The 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 workflow management and visualization VisTrails software. This 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. 

Abstract (from http://www.esajournals.org/doi/abs/10.1890/13-0905.1):  Many protected areas may not be adequately safeguarding biodiversity from human activities on surrounding lands and global change. The magnitude of such change agents and the sensitivity of ecosystems to these agents vary among protected areas. Thus, there is a need to assess vulnerability across networks of protected areas to determine those most at risk and to lay the basis for developing effective adaptation strategies. We conducted an assessment of exposure of U.S. National Parks to climate and land use change and consequences for vegetation communities. We first defined park protected-area centered ecosystems (PACEs) based on ecological principles. We then drew on existing land use, invasive species, climate, and biome data sets and models to quantify exposure of PACEs from 1900 through 2100. Most PACEs experienced substantial change over the 20th century (>740% average increase in housing density since 1940, 13% of vascular plants are presently nonnative, temperature increase of 1°C/100 yr since 1895 in 80% of PACEs), and projections suggest that many of these trends will continue at similar or increasingly greater rates (255% increase in housing density by 2100, temperature increase of 2.5° - 4.5°C/100 yr, 30% of PACE areas may lose their current biomes by 2030). In the coming century, housing densities are projected to increase in PACEs at about 82% of the rate of since 1940. The rate of climate warming in the coming century is projected to be 2.5 - 5.8 times higher than that measured in the past century. Underlying these averages, exposure of individual park PACEs to change agents differ in important ways. For example, parks such as Great Smoky Mountains exhibit high land use and low climate exposure, others such as Great Sand Dunes exhibit low land use and high climate exposure, and a few such as Point Reyes exhibit high exposure on both axes. The cumulative and synergistic effects of such changes in land use, invasives, and climate are expected to dramatically impact ecosystem function and biodiversity in national parks. These results are foundational to developing effective adaptation strategies and suggest policies to better safeguard parks under broad-scale environmental change.

Abstract (from http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0070454): Managers of protected natural areas increasingly are confronted with novel ecological conditions and conflicting objectives to preserve the past while fostering resilience for an uncertain future. This dilemma may be pronounced at range peripheries where rates of change are accelerated and ongoing invasions often are perceived as threats to local ecosystems. We provide an example from City of Rocks National Reserve (CIRO) in southern Idaho, positioned at the northern range periphery of pinyon-juniper (P-J) woodland. Reserve managers are concerned about P-J woodland encroachment into adjacent sagebrush steppe, but the rates and biophysical variability of encroachment are not well documented and management options are not well understood. We quantified the rate and extent of woodland change between 1950 and 2009 based on a random sample of aerial photo interpretation plots distributed across biophysical gradients. Our study revealed that woodland cover remained at approximately 20% of the study area over the 59-year period. In the absence of disturbance, P-J woodlands exhibited the highest rate of increase among vegetation types at 0.37% yr - 1. Overall, late-successional P-J stands increased in area by over 100% through the process of densification (infilling). However, wildfires during the period resulted in a net decrease of woody evergreen vegetation, particularly among early and mid-successional P-J stands. Elevated wildfire risk associated with expanding novel annual grasslands and drought is likely to continue to be a fundamental driver of change in CIRO woodlands. Because P-J woodlands contribute to regional biodiversity and may contract at trailing edges with global warming, CIRO may become important to P-J woodland conservation in the future. Our study provides a widely applicable toolset for assessing woodland ecotone dynamics that can help managers reconcile the competing demands to maintain historical fidelity and contribute meaningfully to the U.S. protected area network in a future with novel, no-analog ecosystems.

