Becky K. Kerns 1, Dominique Bachelet 2 and Michelle Buonopane 1 present 1 USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR 2 Oregon State University and Conservation Biology Institute, Corvallis, OR

 Climatic regimes influence terrestrial ecosystem patterns and processes.  Future climatic changes will continue to alter these patterns and processes.

 Considering possible vegetation responses to climate change is a critical issue.  Useful to consider a range of potential future conditions for vegetation;  Incorporate scenarios into management, adaptation, and conservation efforts.  Many information sources can be used– we focus on models.

 Many types of models  Empirically/statistically driven; and  Mechanistic or process based.  Boundaries can fuzzy.  Debates about “best” models heated.  This talk will not settle this debate.

 To illustrate the possible responses of vegetation to climate change using two different impact models.  Compare and contrast model structures, assumptions, results and usability for management and adaptation purposes.  MC1: Dynamic Global Vegetation Model  MaxEnt – statistical mechanics based SDM

Potential Natural Vegetation (USFS local, OR Gap Program)

 Model developed for global application (Daly et al. 2000, Bachelet al. 2000, 2001)  Application of DGVMs to fine spatial scales in in its infancy  Originated from the joining of MAPSS (Neilson 1995) and CENTURY (Parton et al. 1994), and MCFIRE (Lenihan et al. 1998).  Simulates lifeforms mixtures, vegetation types, wildfire, ecosystem fluxes of carbon, nitrogen and water. Main assumption: ecological processes (production and decomposition) are limited by soil N availability

 MC1 simulations based on Rogers (2009); available at:  Input data:  Monthly gridded climate  Historical data from PRISM (30-arc sec)  Future climate, ambient CO 2 : Hadley CM3 A2  Delta method of downscaling (Rogers 2009).  Gridded soils – held constant  Validation/calibration using observational data.

MC1 Vegetation TypeLocally Representative Species Potential Natural Vegetation Type Percent of MC1 Historical Map Percent of PNVT Map Temperate Evergreen Needle-leaf Forest Ponderosa pine, Lodgepole pine, White fir Ponderosa pine xeric, Lodgepole dry, Mixed conifer dry 4464 Maritime Evergreen Needle-leaf Forest Hemlock, Douglas-firMixed conifer moist96 Subalpine ForestSpruce, true firSpruce-fir0.28 Temperate Evergreen Needle-leaf Woodland Western juniper, ponderosa pine Juniper sage212 (Juniper sage) Temperate ShrublandSagebrush, bitterbrush, greasewood Wyoming Big Sage, Salt Desert Shrub 2413 Temperate GrasslandIdaho fescue, western wheatgrass, squireltail Idaho fescue13

Historical Vegetation End of Century HADCM3 A2e Vegetation

MC1 Vegetation TypeLocally Representative Species Potential Natural Vegetation Type Percent of MC1 Historical Map Percent of MC1 Future Map Temperate Evergreen Needle-leaf Forest Ponderosa pine, Lodgepole pine, White fir Ponderosa pine xeric, Lodgepole dry, Mixed conifer dry 4465 Maritime Evergreen Needle-leaf Forest Hemlock, Douglas-firMixed conifer moist90 Subalpine ForestSpruce, true firSpruce-fir0.20 Temperate Evergreen Needle-leaf Woodland Western juniper, ponderosa pine Juniper sage2122 Temperate ShrublandSagebrush, bitterbrush, greasewood Wyoming Big Sage, Salt Desert Shrub 2414 Temperate GrasslandIdaho fescue, western wheatgrass, squireltail Idaho fescue11

 Utilizes a maximum entropy approach (Phillips 2006).  Estimates the most uniform distribution (maximum entropy) given the constraint that the expected value of each predictor under this estimated distribution matches (within errror bounds) its empirical average.  Overfitting controlled by smoothing (regularization).  Similar to generalized linear and additive models, Bayesian approaches, neural networks.

 Data:  Input variables:  30-arc sec PRISM (1979 – 2008)  STATSGO soils (constant)  Hadley CM3 A2 (2070– 2099)  Species occurrence:  Ponderosa pine, western juniper and sagebrush  Local USFS Ecology Plot  Forest Inventory and Analysis  STATSGO  Model assessment using test data.

End of Century ProbabilityHistorical Probability ProbabilityPercent of Map – – ProbabilityPercent of Map – –

End of Century Vegetation End of Century Probability

Historical Probability of Occurrence ProbabilityPercent of Map – – End of Century Probability ProbabilityPercent of Map – –

End of Century Probability ProbabilityPercent of Map – – Historical Probability of Occurrence ProbabilityPercent of Map – –

MC1 Vegetation TypeModeled Species MaxEnt ResultsMC1 ResultsParsimony Temperate Evergreen Needle-leaf Forest Ponderosa pine Lots of movement on the landscape (e.g. upslope), but general increase probability Expansion into shrublands, cooler forests, and juniper woodland Models agree in sign, pattern Maritime Evergreen Needle-leaf Forest Douglas-fir Subalpine ForestMountain hemlock Temperate Evergreen Needle-leaf Woodland Western juniper Lost from study areaLittle changeModels disagree in sign/pattern Temperate ShrublandSagebrushLost from study areaDecreases and shifts to the east Models agree in sign, not magnitude Temperate GrasslandSanberg’s bluegrass

 If models agree, are we more confident?  When model disagree, examine why – what makes the most sense based on local knowledge and observed patterns.  Because empirical models are fit to data, they are better at producing historical patterns; but does this make them better models for the future?  Degree to which DGVMs can nail present conditions  DGVMs have the potential to provide finer biological resolution.  Only using one impact model is like only using one GCM…  Early days of applying DGVMs to these scales

 Funding support: Western Wildand Environmental Threat Assessment Center  Special Thanks  David Conklin  Brendan Rogers