Bozeman, MT, United States
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Geremia C.,National Park Service | Geremia C.,Colorado State University | White P.J.,National Park Service | Wallen R.L.,National Park Service | And 5 more authors.
PLoS ONE | Year: 2011

Long distance migrations by ungulate species often surpass the boundaries of preservation areas where conflicts with various publics lead to management actions that can threaten populations. We chose the partially migratory bison (Bison bison) population in Yellowstone National Park as an example of integrating science into management policies to better conserve migratory ungulates. Approximately 60% of these bison have been exposed to bovine brucellosis and thousands of migrants exiting the park boundary have been culled during the past two decades to reduce the risk of disease transmission to cattle. Data were assimilated using models representing competing hypotheses of bison migration during 1990-2009 in a hierarchal Bayesian framework. Migration differed at the scale of herds, but a single unifying logistic model was useful for predicting migrations by both herds. Migration beyond the northern park boundary was affected by herd size, accumulated snow water equivalent, and aboveground dried biomass. Migration beyond the western park boundary was less influenced by these predictors and process model performance suggested an important control on recent migrations was excluded. Simulations of migrations over the next decade suggest that allowing increased numbers of bison beyond park boundaries during severe climate conditions may be the only means of avoiding episodic, large-scale reductions to the Yellowstone bison population in the foreseeable future. This research is an example of how long distance migration dynamics can be incorporated into improved management policies.


White P.J.,National Park Service | Davis T.L.,National Park Service | Sheldon J.W.,Yellowstone Ecological Research Center | White J.R.,Gardiner Public School
Journal of Mammalogy | Year: 2012

Understanding mechanisms that influence the grouping tendencies of large herbivores is necessary to predict the influence of environmental and human factors on threatened populations. Locations of 53 adult female pronghorn (Antilocapra americana) in Yellowstone National Park during June 1999April 2005 indicated that mean and typical group sizes and the variation in group size decreased during fawning when females secluded themselves, but became larger and more dynamic during fawn rearing and the rut and winter. Mixed-effects models indicated a strong effect of time of year on mean group sizes, with some evidence that predators negatively affected group sizes during winter. Within-animal variability (0.64) was substantially higher than between-animal variability (0.02). Pronghorn density, snow water equivalent, and predation apparently influenced variations in group size. Multiple regressions indicated effects of pronghorn density and snow water equivalent on typical group size, the size of the group in which the average animal finds itself. Overall, there was fluidity in group cohesion, with female associations changing within and among days. The behavioral plasticity of pronghorn with respect to grouping and social cohesion might confer resilience to changes in environmental conditions, but often makes it difficult to predict the consequences of conservation actions to control disease, protect or restore key habitat, regulate harvests, and limit adverse effects of development and recreation. © 2012 American Society of Mammalogists.


Malanson G.P.,University of Iowa | Resler L.M.,Virginia Polytechnic Institute and State University | Bader M.Y.,University of Oldenburg | Holtmeier F.-K.,University of Munster | And 4 more authors.
Arctic, Antarctic, and Alpine Research | Year: 2011

For over 100 years, mountain treelines have been the subject of varied research endeavors and remain a strong area of investigation. The purpose of this paper is to examine aspects of the epistemology of mountain treeline research-that is, to investigate how knowledge on treelines has been acquired and the changes in knowledge acquisition over time, through a review of fundamental questions and approaches. The questions treeline researchers have raised and continue to raise have undoubtedly directed the current state of knowledge. A continuing, fundamental emphasis has centered on seeking the general cause of mountain treelines, thus seeking an answer to the question, "What causes treeline?" with a primary emphasis on searching for ecophysiological mechanisms of low-temperature limitation for tree growth and regeneration. However, treeline research today also includes a rich literature that seeks local, landscape-scale causes of treelines and reasons why treelines vary so widely in three-dimensional patterns from one location to the next, and this approach and some of its consequences are elaborated here. In recent years, both lines of research have been motivated greatly by global climate change. Given the current state of knowledge, we propose that future research directions focused on a spatial approach should specifically address cross-scale hypotheses using statistics and simulations designed for nested hierarchies; these analyses will benefit from geographic extension of treeline research. © 2011 Arctic, Antarctic, and Alpine Research.