This dataset is a shapefile that contains the grid outlines and identifiers for the tiles produced by the TopoWx ("Topographical Weather/Climate") temperature dataset as applied to the USGS North Central Climate Center Domain and the surrounding area of Montana. The TopoWx dataset contains gridded daily temperature and is an interpolated spatio-temporaldataset in the same vein as the well-known PRISM (http://www.prism.oregonstate.edu) and Daymet products (http://daymet.ornl.gov). Daily Tmin and Tmax are provided at a 30-arcsec resolution (~800m) from 1948-2012 along with the latest 30-year monthly normals (1981-2010). The goals of the TopoWx project were to produce a dataset that: (1) incorporates key landscape-scale physiographic and biophysical factors that influence spatial spatial patterns of temperature;(2) provides estimates of uncertainty; (3) is appropriate for analyzing trends; and (4) is open to the research community for further analysis and improvements.

This data set contains output from the dynamic vegetation model MC1, as modified to simulate future woody encroachment in the northern Great Plains, for 23 monthly variables, 63 yearly variables, and 31 multi-year variables. Variables include simulated plant (by growth form) and soil carbon stocks, net primary production, vegetation type, potential and actual evapotranspiration, stream flow, and fuel mass and moisture. Model output is provided for the EQ, Spinup, Historical, and Future stages of MC1 runs; future stages were run for four climate projections crossed with 10 or 11 fire X grazing X CO2 concentration scenarios for the western and eastern portions of the study area, respectively.

Abstract (from http://www.sciencedirect.com/science/article/pii/S0006320712002388):  U.S. National Park Service land managers face a variety of challenges to preserving the biodiversity in their parks. A principle challenge is to minimize the impacts of surrounding land use on park condition and biodiversity. In the absence of ideal sets of data and models, the present study develops methods and results that demonstrate a coarse-filter approach to understanding the effects of land use change on habitat types for four pilot study-areas. The area of analysis for each park is defined by a protected-area-centered-ecosystem. Habitat types were defined by biophysical factors assumed to represent the distribution of vegetation communities as they may have existed prior to European settlement. Present-day land use was overlaid on historical habitat and change in area and pattern was quantified for private and public lands separately. Results suggest that patterns of development are affecting study-areas differently. Therefore, the conservation challenges faced by each study-area are distinct to their landscape contexts. For some parks, the primary challenge is to work towards maintaining ecosystem condition in its present or near-present state while paying particular attention to habitats that are underrepresented on public lands. For other parks, the challenge is to address spatially aggregated land use that is affecting only a few habitat types. For still other parks, the challenge is to maintain connectivity with a regional network of protected lands and to undertake restoration projects where feasible. The present methods and results help to focus conservation attention on habitats that have been most impacted by land use change.

Abstract (from http://www.esajournals.org/doi/abs/10.1890/12-2174.1):  Recent research on mountain-dwelling species has illustrated changes in species' distributional patterns in response to climate change. Abundance of a species will likely provide an earlier warning indicator of change than will occupancy, yet relationships between abundance and climatic factors have received less attention. We tested whether predictors of counts of American pikas ( Ochotona princeps ) during surveys from the Great Basin region in 1994 - 1999 and 2003 - 2008 differed between the two periods. Additionally, we tested whether various modeled aspects of ecohydrology better predicted relative density than did average annual precipitation, and whether risk of site-wide extirpation predicted subsequent population counts of pikas. We observed several patterns of change in pika abundance at range edges that likely constitute early warnings of distributional shifts. Predictors of pika abundance differed strongly between the survey periods, as did pika extirpation patterns previously reported from this region. Additionally, maximum snowpack and growing-season precipitation resulted in better-supported models than those using average annual precipitation, and constituted two of the top three predictors of pika density in the 2000s surveys (affecting pikas perhaps via vegetation). Unexpectedly, we found that extirpation risk positively predicted subsequent population size. Our results emphasize the need to clarify mechanisms underlying biotic responses to recent climate change at organism-relevant scales, to inform management and conservation strategies for species of concern.

