The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year^–1) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year^–1 from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year^–1 partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year^–1. Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year^–1, with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.

Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stocks 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 a useful surrogate variable for analyses of the impact of disturbance on forest carbon. 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 Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. A companion map of the standard deviations for age estimates was developed for quantifying uncertainty. We discuss the significance of the disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, by analyzing the causes of disturbances from land management 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. 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. 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.

We compared estimates of net primary production (NPP) from the MODIS satellite with estimates from a forest ecosystem process model (PnET‐CN) and forest inventory and analysis (FIA) data for forest types of the mid‐Atlantic region of the United States. The regional means were similar for the three methods and for the dominant oak– hickory forests in the region. However, MODIS underestimated NPP for less‐dominant northern hardwood forests and overestimated NPP for coniferous forests. Causes of inaccurate estimates of NPP by MODIS were (1) an aggregated classification and parameterization of diverse deciduous forests in different climatic environments into a single class that averages different radiation conversion efficiencies; and (2) lack of soil water constraints on NPP for forests or areas that occur on thin or sandy, coarse‐grained soil. We developed the “available soil water index” for adjusting the MODIS NPP estimates, which significantly improved NPP estimates for coniferous forests. The MODIS NPP estimates have many advantages such as globally continuous monitoring and remarkable accuracy for large scales. However, at regional or local scales, our study indicates that it is necessary to adjust estimates to specific vegetation types and soil water conditions.

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha⁻¹ and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Forest biodiversity is the foundation of many ecosystem services, and the effect of biodiversity on ecosystem functioning and processes (BEF) has been a central issue in biodiversity studies. Although many hypotheses have been developed to interpret global gradients of biodiversity, there has not been complete agreement on mechanisms controlling biodiversity patterns and distributions. Differences may be due to limited observation data and inconsistencies of spatial scales in analysis. In this study, we take advantage of USDA Forest Service forest inventory and analysis (FIA) data for exploring regional forest biodiversity and BEF in New England forests. The FIA data provide detailed information of sampled plots and trees for the region, including 6000 FIA plots and more than 33,000 individual trees. Biodiversity models were used to analyze the data. Tree species diversity increases from the north to the south at a rate about 2–3 species per latitudinal degree. Tree species diversity is better predicted by tree height than forest age or biomass. Very different distribution patterns of two common maple species, sugar maple (Acer saccharum) and red maple (Acer rubrum), highlight the vulnerability of sugar maple and its potential replacement by red maple on New England landscapes. Red maple generally already outperforms sugar maple, and will likely and continuously benefit from a changing climate in New England. We conclude that forest structure (height) and resources (biomass) are more likely foundational characteristics supporting biodiversity rather than biodiversity determining forest productivity and/or biomass. The potential replacement of red maple for sugar maple in the New England areas could affect biodiversity and stability of forest ecosystem functioning because sugar maple plays important ecological roles distinct from red maple that are beneficial to other tree species in northern hardwood forests. Such a change may not affect forest resilience in terms of forest productivity and biomass as these are similar in red maple and sugar maple, however, it would almost certainly alter forest structure across the landscape.