Feral swine disturbance at important archaeological sites

Feral swine are well known as environmentally destructive invasive animals in many areas around the world, where they degrade native habitats, harm rare plant and animal species, damage agricultural interests, and spread disease. We provide the first quantification of their potential as agents of disturbance at archaeological sites. Our study was conducted in south-central Florida at Avon Park Air Force Range, a base comprising over 40,000 ha and containing many archaeological sites. To determine the identifiable prevalence of feral swine disturbance, we examined 36 sites registered with the Florida State Historic Preservation Office and also eligible for inclusion in the National Register of Historic Places (NRHP). Moreover, we evaluated the extent of swine disturbance at a prehistoric site of extraordinary significance to Florida’s prehistory, “Dead Cow.” Fifteen of the 36 NRHP-eligible sites (42 %) had some level of swine disturbance, including 14 of 30 (47 %) sites known to have artifacts within 20 cm of the surface (well within swine rooting depths). At the Dead Cow site, we documented disturbance at 74 % of shovel test points. Sites with shallow artifact depositions appeared highly vulnerable to disturbance by feral swine, threatening destruction of artifact stratigraphy and provenience. Our observations likely are a minimal representation of accumulated damage. These irreplaceable sites tell the area’s land use story across the millennia. That they are under threat from feral swine should serve broad notice of potential threats that feral swine may pose to archaeological sites globally, making effective swine management imperative for site protection.     

Simulation of pesticide runoff at Rosemaund Farm (UK) using the SoilFug model

A validation exercise of the SoilFug model using field runoff data from Rosemaund Farm (UK) is described. A comparison has been made of modelled and measured concentrations of several pesticides in surface water and soil during and after specific rain events following application. The field experiments were designed to obtain data on rainfall, outflows of water, pesticide application rates and concentrations in soil and water. The results were satisfactory for the undissociated pesticides (atrazine, carbofuran, dimethoate, isoproturon, lindane, simazine and trifluralin), whose concentrations in water were mostly predicted within an order of magnitude of measured data. The results for the dissociated pesticides (dichlorprop, MCPA, mecoprop) were less satisfactory, giving generally much higher predicted concentrations in water. The use of the SoilFug model is suggested for the calculation of predicted environmental concentrations (PECs) in water, since it generally produces acceptable results from a relatively small set of input data, most of which is generally available.     

Information processing style and decision making

Information processing style, often termed cognitive style, has gained prominence in the organizational behavior literature as researchers use it as a basis for studying decision making behavior, conflict, strategy development, and group processes. However, the many operational definitions and measures of cognitive style have produced inconsistent and confusing results. This study tested the interrelationship among four measures of this construct: the Myers–Briggs Type Indicator, the Group Embedded Figures Test, the Learning Styles Inventory, and the Decision Style Inventory. Measures that appeared to be conceptually linked through their underlying theories were compared. Results indicate that the various measures are not strongly interrelated and appear to be measuring different aspects of information processing and decision making. Copyright © 1999 John Wiley & Sons, Ltd.     

Comparing computing formulas for estimating concentration ratios

The purpose of this paper is to provide guidance on the choice of computing formulas (estimators) for estimating average concentration ratios and other ratio-type measures of radionuclides and other environmental contaminant transfers between ecosystem components. Mathematical expressions for the expected value of three commonly used estimators (arithmetic mean of ratios, geometric mean of ratios, and the ratio of means) are obtained when the multivariate lognormal distribution is assumed. These expressions are used to explain why these estimators will not in general give the same estimate of the average concentration ratio. They illustrate that the magnitude of the discrepancies depends on the magnitude of measurement biases, and on the variance and correlations associated with spatial heterogeneity and measurement errors. This paper also reports on a computer simulation study that compares the accuracy of eight computing formulas for estimating a ratio relationship that is constant over time and/or space. Statistical models appropriate for both controlled spiking experiments and observational field studies for either normal or lognormal distributions are considered. Our results indicate that for either type of study the geometric mean is generally preferred if the lognormal distribution applies. However, the geometric mean has the disadvantage that its expected value depends on n, the number of measurements taken. Ricker's estimator, $\text{R}\text{?}{}_{\mathrm{rt}}$, appears to perform worse than the other estimators studied when the observations are lognormal. All eight estimators appear to be equally accurate for the controlled spiking study when data are normally distributed. For observational field studies when data are normally distributed the ratio of means or slight modifications thereof are preferred to other estimators investigated. Before one chooses a computing formula for estimating a concentration ratio, thought should be given to what target value needs to be estimated to satisfy study objectives, and to whether the normal or lognormal distribution is a more realistic model. The geometric mean performs well for lognormal distributions, but comparison of geometric means or of a geometric mean with environmental limits can be misleading if n is small. The arithmetic mean of ratios is a conservative choice in that it will always give a larger estimate than will the geometric mean. It may also be severely biased when data are lognormal and the variances of measurement errors are large. The ratio of the means is a reasonable choice if the distribution is normal. The median of the observed ratios, $\text{R}\text{?}{}_{\mathrm{<mtext>md</mtext>}}$, is useful estimate since it is easily obtained and has an easily understood interpretation as the point above which and below which 50% of the observed ratios lie. Also, it is appropriate no matter what the distribution of the observed ratios may be. Confidence limits on the median are also easily obtained. Finally, while this paper emphasizes applications in radionuclide research, our results should be applicable to a wide range of environmental contaminants since many contaminants have approximately lognormal distributions.     

