Improving soils data for better vegetation modeling Wendy Peterman, Dominique Bachelet Conservation Biology Institute  Abstract Over the last decade, scientists have documented unprecedented levels of forest dieback in response to prolonged droughts, floods and/or changes in snowpack. As the main determinant of belowground water availability, soils play an integral role in ecological responses to changes in precipitation and temperature. Increasing the realism of belowground processes and the sensitivity of vegetation models to soil conditions will improve their representation of real-world conditions, thus improving their predictive power. For the North Pacific LCC, we are developing new, improved soils datasets and evaluating overall soil vulnerability to climate change. The new soil data will be used to test the sensitivity of statistical models, process models and dynamic global vegetation models (DGVMs) and deepen our understanding of forest mortality in response to changing conditions.  Methods & Materials The over-arching issue of our work is climate change, but it addresses the multiple interconnections between systems affected by it from soils to trees, insects and pathogens, involving processes ranging from hydrological flow to plant species competition. Simulation results used in regional assessments address the various aspects of these issues separately to forecast responses to climate change, but the more we can combine these results using soils as the underlying framework of ecosystem functionality, the more robust our projections will become.  Expected Outcomes This work was made possible by a grant from the North Pacific Landscape Conservation Cooperative Figure 1. Examples of vegetation simulations produced with the MC1 dynamic global vegetation model for western Oregon and Washington.  Questions One aspect of our research involves improving soils datasets for use in vegetation models, and testing how sensitive the models are to these changes. If the model is sensitive to soils, data with coarse resolutions and large gaps like the one labeled A, should produce less realistic vegetation results than finer scale data with fewer gaps (B), and data with no gaps (C). Figure 2. Examples of three soil datasets used in the MC2 dynamic global vegetation model. The model is being run with three different sets of soils data to evaluate the effects of improved soils data on model performance. Can soil characteristics be used to predict forest stand mortality in the Pacific Northwest? Currently, how sensitive are vegetation models to soils data? Can soils data be improved to increase the predictive power of vegetation models?  Methods & Materials  Acknowledgements Soil texture, depth and bulk density characteristics are used to govern both hydrological and nutrient cycling in vegetation models. We expect that models with good sensitivity to soil conditions will produce different results not just in vegetation, but in above and belowground carbon, runoff and streamflow.