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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. 

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 

Abstract In the Arctic, winter soil temperatures exert strong control over mean annual soil temperature and winter CO₂emissions. In tundra ecosystems there is evidence that plant canopy influences on snow accumulation alter winter soil temperatures. By comparison, there has been relatively little research examining the impacts of heterogeneity in boreal forest cover on soil temperatures. Using seven years of data from six sites in northeastern Siberia that vary in stem density we show that snow-depth and forest canopy cover exert equally strong control on cumulative soil freezing degrees days (FDD_soil). Together snow depth and canopy cover explain approximately 75% of the variance in linear models of FDD_soiland freezingn-factors (n_f; calculated as the quotient of FDD_soiland FDD_air), across sites and years. Including variables related to air temperature, or antecedent soil temperatures does not substantially improve models. The observed increase in FDD_soilwith canopy cover suggests that canopy interception of snow or thermal conduction through trees may be important for winter soil temperature dynamics in forested ecosystems underlain by continuous permafrost. Our results imply that changes in Siberian larch forest cover that arise from climate warming or fire regime changes may have important impacts on winter soil temperature dynamics. 

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 Helminths infect humans, livestock, and wildlife, yet remain understudied despite their significant impact on public health and agriculture. Because many of the most prevalent helminth‐borne diseases are zoonotic, understanding helminth transmission among wildlife could improve predictions and management of infection risks across species. A key challenge to understanding helminth transmission dynamics in wildlife is accurately and quantitatively tracking parasite load across hosts and environments. Traditional methods, such as visual parasite identification from environmental samples or infected hosts, are time‐consuming, while standard molecular techniques (e.g., PCR and qPCR) often lack the sensitivity to reliably detect lower parasite burdens. These limitations can underestimate the prevalence and severity of infection, hindering efforts to manage infectious diseases. Here, we developed a multiplexed droplet digital PCR (ddPCR) assay to quantify helminth loads in aquatic habitats using 18S rRNA target genes. UsingSchistocephalus solidusand their copepod hosts as a case study, we demonstrate ddPCR's sensitivity and precision. The assay is highly reproducible, reliably detecting target genes at concentrations as low as 1 pg of DNA in lab standards and field samples (multi‐species and eDNA). Thus, we provide a toolkit for quantifying parasite load in intermediate hosts and monitoring infection dynamics across spatio‐temporal scales in multiple helminth systems of concern for public health, agriculture, and conservation biology. 

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. 

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 impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis 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 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 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 (https://doi.org/10.18739/A2RN3092P, Talucci et al., 2024).