An official website of the United States government
Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.
more »
« less
This content will become publicly available on May 1, 2026
Moderate-resolution mapping of aboveground biomass stocks, forest structure, and composition in coastal Alaska and British Columbia
The forests of coastal Alaska and British Columbia are globally significant for their high carbon storage capacity and complex forest structure, hosting some of the densest values of aboveground biomass in the world. These ecosystems support biodiversity, provide critical habitat, and serve as long-term carbon sinks, offering resilience to climate change. However, comprehensive, spatially continuous estimates of forest structure across this region have been limited, particularly across political boundaries. In this study, we used a Gradient Nearest Neighbor (GNN) modeling approach to integrate extensive forest inventory plot data with satellite-derived environmental variables. This approach enabled us to produce moderate-resolution (30-meter) maps of aboveground biomass, species biomass, forest age, basal area, and additional structural attributes. Our results indicated that climate and topography accounted for the majority of the explainable variation across all modeling regions. Predictions of aboveground live biomass were higher than previous estimates, particularly in Southeast Alaska, where estimates were 30–53 % greater than previous studies. Forest structure varied across the region, with older forests found in Southeast Alaska and higher tree densities in British Columbia. Collectively, the coastal forests of Alaska and British Columbia store approximately 3.58 petagrams of carbon. These spatially explicit maps offer critical insights for carbon monitoring, forest management, and biodiversity conservation across this ecologically diverse and politically fragmented landscape.
more »
« less
- Award ID(s):
- 2025726
- PAR ID:
- 10644047
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Forest Ecology and Management
- ISSN:
- 0378-1127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Seasonally dry tropical forests (SDTFs) account for one-third of the interannual variability of global net primary productive (NPP). Large-scale shifts in dry tropical forest structure may thus significantly affect global CO2fluxes in ways that are not fully accounted for in current projections. This study quantifies how changing climate might reshape one of the largest SDTFs in the world, the Caatinga region of northeast Brazil. We combine historical data and future climate projections under different representative concentration pathways (RCPs), together with spatially explicit aboveground biomass estimates to establish relationships between climate and vegetation distribution. We find that physiognomies, aboveground biomass, and climate are closely related in the Caatinga—and that the region’s bioclimatic envelope is shifting rapidly. From 2008–2017, more than 90% of the region has shifted to a dryer climate space compared to the reference period 1950–1979. An ensemble of global climate models (based on IPCC AR5) indicates that by the end of the 21st century the driest Caatinga physiognomies (thorn woodlands to non-vegetated areas) could expand from 55% to 78% (RCP 2.6) or as much as 87% (RCP8.5) of the region. Those changes would correspond to a decrease of 30%–50% of the equilibrium aboveground biomass by the end of the century (RCP 2.6 and RCP8.5, respectively). Our results are consistent with historic vegetation shifts reported for other SDTFs. Projected changes for the Caatinga would have large-scale impacts on the region’s biomass and biodiversity, underscoring the importance of SDTFs for the global carbon budget. Understanding such changes as presented in this study will be useful for regional planning and could help mitigate their negative social impacts.more » « less
-
Abstract Given that terrestrial ecosystems globally are facing the loss of biodiversity from land use conversion, invasive species, and climate change, effective management requires a better understanding of the drivers and correlates of biodiversity. Increasingly, biodiversity is co‐managed with aboveground carbon storage because high biodiversity in animal species is observed to correlate with high aboveground carbon storage. Most previous investigations into the relationship of biodiversity and carbon co‐management do not focus on the biodiversity of the species rich plant kingdom, which may have tradeoffs with carbon storage. To examine the relationships of plant species richness with aboveground tree biomass carbon storage, we used a series of generalized linear models with understory plant species richness and diversity data from the USDA Forest Service Forest Inventory and Analysis dataset and high‐resolution modeled carbon maps for the Tongass National Forest. Functional trait data from the TRY database was used to understand the potential mechanisms that drive the response of understory plants. Understory species richness and community weighted mean leaf dry matter content decreased along an increasing gradient of tree biomass carbon storage, but understory diversity, community weighted mean specific leaf area, and plant height at maturity did not. Leaf dry matter content had little variance at the community level. The decline of understory plant species richness but not diversity to increases in aboveground biomass carbon storage suggests that rare species are excluded in aboveground biomass carbon dense areas. These decreases in understory species richness reflect a tradeoff between the understory plant community and aboveground carbon storage. The mechanisms that are associated with observed plant communities along a gradient of biomass carbon storage in this forest suggest that slower‐growing plant strategies are less effective in the presence of high biomass carbon dense trees in the overstory.more » « less
-
Abstract Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g. trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up’. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine(Pinus contorta)remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change.more » « less
-
Understanding patterns of aboveground carbon storage across forest types is increasingly important as managers adapt to threats of global change. We combined field measures of aboveground biomass with lidar to model fine-scale biomass in deciduous forests located in two watersheds; one watershed was underlain by sandstone and the other by shale. We measured tree and shrub biomass across three topographic positions for both watersheds and analyzed biomass using mixed models. The watershed underlain by shale had 60% more aboveground biomass than the sandstone watershed. Although spatial patterns of biomass were different across watersheds, both had higher (between about 40% and 55%) biomass values at the toe-slope position than at the ridge-top position. To model fine-scale spatial patterns of biomass, we tested the effectiveness of leaf-on and leaf-off lidar combined with topographic metrics to develop a spatially explicit random forest model of tree and shrub biomass across both watersheds. Leaf-on variables were more important for modeling shrub biomass, while leaf-off variables were more effective at modeling tree biomass. Our model of tree and shrub biomass reflects the distribution of biomass across both watersheds at a fine scale and highlights the potential of abiotic factors such as topography and bedrock to affect carbon storage.more » « less
