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Abstract BackgroundThe increasing size, severity, and frequency of wildfires is one of the most rapid ways climate warming could alter the structure and function of high-latitude ecosystems. Historically, boreal forests in western North America had fire return intervals (FRI) of 70–130 years, but shortened FRIs are becoming increasingly common under extreme weather conditions. Here, we quantified pre-fire and post-fire C pools and C losses and assessed post-fire seedling regeneration in long (> 70 years), intermediate (30–70 years), and short (< 30 years) FRIs, and triple (three fires in < 70 years) burns. As boreal forests store a significant portion of the global terrestrial carbon (C) pool, understanding the impacts of shortened FRIs on these ecosystems is critical for predicting the global C balance and feedbacks to climate. ResultsUsing a spatially extensive dataset of 555 plots from 31 separate fires in Interior Alaska, our study demonstrates that shortened FRIs decrease the C storage capacity of boreal forests through loss of legacy C and regeneration failure. Total wildfire C emissions were similar among FRI classes, ranging from 2.5 to 3.5 kg C m⁻². However, shortened FRIs lost proportionally more of their pre-fire C pools, resulting in substantially lower post-fire C pools than long FRIs. Shortened FRIs also resulted in the combustion of legacy C, defined as C that escaped combustion in one or more previous fires. We found that post-fire successional trajectories were impacted by FRI, with ~ 65% of short FRIs and triple burns experiencing regeneration failure. ConclusionsOur study highlights the structural and functional vulnerability of boreal forests to increasing fire frequency. Shortened FRIs and the combustion of legacy C can shift boreal ecosystems from a net C sink or neutral to a net C source to the atmosphere and increase the risk of transitions to non-forested states. These changes could have profound implications for the boreal C-climate feedback and underscore the need for adaptive management strategies that prioritize the structural and functional resilience of boreal forest ecosystems to expected increases in fire frequency. 

ABSTRACT Siberian boreal forests have experienced increases in fire extent and intensity in recent years, which may threaten their role as carbon (C) sinks. Larch forests (Larixspp.) cover approximately 2.6 million km²across Siberia, yet little is known about the magnitude and drivers of carbon combustion in these ecosystems. To address the paucity of field‐based estimates of fuel load and consumption in Siberian larch forests, we sampled 41 burned plots, one to two years after fire, in Cajander larch (Larix cajanderi) forests in the Republic of Sakha (Yakutia), Russia. We estimated pre‐fire carbon stocks and combustion with the objective of identifying the main drivers of carbon emissions. Pre‐fire aboveground (trees and woody debris) and belowground carbon stocks at our study plots were 3.12 ± 1.26 kg C m⁻²(mean ± standard deviation) and 3.50 ± 0.93 kg C m⁻². We found that combustion averaged 3.20 ± 0.75 kg C m⁻², of which 78% (2.49 ± 0.56 kg C m⁻²) stemmed from organic soil layers. These results suggest that severe fires in Cajander larch forests can result in combustion rates comparable to those observed in North American boreal forests and exceeding those previously reported for other forest types and burning conditions in Siberia. Carbon combustion was driven by both fire weather conditions and landscape variables, with pre‐fire organic soil depth being the strongest predictor across our plots. Our study highlights the need to better account for Siberian larch forest fires and their impact on the carbon balance, especially given the expected climate‐induced increase in fire extent and severity in this region. 

Abstract The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO₂fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO₂sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr⁻¹; trend, −14 Tg C yr⁻¹;P < 0.001), more than 30% of the region was a net CO₂source. Tundra regions may have already started to function on average as CO₂sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr⁻¹; trend, −9 Tg C yr⁻¹), and the permafrost region becomes CO₂neutral (budget, −24 ± 123 Tg C yr⁻¹; trend, −3 Tg C yr⁻¹), underscoring the importance of fire in this region. 

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m⁻²) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic. 

Abstract Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development. 

Abstract Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study. 

Abstract With climate warming and drying, fire activity is increasing in Cajander larch (Larix cajanderiMayr.) forests underlain by continuous permafrost in northeastern Siberia, and initial post-fire tree demographic processes could unfold to determine long-term forest carbon (C) dynamics through impacts on tree density. Here, we evaluated above- and belowground C pools across 25 even-aged larch stands of varying tree densities that established following a wildfire in ~ 1940 near Cherskiy, Russia. Total C pools increased with increased larch tree density, from ~ 9,000 g C m⁻²in low-density stands to ~ 11,000 g C m⁻²in high and very high-density stands, with increases most pronounced at tree densities < 1 stem m⁻²and driven by increased above- and belowground (that is, coarse roots) and live and dead (that is, woody debris and snags) larch biomass. Total understory vegetation and non-larch coarse root C pools declined with increased tree density due to decreased shrub C pools, but these pools were relatively small compared to larch biomass. Fine root, soil organic matter (OM), and near surface (0–30 cm) mineral soil (MS) C pools varied little with tree density, although soil C pools held most (18–28% in OM and 44–51% in MS) C stored in these stands. Thus, if changing fire regimes promote denser stands, C storage will likely increase, but whether this increase offsets C lost during fires remains unknown. Our findings highlight how post-fire tree demographic processes impact C pool distribution and stability in larch forests of Siberian permafrost regions. 

Abstract Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage. 

Abstract Understanding the factors influencing species range limits is increasingly crucial in anticipating migrations due to human‐caused climate change. In the boreal biome, ongoing climate change and the associated increases in the rate, size, and severity of disturbances may alter the distributions of boreal tree species. Notably, Interior Alaska lacks native pine, a biogeographical anomaly that carries implications for ecosystem structure and function. The current range of lodgepole pine (Pinus contortavar.latifolia) in the adjacent Yukon Territory may expand into Interior Alaska, particularly with human assistance. Evaluating the potential for pine expansion in Alaska requires testing constraints on range limits such as dispersal limitations, environmental tolerance limits, and positive or negative biotic interactions. In this study, we used field experiments with pine seeds and transplanted seedlings, complemented by model simulations, to assess the abiotic and biotic factors influencing lodgepole pine seedling establishment and growth after fire in Interior Alaska. We found that pine could successfully recruit, survive, grow, and reproduce across our broadly distributed network of experimental sites. Our results show that both mammalian herbivory and competition from native tree species are unlikely to constrain pine growth and that environmental conditions commonly found in Interior Alaska fall well within the tolerance limits for pine. If dispersal constraints are released, lodgepole pine could have a geographically expansive range in Alaska, and once established, its growth is sufficient to support pine‐dominated stands. Given the impacts of lodgepole pine on ecosystem processes such as increases in timber production, carbon sequestration, landscape flammability, and reduced forage quality, natural or human‐assisted migration of this species is likely to substantially alter responses of Alaskan forest ecosystems to climate change. 

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling