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functions and services for human societies. Temperatures are increasing most rapidly in high northern latitudes, altering tree growth and competition dynamics, and modifying disturbance regimes. The effect of these cumulative changes on the ecosystem functions provided by boreal forests is difficult to predict. We used the process-based LANDIS-II forest landscape model to evaluate how climate change and timber harvesting will interact to alter the production of ecosystem functions and services in boreal forests on three study areas across a large latitudinal gradient (11°) in central Siberia. We found that the relative importance of wood harvesting as a disturbance type varied depending on latitude and its impact was always far less than that of fire. Moderate climate change increased the availability of wood for harvest in the northern landscape, but wood availability declined in the southern landscapes under any amount of climate change likely because of an increase in the frequency of fire that kept forests too young for harvest. Modest climate change (RCP6.0) increased productivity and the storage of carbon in all landscapes but severe climate change (RCP8.5) reduced both in the southernmost landscape. Harvesting as a specific driver of change in these boreal forests is likely to be relatively minor except as a forest fragmentation process. Our results provide compelling evidence that status quo forest management in these landscapes is likely not sustainable, suggesting that climate-smart forestry will be needed. 

Boreal forests form the largest terrestrial biome globally. Climate change is expected to induce large changes in vegetation of high latitude ecosystems, but there is considerable uncertainty about where, when, and how those changes will occur. Such vegetation change produces major feedback to the climate system, including by modifying albedo (reflectivity). Our study used the LANDIS-II forest landscape model to project forest dynamics on four representative landscapes (1 M ha) for 280 years into the future under a range of climate scenarios across a broad latitudinal gradient in Siberia. The model estimated the albedo of the vegetation and any snow on each landscape grid-cell through time to quantify surface albedo change in response to climate change and disturbances. We found that the shortening of the snow-covered season (winter) decreased annual average albedo dramatically, and climate change facilitated the invasion of tundra by boreal trees in the northernmost landscape (reducing albedo in all seasons). However, in other landscapes, albedo increased in summer due to disturbances (fire, wind, insects, harvest), eliminating or reducing leaf area in the short-term, and in the mid-term by promoting more reflective forest types deciduous, light conifers). This increased albedo was somewhat ephemeral and under climate change was overwhelmed by the shortening of the snow-covered season that greatly reduced albedo. We conclude that the primary driver of the overall reflectivity of boreal ecosystems is not vegetation, but rather, the length of the snow-covered season. Because climate change is likely to dramatically shorten the snow season, the concurrent reduction of albedo has the potential to act as a powerful positive feedback for climate change. Managing natural and anthropogenic disturbances may be the only tool with potential to mitigate the reduction of albedo by climate change in boreal ecosystems because management to encourage more reflective forest types has relatively small effect. 

Abstract BackgroundClimate change is expected to increase fire activity across the circumboreal zone, including central Siberia. However, few studies have quantitatively assessed potential changes in fire regime characteristics, or considered possible spatial variation in the magnitude of change. Moreover, while simulations indicate that changes in climate are likely to drive major shifts in Siberian vegetation, knowledge of future forest dynamics under the joint influence of changes in climate and fire regimes remains largely theoretical. We used the forest landscape model, LANDIS-II, with PnET-Succession and the BFOLDS fire extension to simulate changes in vegetation and fire regime characteristics under four alternative climate scenarios in three 10,000-km²study landscapes distributed across a large latitudinal gradient in lowland central Siberia. We evaluated vegetation change using the fire life history strategies adopted by forest tree species: fire resisters, fire avoiders, and fire endurers. ResultsAnnual burned area, the number of fires per year, fire size, and fire intensity all increased under climate change. The relative increase in fire activity was greatest in the northernmost study landscape, leading to a reduction in the difference in fire rotation period between study landscapes. Although the number of fires per year increased progressively with the magnitude of climate change, mean fire size peaked under mild or moderate climate warming in each of our study landscapes, suggesting that fuel limitations and past fire perimeters will feed back to reduce individual fire extent under extreme warming, relative to less extreme warming scenarios. In the Southern and Mid-taiga landscapes, we observed a major shift from fire resister-dominated forests to forests dominated by broadleaved deciduous fire endurers (BetulaandPopulusgenera) under moderate and extreme climate warming scenarios, likely associated with the substantial increase in fire activity. These changes were accompanied by a major decrease in average cohort age and total vegetation biomass across the simulation landscapes. ConclusionsOur results imply that climate change will greatly increase fire activity and reduce spatial heterogeneity in fire regime characteristics across central Siberia. Potential ecological consequences include a widespread shift toward forests dominated by broadleaved deciduous species that employ a fire endurer strategy to persist in an increasingly fire-prone environment. 

Abstract Fire frequency is increasing with climate warming in the boreal regions of interior Alaska, with short fire return intervals (< 50 years) becoming more common. Recent studies suggest these “reburns” will reduce the insulating surface organic layer (SOL) and seedbanks, inhibiting black spruce regeneration and increasing deciduous cover. These changes are projected to amplify soil warming, increasing mineral soil organic carbon (SOC) decomposition rates, and impair re-establishment of understorey vegetation and the SOL. We examined how reburns changed soil temperature, heterotrophic soil respiration (RH), and understorey gross primary production (GPP), and related these to shifts in vegetation composition and SOL depths. Two distinct burn complexes previously covered by spruce were measured; both included areas burned 1x, 2x, and 3x over 60 years and mature (≈ 90 year old) spruce forests underlain by permafrost. A 2.7 °C increase in annual near-surface soil temperatures from 1x to 3x burns was correlated with a decrease in SOL depths and a 1.9 Mg C ha⁻¹increase in annual RH efflux. However, near-surface soil warming accounted for ≤ 23% of higher RH efflux; increases in deciduous overstorey vegetation and root biomass with reburning better correlated with RH than soil temperature. Reburning also warmed deeper soils and reduced the biomass and GPP of understory plants, lessening their potential to offset elevated RH and contribute to SOL development. This suggests that reburning led to losses of mineral SOC previously stored in permafrost due to warming soils and changes in vegetation composition, illustrating how burn frequency creates pathways for accelerated regional C loss. 

Abstract Prescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk. 

Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public. 

Abstract Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data‐sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one‐dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13‐year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five‐year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm³/cm³. Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight‐year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non‐stationary climates.