Alaska Biological Research (United States)

Abstract. The boreal forest has experienced the fastest warming of any forested biome in recent decades. While vegetation–climate models predict a northward migration of boreal tree cover, the long-term studies required to test the hypothesis have been confined to regional analyses, general indices of vegetation productivity, and data calibrated to other ecoregions. Here we report a comprehensive test of the magnitude, direction, and significance of changes in the distribution of the boreal forest based on the longest and highest-resolution time-series of calibrated satellite maps of tree cover to date. From 1985 to 2020, boreal tree cover expanded by 0.844 million km2, a 12 % relative increase since 1985, and shifted northward by 0.29° mean and 0.43° median latitude. Gains were concentrated between 64–68° N and exceeded losses at southern margins, despite stable disturbance rates across most latitudes. Forest age distributions reveal that young stands (up to 36 years) now comprise 15.4 % of forest area and hold 1.1–5.9 Pg of aboveground biomass carbon, with the potential to sequester an additional 2.3–3.8 Pg C if allowed to mature. These findings confirm the northward advance of the boreal forest and implicate the future importance of the region's greening to the global carbon budget.

Subarctic floodplains are “hotspots” of biological productivity, diversity, and cross-trophic habitat quality embedded in otherwise relatively unproductive ecosystems. Broad-scale and rapid changes to the discharge regime, biogeochemistry, and seasona

Abstract Tundra shrub expansion is a central form of change in warming Arctic ecosystems, but the pace of shrubification varies across spatial scales, complicating efforts to understand its drivers and consequences. Here, we apply convolutional neural networks (CNNs) to very‐high resolution satellite image pairs acquired 10–15 years apart (circa 2005–2019) to identify spatio‐temporal patterns of tall shrub (> ~1.5 m height) expansion and their relationships to environmental covariates and antecedent shrub cover in three upland‐dominated Siberian Arctic landscapes. We developed human‐interpreted training datasets for CNN modelling of tall shrub occurrence and change for nearly 1 million 12 × 12 m image tiles using four canopy cover classes: Tundra lacking tall shrubs; Colonization with isolated shrubs; Open Shrub with discontinuous cover of mature shrubs; and Closed Shrub with dense cover of mature shrubs. F1 scores for the canopy cover maps ranged 0.83–0.92, and classification confidence was high (>0.8) in both time periods for 62% of image tiles. We evaluated canopy class occurrence with respect to landscape‐scale environmental covariates related to topography, insolation, wetness and proximity to established shrubs. We detected increases in tall shrub cover in all three landscapes, but the rate of increase varied substantially (+2.4 to 26.1% decade−1). Locally, the distribution of canopy cover classes was strongly influenced by topographically derived metrics of wetness and potential insolation. Shrub colonization was further conditioned by proximity to pre‐existing shrubs. Synthesis. We found that mature, long‐established shrubs (i.e. the Open Shrub and Closed Shrub classes) were closely linked to well‐drained landscape positions; however, closed shrub stands were more likely to occur on warmer, south‐facing slopes, while Open Shrub predominated on cooler slope aspects. Contemporary shrub colonization has occurred on flatter landscape positions and across wider gradients of insolation and moisture. In our study region, northward shrubline advance appears most closely tied to microsites with favourable drainage and edaphic conditions; subsequent seed production and ameliorating effects support canopy infilling, particularly on warmer hillslope aspects. The techniques employed here provide insights into the susceptibility of landscapes to future shrub expansion and help address geographic bias regarding Arctic shrubification and its environmental drivers.

Abstract The engaging underrepresented voices (EUVs) in climate research project is an exploratory study into factors at the intersection of climate change and race, as well as their potential relationships to racial disparities affecting Asian Pacific Islander Desi American (APIDA), and Black residents of interior Alaska. Nationally, minority communities have been shown to suffer disproportionate effects of pollution and other forms of environmental change, but the extent to which this is also true in Alaska has been unknown due to a lack of data for non-Indigenous minorities in the state. We explore this topic at the borough (county) scale in the Fairbanks North Star Borough (FNSB), chosen due to its extreme weather, urban condition, diverse population, and the close connection of residents to the surrounding ecosystem. We employed community-based methods to determine whether APIDA and Black residents of interior Alaska are likely to experience disproportionate impacts from climate change compared to the majority White population, and if so, what is the nature of those impacts? The project compiled and analyzed existing data and employed mixed methods (survey and focus groups) to collect in-depth narratives shared by interior Alaska APIDA and Black residents. The findings provide evidence showing the potential for disproportionate impacts experienced by both APIDA and Black interior Alaskans due to several social-environmental variables. Given our results, we include recommendations to further engage underrepresented populations in climate change research and to resolve the data needs that limit our understanding and ability to address structural inequality. Significance Statement Nationally, minority communities have been shown to suffer greater effects of pollution and other forms of environmental change, but the level to which this is also true in Alaska is largely unknown due to a lack of data for non-Indigenous minorities in the state. This knowledge gap limits our ability to identify local needs and design solutions to address them. Our research identified key social and environmental factors that, in relation to race, have the potential to result in outsized impacts to Asian Pacific Islander Desi American and Black residents of interior Alaska. Additional data are needed to better understand and address these inequalities, and more inclusive research methods can help overcome barriers to collecting the needed information.

