Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods

Vocalisations of Killer Whales (Orcinus orca) in the Bremer Canyon, Western Australia

To date, there has been no dedicated study in Australian waters on the acoustics of killer whales. Hence no information has been published on the sounds produced by killer whales from this region. Here we present the first acoustical analysis of recordings collected off the Western Australian coast. Underwater sounds produced by Australian killer whales were recorded during the months of February and March 2014 and 2015 in the Bremer Canyon in Western Australia. Vocalisations recorded included echolocation clicks, burst-pulse sounds and whistles. A total of 28 hours and 29 minutes were recorded and analysed, with 2376 killer whale calls (whistles and burst-pulse sounds) detected. Recordings of poor quality or signal-to-noise ratio were excluded from analysis, resulting in 142 whistles and burst-pulse vocalisations suitable for analysis and categorisation. These were grouped based on their spectrographic features into nine Bremer Canyon (BC) "call types". The frequency of the fundamental contours of all call types ranged from 600 Hz to 29 kHz. Calls ranged from 0.05 to 11.3 seconds in duration. Biosonar clicks were also recorded, but not studied further. Surface behaviours noted during acoustic recordings were categorised as either travelling or social behaviour. A detailed description of the acoustic characteristics is necessary for species acoustic identification and for the development of passive acoustic tools for population monitoring, including assessments of population status, habitat usage, migration patterns, behaviour and acoustic ecology. This study provides the first quantitative assessment and report on the acoustic features of killer whales vocalisations in Australian waters, and presents an opportunity to further investigate this little-known population

Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

<p>Input datasets and model results for associated PLOS ONE publication. </p

Importance of predictor variables measured as out-of-bag mean decrease in accuracy.

<p>Based on Random Forests model results.</p

High coal production scenario.

<p>Future mining footprint for coal production through 2035 (based on EIA low coal production cost scenario or high coal production). Future surface mining spatial footprint in order to meet coal production estimates for high coal production scenario through 2035 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128813#pone.0128813.ref002" target="_blank">2</a>]. Areas with predicted new surface mining through the year 2035 with modeled probability < 0.9 (based on Random Forests results) are shown in orange, areas with modeled probability > 0.9 are shown in dark red.</p

Overburden of coal seam comparison to random forests model results.

<p>In general, coal seams with higher amounts of overburden indicate increased costs for mining and recovery of coal resources. Coal seam overburden data were obtained from U.S. Geological Survey [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128813#pone.0128813.ref040" target="_blank">40</a>] for selected seams including the Pittsburgh coal seam (within the Appalachian basin) and the Baker-Danville coal seam (within the Illinois basin), shown here. Modeled areas of high probably of future surface coal mining (Random Forests probability > = 0.90) are shown for comparison, and are indicated by dark purple.</p

Surface mining probability from Random Forests model results.

<p>Model extent was limited to the known extent of coal in the region (coal field extents obtained from U.S. Geological Survey) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128813#pone.0128813.ref025" target="_blank">25</a>]. Random Forests model result ranges from 0 (lowest modeled probability of future surface mining activity) to 100 (highest probability), shown here in a blue to red color ramp.</p

Supply regions and probability (prob) results.

<p>EIA coal supply regions, with area of relatively high (0.90 or higher) probability of future surface coal mining, based on Random Forests model results.</p><p>Supply regions and probability (prob) results.</p