We are modeling species- and population-level growth responses to changes in climate using provenance and common garden tests data. These are experiments in spatial climate change where the climate at which the seed source has originated differs from that of the test site. When populations are tested in several sites, differences in growth responses to changes in climate can be elucidated. These models are providing crucial insights on species and populations phenotypic responses to climate change as well as guidelines for seed transfer under an adaptive management context.

We are interested in downscaling forest communities and species habitat suitability models. In general, these models have a regional spatial resolution. A finer resolution will match the spatial scale of these models to the scale at which management and conservation decisions occur. We approach this task by increasing the resolution of all the variables in the model including the response variables

We are modeling forest attributes such as canopy height and forest density with inventory, topographic, and remotely sensed data. We have mapped forest canopy height of the entire PA State Forests using leaf-off, low point density lidar data.

We are developing species-specific predictive models of advance regeneration using an extensive field dataset and ensemble classification trees. The goal is for these models to eventually be incorporated into the US Forest Service Forest Vegetation (FVS) Simulator Northeastern variant.