Age structure and disturbance legacy of North American forests

Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stock and fluxes in forest ecosystems, yet it has been a great challenge to assess disturbance impacts in estimates of forest carbon budgets. Net sequestration or loss of CO2 by forests after disturbance follows a predictable pattern with forest recovery. Forest age, which is related to time since disturbance, is the most available surrogate variable for various forest carbon analyses that concern the impact of disturbance. In this study, we compiled the first continental forest age map of North America by combining forest inventory data, historical fire data, optical satellite data and the dataset from NASA's LEDAPS project. Mexico and interior Alaska are excluded from this initial map due to unavailability of all required data sets, but work is underway to develop some different methodology for these areas. We discuss the significance of disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, tracking back disturbances caused by human and nature over centuries and at various scales. We also show how such information can be used with inventory data for analyzing carbon management opportunities, and other modeling applications. By combining geographic information about forest age with estimated C dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. The forest age map may also help address the recent concern that the terrestrial C sink from forest regrowth in North America may saturate in the next few decades. Finally, we describe how the forest age data can be used in large-scale carbon modeling, both for land-based biogeochemistry models and atmosphere-based inversion models, in order to improve the spatial accuracy of carbon cycle simulations.