Hatala J.A.,University of California at Berkeley | Crabtree R.L.,Yellowstone Ecological Research Center | Halligan K.Q.,Sanborn Map Company | Moorcroft P.R.,Harvard University
Remote Sensing of Environment | Year: 2010

Pathogen and pest outbreaks are recognized as key processes in the dynamics of Western forest ecosystems, yet the spatial patterns of stress and mortality are often complex and difficult to describe in an explicit spatial context, especially when considering the concurrent effects of multiple agents. Blister rust, a fungal pathogen, and mountain pine beetle, an insect pest, are two dominant sources of stress and mortality to high-altitude whitebark pine within the Greater Yellowstone Ecosystem (GYE). In whitebark pine populations infested with blister rust or mountain pine beetle, the shift from green to red needles at the outer-most branches is an early sign of stress and infestation. In this analysis, we investigated a method that combines field surveys with a remote sensing classification and spatial analysis to differentiate the effects of these two agents of stress and mortality within whitebark pine. Hyperspectral remotely sensed images from the airborne HyMap sensor were classified to determine the locations of stress and mortality in whitebark pine crowns through sub-pixel mixture-tuned matched-filter analysis in three areas of the GYE in September 2000 and July 2006. Differences in the spatial pattern of blister rust and mountain pine beetle infestation allowed us to separate areas dominated by mountain pine beetle versus blister rust by examining changes in the spatial scale of significant stress and mortality clusters computed by the Ripley's K algorithm. At two field sites the distance between clusters of whitebark pine stress and mortality decreased from 2000 to 2006, indicating domination by the patchy spatial pattern of blister rust infestation. At another site, the distance between significant stress and mortality clusters increased from 2000 to 2006, indicating that the contiguous pattern of mountain pine beetle infestation was the primary source of disturbance. Analysis of these spatial stress and mortality patterns derived from remote sensing yields insight to the relative importance of blister rust and mountain pine beetle dynamics in the landscape. © 2009 Elsevier Inc. All rights reserved.


Grafius D.,University of Iowa | Malanson G.,University of Iowa | Weiss D.,Yellowstone Ecological Research Center
Physical Geography | Year: 2012

Alpine treelines can be explained by lower temperatures with increasing elevation at global scales, but regional and local treelines vary because of additional constraints. We examine data from treelines across the western USA, divided into subregions to elucidate the patterns of these other constraints. We determined the best predictor of elevation for 930 treeline sites in 26 mountain ranges by finding a climate variable with least variance. We used the variance at each site from this predictor to compute an elevation anomaly that became the dependent variable in correlation and regression analyses. We derived independent variables from digital elevation models and regional climate interpolations. A "best subsets" regression, which compares all possible combinations, revealed that at the continental scale, latitude and longitude stand out among many significant but weak correlations between the elevation anomaly and the other variables; these retain importance within regions. Our results show variables related to water and energy play a secondary role. We found that the division of the observations into regions and interpretation of spatial patterns within regions was necessary to interpret the relations between the potential predictor variables and the elevation anomaly.


Huang S.,NASA | Huang S.,Yellowstone Ecological Research Center | Potter C.,NASA | Crabtree R.L.,Yellowstone Ecological Research Center | And 3 more authors.
Remote Sensing of Environment | Year: 2010

The arid and semi-arid sagebrush-grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of "pure grass" and "mixed sagebrush and grass". Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of "low", "medium", and "high", the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring. © 2009 Elsevier Inc.


Potter C.,NASA | Li S.,NASA | Li S.,Henan University | Huang S.,NASA | And 3 more authors.
Remote Sensing of Environment | Year: 2012

The density of lodgepole pine (Pinus contorta) sapling regeneration was mapped in areas burned during the 1988 wildfires across Yellowstone National Park (YNP), Wyoming, USA. Hyperspectral image analysis and field measurements were combined across the entire YNP extent. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image data from 2006 were used to compute ten different vegetation indices (VI). The ten VIs were combined to build multiple regression models for predicting and mapping post-fire sapling density. Four different forms of regression modeling were applied to derive the highest possible prediction accuracy (correlation coefficient of R 2=0.83). Pine sapling regeneration 19years after large-scale wildfires showed a high level of variability in patch density (ranging from 14/100ha to 57/100ha), whereas sapling density measured previously from the first decade following wildfire was more uniform (10/100ha to 21/100ha). The ecosystem-level clumpiness index showed major shifts in aggregation of different sapling density classes, and was consistent with an overall decrease in estimated sapling density of nearly 50% between 1998 and 2007. This analysis revealed important succession patterns and processes in post-fire forest regeneration for the Greater Yellowstone Area (GYA). © 2012.


Martin J.G.,Oregon State University | Phillips C.L.,Lawrence Livermore National Laboratory | Schmidt A.,Oregon State University | Irvine J.,Yellowstone Ecological Research Center | Law B.E.,Oregon State University
Tree Physiology | Year: 2012

High-frequency soil CO 2 flux data are valuable for providing new insights into the processes of soil CO 2 production. A record of hourly soil CO 2 fluxes from a semi-arid ponderosa pine stand was spatially and temporally deconstructed in attempts to determine if variation could be explained by logical drivers using (i) CO 2 production depths, (ii) relationships and lags between fluxes and soil temperatures, or (iii) the role of canopy assimilation in soil CO 2 flux variation. Relationships between temperature and soil fluxes were difficult to establish at the hourly scale because diel cycles of soil fluxes varied seasonally, with the peak of flux rates occurring later in the day as soil water content decreased. Using a simple heat transport/gas diffusion model to estimate the time and depth of CO 2 flux production, we determined that the variation in diel soil CO 2 flux patterns could not be explained by changes in diffusion rates or production from deeper soil profiles. We tested for the effect of gross ecosystem productivity (GEP) by minimizing soil flux covariance with temperature and moisture using only data from discrete bins of environmental conditions (±1°C soil temperature at multiple depths, precipitation-free periods and stable soil moisture). Gross ecosystem productivity was identified as a possible driver of variability at the hourly scale during the growing season, with multiple lags between ∼5, 15 and 23 days. Additionally, the chamber-specific lags between GEP and soil CO 2 fluxes appeared to relate to combined path length for carbon flow (top of tree to chamber center). In this sparse and heterogeneous forested system, the potential link between CO 2 assimilation and soil CO 2 flux may be quite variable both temporally and spatially. For model applications, it is important to note that soil CO 2 fluxes are influenced by many biophysical factors, which may confound or obscure relationships with logical environmental drivers and act at multiple temporal and spatial scales; therefore, caution is needed when attributing soil CO 2 fluxes to covariates like temperature, moisture and GEP. © 2012 The Author.


Fairweather S.,Montana State University | Fairweather S.,Yellowstone Ecological Research Center | Potter C.,NASA | Crabtree R.,Yellowstone Ecological Research Center | Li S.,NASA
Photogrammetric Engineering and Remote Sensing | Year: 2012

Remote sensing techniques can provide information on habitat quality, biodiversity, and cover change at the regional scales of sagebrush-steppe dominated systems and with the repeatability necessary for resource management. In this paper, we present the results of multiple endmember spectral mixture analysis applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery, comparing affects of seasonality and spectral resolution on the discrimination of soil, grass/forb, and sagebrush abundance in Yellowstone National Park. Our results showed that AVIRIS, despite late season phenology, correlated well with herbaceous and sagebrush cover field measurements (R 2 = 0.77 and 0.75, respectively), utilizing high spectral resolution to separate soils from vegetation. However, ASTER-derived values during peak green correlated best with field measurements (0.75 and 0.78). These results demonstrated an effective method to monitor semi-arid regions with readily available imagery and highlight applications for future hyperspectral satellite missions. © 2012 American Society for Photogrammetry and Remote Sensin.


Weiss D.J.,Yellowstone Ecological Research Center | Crabtree R.L.,Yellowstone Ecological Research Center
Remote Sensing of Environment | Year: 2011

Four models for deriving percent surface water estimates were developed for use with MODIS16-day Bidirectional Reflectance Distribution Function (BRDF) corrected composite images. The models allow intra-annual surface water estimates to be produced with 1km spatial resolution and an 8-day temporal resolution when applied to image composites from sensors on both the Aqua and Terra platforms. The surface water models are conceptually simple, relying on widely used indices (NDVI, NDWI, and tasseled cap), but computationally intensive. The models differ in the time and effort required to produce or acquire the inputs necessary for model training. The models were applied and tested in Yukon Flats National Wildlife Refuge, an area with varied surface water types including ponds, fens, and the Yukon River and its tributaries. Resulting accuracies peaked with an R 2 of approximately 0.625, and model accuracies were higher for pixels with higher percentages of water. © 2011 Elsevier Inc.

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