Supplemental Material
Recovery Dynamicsand Climate Change Effects to Future New England Forests

Appendix I: Model and parameter description.

Appendix II: Forest age plotted against forest growth by ecoregion.

Appendix III: Less-dominant species response.

Appendix I: Model and parameter description

LANDIS-II simulates tree species establishment, growth, and mortality by tracking species-age cohorts within interacting cells within a forested landscape. Within a cell, competition between cohorts and biomass accumulation can also be simulated. Seed dispersal is simulated as a dynamic process where mature cohorts within a cell act as a seed source. The probability of dispersal to surrounding cells is simulated with a declining exponential function with distance from the source cell. Several LANDIS-II extensions allow the user to choose succession and disturbance process functions that vary in complexity (Scheller et al. 2007).
We used the Base Wind v2.0(Scheller and Mladenoff 2004) extension to simulate a general, simplified disturbance regime. This disturbance regime resulted in approximately 0.73% of the landscape disturbed at every ten-year timestep within patches with a mean size of 17 hectare (SE=.05). This regime was meant to create a limited number of forest gaps on the landscape to allow some early successional habitat without having disturbances drive the landscape level response.
We simulated New England’s 32 most abundant tree species observed in the U.S. Forest Service’s Forest Inventory and Analysis (FIA) database (Bechtold and Patterson 2005). Species-specific attributes were obtained from the literature when possible, from expert opinion, or were tuned during calibration (Supplementary Table 1). To represent initial conditions, we used a previously described imputation map of FIA plots (Wilson et al. 2012, Duveneck et al. 2015), where the attributes of each map cell were imputed from FIA plots. We then aggregated plot data of individual tree stems into species-age cohorts, calculating tree age with site index curve equations (Carmean et al. 1989) followingDuveneck et al.(2014). Initial condition cohorts are used during spin-up within PnET-Succession to calculate and track biomass (de Bruijn et al. 2014). We delineated soil regions and assigned soil types using the STATSGO database (STATSGO 1994). We delineated 8 climate regions based on a cluster analysis of observed PRISM gridded climate data (Daly and Gibson 2002) (Figure 1). Cluster analysis inputs included 30-year monthly average annual precipitation as well as minimum temperatures for fall and spring months when many critical thresholds for photosynthesis occur.

Figure below shows temporal trends of climate change projections from selected climate regions (July temperature and annual precipitation).

Literature Cited in Appendix I:

Bechtold, W. A., and P. L. Patterson. 2005. The Enhanced Forest Inventory and Analysis Program — National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80. Ashville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station.

de Bruijn, A., E. J. Gustafson, B. R. Sturtevant, J. R. Foster, B. R. Miranda, N. I. Lichti, and D. F. Jacobs. 2014. Toward more robust projections of forest landscape dynamics under novel environmental conditions: Embedding PnET within LANDIS-II. Ecological Modelling 287:44–57.

Carmean, W. H., J. T. Hahn, and R. D. Jacobs. 1989. Site index curves for forest tree species in the Eastern United States 153. US Department of Agriculture, Forest Service.

Daly, C., and W. Gibson. 2002. 103-Year high-resolution temperature climate data set for the conterminous United States, The PRISM Climate Group, Oregon State University, Corvallis, Oregon .

Duveneck, M. J., R. M. Scheller, M. A. White, S. D. Handler, and C. Ravenscroft. 2014. Climate change effects on northern Great Lake (USA) forests: A case for preserving diversity. Ecosphere 5:1–26.

Duveneck, M. J., J. R. Thompson, and B. T. Wilson. 2015. An imputed forest composition map for New England screened by species range boundaries. Forest Ecology and Management 347:107–115.

Scheller, R. M., J. B. Domingo, B. R. Sturtevant, J. S. Williams, A. Rudy, E. J. Gustafson, and D. J. Mladenoff. 2007. Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecological Modelling 201:409–419.

Scheller, R. M., and D. J. Mladenoff. 2004. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecological Modelling 180:211–229.

STATSGO. 1994. State soil geographic database. Data use information. U.S. Department of Agriculture National Cartography and GIS Center. Fort Worth, Texas, USA.

Wilson, B. T., A. J. Lister, and R. I. Riemann. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management 271:182–198.

Appendix I, Table 1. Species specific parameters used in PnET-Succession.Foliar_N = percent foliar nitrogen content. SLW_Max=maximum specific leaf weight (g m-2). HALF_SAT=half saturation light level for photosynthesis (umolm-2sec-1). PSN_MIN = Minimum temperature for photosynthesis (°C). PSN_OPT = Optimal temperature for photosynthesis (°C). WUE_CONSTANT = Constant in equation for computing water use efficiency (WUE) as a function of VPD.

Appendix II:

Mean ecoregion maximum cohort age during initial conditions plotted against percent increase in aboveground biomass from 2010 to 2110 simulated under each climate scenario (Figure below). Numbers below climate symbols reference level-IV ecoregions.

Appendix III:

Mean aboveground biomass of less-dominate species under five climate scenarios.