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

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.

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

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.

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

Forest dieback in LCC’s 1997 to 2010

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

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

GIS analysis

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.

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

Decision Tree Analysis

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

Peterman et al., Ecohydrology (in revision)

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

N fixers CO2 evolution

Coops et al. (Applied Vegetation Science, 2011)

Lodgepole pine future viability scores under Hadley GCM A2 scenario 0 - 0.5 0.5 - 0.75 0.75 - 1 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

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

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