1. Mapping of stress on native tree species across western U.S.A. & Canada: interpretation of climatically-induced changes using a physiologically-based approach Richard Waring 1 Thomas Hilker 1 Nicholas Coops 2 Amanda Mathys 2 1 Oregon State University 2 University of British Columbia Richard Waring 1 Thomas Hilker 1 Nicholas Coops 2 Amanda Mathys 2 1 Oregon State University 2 University of British Columbia I R S S

2. Challenge: can we map where some tree species will die & others migrate in response to a changed climate? In a previous NASA grant we predicted where a majority of 15 species in the Pacific N.W. might expect to be subject to fire, insect, or disease attack within broadly defined ecoregions with 70 to 80% accuracy. In this NASA grant, we seek to expand the areas to the entire West for 25 tree species and to improve spatial accuracy to 1km 2 by: a) mapping variation in soil properties b) limiting predictions to areas where a tree species is known to be present on Forest Service survey plots c) field verification (or falsification) of model predictions

3. Site productivity varies with climate and soil conditions: The maximum greenness (Leaf Area Index) of the vegetation during mid- summer directly mirrors variation in site productivity, even with disturbance Coops, Waring, Hilker. 2012. Remote Sensing of Env. 126:160-173 3-PG Modeled LAI MODIS LAI

4. Modeling gross photosynthesis (GPP) Assume Max GPP is the product of light absorbed by LAI each month and the photosynthetic efficiency of leaves, the latter dependent on soil fertility. Actual GPP = Max GPP * f(temp)*f(frost)*f(vpd)*f(avail H 2 0) Each of the functions vary from 0 (shutdown) to 1 (optimum)

5. Decision tree analysis [Values in reference to optimum for Douglas-fir] (After Coops & Waring. 2011. Climatic Change 105:313-328) (more frost) (cooler temp) (< humid) (soils saturated)

6. Presence & absence of 25 tree species on >43,400 survey plots predicted with an average accuracy of 80% Species Presence accuracy % Absence accuracy %Average accuracy %K statistic Alaska yellow cedar9590920.89 Bristlecone pine7385790.66 Colo. blue spruce8387830.73 Douglas-fir7480780.60 Engelmann spruce8472780.89 Grand fir8579810.67 Lodgepole pine6878700.63 Mt. hemlock8588870.75 Noble fir9390910.86 One-seeded juniper9791940.93 Pacific silver fir9087880.79 Pinyon pine8496900.91 Ponderosa pine9173820.78 Quaking aspen8271770.89 Rocky Mt. juniper9082860.78 Single-leaf pinyon9383880.86 Sitka spruce9692940.92 Subalpine fir9562790.86 Utah juniper9582890.89 Western hemlock9580870.88 Western larch8883850.74 Western red cedar8779830.72 Western white pine8170750.57 White-bark pine9181860.81 White fir9380870.84 Unpublished material :NASA Grant NNX11A029G

7. Process-based decision-tree models predict where climatic conditions since 2000 are more or less favorable than they were previously (1950-75) Unpublished material :NASA Grant NNX11A029G Douglas-fir Ponderosa pine

8. Modeled change in summer soil water stress since 2000 compared to 1950-75 period Range: --0.5 to +0.5

9. Modeled change in spring frost since 2000 compared to 1950-75 period No change in 2000 Range:- 0.06 to 0.3

10. Ponderosa pine: predicted area under stress in N. California, and vulnerable since 2000 compared to 1950-75 climatic conditions Unpublished material :NASA Grant NNX11A029G CALIFORNIA

11. Whitebark pine: predicted areas under stress since 2000 in the vicinity of Bozeman, MT Unpublished material :NASA Grant NNX11A029G

12. USFS species–level disturbance patterns COLORADO

13. Summary Inverting the process-based model to predict soil fertility & soil water holding capacity improves our ability to explain local patterns of tree death (Peterman et al. 2012). Including soil properties did not improve broad scale accuracy in predicting presence or absence on field survey plots (80%), but we expect improved accuracy at local scales for specific species We will restrict field inspections to areas where tree species have been recorded and are modeled to be in a changed state

14. AUSTRALIA Smettem, K.R.J., R.H. Waring, N. Callow, M. Wilson & Q. Mu (2013) Satellite-derived estimates of forest leaf area index in south west Western Australia are not tightly coupled to inter- annual variations in rainfall: implications for groundwater decline in a drying climate. Global Change Biology (in press). SOUTHWEST USA Peterman, W., R.H. Waring, T. Seager, and W.L. Pollock (2012) Soil properties affect pinyon pine-juniper response to drought. Ecohydrology (on line June 13th, 2012). BRITISH COLUMBIA & ALBERTA, CANADA Coops, N.C., M.A. Wulden, and R.H. Waring (2012) Modeling lodgepole and jack pine vulnerability to mountain pine beetle expansion into the Canadian boreal forest. Forest Ecology and Management 274:1601-171. ALL WESTERN STATES AND CANADIAN PROVINCES Coops, N.C., R.H. Waring & T. Hilker (2012) Prediction of soil properties using a process-based forest growth model to match satellite-derived estimates of leaf area index. Remote Sensing of Environment 126:160-173. PUBLICATIONS