Forests have a prominent role in carbon sequestration and storage. Climate change and anthropogenic forcing have altered the dominant characteristics of some forested ecosystems through changes to their disturbance regimes, particularly fire. Ecosystems that historically burned frequently, like pinelands in the southeastern United States, risk changes in their structure and function when the fire regime they require is altered. Although the carbon storage potential in an unburned southeastern U.S. forest would be larger, this scenario is unrealistic due to the likelihood of wildfire. Additionally, fire exclusion can have negative consequences on these forests health, biodiversity, and species endemism. There is a need, specifically for the southeast, to estimate carbon and species dynamics based on the differences between various fire regimes, and particularly the differences between prescribed fire and wildfire. These are important factors to consider given that prescribed fire is a common tool used in the southeast, and wildfires are ever more present. Field data from an experimentalPinus palustris(longleaf pine) forest of southwest Georgia were used to parametrize the forest landscape modelLANDIS‐II. The model simulated how carbon and species dynamics differ under a fire exclusion, a prescribed fire, and multiple wildfire scenarios. All scenarios except fire exclusion resulted in net emissions to the atmosphere, but prescribed fire produced the least carbon emissions from fire and maintained the most stable aboveground biomass compared to wildfire scenarios. Removing fire for approximately a century was necessary to obtain an average stand‐level biomass greater than that of prescribed fire and net emissions less than that of prescribed fire. The prescribed fire scenario produced a longleaf pine‐dominated forest, the exclusion scenario converted to predominantly oak speciesQuercus virginiana(live oak),Q. stellata(post oak), andQ. margaretta(sand post oak), while scenarios with intermediate wildfire regimes supported a mix of other fire‐facilitator hardwoods and pine species, such asQ. incana(bluejack oak) andPinus elliotti(slash pine). Overall, this study supports prescribed fire regimes in southeastern U.S. pinelands to both minimize carbon emissions and preserve native biodiversity.

Disturbances, both natural and anthropogenic, are critical determinants of forest structure, function, and distribution. The vulnerability of forests to potential changes in disturbance rates remains largely unknown. Here, we developed a framework for quantifying and mapping the vulnerability of forests to changes in disturbance rates. By comparing recent estimates of observed forest disturbance rates over a sample of contiguous US forests to modeled rates of disturbance resulting in forest loss, a novel index of vulnerability, Disturbance Distance, was produced. Sample results indicate that 20% of current US forestland could be lost if disturbance rates were to double, with southwestern forests showing highest vulnerability. Under a future climate scenario, the majority of US forests showed capabilities of withstanding higher rates of disturbance then under the current climate scenario, which may buffer some impacts of intensified forest disturbance.

There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.

Terrestrial ecosystems and their vegetation are linked to climate. With the potential of accelerated climate change from anthropogenic forcing, there is a need to further evaluate the transient response of ecosystems, their vegetation, and their influence on the carbon balance, to this change. The equilibrium response of ecosystems to climate change has been estimated in previous studies in global domains. However, research on the transient response of terrestrial vegetation to climate change is often limited to domains at the sub-continent scale. Estimation of the transient response of vegetation requires the use of mechanistic models to predict the consequences of competition, dispersal, landscape heterogeneity, disturbance, and other factors, where it becomes computationally prohibitive at scales larger than sub-continental. Here, we used a pseudo-spatial ecosystem model with a vegetation migration sub-model that reduced computational intensity and predicted the transient response of vegetation and carbon to climate change in northern North America. The ecosystem model was first run with a current climatology at half-degree resolution for 1000 years to establish current vegetation and carbon distribution. From that distribution, climate was changed to a future climatology and the ecosystem model run for an additional 2000 simulation years. A model experimental design with different combinations of vegetation dispersal rates, dispersal modes, and disturbance rates produced 18 potential change scenarios. Results indicated that potential redistribution of terrestrial vegetation from climate change was strongly impacted by dispersal rates, moderately affected by disturbance rates, and marginally impacted by dispersal mode. For carbon, the sensitivities were opposite. A potential transient net carbon sink greater than that predicted by the equilibrium response was estimated on time scales of decades–centuries, but diminished over longer time scales. Continued research should further explore the interactions between competition, dispersal, and disturbance, particularly in regards to vegetation redistribution.