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Abstract Forested landscapes have the potential to help offset global carbon emissions. However, current global models do not, nor are they intended to, capture the fine‐scale variability of the distributions of carbon in aboveground or belowground stocks or their simultaneous variability. Regional investigations are necessary to resolve patterns in carbon that can guide policy and planning, but regional maps that quantify multiple carbon pools are scarce. We quantified the spatial relationships of aboveground and belowground carbon stocks to understand their simultaneous variability across the forested area of the perhumid ecoregion of the Pacific Coastal Temperate Rainforest. Further, we identified topo‐climatic contexts associated with unique patterns in both aboveground and belowground carbon stocks by conducting an overlay analysis across the entire ecoregion. We utilized previously published estimates of carbon stocks based on extensive governmental data and machine learning techniques to model simultaneous spatial relationships of aboveground and belowground carbon stocks and generate a map for a high carbon region. We employed Pearson's correlations as well as ANOVA and Tukey honestly significant difference (HSD) tests for comparisons of topography and climate. Approximately 25% (2.6 million ha) of the area across the perhumid ecoregion had similar trends in aboveground and belowground stocks (convergence). Likewise, 20% of the ecoregion had opposite trends of aboveground and belowground stocks (divergence), and 56% of the ecoregion experienced no relationship (moderate conditions) between aboveground and belowground stocks. Convergence areas consist of carbon hotspots associated with 1.3 million ha and 794 Mg C ha⁻¹on average, or carbon cold spots associated with 1.2 million ha and 224 Mg C ha⁻¹. Areas with convergence, divergence, and moderate carbon stocks all had unique associations with slope, elevation, aspect, mean annual precipitation, and annual mean temperature. High levels of aboveground carbon were associated with steeper slopes, while high levels of belowground carbon were associated with high levels of precipitation. The interactions between slope, precipitation, and temperature correspond with carbon convergence and divergence, likely due to water accumulation which impacts the decomposition of organic matter in soil. These data are critical to regional planning and carbon policy and inform expectations for future carbon storage as the climate changes. 

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. 

Abstract Landslides, a forest disturbance, mobilize carbon (C) sequestered in vegetation and soils. Mobilized C is deposited either onto hillslopes or into the water, sequestering C from and releasing C to the atmosphere at different time scales. The C‐dense old‐growth temperate forests of SE Alaska are a unique location to quantify C mobilization rate by frequent landslides that often evolve into saturated moving masses known as debris flows. In this study, the amount of C mobilized by debris flows over historic time scales was estimated by combining a landslide inventory with maps of modeled biomass and soil carbon. We analyzed SE Alaskan landslides over a 55‐year period where a total of 4.69 ± 0.21 MtC was mobilized, an average rate of 2.5 tC km⁻² yr⁻¹. A single event in August 2015 mobilized 57,651 ± 3,266 tC, an average of 63 tC km⁻². Depositional fate was inferred using two methods, a standard stream intersection analysis and a second novel approach using simulated debris flow deposition modeling calibrated to the study area. Approximately 60% of debris flow deposits intersected the stream network (9% into mainstem channels, 91% into small tributaries), consistent with long‐term modeled connectivity, suggesting that debris flows are likely to contribute to globally significant amounts of C buried in local fjord sediments. Our results are consistent with an emerging consensus that landslide disturbances that mobilize organic carbon may play an important role in the global carbon cycle over geologic time, with coastal temperate forests being hotspots of potential carbon sequestration. 

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. 

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.