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The tassel‐eared squirrel (Sciurus aberti) is often used as an indicator species in southwestern ponderosa pine (Pinus ponderosa) forests. Because of more than a century of fire suppression, grazing, and timber harvest, these forests have become increasingly prone to catastrophic wildfire, resulting in pressure to implement large‐scale treatments to reduce fire threat and restore ecosystem function. However, such treatments could have dramatic effects on tassel‐eared squirrels and other wildlife. Because of emerging plans for thinning southwestern forests to reduce fire threat, we undertook a modeling effort to produce spatial data to examine the results of proposed management actions on squirrel habitat. We used squirrel density and recruitment data from 9 study areas located in the Flagstaff region of northern Arizona, USA, linked with spatial data on forest structure developed from remote‐sensing imagery. We used a multiscale approach to analyze relationships between forest structure and squirrel density and recruitment. We then used an information‐theoretic approach to identify the most parsimonious models for both squirrel density and recruitment. The most strongly supported models of squirrel density included local‐scale basal area and >60% canopy cover at the 65‐ha spatial scale. For squirrel recruitment, 4 different models that included both local‐scale basal area (m2/ha) and variations of canopy cover over extents of approximately 160–305 ha were strongly supported. Using the most parsimonious models, we created spatial data layers representing both squirrel density and recruitment across an 800,000‐ha landscape in northern Arizona. Our approach resulted in spatially explicit models that can be used in efforts to predict the effects of forest management on squirrel populations.

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This paper reports the geochemical compositions of coals and non-coal samples from a complete seam section in the Late Permian Longtan Formation from the Yueliangtian mine, western Guizhou, southwestern China. The abundances, modes of occurrence, and origin of elements and minerals in the Yueliangtian coal were investigated using optical microscopy, scanning electron microscopy with an energy dispersive X-ray spectrometer, X-ray powder diffraction, X-ray fluorescence spectrometry, and inductively coupled plasma mass spectrometry. The host rocks (roof and floor) and one parting sample of the coal seam have high TiO2 contents, which is in accordance with the high TiO2 content in the Emeishan basalt from the Kangdian Upland. The coal bench samples are rich in SiO2 (14.52%, whole-coal basis) compared with the average for the common Chinese coals, and the high SiO2 present in this study is consistent with the abundant quartz, which was mainly precipitated from siliceous solutions produced by weathering of the Emeishan basalt. Compared to the average values for world hard coals, the coal bench samples are enriched in V (77.0 μg/g), Cu (41.9 μg/g), Se (4.77 μg/g), Zr (93.8 μg/g), Hg (0.375 μg/g), and Pb (21.4 μg/g). In contrast to many other Permian coals from southwestern China, the transition elements, including Cr, Co, Ni, and Zn, are not enriched in the coal bench samples, possibly due to the input of the terrigenous materials with felsic and felsic-intermediate rock compositions. The highfield strength elements are relatively enriched not only in the parting samples but also in the adjacent coal bench samples, indicating that the partings were subjected to leaching by groundwater during the diagenetic process. Elements in coal, including B, Cr, Co, Zn, and Ni, are mainly associated with clay minerals, while As, Se, Sb, and Pb mainly occur in sulfide minerals (pyrite and marcasite). An intra-seam volcanic ash-derived tonstein layer identified in the coal is characterized by strong negative Eu anomaly in the Upper Continental Crust-normalized rare earth elements and Y distribution pattern, indicating the input of felsic or felsic-intermediate terrigenous materials.

A regional geochemical map interpolated from point data, usually sampled in surficial media such as stream sediments or lake sediments, may contain a large amount of information critical for mineral exploration and environmental studies. The geochemical map is, however, not 'ready-to-use' for such tasks as the determination of a local 'anomaly' or the characterization of a regional trend of one or more chemical elements as may be required for the purpose of mineral resource prediction. This becomes possible only after the map has been clearly divided into different components. Fractal filtering, a recently developed technique for decomposing a map or image into different components, helps to separate the anomaly from background or to extract other meaningful patterns from the geochemical map using both frequency and spatial information.The fractal filters are formed by applying the fractal concentration-area model to the power spectrum of the processed geochemical field. They often constitute a group of irregularly shaped filters in the frequency domain that can separate the domain of wave numbers into distinct regions, each with a power spectrum following a similar power-law or fractal property. The corresponding patterns of the separate components are obtained after transformation back to the spatial domain.The fractal filter can be applied to decompose the original geochemical field into a set of map components with distinct scaling ranges and anisotropy. The analysis of relationships among these decomposed maps can provide useful information for the interpretation and evaluation of anomalies or trends.This paper briefly introduces the theory behind the fractal filtering technique. A case study of regional geochemical data of lake sediments from western Meguma Terrain, Southern Nova Scotia, Canada, is used to illustrate application of this technique to process the regional geochemical maps of the study area for the prediction of the turbidite-hosted gold deposits.