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.

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).

Abstract (from http://www.srmjournals.org/doi/abs/10.2111/REM-D-13-00079.1):  Big sagebrush,  Artemisia tridentata  Nuttall (Asteraceae), is the dominant plant species of large portions of semiarid western North America. However, much of historical big sagebrush vegetation has been removed or modified. Thus, regeneration is recognized as an important component for land management. Limited knowledge about key regeneration processes, however, represents an obstacle to identifying successful management practices and to gaining greater insight into the consequences of increasing disturbance frequency and global change. Therefore, our objective is to synthesize knowledge about natural big sagebrush regeneration. We identified and characterized the controls of big sagebrush seed production, germination, and establishment. The largest knowledge gaps and associated research needs include quiescence and dormancy of embryos and seedlings; variation in seed production and germination percentages; wet-thermal time model of germination; responses to frost events (including freezing/thawing of soils), CO2  concentration, and nutrients in combination with water availability; suitability of microsite vs. site conditions; competitive ability as well as seedling growth responses; and differences among subspecies and ecoregions. Potential impacts of climate change on big sagebrush regeneration could include that temperature increases may not have a large direct influence on regeneration due to the broad temperature optimum for regeneration, whereas indirect effects could include selection for populations with less stringent seed dormancy. Drier conditions will have direct negative effects on germination and seedling survival and could also lead to lighter seeds, which lowers germination success further. The short seed dispersal distance of big sagebrush may limit its tracking of suitable climate; whereas, the low competitive ability of big sagebrush seedlings may limit successful competition with species that track climate. An improved understanding of the ecology of big sagebrush regeneration should benefit resource management activities and increase the ability of land managers to anticipate global change impacts.

Abstract (from  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0111669): Projected climate change at a regional level is expected to shift vegetation habitat distributions over the next century. For the sub-alpine species whitebark pine (Pinus albicaulis), warming temperatures may indirectly result in loss of suitable bioclimatic habitat, reducing its distribution within its historic range. This research focuses on understanding the patterns of spatiotemporal variability for future projected P.albicaulis suitable habitat in the Greater Yellowstone Area (GYA) through a bioclimatic envelope approach. Since intermodel variability from General Circulation Models (GCMs) lead to differing predictions regarding the magnitude and direction of modeled suitable habitat area, nine bias-corrected statistically down-scaled GCMs were utilized to understand the uncertainty associated with modeled projections. P.albicaulis was modeled using a Random Forests algorithm for the 1980-2010 climate period and showed strong presence/absence separations by summer maximum temperatures and springtime snowpack. Patterns of projected habitat change by the end of the century suggested a constant decrease in suitable climate area from the 2010 baseline for both Representative Concentration Pathways (RCPs) 8.5 and 4.5 climate forcing scenarios. Percent suitable climate area estimates ranged from 2-29% and 0.04-10% by 2099 for RCP 8.5 and 4.5 respectively. Habitat projections between GCMs displayed a decrease of variability over the 2010-2099 time period related to consistent warming above the 1910-2010 temperature normal after 2070 for all GCMs. A decreasing pattern of projected P.albicaulis suitable habitat area change was consistent across GCMs, despite strong differences in magnitude. Future ecological research in species distribution modeling should consider a full suite of GCM projections in the analysis to reduce extreme range contractions/expansions predictions. The results suggest that restoration strageties such as planting of seedlings and controlling competing vegetation may be necessary to maintain P.albicaulis in the GYA under the more extreme future climate scenarios. This publication was developed as part of the project,  Informing Implementation of the Greater Yellowstone Coordinating Committee’s (GYCC) Whitebark Pine (WBP) Strategy Based on Climate Sciences, Ecological Forecasting, and Valuation of WBP-Related Ecosystem Services.

Covering 120 million acres across 14 western states and 3 Canadian provinces, sagebrush provides critical habitat for species such as pronghorn, mule deer, and sage-grouse – a species of conservation concern. The future of these and other species is closely tied to the future of sagebrush. Yet this important ecosystem has already been affected by fire, invasive species, land use conversion, and now, climate change.   In the western U.S., temperatures are rising and precipitation patterns are changing. However, there is currently a limited ability to anticipate the impacts of climate change on sagebrush. Current methods suffer from a range of weakness that limits the reliability of results. In fact, the current uncertainty about future changes in sagebrush has been identified as a critical constraint on climate change adaptation planning in the West. To address this need, researchers forecasted the effects of climate change on the distribution and abundance of sagebrush, and integrated several modeling approaches that take into account historical data, disturbances such as fire, and changes in temperature and precipitation. This integrated method is expected to produce more accurate estimates of future sagebrush distribution and abundance.   The results of this research will be effectively communicated to land managers so that they can inform conservation planning, and sage-grouse management in particular, across the Intermountain West. Improved sagebrush forecasting will increase the capacity of land managers to prioritize future investments in sagebrush conservation and management by identifying areas where sagebrush are most and least vulnerable to climate change.

Ecological niche models predict plant responses to climate change by circumscribing species distributions within a multivariate environmental framework. Most projections based on modern bioclimatic correlations imply that high-elevation species are likely to be extirpated from their current ranges as a result of rising growing-season temperatures in the coming decades. Paleoecological data spanning the last 15,000 years from the Greater Yellowstone region describe the response of vegetation to past climate variability and suggest that white pines, a taxon of special concern in the region, have been surprisingly resilient to high summer temperature and fire activity in the past. Moreover, the fossil record suggests that winter conditions and biotic interactions have been critical limiting variables for high-elevation conifers in the past and will likely be so in the future. This long-term perspective offers insights on species responses to a broader range of climate and associated ecosystem changes than can be observed at present and should be part of resource management and conservation planning for the future.