• Energy Research

This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).

AbstractRestoring forest cover is a key action for mitigating climate change. Although monoculture plantations dominate existing commitments to restore forest cover, we lack a synthetic view of how carbon accumulates in these systems. Here, we assemble a global database of 4756 field-plot measurements from monoculture plantations across all forested continents. With these data, we model carbon accumulation in aboveground live tree biomass and examine the biological, environmental, and human drivers that influence this growth. Our results identify four-fold variation in carbon accumulation rates across tree genera, plant functional types, and biomes, as well as the key mediators (e.g., genus of tree, endemism of species, prior land use) of variation in these rates. Our nonlinear growth models advance our understanding of carbon accumulation in forests relative to mean annual rates, particularly during the next few decades that are critical for mitigating climate change.

Abstract Forests play a central role in addressing climate change, and accurate estimates of forest carbon are critical for the development of actions that reduce emissions from forests and that maximize sequestration by forests. Methodological challenges persist regarding how best to estimate forest carbon stocks and flux at regulatory-relevant scales. Using California, USA as a case study, we compare two approaches to stock-difference forest carbon estimation for aboveground live trees: one based on ground inventories and one on land cover classification of remotely-sensed data. Previous work using ground inventory data from the Forest Inventory and Analysis Program (FIA) showed net aboveground carbon (AGC) sequestration by live trees in California forests, while estimates using land cover classification from the Landscape Fire and Resource Management Planning Tools (Landfire) showed net reductions in live tree AGC over a similar time period. We examined the discrepancy by re-analyzing the FIA inventory data through the lens of a category-change analysis based on Landfire. This analysis showed more than 50% of the live tree AGC in fewer than 4% of Landfire-equivalent categories and that the overwhelming majority (>80%) of forest area did not change height category between measurement periods. Despite the lack of categorical change, the majority of FIA plots increased in both 95th percentile tree height and in live tree AGC. These findings suggest that an approach based on observing categorical changes risks undercounting AGC sequestration resulting from growth and thus overstating the relative importance of AGC reductions that result from disturbances. This would bias AGC flux estimates downward, leading us to validate the conclusion that live trees in California were a net sink of aboveground carbon in the decade ending in 2016. Our findings suggest an inventory-based or hybrid approach is preferable to methods that depend on categorical bins for estimating AGC in disturbance-prone forest ecosystems.