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Species with different ecological niches will likely exhibit distinct responses to a changing environment. Differences in the magnitude of niche specialization may also indicate which species may be more vulnerable to environmental change, as many life-history characteristics are known to affect climate change vulnerability. We characterized the niche space of three sympatric high-elevation ground-dwelling squirrels, yellow-bellied marmot (Marmota flaviventer), Belding's ground squirrel (Urocitellus beldingi), and golden-mantled ground squirrel (Callospermophilus lateralis), in the alpine and upper subalpine regions of the Sierra Nevada in California. We used 5879 observations of individual squirrels, collected from 4 years (2009-2012) of transect survey data, to quantify which ecogeographical variable types (climate, topography, or landcover) were most important in defining the niche of each species. We conducted Ecological Niche Factor Analysis to quantify the niche and generate indices of "marginality" (magnitude of selection) and "specialization" (narrowness of niche space). All three species demonstrated differential use of niche space when compared to the available niche space. Moreover, the relative importance of the variables shaping the niche differed among these species. For example, the presence of meadows was important in defining the niche for U. beldingi and M. flaviventer, but the presence of conifers was important to C. lateralis. Precipitation was important in defining the niche for all three species, positively so for U. beldingi, and negatively for the other two species. The niche breadth of these three species was also positively associated with geographic range size. Mammals in high-elevation mountain systems often are perceived as vulnerable to climate shifts, but our results underscore the importance of also including non-climate-based factors in defining the niche. The overall magnitude of niche selection for all three species was driven by a combination of topographic, climatic, and landcover factors; thus, efforts to forecast areas where these species can persist in the future need to evaluate from more than just a climatic perspective.

Native bunchgrass communities dominated by Stipa pulchra are widely distributed in California but share dominance with non‐native annual grasses. Restoration of these grasslands focuses on altering the balance of native to non‐native grasses to favor the former. This study investigated the impact of burning on vegetation recovery. In the first postfire year burning showed a 70% reduction in cover of non‐native annual grasses (Bromus diandrus exhibited the greatest reduction) and minimal impact on S. pulchra recovery. In the following 3 years, S. pulchra recovered to levels comparable to controls, whereas the annual grasses remained below control levels until the fifth year. Also, in response to reduced annual grass cover on burned sites several species of non‐native Erodium increased from 10 to 30% relative cover, however, the low growth form of these forbs presented a less competitive threat to bunchgrasses than the non‐native annual grasses, and by the third postfire year returned to near control levels. The rare native geophyte Brodiaea kinkiensis was present throughout these grasslands and was not inhibited by burning treatments. To document the reliability of these patterns a second prescription burn was conducted on these sites 5 years after the first burn and vegetation recovery followed for the subsequent 4 years. Patterns observed after the first burn were duplicated following the second burn. The cover of S. pulchra varied in response to precipitation, with the 95% credible intervals of precipitation parameters overlapping zero, however, the cover of non‐native grasses varied greatly with precipitation and had similar trajectories in unburned and burned plots. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline ( TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index ( NDVI) products derived from Advanced Very High Resolution Radiometer ( AVHRR) and Moderate Resolution Imaging Spectroradiometer ( MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study ( ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains. [ABSTRACT FROM AUTHOR]