Solomon Dobrowski

PhD, Ecology. University of California, Davis. 2005.

Master of Science, Horticulture. University of California, Davis. 2001.

Bachelor of Science, Resource Management. University of California, Berkeley. 1997.

1) FOR 202 Forest Mensuration (spring semester)

2) FOR 503 Predictive distribution modeling (spring semester every other year)

3) FOR 595 Statistical models for ecological data analysis

General
My long-term research interests are to understand the abiotic and biotic controls of plant species distributions, vegetation structure, and productivity in managed and unmanaged forests. I am particularly interested in 1) the processes by which topography shapes coarse-scaled climate influences into locally-scaled biophysical drivers, 2) physiological response of organisms to changes in their environment, 3) the emergent response of vegetation structure and composition to shifts in environmental gradients, and 4) approaches to modeling biophysical processes and vegetation response in space and time. To address questions in these areas of research, I employ a wide range of analytical tools including remote sensing methodologies aimed at producing spatially continuous estimates of vegetation composition, structure, and physiological functioning, statistical and terrain modeling techniques to develop spatial estimates of biophysical drivers, and modern statistical methods to link the two. The main thrust of my research is to understand the linkages between biophysical drivers and vegetation response, the physiological processes involved in these linkages, the spatial scales at which these linkages operate, and approaches to modeling and mapping these processes.

Species distributions and historic data
Much of our understanding about the response of forest species distributions to climate change comes from species distribution models (SDM). SDMs are empirical models that relate field observations of species to environmental predictors based on statistically or theoretically derived response functions. SDM climate change projections are largely untested and rely on a number of critical assumptions. Existing approaches to the validation of SDM projections partition contemporary datasets into calibration and validation sets. The working assumption is that a model with a strong goodness-of-fit will accurately project distributions under novel climates. An alternative approach to this form of validation is to assess the predictive performance of SDMs directly by fitting models retrospectively with historic data to produce projections of the present day. I am working with historic climate conditions, historic vegetation distribution data, and measured climate change, to predict current species distributions. My overall objectives are to put real numbers on estimates of SDM climate-change projection accuracies, determine if projection accuracies vary by species and are related to species autecological traits, and assess the role of land-use type, intensity, and disturbance on projection accuracies.

Dobrowski, S.Z., J.T. Abatzoglou, J.A. Greenberg, S.G. Schladow (2009) How much influence does landscape-scale physiography have on air temperature in a mountain environment? Agricultural and Forest Meteorology 149: 1751-1758

Greenberg, J.A., S.Z. Dobrowski, V.C. Vanderbilt. (2009) Limitations of maximum tree density using hyperspatial remote sensing and envrionmental gradient analysis. Remote Sensing of Environment 113: 94-101

Dobrowski, S.Z., H.D. Safford, C.A. Rueda, and S.L. Ustin (2008) Mapping mountain vegetation using image-based texture analysis, predictive species distribution modeling, and object-based classification. Applied Vegetation Science 11: 499-508.

Hammersmark, C.T, S.Z. Dobrowski, M.C. Rains, and J.F. Mount (2008) Simulated effects of stream restoration on the distribution of wet meadow vegetation. Restoration Ecology (in press)

Dobrowski, S.Z. and S.K. Murphy (2006) A practical look at the variable area transect. Ecology 87 (7): 1856-1860.

Dobrowski, S.Z., J.A. Greenberg, C.M. Ramirez, and S.L. Ustin (2006) Improving image derived vegetation maps with regression based distribution modeling. Ecological Modeling 192: 126-142.

Greenberg, J.A., S.Z. Dobrowski, C.M. Ramirez, J.L. Tuil, and S.L. Ustin (2006) A bottom-up approach to vegetation mapping of the Lake Tahoe Basin using hyperspatial image analysis. Photogrammetric Engineering and Remote Sensing 72 (5): 581-589.

Greenberg, J.A., S.Z. Dobrowski, and S.L. Ustin (2005) Shadow allometry: estimating tree structural parameters using hyperspatial image analysis. Remote Sensing of Environment 97: 15-25.

Dobrowski, S.Z., J.C. Pushnik, P.J. Zarco-Tejada, and S.L. Ustin (2005) Simple reflectance indices track heat and water stress induced changes in steady state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment 97: 403-414.

Roberts, D.A., S.L. Ustin, S. Ogunjemiyo, J. Greenberg, S.Z. Dobrowski, J. Chen, and T.M. Hinckley (2004) Scaling up the forests of the Pacific Northwest using remote sensing. Ecosystems 7 (5): 545-562.

Zarco-Tejada, P.J., J.C. Pushnik, S.Z. Dobrowski, and S.L Ustin (2002) Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red edge effects. Remote Sensing of Environment 84: 283-294.