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Th e Software for Assisted Habitat Modeling (SAHM) has been created to both expedite habitat modeling and help maintain a record of the various input data, pre-and post-processing steps and modeling options incorporated in the construction of a species distribution model through the established workfl ow management and visualization VisTrails software. Th is paper provides an overview of the VisTrails:SAHM software including a link to the open source code, a table detailing the current SAHM modules, and a simple example modeling an invasive weed species in Rocky Mountain National Park, USA.Understanding where species will thrive is a useful and important consideration for resource managers concerned with either promoting (for threatened or endangered species) or controlling (for invasive or unwanted species). Th e fi eld of species distribution or habitat niche modeling is contributing to this understanding. With an increasing availability of both ecological data (Graham et al. 2004) and software packages to fi t ecological niche models (Phillips et al. 2006, Franklin 2009, Th uiller et al. 2009, Guo and Liu 2010, Peterson et al. 2011 as well as new tools to evaluate model performance (Allouche et al. 2006, Phillips and Elith 2010, Warren et al. 2010), researchers and land managers now have an unprecedented opportunity to explore many parameters and iterations for any given habitat niche modeling exercise. Each niche modeling technique has multiple parameters and options that can be adjusted and choices for input and output data. For habitat models that consider climate change, there are future climate projections from diff erent climate modeling centers and multiple emissions scenarios to consider (IPCC 2007). Land managers might want to evaluate diff erent biological responses; such as diff erent/multiple species or, for a given species, diff erent life cycles (e.g. breeding vs nesting habitat). Furthermore, it may be of interest to modify the spatial extent and spatial resolution of both input/ predictor layers and output/model results. With these options and others not listed here, the potential number of model runs and related results can be overwhelming. Th ere is a need for careful documentation of the precise model confi guration as well as meaningful interpretation of results. Scientifi c workfl ow systems help address this need.

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The Topologically Integrated Geographic Encoding and Referencing (TIGER) data are an essential part of the US Census and represent a critical element in the nation's spatial data infrastructure. TIGER data for the year 2000, however, are of limited positional accuracy and were deemed of insufficient quality to support the 2010 Census. In response the US Census Bureau embarked on the MAF/TIGER Accuracy Improvement Project (MTAIP) in an effort to improve the positional accuracy of the database, modernize the data processing environment and improve cooperation with partner agencies. Improved TIGER data were released for the entire US just before the 2010 Census. The current study characterizes the positional accuracy of the TIGER 2009 data compared with the TIGER 2000 data based on selected road intersections. Three US counties were identified as study areas and in each county 100 urban and 100 rural sample locations were selected. Features in the TIGER 2000 and 2009 data were compared with reference locations derived from high resolution natural color orthoimagery. Results indicate that TIGER 2009 data are much improved in terms of positional accuracy compared with the TIGER 2000 data, by at least one order of magnitude across urban and rural areas in all three counties for most accuracy metrics. TIGER 2009 is consistently more accurate in urban areas compared with rural areas, by a factor of at least two for most accuracy metrics. Despite the substantial improvement in positional accuracy, large positional errors of greater than 10 m are relatively common in the TIGER 2009 data, in most cases representing remnant segments of minor roads from older versions of the TIGER data. As a result, based on the US Census Bureau's suggested accuracy metric, the TIGER 2009 data meet the accuracy expectation of 7.6 m for two of the three urban areas but for none of the three rural areas. The suggested metric is based on the National Standard for Spatial Data Accuracy (NSSDA) protocol and was found to be very sensitive to the presence of a small number of very large errors. This presents challenges during attempts to characterize the accuracy of TIGER data or other spatial data using this protocol.

Abstract. More than 5957 km 2 in southwestern Wyoming is currently covered by operational gas fields, and further development is projected through 2030. Gas fields fragment landscapes through conversion of native vegetation to roads, well pads, pipeline corridors, and other infrastructure elements. The sagebrush steppe landscape where most of this development is occurring harbors 24 sagebrush-associated species of greatest conservation need, but the effects of gas energy development on most of these species are unknown. Pygmy rabbits (Brachylagus idahoensis) are one such species. In 2011, we began collecting three years of survey data to examine the relationship between gas field development density and pygmy rabbit site occupancy patterns on four major Wyoming gas fields (Continental Divide-Creston-Blue Gap, Jonah, Moxa Arch, Pinedale Anticline Project Area). We surveyed 120 plots across four gas fields, with plots distributed across the density gradient of gas well pads on each field. In a 1 km radius around the center of each plot, we measured the area covered by each of 10 gas field infrastructure elements and by shrub cover using 2012 National Agriculture Imagery Program imagery. We then modeled the relationship between gas field elements, pygmy rabbit presence, and two indices of pygmy rabbit abundance. Gas field infrastructure elements-specifically buried utility corridors and a complex of gas well pads, adjacent disturbed areas, and well pad access roads-were negatively correlated with pygmy rabbit presence and abundance indices, with sharp declines apparent after approximately 2% of the area consisted of gas field infrastructure. We conclude that pygmy rabbits in southwestern Wyoming may be sensitive to gas field development at levels similar to those observed for greater sage-grouse, and may suffer local population declines at lower levels of development than are allowed in existing plans and policies designed to conserve greater sage-grouse by limiting the surface footprint of energy development. Buried utilities, gas well pads, areas adjacent to well pads, and well pad access roads had the strongest negative correlation with pygmy rabbit presence and abundance. Minimizing the surface footprint of these elements may reduce negative impacts of gas energy development on pygmy rabbits.