Mapping of stress on native tree species across western U.S.A. & Canada: interpretation of

climatically-induced changes using a physiologically-based approach

Richard Waring1

Thomas Hilker1

Nicholas Coops2

Amanda Mathys2

1 Oregon State University 2 University of British Columbia

I R S S

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

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

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 H20)• Each of the functions vary from 0 (shutdown) to 1

(optimum)

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)

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 cedar 95 90 92 0.89Bristlecone pine 73 85 79 0.66Colo. blue spruce 83 87 83 0.73Douglas-fir 74 80 78 0.60Engelmann spruce 84 72 78 0.89Grand fir 85 79 81 0.67Lodgepole pine 68 78 70 0.63Mt. hemlock 85 88 87 0.75Noble fir 93 90 91 0.86One-seeded juniper 97 91 94 0.93Pacific silver fir 90 87 88 0.79Pinyon pine 84 96 90 0.91Ponderosa pine 91 73 82 0.78Quaking aspen 82 71 77 0.89Rocky Mt. juniper 90 82 86 0.78Single-leaf pinyon 93 83 88 0.86Sitka spruce 96 92 94 0.92Subalpine fir 95 62 79 0.86Utah juniper 95 82 89 0.89Western hemlock 95 80 87 0.88Western larch 88 83 85 0.74Western red cedar 87 79 83 0.72Western white pine 81 70 75 0.57White-bark pine 91 81 86 0.81White fir 93 80 87 0.84

Unpublished material :NASA Grant NNX11A029G

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-firPonderosa pine

Modeled change in summer soil water stress since 2000 compared to 1950-75 period

Range: --0.5 to +0.5

Modeled change in spring frost since 2000 compared to 1950-75 period

No change in 2000

Range:- 0.06 to 0.3

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

Whitebark pine: predicted areas under stress since 2000 in the vicinity of Bozeman, MT

Document generated by Richard Waring on April 10, 2013

Legend

Whitebark pine (Pinus albicaulis) extent, North America

New range expansion and contraction

Displaying: VALUE

New range expansion

Historic range

Stressed in current range

Whitebark pineDisplaying: VALUE1

Unpublished material :NASA Grant NNX11A029G

USFS species–level disturbance patterns

COLORADO

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

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 USAPeterman, 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, CANADACoops, 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.

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