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Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified snag and woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Climate change is intensifying the fire regime across Siberia, with the potential to alter carbon combustion and post‐fire carbon re‐accumulation trajectories. Few field‐based estimates of fire severity (e.g., carbon combustion and tree mortality) exist in Siberian larch forests (Larixspp.), which limits our ability to project how an intensified fire regime will affect regional and global climate feedbacks. Here, we present field‐based estimates of fire‐induced tree mortality and carbon loss in eastern Siberian larch forests. Our results suggest that fires in this region result in high tree mortality (means of 83% and 76% at Arctic and subarctic sites, respectively). In both absolute and relative terms, aboveground carbon loss following fire is higher in Siberian larch forests than in North America, but belowground carbon loss is considerably lower. This suggests fundamental differences in wildfire behavior and carbon dynamics between dominant vegetation types across the boreal biome. 

Abstract. As the northern high latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impact permafrost stability through the combustion of insulating organic matter, vegetation and post-fire changes in albedo. Efforts to synthesise the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52,466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end of season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 47,952 ALT estimates (27,747 burned, 20,205 unburned) with 32 attributes. There are 193 unique paired burned/unburned sites spread across 12 ecozones that span Canada, Russia, and the United States. The data span fire events from 1900 to 2022. Time since fire ranges from zero to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end of season maximum. This dataset can be used to address understudied research areas particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost-wildfire interactions under accelerated warming across the high northern latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center. 

Cajander larch (Larix cajanderi Mayr.) forests of the Siberian Arctic are experiencing increased wildfire activity in conjunction with climate warming. These shifts could affect postfire variation in the density and arrangement of trees and understory plant communities. To better understand how understory plant composition, abundance, and diversity vary with tree density, we surveyed understory plant communities and stand characteristics (e.g., canopy cover, active layer depth, and soil organic layer depth) within 25 stands representing a density gradient of similarly-aged larch trees that established following a 1940 fire near Cherskiy, Russia. Understory plant diversity and mean total plant abundance decreased with increased canopy cover. Canopy cover was also the most important variable affecting individual species’ abundances. In general, tall shrubs (e.g., Betula nana subsp. exilis) were more abundant in low-density stands with high light availability, and mosses (e.g., Sanionia spp.) were more abundant in high-density stands with low light availability. These results provide evidence that postfire variation in tree recruitment affects understory plant community composition and diversity as stands mature. Therefore, projected increases in wildfire activity in the Siberian Arctic could have cascading impacts on forest structure and composition in both overstory and understory plant communities. 

This data set contains the raw files from flight RU_ALN_TR1_FL007R. The remote sensing imagery is collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018). Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27. 

This data set contains the raw files from flight RU_ALN_TR1_FL007R. The remote sensing imagery is collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018). Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27. 

This data set contains the raw files from flight RU_ALN_TR1_FL007R. The remote sensing imagery is collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018). Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27. 

This data set contains the raw files from flight RU_ALN_TR1_FL007R. The remote sensing imagery is collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018). Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27.