Objectives and status of fabric filter performance study

In October 1977, Southwestern Public Service Company executed a contract with the U.S. Environmental Protection Agency that called for a study to assess the performance of a fabric filter system installed on a large utility boiler that utilizes low sulfur Western coal. The project is now into its second year and the objectives of this paper are to describe the scope and intent of the study, as well as to report progress to date. In addition, some of the difficulties that we have encountered are discussed. Although some of these problems have resulted in procedural changes, the intent of the study has not been altered. This paper describes work being done in specific areas with which both the EPA and Southwestern are connected. These include fabric assessment, data collection, selection and installation of instrumentation, and overall fabric filter system performance. Results of the first performance test are also reviewed and the installation of a pilot baghouse is discussed.     

Development of quantitative estimates of uncertainty in environmental risk assessments when the scientific data base is inadequate

The reasons for developing quantitative estimates of uncertainty in environmental risk assessments are discussed along with a method for developing them which involves scientific judgement. In the situation considered here the regulatory needs are ahead of the science, which makes the development of the estimates on uncertainty more difficult, but not impossible. Quantitative estimates for all uncertainties involved in the estimation of risk resulting from exposure to volatile organic compounds (VOCs) in drinking water are developed and tabulated. By far the largest contribution to the uncertainty in the risk estimates for VOCs in drinking water are due to uncertainty in the extrapolation of the dose-response curve to low levels. The uncertainty due to extrapolation is on the order of 10⁴ and 10⁶. Other components of the analysis may contribute uncertainties of a few orders of magnitude. In general the largest uncertainties are in the toxicological data base and the manipulation of it needed to estimate risk. The data base and manipulations needed to estimate exposure due to VOCs in drinking water were at the more an order to magnitudes in uncertainty.     

Perceived stressors of climate vulnerability across scales in the Savannah zone of Ghana: a participatory approach

Smallholder farmers in sub-Saharan Africa are confronted with climatic and non-climatic stressors. Research attention has focused on climatic stressors, such as rainfall variability, with few empirical studies exploring non-climatic stressors and how these interact with climatic stressors at multiple scales to affect food security and livelihoods. This focus on climatic factors restricts understanding of the combinations of stressors that exacerbate the vulnerability of farming households and hampers the development of holistic climate change adaptation policies. This study addresses this particular research gap by adopting a multi-scale approach to understand how climatic and non-climatic stressors vary, and interact, across three spatial scales (household, community and district levels) to influence livelihood vulnerability of smallholder farming households in the Savannah zone of northern Ghana. This study across three case study villages utilises a series of participatory tools including semi-structured interviews, key informant interviews and focus group discussions. The incidence, importance, severity and overall risk indices for stressors are calculated at the household, community, and district levels. Results show that climatic and non-climatic stressors were perceived differently; yet, there were a number of common stressors including lack of money, high cost of farm inputs, erratic rainfall, cattle destruction of crops, limited access to markets and lack of agricultural equipment that crossed all scales. Results indicate that the gender of respondents influenced the perception and severity assessment of stressors on rural livelihoods at the community level. Findings suggest a mismatch between local and district level priorities that have implications for policy and development of agricultural and related livelihoods in rural communities. Ghana’s climate change adaptation policies need to take a more holistic approach that integrates both climatic and non-climatic factors to ensure policy coherence between national climate adaptation plans and District development plans.     

When experts disagree: the need to rethink indicator selection for assessing sustainability of agriculture

Sustainability indicators are well recognized for their potential to assess and monitor sustainable development of agricultural systems. A large number of indicators are proposed in various sustainability assessment frameworks, which raises concerns regarding the validity of approaches, usefulness and trust in such frameworks. Selecting indicators requires transparent and well-defined procedures to ensure the relevance and validity of sustainability assessments. The objective of this study, therefore, was to determine whether experts agree on which criteria are most important in the selection of indicators and indicator sets for robust sustainability assessments. Two groups of experts (Temperate Agriculture Research Network and New Zealand Sustainability Dashboard) were asked to rank the relative importance of eleven criteria for selecting individual indicators and of nine criteria for balancing a collective set of indicators. Both ranking surveys reveal a startling lack of consensus amongst experts about how best to measure agricultural sustainability and call for a radical rethink about how complementary approaches to sustainability assessments are used alongside each other to ensure a plurality of views and maximum collaboration and trust amongst stakeholders. To improve the transparency, relevance and robustness of sustainable assessments, the context of the sustainability assessment, including prioritizations of selection criteria for indicator selection, must be accounted for. A collaborative design process will enhance the acceptance of diverse values and prioritizations embedded in sustainability assessments. The process by which indicators and sustainability frameworks are established may be a much more important determinant of their success than the final shape of the assessment tools. Such an emphasis on process would make assessments more transparent, transformative and enduring.     

Coupled radon, methane and nitrate sensors for large-scale assessment of groundwater discharge and non-point source pollution to coastal waters

We constructed a survey system of radon/methane/nitrate/salinity to find sites of submarine groundwater discharge (SGD) and groundwater nitrate input. We deployed the system in Waquoit Bay and Boston Harbor, MA where we derived SGD rates using a mass balance of radon with methane serving as a fine resolution qualitative indicator of groundwater. In Waquoit Bay we identified several locations of enhanced groundwater discharge, out of which two (Childs and Quashnet Rivers) were studied in more detail. The Childs River was characterized by high nitrate input via groundwater discharge, while the Quashnet River SGD was notable but not a significant source of nitrate. Our radon survey of Boston Harbor revealed several sites with significant SGD, out of these Inner Harbor and parts of Dorchester Bay and Quincy Bay had groundwater fluxes accompanied by significant water column nitrogen concentrations. The survey system has proven effective in revealing areas of SGD and non-point source pollution.