Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Spatiotemporal mapping of mountain pine beetle infestation severity and probability of new infestation in the central U.S. Rocky Mountains(Montana State University - Bozeman, College of Agriculture, 2018) Bode, Emma Taylor; Chairperson, Graduate Committee: Rick L. LawrenceSynchronous, widespread, and severe mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks impacted forests of western North America at unprecedented levels in recent decades. Severe MPB epidemics can degrade ecosystem services and socio-economic assets. Mapping outbreak progression informs mitigation efforts and enables analysis of MPB attack processes on a landscape scale. Existing time-series methods for mapping disturbance focus on extent rather than severity. Infestation severity, expressed as within-pixel mortality percentage, is more robust for answering a variety of ecological questions. Our objectives were to: (1) map infestation severity from 2005-2015 using a time-series regression; and (2) analyze MPB attack processes by modeling new infestation probability using spatial and environmental variables in the central U.S. Rocky Mountains. We used spectral data from all available Landsat images, topographic data, and data from U.S. Forest Service aerial detection survey (ADS) polygons to model infestation severity. We collected reference data by interpreting National Agricultural Imagery Program images. We then employed logistic regression model-based recursive partitioning (MOB) to determine: (a) to what degree nearby infestation severity increased probability of new infestation; (b) the degree of variation in probability across space and time with respect to other spatial and environmental risk factors; and (c) the extent to which these effects were directional relative to prevailing winds. Validation of our infestation severity model against a randomly selected subset of the data resulted in no statistical difference between predicted and observed severity. Our raster maps allowed us to identify lower severity infestation not recorded by the ADS. The final MOB model obtained 72.1% accuracy in predicting new infestation. Nearby infestation severity strongly influenced the probability of new infestations. This effect varied with elevation, aspect, temperature, phase of the outbreak, and spatial location. Variation in probability of infestation was highest when surrounding infestation severity was low. Use of wind-informed directional effects did not improve the model. This analysis establishes the efficacy of mapping an infestation severity time series and demonstrates that severity maps facilitate novel analyses of MPB attack processes. The processes developed here can support management decisions with timely maps of MPB infestation severity and probability of new infestation.Item The impacts mountain pine beetle on forested snowpacks : accumulation and ablation(Montana State University - Bozeman, College of Agriculture, 2013) Welch, Christopher Michael; Chairperson, Graduate Committee: Paul C. StoyThe future of water resources in the west is tenuous, as climatic changes have resulted in earlier spring melts that have exacerbated summer droughts. Associated with climate changes to the physical environment are changes to the biological environment that may impact snow dynamics; namely via the massive outbreaks of Mountain Pine Beetle (MPB; Dendroctonus ponderosae) that have devastated several million hectares of Lodgepole Pine forests in the western U.S. and Canada. If snow accumulation and melt are determined by the physical environment of the snowpack, and forest canopies define in part this physical environment, how might recent insect outbreaks alter the timing and intensity of snowmelt? MPB often attack in large numbers, and within a few years, the canopy of an infected forest will turn from green but dying, to red, to grey. As needles fall, impacts on the snow pack include changes to wind driven transport, temperature gradients, and snow interception. Additionally, the shifting canopy alters the radiated physics of the canopy, specifically the shortwave/longwave flux density. Combined with a corresponding decrease of snow reflectance (albedo) from litter fall, the dying canopy will provide more energy available to the surface and likely drive snowpacks to melt more rapidly. Conversely, the diminished canopy cover will presumably decrease net longwave radiation of the snowpack. Canopy interception of snow is expected to decrease, and an increase in accumulation will result. I investigate the impacts of MPB disturbance on snow melt through modeling and micrometeorological measurements in intact lodgepole pine and mixed coniferous forests, a MPB-infested forest in the red stage, and a clearcut stand. Albedo at the homogenous intact stand is found to be 16 and 34% higher than the red stand during the melt periods of 2011 and 2012, but no significant difference is found between the red stand and the more heterogeneous 'healthy' stand. Modeled sensible heat over-predicts sensible heat by over 300% during the melt period of 2012. Results highlight the role of beetle-infested and mixed stands on altering snow albedo, and additionally suggest that model formulations for turbulent exchange between snow and atmosphere below forest canopies require improvement.Item Spatiotemporal relationships between climate and whitebark pine mortality in the greater Yellowstone ecosystem(Montana State University - Bozeman, College of Agriculture, 2009) Jewett, Jeffrey Thomas; Chairperson, Graduate Committee: Rick L. Lawrence.Whitebark pine (Pinus albicaulis) serves as a subalpine keystone species by regulating snowmelt runoff, reducing soil erosion, facilitating the growth of other plants, and providing food for wildlife, particularly grizzly bears (Ursus arctos horribilis). Mountain pine beetle (Dendroctonus ponderosae) is an ideal bio-indicator of climate change, as its life cycle is entirely temperature dependent. Western North America is currently experiencing the largest outbreak of mountain pine beetle on record, and evidence suggests that a changing climate has accelerated the life-cycle of this bark beetle, allowing it to expand into new habitat. This study explored the relationships between climate, mountain pine beetles, and whitebark pine mortality in the Greater Yellowstone Ecosystem (GYE). A time-series of Landsat satellite imagery was used to monitor whitebark pine mortality in the GYE from 1999 to 2008. The patterns of mortality were analyzed with respect to monthly climate (temperature and precipitation) variations over the 9-year period. The impacts of topography and autocorrelation (both spatial and temporal) were also analyzed with respect to whitebark pine mortality. Whitebark pine mortality was assessed using the Enhanced Wetness Difference Index (EWDI), a Landsat-derived measure of canopy moisture. Regression tree models were built to predict yearly changes in EWDI. Thirty-eight percent of the deviance in whitebark pine was explained by a regression tree with 10 predictor variables. The most important predictor variables were autocorrelation terms, indicating a strong host-tree depletion effect, where mountain pine beetles were much less likely to attack recently attacked areas. Topographic variables (elevation, slope, aspect) were not useful in predicting whitebark pine mortality. Climate variables alone were used to construct a regression tree with 14 predictor variables which predicted 15% of the dataset deviance in whitebark pine mortality. Drier climatic conditions favored increased whitebark pine mortality, likely due to the decreased ability of whitebark pine to repel beetle attacks. Warmer climatic conditions also favored increased whitebark pine mortality, likely due to a decrease in winter mortality of mountain pine beetle. These results show for the first time a statistical link between climate variability and whitebark pine mortality, likely mediated by mountain pine beetles.