Forecasting effects of tree species reintroduction strategies on carbon stocks in a future without historical analog.

American chestnut (Castanea dentata) was once an important component forests in the central Appalachians (USA), but it was functionally extirpated nearly a century ago. Attempts are underway to reintroduce blight-resistant chestnut to its former range, but it is uncertain how current forest composition, climate, and atmospheric changes and disturbance regimes will interact to determine future forest dynamics and ecosystem services. The combination of novel environmental conditions (e.g. climate change), a reintroduced tree species and new disturbance regimes (e.g. exotic insect pests, fire suppression) have no analog in the past that can be used to parameterize phenomenological models. We therefore used a mechanistic approach within the LANDIS-II forest landscape model that relies on physiological first principles to project forest dynamics as the outcome of competition of tree cohorts for light and water as a function of temperature, precipitation, CO2 concentration, and life history traits. We conducted a factorial landscape simulation experiment to evaluate specific hypotheses about future forest dynamics in two study sites in the center of the former range of chestnut. Our results supported the hypotheses that climate change would favor chestnut because of its optimal temperature range and relative drought resistance, and that chestnut would be less competitive in the more mesic Appalachian Plateau province because competitors will be less stressed. The hypothesis that chestnut will increase carbon stocks was supported, although the increase was modest. Our results confirm that aggressive restoration is needed regardless of climate and soils, and that increased aggressiveness of chestnut restoration increased biomass accumulation. The hypothesis that chestnut restoration will increase both compositional and structural richness was not supported because chestnut displaced some species and age cohorts. Although chestnut restoration did not markedly enhance carbon stocks, our findings provide hope that this formerly important species can be successfully reintroduced and associated ecosystem services recovered.

Integrating ecophysiology and forest landscape models to improve projections of drought effects under climate change.

Fundamental drivers of ecosystem processes such as temperature and precipitation are rapidly changing and creating novel environmental conditions. Forest landscape models (FLM) are used by managers and policy-makers to make projections of future ecosystem dynamics under alternative management or policy options, but the links between the fundamental drivers and projected responses are weak and indirect, limiting their reliability for projecting the impacts of climate change. We developed and tested a relatively mechanistic method to simulate the effects of changing precipitation on species competition within the LANDIS-II FLM. Using data from a field precipitation manipulation experiment in a piñon pine (Pinus edulis) and juniper (Juniperus monosperma) ecosystem in New Mexico (USA), we calibrated our model to measurements from ambient control plots and tested predictions under the drought and irrigation treatments against empirical measurements. The model successfully predicted behavior of physiological variables under the treatments. Discrepancies between model output and empirical data occurred when the monthly time step of the model failed to capture the short-term dynamics of the ecosystem as recorded by instantaneous field measurements. We applied the model to heuristically assess the effect of alternative climate scenarios on the piñon-juniper ecosystem and found that warmer and drier climate reduced productivity and increased the risk of drought-induced mortality, especially for piñon. We concluded that the direct links between fundamental drivers and growth rates in our model hold great promise to improve our understanding of ecosystem processes under climate change and improve management decisions because of its greater reliance on first principles.