change and sea-level rise at the regional scale. This approach allowed to divide the Veneto shoreline into 140 segments with an average length of about 1 km, while the global scale approach identified four coastal segments with an average length of about 66 km. The performed comparison indicated how the more detailed approach adopted at the regional scale is essential to understand and manage the complexities of the specific study area. In fact, the 25-m DEM employed at the regional scale provided a more accurate differentiation of the coastal area's elevation and thus of coastal susceptibility to the inundation risks, compared to the 1-km DEM used at the global level. Moreover, at the regional level the use of a 1:20,000 geomorphological map allowed to differentiate the unique landform class detected at the global level (e.g., fluvial plain) in a variety of more detailed coastal typologies (e.g., open coast eroding sandy shores backed by bedrock) characterized by a different sensitivity to climate change and sea-level rise. Accordingly, the information provided by regional indicators can support decision-makers in improving the management of coastal resources by considering the potential impacts of climate change and in the definition of appropriate actions to reduce inundation risks, to avoid the potential loss of valuable wetlands and vegetation and to plan the nourishment of sandy beaches subject to erosion processes.     

Mercury Capture on Fly Ash and Sorbents: The Effects of Coal Properties and Combustion Conditions

The US fleet of coal-fired power plants, with generating capacity of just over 300 GW, is known to be a major source of domestic mercury (Hg) emissions. To address this, in March 2005, the Environmental Protection Agency (EPA) promulgated the Clean Air Mercury Rule (CAMR) to reduce emissions of mercury from these plants. It is generally believed that most of the initial (Phase I) mercury reductions will come as a co-benefit of existing controls used to remove particulate matter (PM), SO₂, and NO _X . Deeper reductions in emissions (as required in Phase II of CAMR) may require the installation of mercury-specific control technology. Duct injection of activated carbon sorbents is the mercury-specific control technology that has been most widely studied and has been demonstrated over a wide range of coal types and combustion conditions. The effectiveness of the mercury control options (both “co-benefit control” and “mercury-specific control”) is significantly impacted by site-specific characteristics such as the combustion conditions, the configuration of existing air pollution controls, and the type of coal burned. This paper identifies the role of coal properties and combustion conditions in the capture of mercury by fly ash and injected sorbents.     

Accumulation and fractionation of copper, iron, manganese, and zinc in calcareous soils amended with composts.

Amending soils with compost may lead to accumulation of metals and their fractions at various concentrations in the soil profile. The objectives of this study were to determine 1) the accumulation of Cu, Fe, Mn, and Zn with depth and 2) the distribution of water soluble, exchangeable, carbonate, Fe-Mn oxides, organic and residual forms of each metal in soils amended with MSW compost, co-compost, biosolids compost and inorganic fertilizer (as control). Total concentrations of Cu, Fe, Mn and Zn were concentrated in the 0-22 cm soil layer and scant in the rock layer. These metals were in the decreasing order of Fe > Mn > Zn > or = Cu. Copper, Fe, and Zn were predominantly in the residual form followed by fractions associated with Fe-Mn oxides, carbonate, organic, exchangeable and water soluble in all treatments except MSW compost amended soil where the organic fraction was higher than the carbonate fraction. In fertilizer, co-compost and biosolids compost treated soils Mn concentrated mainly in the Fe-Mn oxides form followed by residual, carbonate, and organic forms whereas, in MSW compost treated soil the same pattern occurred except that Mn organic fraction was higher than that in the carbonate form. The MSW compost has a greater potential to be used as a soil amendment to supply plants with Cu, Mn and Zn than other treatments in calcareous soils of south Florida.     

Hazards, spatial transmission and timing of outbreaks in epidemic metapopulations

Highly infectious, immunizing pathogens can cause violent local outbreaks that are followed by the agent’s extinction as it runs out of susceptible hosts. For these pathogens, regional persistence can only be secured through spatial transmission and geographically asynchronous epidemics. In this paper we develop a hazard model for the waiting time between epidemics. We use the model, first, to discuss the predictability in timing of epidemics, and, second, to estimate the strength of spatial transmission. Based on the hazard model, we conclude that highly epidemic pathogens can at times be predictable in the sense that the waiting-time distribution between outbreaks is probabilistically bounded; The greater the spatial transmission the more periodic the outbreak dynamics. When we analyze the historical records of measles outbreaks in England and Wales between 1944 and 1965, we find the waiting-time between epidemics to depend inversely on community size. This is because large communities are much more tightly coupled to the regional metapopulation. The model further help identify the most important areas for spatial transmission. We conclude that the data on absence of these pathogens is the key to understanding spatial spread.     

A WINDOWS‐BASED GIS‐AGNPS INTERFACE1

ABSTRACT: ArcView Nonpoint Source Pollution Modeling (AVNPSM), an interface between ArcView GIS and AGNPS (Agricultural Nonpoint Source Pollution Model) is developed in support of agricultural watershed analysis and nonpoint source pollution management. The interface is PC‐based and operates in a Windows environment. It consists of seven modules: AGNPS utility, parameter generator, input file processor, model executor, output visualizer, statistical analyzer, and land use simulator. Basic input data to the interface include: soil, digital elevation model, land use/cover, water features, climate, and information on management practices. Application of the AVNPSM to a sample watershed indicates that it is user friendly, flexible, and robust, and it significantly improves the efficiency of the nonpoint source pollution modeling process.     

How complex do models need to be to predict dispersal of threatened species through matrix habitats?

Persistence of species in fragmented landscapes depends on dispersal among suitable breeding sites, and dispersal is often influenced by the "matrix" habitats that lie between breeding sites. However, measuring effects of different matrix habitats on movement and incorporating those differences into spatially explicit models to predict dispersal is costly in terms of time and financial resources. Hence a key question for conservation managers is: Do more costly, complex movement models yield more accurate dispersal predictions? We compared the abilities of a range of movement models, from simple to complex, to predict the dispersal of an endangered butterfly, the Saint Francis' satyr (Neonympha mitchellii francisci). The value of more complex models differed depending on how value was assessed. Although the most complex model, based on detailed movement behaviors, best predicted observed dispersal rates, it was only slightly better than the simplest model, which was based solely on distance between sites. Consequently, a parsimony approach using information criteria favors the simplest model we examined. However, when we applied the models to a larger landscape that included proposed habitat restoration sites, in which the composition of the matrix was different than the matrix surrounding extant breeding sites, the simplest model failed to identify a potentially important dispersal barrier, open habitat that butterflies rarely enter, which may completely isolate some of the proposed restoration sites from other breeding sites. Finally, we found that, although the gain in predicting dispersal with increasing model complexity was small, so was the increase in financial cost. Furthermore, a greater fit continued to accrue with greater financial cost, and more complex models made substantially different predictions than simple models when applied to a novel landscape in which butterflies are to be reintroduced to bolster their populations. This suggests that more complex models might be justifiable on financial grounds. Our results caution against a pure parsimony approach to deciding how complex movement models need to be to accurately predict dispersal through the matrix, especially if the models are to be applied to novel or modified landscapes.