No abstract

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Hydraulic fracturing of unconventional hydrocarbon reservoirs is critical to the United States energy portfolio; however, hydrocarbon production from newly fractured wells generally declines rapidly over the initial months of production. One possible reason for this decrease, especially over time scales of several months, is the mineralization and clogging of microfracture networks and pores proximal to propped fractures. One important but relatively unexplored class of reactions that could contribute to these problems is oxidation of Fe(II) derived from Fe(II)-bearing phases (primarily pyrite, siderite, and Fe(II) bound directly to organic matter) by the oxic fracture fluid and subsequent precipitation of Fe(III)-(oxy)hydroxides. The extent to which such reactions occur and their rates, mineral products, and physical locations within shale pore spaces are unknown. To develop a foundational understanding of potential impacts of shale iron chemistry on hydraulic stimulation, we reacted sand-sized (150-250 μm) and whole rock chips (cm-scale) of shales from four different formations

Summary. We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly autocorrelated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

We review 87 articles published in the Journal of Wildlife Management from 2000 to 2004 to assess the current state of practice in the design and analysis of resource selection studies. Articles were classified into 4 study designs. In design 1, data are collected at the population level because individual animals are not identified. Individual animal selection may be assessed in designs 2 and 3. In design 2, use by each animal is recorded, but availability (or nonuse) is measured only at the population level. Use and availability (or unused) are measured for each animal in design 3. In design 4, resource use is measured multiple times for each animal, and availability (or nonuse) is measured for each use location. Thus, use and availability measures are paired for each use in design 4. The 4 study designs were used about equally in the articles reviewed. The most commonly used statistical analyses were logistic regression (40%) and compositional analysis (25%). We illustrate 4 problem areas in resource selection analyses: pooling of relocation data across animals with differing numbers of relocations, analyzing paired data as though they were independent, tests that do not control experiment wise error rates, and modeling observations as if they were independent when temporal or spatial correlations occurs in the data. Statistical models that allow for variation in individual animal selection rather than pooling are recommended to improve error estimation in population‐level selection. Some researchers did not select appropriate statistical analyses for paired data, or their analyses were not well described. Researchers using one‐resource‐at‐a‐time procedures often did not control the experiment wise error rate, so simultaneous inference procedures and multivariate assessments of selection are suggested. The time interval between animal relocations was often relatively short, but existing analyses for temporally or spatially correlated data were not used. For studies that used logistic regression, we identified the data type employed: single sample, case control (used‐unused), use‐availability, or paired use‐availability. It was not always clear whether studies intended to compare use to nonuse or use to availability. Despite the popularity of compositional analysis, we do not recommend it for multiple relocation data when use of one or more resources is low. We illustrate that resource selection models are part of a broader collection of statistical models called weighted distributions and recommend some promising areas for future development.