Search

Great hope is being placed in the ability of forest ecosystems to contribute to greenhouse gas (GHG) emission reduction targets to limit global warming. Many nations plan to rely on forest-based climate mitigation activities to create additional and long-term carbon sequestration. Here, we take a critical look at the state of the policy and ecology surrounding forest-based natural climate solutions (NCS), with a focus on temperate forests of the United States (US). We first provide a high-level overview of carbon accounting, including key concepts used in the monitoring, reporting and verification of forest-based NCS. Second, we provide a high-level overview of forest carbon dynamics, including pools and fluxes, and drivers of their change. We then identify gaps in the current systems of GHG accounting, and between current ambitions and basic forest ecology. Improved use of data in models provides a path forward to better assessment and anticipation of forest-based climate mitigation. We illustrate this with the creation of a climate-sensitive forestry model, using tree-ring time series data. This climate-sensitive forest simulator will improve planning of site-level climate mitigation activities in the US by providing more realistic expectations of the carbon sequestration potential of forests undergoing climate change. Our review highlights the sobering complexity and uncertainty surrounding forest carbon dynamics, along with the need to improve carbon accounting. If we are to expect forests to play the significant emissions reduction role that is currently planned, we should view immediate emissions reductions as critical to preserve the climate mitigation capacity of forest ecosystems. [ABSTRACT FROM AUTHOR]

Several initiatives have been proposed to mitigate forest loss and climate change through tree planting as well as maintaining and restoring forest ecosystems. These initiatives have both inspired and been inspired by global assessments of tree and forest attributes and their contributions to offset carbon dioxide (CO2) emissions. Here we use data from more than 130,000 national forest inventory plots to describe the contribution of nearly 1.4 trillion trees on forestland in the conterminous United States to mitigate CO2 emissions and the potential to enhance carbon sequestration capacity on productive forestland. Forests and harvested wood products uptake the equivalent of more than 14% of economy-wide CO2 emissions in the United States annually, and there is potential to increase carbon sequestration capacity by ~20% (-187.7 million metric tons [MMT] CO2 ±9.1 MMT CO2) per year by fully stocking all understocked productive forestland. However, there are challenges and opportunities to be considered with tree planting. We provide context and estimates from the United States to inform assessments of the potential contributions of forests in climate change mitigation associated with tree planting. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim For trees, wood density is linked to competing energetic demands and therefore reflects responses to the environment. Climatic trends in wood density are recognized, yet their contribution to regional biogeographical patterns or impact on forest biomass stocks is not understood. This study has the following two objectives: (O1) to characterize wood density-climate trends for coarse (i.e., angiosperm versus gymnosperm) and fine (i.e., within-species) taxonomic units and test a predictive model that incorporates these trends into a model that assumes range-wide wood density is constant; and (O2) to assess the impact of climate-driven intraspecific variation on forest biomass stocks for major tree species. Location We use an assemblage of eastern U.S. tree species for assessing climatic trends (O1), and then apply fitted models to forest inventory data spanning the eastern U.S.A. to assess impacts of forest carbon estimation procedures (O2). Methods We compared hierarchical models fitted to the full data to characterize wood density/climate gradients and to assess the impact of within-species variation (O1). Then, we compared predictions of biomass stocks from the climate-variable model with those of the static model using the Forest Inventory and Analysis (FIA) database (O2). Results We found among- and within-species trends related to temperature and moisture regimes, with differing responses between angiosperms and gymnosperms. Incorporating within-species variation in wood density increases the carbon stock of the study region by an estimated 242 Tg when compared with a species-only model. Main conclusions Intraspecific variation in wood density across species ranges suggests that climate influences investment in stem wood within tree species and contributes to biogeographical patterns in wood density in the eastern U.S.A. This variation impacts forest biomass stock assessments, and thus contributes refinements to the U.S. National Greenhouse Gas Inventory. In addition, our work highlights the potential for combining trait data and forest inventory to infer forest ecological processes at broad spatial scales. [ABSTRACT FROM AUTHOR]

Carbon accounting is at the heart of efforts to mitigate the effects of climate change. One approach for estimating population parameters for live tree stem carbon entails three primary steps: (1) construction of an individual tree, allometric carbon model, (2) application of the model to tree-level data for a probability sample of plots, and (3) use of a probability-based (design-based) estimator of mean carbon per unit area for a population of interest. Compliance with the IPCC good practice guidance requires satisfaction of two criteria, one related to minimizing bias and one related to minimizing uncertainty. For this carbon estimation procedure, the portion of uncertainty attributed to the variance of the probability-based estimator of the population mean using the plot-level predictions is usually correctly estimated, but the portion attributed to the variance of the allometric model estimator is usually ignored. The result is that the total variance of the population mean estimator cannot be asserted to comply with the IPCC good practice criteria because not only is it not minimized, it is not even correctly estimated. Within the framework of what is coming to be characterized as hybrid inference, model-based inferential methods were used to estimate the variance of the tree-level allometric model estimator which was then propagated through to the variance of the probability-based estimator of mean carbon per unit area. This combined estimator, consisting of a model-based estimator used to predict a variable for a probability sample of a population followed by a probability-estimator of the population total or mean using the sample predictions, is characterized as a hybrid estimator. For this study, two probability-based estimators of the mean were considered, simple random sampling estimators and model-assisted regression estimators that used airborne laser scanning (ALS) data as auxiliary information. The variance of the allometric model estimator incorporated variances of distributions of diameter and height measurement errors, covariances of model parameter estimators, model residual variance, and variances of distributions of wood densities and carbon content proportions. The novel features of the study included the hybrid inferential framework, consideration of six sources of uncertainty including the variances of distributions of wood densities and carbon content proportions, use of ALS data with model-assisted regression estimators of the population mean, and use of confidence intervals for the population mean as the basis for comparisons rather than intermediate products such as model prediction accuracy. The primary conclusions were that the variance of the allometric model estimator was negligible or marginally negligible relative to the variance of the probability estimator when using species-specific allometric models and simple random sampling estimators, but non-negligible when using species-specific models and model-assisted regression estimators and when using a non-specific model with either estimator. [ABSTRACT FROM AUTHOR]