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 ~6,000 g m-2 (mean ≈ 350 g m-2), while predicted values ranged from 0 to ~4,000 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. 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 Milne Point Field initiated the first polymer injection pilots on the North Slope of Alaska starting in 2018 and has rapidly progressed to full-field polymer injection within four years. The two initial pilot projects injected at an initial total rate of 6,000 barrels of water per day (bwpd) utilizing five horizontal injection wells. Expansion activities began two years later, in 2020, with the addition of three more polymer injection units. By the end of 2021, two more polymer injection units were added, followed by one in 2022 and another in 2023. Currently, Milne Point operates nine active polymer skids, and the field's polymer injection rate has reached 60,000 bwpd via 50 horizontal injection wells. Targeted reservoirs have average permeabilities ranging from 100 to 1000 md and in-situ oil viscosities ranging from 13 cp to 1300 cp. Both secondary and tertiary floods are being conducted in both greenfield and brownfield development areas, each with varying, yet all promising, results. The highest observed recovery is in a secondary polymer flood pattern at 34% of OOIP with an oil viscosity of 850 cp and water breakthrough after 5 years or 0.2 PV injection. Responses in injection well injectivities have ranged from as low as 0% up to a 50% loss to date and are observed to be correlated with well spacing and total reservoir mobility. The intent of this paper is to: a) provide an update on previously published results from 2022; b) highlight what has been done to date regarding moving from polymer flood concept to near full-field expansion; and c) present observed results with the hope that they can help set expectations for future polymer flood projects.

Earth's global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the "triple dip" La Niña conditions that

Polyploidy has been suggested to negatively impact environmental stress tolerance, resulting in increased susceptibility to extreme climate events. In this study, we compared the genomic and physiological response of diploid (2n) and triploid (3n) Pacific oysters (Crassostrea gigas) to conditions present during an atmospheric heatwave that impacted the Pacific Northwestern region of the United States in the summer of 2021. Climate stressors were applied either singly (single stressor; elevated seawater temperature, 30°C) or in succession (multiple stressor; elevated seawater temperature followed by aerial emersion at 44°C), replicating conditions present within the intertidal over a tidal cycle during the event. Oyster mortality rate was elevated within stress treatments with respect to the control and was significantly higher in triploids than diploids following multiple stress exposure (36.4% vs. 14.8%). Triploids within the multiple stressor treatment exhibited signs of energetic limitation, including metabolic depression, a significant reduction in ctenidium Na+ /K+ ATPase activity, and the dysregulated expression of genes associated with stress response, innate immunity, glucose metabolism, and mitochondrial function. Functional enrichment analysis of ploidy-specific gene sets identified that biological processes associated with metabolism, stress tolerance, and immune function were overrepresented within triploids across stress treatments. Our results suggest that triploidy impacts the transcriptional regulation of key processes that underly the stress response of Pacific oysters, resulting in downstream shifts in physiological tolerance limits that may increase susceptibility to extreme climate events that present multiple environmental stressors. The impact of chromosome set manipulation on the climate resilience of marine organisms has important implications for domestic food security within future climate scenarios, especially as triploidy induction becomes an increasingly popular tool to elicit reproductive control across a wide range of species used within marine aquaculture.

Many studies using convolutional neural networks, including in the field of satellite images, are aimed at recognizing clearly defined objects, such as cars or individual trees. Here we present the first results on mapping the growth stage of tundra shrubs, which is a ‘‘fuzzy’’ target for a network: there are no obvious geometric boundaries of the growth stages, as well as a clear division between the classes representing the growth stages. For six high-resolution satellite images of three Low Arctic landscapes, we achieved an F-score of 0.8-0.9, what is very promising for further ecological research. We also found that different landscapes and stages of shrub growth are not equally error-prone.