1. Soils, Climate Change and Vegetation Modeling
Wendy Peterman
Conservation Biology Institute/OSU
Corvallis, Oregon

2. In recent decades, scientists have reported an escalation in forest mortality connected to physiological stress (hydrological failure, carbon starvation), insect infestations, and wildfires likely due to increased temperatures and drought intensity.

3. In Western states
Van Mantgem et al. (2009): Increased mortality rates, reduced recruitment at all elevations, tree sizes, species & fire histories likely not caused by competition or aging most likely culprits: regional warming & water deficits

4. Craig Allen et al. (2010) identified seven major knowledge gaps and scientific uncertainties hindering the ability of the scientific community to fully understand the causes of these diebacks and allow them to forecast areas of future tree mortality.

5. First Gap = Documentation
Joerg Steinkamp, Biodiversity Climate Research Center
& Wendy Peterman, Conservation Biology Institute

6. Forest dieback in LCC’s 1997 to 2010

7. 2 nd gap - improved belowground process modeling
incorporating effects of:
increased CO2,
nitrogen deposition,
and drought
on root dynamics, productivity,
exudation rates and
mycorrhizal interactions.
Solution: revise belowground algorithms through improved understanding based on new measurements and analyses

8. Above-ground
Abiotic
Biotic
Plant physiology (C, N, H2O)
Fuels
Insects and diseases
Herbivory
Climate (Temp & Precip)
Evaporation
Hydrological cycle
Fire weather
Oceans & sea ice
Below-ground
Abiotic
Biotic
Hydrology
Soil physical conditions (temp & moisture regimes)
Mineralogy/chemistry
Texture
Microbial communities
Roots and rhizosphere
C & N cycling/storage
H2O cycling
Decomposition
Disease agents, herbivory

9. GIS analysis

10. Correlations between soil characteristics and mortality
Example: Southwest pinyon pine decline area
% of area
% of area
Mineral and salt Accumulations
Climate regime
% of area
% of area
Soil texture
Soil order
Peterman, unpub.

11. Trends in combinations of soil characteristics
Combo
Caliche
Climate regime
Order
texture
% mort area
% hab area 1
calcic
torric/ustic
Entisols
medium/skeletal
5.8
0.39 2
calcidic
ustic
Aridisols
medium/coarse
5.6
0.24 3
unknown
Inceptisols/Alfisols
4.0
0.11 4
cryic/ustic
mixed
fine
3.7
0.15 5
Alfisols
1.24 6
torric
Inceptisols/Entisols
3.6
0.17 7
Entisols/Aridisols
3.5
0.42 8
medium
3.1
1.01
% of area
Peterman, unpub.
DO NOT SHARE
Combinations of Soil Characteristics

12. Decision Tree Analysis

13. Utah
Colorado
Arizona
New Mexico
Peterman, unpub.
DO NOT SHARE

15. Peterman et al., Ecohydrology (in revision)

16. 3-PG results based on GIS soil and tree mortality analyses
Peterman et al., Ecohydrology (in revision)

17. N fixers
CO2 evolution

19. Coops et al. (Applied Vegetation Science, 2011)

20. Lodgepole pine future viability scores under Hadley GCM A2 scenario
Lodgepole pine future viability scores under Hadley GCM A2 scenario
2010
2030
Crookston et al. 2010
Crookston et al. 2010
2060
2090
Crookston et al. 2010
Crookston et al. 2010

21. Improving DGVM soil treatment
Run sensitivity analysis of the dynamic global vegetation model MC1 with different soil data.
Revise belowground algorithms in MC1 about root processes (water, nutrient uptake, leaching, turnover)
Create new plant functional types based on plants response to disturbance (traits): ex.
insect vulnerability: cuticle thickness, II compounds
drought vulnerability: root depth, phenology, stomate density, wilting point

22. Products Forecasting tools to project future dieback based
Web-based spatial datasets to visualize soil characteristics and projected changes