Background: Refined estimation of carbon (C) stocks within forest ecosystems is a critical component of efforts to reduce greenhouse gas emissions and mitigate the effects of projected climate change through forest C management. Specifically, belowground C stocks are currently estimated in the United States' national greenhouse gas inventory (US NGHGI) using nationally consistent species- and diameter-specific equations applied to individual trees. Recent scientific evidence has pointed to the importance of climate as a driver of belowground C stocks. This study estimates belowground C using current methods applied in the US NGHGI and describes a new approach for merging both allometric models with climate-derived predictions of belowground C stocks. Results: Climate-adjusted predictions were variable depending on the region and forest type of interest, but represented an increase of 368.87 Tg of belowground C across the US, or a 6.4 % increase when compared to currently-implemented NGHGI estimates. Random forests regressions indicated that aboveground biomass, stand age, and stand origin (i.e., planted versus artificial regeneration) were useful predictors of belowground C stocks. Decreases in belowground C stocks were modeled after projecting mean annual temperatures at various locations throughout the US up to year 2090. Conclusions: By combining allometric equations with trends in temperature, we conclude that climate variables can be used to adjust the US NGHGI estimates of belowground C stocks. Such strategies can be used to determine the effects of future global change scenarios within a C accounting framework. [ABSTRACT FROM AUTHOR]

The contribution of understorey vegetation (UVEG) to forest ecosystem biomass and carbon (C) across diverse forest types has, to date, eluded quantification at regional and national scales. Efforts to quantify UVEG C have been limited to field-intensive studies or broad-scale modelling approaches lacking field measurements. Although large-scale inventories of UVEG C are not common, species- and community-level inventories of vegetation structure are available and may prove useful in quantifying UVEG C stocks. This analysis developed a general framework for estimating UVEG C stocks by employing per cent cover estimates of UVEG from a region-wide forest inventory coupled with an estimate of maximum UVEG C across the US Lake States (i.e. Michigan, Minnesota and Wisconsin). Estimates of UVEG C stocks from this approach reasonably align with expected C stocks in the study region, ranging from 0.86 ± 0.06 Mg ha−1 in red pine-dominated to 1.59 ± 0.06 Mg ha−1 for aspen/birch-dominated forest types. Although the data employed here were originally collected to assess broad-scale forest structure and diversity, this study proposes a framework for using UVEG inventories as a foundation for estimating C stocks in an often overlooked, yet important ecosystem C pool. [ABSTRACT FROM AUTHOR]

Downed coarse woody debris, also known as coarse woody detritus or downed dead wood, is challenging to estimate for many reasons, including irregular shapes, multiple stages of decay, and the difficulty of identifying species. In addition, some properties are commonly not measured, such as wood density and carbon concentration. As a result, there have been few previous evaluations of uncertainty in estimates of downed coarse woody debris, which are necessary for analysis and interpretation of the data. To address this shortcoming, we quantified uncertainties in estimates of downed coarse woody debris volume and carbon storage using data collected from permanent forest inventory plots in the northeastern United States by the Forest Inventory and Analysis program of the USDA Forest Service. Quality assurance data collected from blind remeasurement audits were used to quantify error in diameter measurements, hollowness of logs, species identification, and decay class determination. Uncertainty estimates for density, collapse ratio, and carbon concentration were taken from the literature. Estimates of individual sources of uncertainty were combined using Monte Carlo methods. Volume estimates were more reliable than carbon storage, with an average 95% confidence interval of 15.9 m3/ha across the 79 plots evaluated, which was less than the mean of 31.2 m3/ha. Estimates of carbon storage (and mass) were more uncertain, due to poorly constrained estimates of the density of wood. For carbon storage, the average 95% confidence interval was 11.1 Mg C/ha, which was larger than the mean of 4.6 Mg C/ha. Accounting for the collapse of dead wood as it decomposes would improve estimates of both volume and carbon storage. On the other hand, our analyses suggest that consideration of the hollowness of downed coarse woody debris pieces could be eliminated in this region, with little effect. This study demonstrates how uncertainty analysis can be used to quantify confidence in estimates and to help identify where best to allocate resources to improve monitoring designs. [ABSTRACT FROM AUTHOR]