The North Central Climate Science Center Paleoenvironmental Database serves as an archive of Pleistocene proxy records, metadata and derivative products (e.g., chronologies, vegetation and climate reconstructions), and provides a resource for environmental research, facilitating data viewing, synthesis and joint analysis of multiproxy datasets.  As of March 2014, the database consists of 1270 paleoenvironmental records, including proxies of climate (i.e., tree-rings, borehole temperatures, isotopes, diatoms, electrical conductivity, ice cores, loess accumulation), streamflow (i.e., tree rings), fauna (i.e., fossils), vegetation (i.e., pollen, plant macrofossils) and fire (i.e., tree-scars, charcoal). 

ABSTRACT: U.S. National Park Service land managers face a variety of challenges to preserving the biodiversity in their parks. A principle challenge is to minimize the impacts of surrounding land use on park condition and biodiversity. In the absence of ideal sets of data and models, the present study develops methods and results that demonstrate a coarse-filter approach to understanding the effects of land use change on habitat types for four pilot study-areas. The area of analysis for each park is defined by a protected-area-centered-ecosystem. Habitat types were defined by biophysical factors assumed to represent the distribution of vegetation communities as they may have existed prior to European settlement. Present-day land use was overlaid on historical habitat and change in area and pattern was quantified for private and public lands separately. Results suggest that patterns of development are affecting study-areas differently. Therefore, the conservation challenges faced by each study-area are distinct to their landscape contexts. For some parks, the primary challenge is to work towards maintaining ecosystem condition in its present or near-present state while paying particular attention to habitats that are underrepresented on public lands. For other parks, the challenge is to address spatially aggregated land use that is affecting only a few habitat types. For still other parks, the challenge is to maintain connectivity with a regional network of protected lands and to undertake restoration projects where feasible. The present methods and results help to focus conservation attention on habitats that have been most impacted by land use change. Remote sensing for inventory and monitoring of the U.S. national parks - ResearchGate. Available from: http://www.researchgate.net/publication/230720086_Remote_sensing_for_inventory_and_monitoring_of_the_U.S._national_parks [accessed Apr 23, 2015].

Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.All negative NDVI values which are thought to correspond to standing water, partial snow-cover or wet bare soil were set to ‘NA’values (Huete, Justice and van Leeuwen 1999)The following steps were used to remove any conifer/evergreen signal from NDVI data and are based on an understanding that each pixel has a different “background”(i.e. no-growth) greenness against which any seasonal change must be compared (Beever et al. 2013; Piekielek and Hansen 2013). These methods also help to eliminate long gaps in data that can allow smoothing algorithms to interpolate beyond the valid range of data (in the case of NDVI from 0 –1):Annual minimum NDVI values that were labeled as high-quality were identified in the 13 year time-series.The bottom first percentileof a distribution of minimum values was used as the “background”value to fill-in missing values when the target was identified as being under snow cover.All NDVI values identified by pixel-reliability grids as being of high or marginal quality werepreserved and snow-covered pixels and dates were filled in with each pixels “background”value.Composite day of year grids were used to identify the actual date from which the 16-day maximum composite NDVI value came.Each pixel’s entire time-series (2000 –2012) was smoothed in a weighted regression framework against time using smoothing splines (Chambers and Hastie 1992). NDVI data of marginal quality and snow-covered background values contributed half the weight to final smoothed values as did high-quality values. The final smoothed values were used to interpolate the time-series to a daily time-step and to record annual NDVI amplitudes. Land surface phenology metrics were calculated as follows:Start of season (SOS) –the first annual day of year when smoothed NDVI crosses half of its annual amplitude (White et al. 2009).End of Season (EOS) –the last day of year when smoothed NDVI crosses half of its annual amplitude.Maximum annual NDVI (MAX) –the highest annual smoothed NDVITiming of annual maximum (DOYMAX) –the smoothed day of year when NDVI reaches its maximum valueEstimated annual productivity (INDVI) –the integrated area under the growing season (SOS to EOS) NDVI curve (Goward et al 1985).