Forecasting carbon storage of eastern forests: interactions of pests, climate change and chestnut restoration.

Arjan De Bruijn1,2, Eric Gustafson2, Brian Sturtevant2, Nathanael Lichti1 ,Douglass F. Jacobs1*,

(1) Purdue University, West Lafayette, IN(2) Northern Research Station, USDA-ARS Forest Service, Rhinelander, WI

* Author for correspondence. Email: djacobs@purdue.edu

References[1] Botkin, D. B., L. G. Simpson, and R. A. Nisbet. 1993. Biomass and carbon storage of the North American deciduous forest. Biogeochemistry 20:1-17. [2] Dong, J., R. K. Kaufmann, R. B. Myneni, C. J. Tucker, P. E. Kauppi, J. Liski, W. Buermann, V. Alexeyev, and M. K. Hughes. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment 84:393-410. [3] 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 spatial and temporal resolution. Ecological Modeling 201: 409-419.[4] Aber, J. D. and C. A. Federer. 1992. A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92: 463-474.

Papers and manuscripts (at least partially) attributed to this project[5] Jacobs, D.F., Dalgleish, H.J., Nelson, C.D., 2013. A conceptual framework for restoration of threatened plants: the effective model of American chestnut (Castanea dentata) reintroduction. New Phytologist 197, 378-393.[6] Lichti, N., Sturtevant, B.R., Miranda, B.R., Gustafson, E.J. Improving the simulation of seed dispersal in forest landscape models (In prep)[7] De Bruijn, A.M.G., Gustafson, E.J., Kashian, D.M., Dalgleish, H.J., Sturtevant, B.R., Jacobs, D.F. Decomposition rates of American chestnut (Castanea dentata) wood and implications for coarse woody debris pools (Canadian Journal of Forest Research 44(12): 1575-1585, 2014)[8] De Bruijn, A.M.G., Gustafson, E.J. Sturtevant, B.R., Foster, J., Miranda, B, Lichti, N. Jacobs, D.F. Toward more robust projections of forest landscape dynamics under novel environmental conditions: more mechanistic simulations of competition for water and light in LANDIS-II (Ecological Modelling, 287: 44-57,

2014)[9] Gustafson, E.J. 2013. When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world.  Landscape Ecology 28:1429-1437.[10] Gustafson, E.J., D.J. Shinneman. Approaches to modeling landscape-scale drought-induced forest mortality. Ch. 5 in Mapping and Modeling Forest Landscape Patterns, Perera A., T. Remmel and L. Buse (eds.).  Springer. (in press)[11] Gustafson, E.J., A.M.G. De Bruijn, M.E. Kubiske, B.R. Sturtevant. 2014, Mechanistically linking fundamental environmental drivers to landscape dynamics: integrating ecophysiology and forest landscape models to better predict drought effects under climate change (Global Change Biology 2015 21(2): 843-

856)[12] Gustafson, E.J., A.M.G. De Bruijn, M.E. Kubiske, Robert E. Pangle, Jean-Marc Limousin, Nate McDowell, B.R. Sturtevant, Jordan Muss, William T. Pockman. 2015. Integrating ecophysiology and forest landscape models to better project drought effects under climate change. (in press, Global Change Biology

12(21)) [13] A.M.G. De Bruijn et al. Landscape and carbon sequestration implications of American chestnut re-introduction: simulating the outcome of complex life history and disturbance interactions. (in prep.)

AcknowledgmentsThis project was supported by Agriculture and Food Research Initiative Competitive Grant no. 105321 from the USDA National Institute of Food and Agriculture. 

Eastern deciduous forests sequester a majority of the nation’s forest carbon, but impending forest changes such as exotic insect pests (e.g., gypsy moth, emerald ash borer), and successional changes stemming from suppression of fire threaten to reduce carbon stocks. Reintroduction of blight-resistant American chestnut may increase carbon sequestration because of its high

growth rate, longevity and decay resistance. We developed a new succession extension (PnET-Succession) for the LANDIS-II forest landscape model that uses concepts of the PnET ecophysiology model to simulate species competition for light and water at landscape scale. We applied the model in western Maryland to explore the carbon implications of scenarios that

include interactions of current forest management strategies (e.g., prescribed fire), chestnut restoration, climate change, and exotic pests. Our results show the relative impact of each factor on forest composition, successional trends, susceptibility of forests to insect outbreaks and carbon stocks. Because of its first-principles modeling approach, our study allows rigorous

assessment of the landscape-scale carbon-storage implications of a potential restoration opportunity in eastern forests in the face of novel threats posed by climate change and exotic pests.

CalibrationSpecies traits Factorial simulationsLocations

Pests Harvests Carbon, water, light

Factors Treatment Levels Management Harvest

Harvest + BurningClimate Change Moderate change (A2)

Extreme change (A1F1)

Pests Gypsy moth, EAB, Sudden Oak Death, Asian Longhorned Beetle, hemlock wooly adelgid

Chestnut Restoration None Restricted Aggressive

New chestnutClimate change

• Top: From a source northeast of the map (~100 m /100 yr)

• Bottom: From planting after commercial oak harvests

2030 2070 2180

Species dominance in constant and A1F1 climate

2030 2070 2180

• Literature review for chestnut traits, other species data from previous LANDIS-II projects [e.g., 1-4] and published data

• Field work to determine chestnut leaf mass area, foliar nitrogen, leaf lignin, wood decay rates, dispersal distance.

Top: Spread of Asian longhorned beetje through Green RidgeMiddle: Simulated demise of host species Sugar maple Bottom: Simulated area infected by three main insect pests

• Calibrate species growth using FIA data

• Use historic accounts for chestnut [7].

Above: Random simulation of an annual harvest cycleBelow: Effect of harvests on aboveground carbon