A closer look at detectability

Environmental and Ecological Statistics - The theory underlying line transect and variable circular plot surveys—distance sampling—begins with an assumed detectability function, giving...     

Conditional and unconditional estimators of population size

Estimators for the population sizes of animal species are similar to Horvitz-Thompson estimators — they involve dividing counts of detected animals by the probabilities of detection. Knowing detection probabilities for different subpopulations allows one to estimate each sub-population size with such an estimator, and to add the results for an estimator of the total population. In the case where the proportions of animals belonging to the different sub-populations are also known, this paper shows that using those proportions to arrive at a common average detection probability will, when used in conjunction with the total number of animals detected, result in a better estimator. We provide two examples where the inferior estimator may seem sensible.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

Optimized contaminated land investigation at minimum overall cost to achieve fitness-for-purpose

A methodology for optimized contaminated land investigation (OCLI) is described that balances the uncertainty of measurements against the cost of taking the measurements and the financial losses that may arise from misclassification of the land. Uncertainty from the sources of both field sampling and chemical analysis is estimated using existing techniques, based on the taking of duplicated samples. The actual costs of sampling and analysis and the expected costs that could arise from either 'false positive' or 'false negative' classification of areas of land were estimated. A loss function was constructed that calculates the expectation of financial loss that will arise for a given uncertainty of measurement. The function shows a clear minimum value of cost at an optimal value of uncertainty. Application of this OCLI technique to two case studies demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum, the costs increased due to increasing risk of factors such as unnecessary remediation or potential litigation over undetected contamination. Many areas for further development of OCLI are identified, but the technique is demonstrated as a useful new approach to judging fitness-for-purpose of such measurements.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

Uncertainty in the assessment of hazard,exposure and risk

The terminology, concepts and current approaches to uncertainty in the assessment of hazard, exposure and risk are reviewed. Five generic questions are discussed on uncertainty, including sources, levels, when and how it should be dealt with or reduced, what are our gaps in understanding and how they can be addressed. A case study of lead exposure of children in Lavrion, Greece, is used to exemplify these questions and possible answers. Estimation of uncertainty may be improved by the use of interorganisational studies to capture sources of uncertainty that are often overlooked. Gaps identified in our understanding of uncertainty include: a limitation in the availability of basic measurements, a lack of knowledge of the environmental processes, an inability to predict the effects of mixtures, the aetiology of disease and devising procedures for optimal resource allocation in impact assessment.     

Discrimination between aluminium held within vegetation and that contributed by soil contamination using a combination of Electron Probe Micro Analysis (EPMA) and Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES)

Aluminium concentrations in NIST (formally NBS) Standard Reference Material Tomato Leaves and Citrus Leaves were determined by ICP-AES after acid digestion. The normal total acid attack (nitric and perchloric acids) gave very low recovery (40%) of Al in NIST tomato leaves, but not for citrus leaves. The contribution of Al from Al-rich soil particles in both tomato leaves and citrus leaves was estimated semi-quantitatively by computer-controlled EPMA. The Al held in these particles corresponded in approximate concentration to the shortfall between the acid soluble component determined in this study and the certified value. Analysis by EPMA can provide, therefore, a method of estimating Al contributed by soil contamination to plant materials. The limitations of the NIST tomato and citrus leaf reference material for the validation of methods for the determination of Al in vegetation are demonstrated.     

Influence of soil-extractable aluminium and pH on the uptake of aluminium from soil into the soybean plant (Glycine max)

The effects of soil pH and other soil properties on the uptake of AI by soybean plants have been investigated in a greenhouse experiment. Six soils were compared that were developed over six contrasting bedrock types ranging widely in their AI content and other chemical and physical characteristics, namely Oxford Clay, Chalk, Lower Lias Clay, Devonian Shale, Granite and Lower Greensand. Soil pH varied naturally between soil types and each soil was also amended to give two other pH levels using elemental sulphur and/or calcium carbonate. AI concentrations in various parts of the soybean plants were determined by ICP-AES after acid digestion. The AI solubility in the soils and hence its availability to the plants was estimated using a number of different reagents designed to extract different forms of AI. The AI concentration measured in the soybean leaves was found to be predicted most accurately by the ‘available’ AI extracted from soils by 0.02 M CaCl₂. The relationship appears to the linear, with a correlation coefficient of 0.97 (p <0.01). The AI content of the leaves increases with decreasing soil pH. The relationship is non-linear with a marked increase in leaf AI for soils with pH <4.4. The amounts of ‘plant-available’ AI in the soils extracted with 0.02 M CaCl₂ was much less than that extracted with 0.05 M EDTA, although both increased markedly with decreasing soil pH. The amount of AI measured in the soybean plants was directly related to both the ‘available’ forms of AI in the soils, and also to the pH of the soils. Soil pH was identified as a major factor that controls the uptake of Al from soil into the soybean plant.     

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.     

Sustainable oil-in-water analysis using a supercritical fluid carbon dioxide extraction system directly interfaced with infrared spectroscopy

A direct aqueous supercritical fluid extraction (SFE) system using carbon dioxide provides a sustainable means by which a vast range of industries may continue to depend on well established infrared (IR) techniques to determine oil-in-water. The SFE-IR method provides an environmentally friendly substitute for current national standard IR reference methods for measuring oil-in-water that rely on using increasingly restricted ozone depleting solvents whose manufacture is being phased out in accordance with international law. The SFE-IR analysis of a 500 mL water sample can be accomplished in 15 min. A rapid on-line SFE-IR calibration method has been implemented. With this calibration method, SFE-IR accuracy for determining diesel oil in 500 mL spiked water samples using single wave number measurement was 86.0%–98.8% with precision (RSD) ranging from 2.5%–7.0%. Using a general purpose calculation which involves measuring infrared absorbance values at three di erent wave numbers, SFE-IR method accuracy for determining diesel oil in 500 mL spiked water samples was 83.7%–92.2% with RSD 1.0%–9.3%. Data is presented that indicates current long established national standard IR reference methods involving three wave number calculations should be reviewed since, without careful consideration, the inclusion of calculated aromatic hydrocarbon species contributions to final oil-in-water concentration values may provide less accurate results.