A novel method for fitting spatio-temporal models to data, withapplications to the dynamics of Mountain Pine Beetle

Abstract We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage-structured forest of lodgepole pine (Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This modeling technique yields four model candidates. These models allow discrimination between four broad possibilities at the landscape scale: whether or not beetles are subject to an Allee effect at the landscape scale and whether or not host selection is random or directed. We fit the models with aerial damage survey data to the Sawtooth National Recreation Area using estimating functions, which allows for more rapid and complete parameter determination. We then introduce a novel model selection procedure based on facial recognition technology to compliment traditional nonspatial selection metrics. Together with these we are able to select a best model and draw inferences regarding the behavior of the beetle in outbreak conditions

Development and parametrization of a model forbark beetle disturbance in lodgepole forest

Native forest insects are the greatest forces of change in forest ecosystems of North America. In aggregate, insect disturbances affect an area that is almost 45 times as a great as that affected by the fire, resulting in an economic impact nearly five times as great (Dale et all., 2001). Of these natural agents of ecosystem disturbance and change, the bark beetles are the most obvious in their impact, and of these, the mountain pine beetles (Dendroctonus ponderosae Hopkins has the greatest economic importance in the forest of western North America (Samman and Logan, 2000). The primary reason for this impact is that the mountain pine beetle is one of a handful of bark beetles that are true predators in that they must kill their host to successfully reproduce, and they often do so in truly spectacular numbers

Incorporating Channel Spatial Variability into Two-Zone Transient Storage Modeling

The two-zone.temperature and solute (TZTS) transport model separates transient storage into surface and subsurface (hyporheic) storage zones due to the potentially different residence time distributions and processes that individually influence each zone. Despite this approach to better represent individual transport processes and storage volumes, the effects of channel spatial heterogeneity on solute transport predictions are not well understood. Since constant channel geometry is often assumed among transient storage modeling efforts and numerical approximation methods are typically used, analytical solutions were developed for the solute component of the TZTS model to serve as verification of numerical results, aid in interpretation of results, and provide a more stable means to account for spatial variability in parameters. In a previous effort, the TZTS model was applied to a 6.5 km reach of the Virgin River, a low gradient desert river with sand to gravel substrate and negligible groundwater exchange, in southwestern Utah, United States. Parameters, including main channel and surface storage widths, were initially estimated using solute tracer data and assumed constant for the entire study reach. Although reasonable solute transport predictions were provided by the calibrated model, the significance of neglecting spatial variability in channel geometry on predictions was uncertain. We investigated the effects of these assumptions by using main channel and surface storage width parameters extracted from aerial high resolution multispectral and thermal infrared imagery. We then used the TZTS analytical solutions with a convolution approach to incorporate these spatially variable parameters into solute transport predictions. In comparing these results to the constant channel geometry predictions, we found significant spatial variability in solute residence times and, therefore, a requirement of further calibration resulting in different parameter sets