Search Results (33)

Portable X-ray fluorescence (pXRF) is a useful geochemical technique employed to explore toolstone procurement strategies in the lithic record, commonly utilized in sourcing obsidians. Non-obsidian volcanic toolstones (e.g., dacites, rhyolites, basalts, and andesites) are abundant in interior Alaskan assemblages yet understudied compared to obsidian. Geochemical analyses of these non-obsidian materials offer the potential to gain new insights into ancient toolstone provisioning behaviors. This paper presents a synthesis of geochemical (pXRF) analyses of rhyolite artifacts, systematic regional raw material surveys, and lithic technological analyses collected from nineteen late Pleistocene and Holocene assemblages from the Nenana valley, interior Alaska. Previous research studies on archaeological rhyolites from the region are replicated, new rhyolite artifact groups are identified, and one new rhyolite source is reported and described here. Ultimately, this paper contributes to a growing body of geochemical research seeking to provide a more nuanced look at the complex late Pleistocene and Holocene record of eastern Beringia. Full article

The boreal forest of northwestern North America covers an extensive area, contains vast amounts of carbon in its vegetation and soil, and is characterized by extensive wildfires. Catastrophic crown fires in these forests are fueled predominantly by only two evergreen needle-leaf tree species, black spruce (Picea mariana (Mill.) B.S.P.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). Identifying where these flammable species grow through time in the landscape is critical for understanding wildfire risk, damages, and human exposure. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas. Full article

This study will review previously published Proto-Athabaskan (P-A) linguistic reconstructions related to weapons and ceramics technologies present on both sides of the Bering Strait. Na-Dene (N-D) is a large family of indigenous languages of North America, consisting mostly of the Athabaskan languages of the western interior, plus the Eyak and Tlingit languages of the southern Alaska coast. Athabaskan-Eyak (A-E) constitutes a distinct branch of Na-Dene. Dene-Yeniseian (D-Y) is a proposed transpacific family comprised of Na-Dene in addition to the Yeniseian languages of Siberia. Reconstructions pertaining to several specific technologies will be discussed in relation to likely cognates within broader A-E, N-D and D-Y historical contexts. Although D-Y is sometimes assumed to have originated near the conclusion of the Pleistocene Epoch (prior to ~11,500 years BP), this study will refocus fundamental questions on the current Holocene Epoch (after ~11,500 BP), and particularly the Late Holocene (after ~3000 BP). Full article

Community-led bioenergy projects show great promise to address a range of issues for remote and Indigenous Arctic communities that typically rely on diesel for meeting their energy demands. However, there is very little research devoted to better understanding what makes individual projects successful. In this study, we analyze the case of the Galena Bioenergy Project (Alaska)—a biomass heating project that uses locally sourced woody biomass to help meet the heating demands of a large educational campus. Using project documents and other publicly available reports, we evaluate the project’s success using three indicators: operational, environmental, and community level socio-economic benefits. We find that the project shows signs of success in all three respects. It has a reliable fuel supply chain for operations, makes contributions towards greenhouse gas reductions by replacing diesel and has improved energy and economic security for the community. We also examine enabling factors behind the project’s success and identify the following factors as crucial: community-level input and support, state level financial support, access to forest biomass with no competing use, predictable demand and committed leadership. Our findings have important implications for other remote communities across the Boreal zone—especially those with nearby forest resources. Our examination of this case study ultimately highlights potential pathways for long-term success and, more specifically, shows how biomass resources might be best utilized through community-led initiatives to sustainably support energy security in Arctic communities. Full article

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure. Full article

The effect of climate on tree growth has received increased interest in the context of climate change. However, most studies have been limited geographically and with respect to species. Here, sixteen tree species of western North America were used to investigate the response of trees to climate change. Forest inventory data from 36,944 stands established between 1600 and 1968 throughout western North America were summarized. The height growth (top height at a breast-height age of 50 years) of healthy dominant and co-dominant trees was related to annual and summer temperatures, the annual and summer Palmer Drought Severity Indexes (PDSIs), and the tree establishment date (ED). Climate-induced height growth patterns were then tested to determine links to the spatial environment (geographic locations and soil properties), the species’ range (coastal, interior, or both), and traits (shade tolerance and leaf form). Analysis was performed using a linear mixed model (total species) and a general linear model (species scale). Climate change was globally beneficial, except for Alaska yellow-cedar (Chamaecyparis nootkatensis (D. Don) Spach), and growth patterns were magnified for coastal-ranged, high-shade-tolerant, and broadleaf species, and mostly at the northernmost extents of these species’ ranges. Nevertheless, growth patterns were more complex with respect to soil properties. A growth decline for some species was observed at higher latitudes and elevations and was possibly related to increased cloudiness, precipitation, or drought (in interior areas). These results highlight the spatio-temporal complexity of the growth response to recent global climate change. Full article

While recent increases in heavy precipitation events in some midlatitude regions are consistent with climate model simulations, evidence of such increases in high latitudes is more tenuous, partly because of data limitations. The present study evaluates historical and future changes in extreme precipitation events in Alaska. Using the ERA5 reanalysis, station data, and output from two downscaled global climate models, we examine precipitation-driven flood events at five diverse locations in Alaska where major historical floods provide benchmarks: Fairbanks (August 1967), Seward (October 1986), Allakaket/Bettles (August 1994), Kivalina (August 2012), and Haines (December 2020). We place these precipitation events into a framework of historical trends and end-of-century (2065–2100) model projections. In all but one of the flood events, the amount of rainfall was the highest on record for the event duration, and precipitation events of this magnitude are generally projected by the models to remain infrequent. All of the cases had subtropical or tropical moisture sources. None of the locations show statistically significant historical trends in the magnitude of extreme precipitation events. However, the frequencies of heavy precipitation events are projected to increase at most of the locations. The frequency of events with 2 year and 5 year historical return intervals is projected to become more frequent, especially in the Interior, and in some cases increase to several times per year. Decreases are projected only for Seward along Alaska’s southern coast. Full article

As climate warms, snow-water storage is decreasing while forest fires are increasing in extent, frequency, and duration. The majority of forest fires occur in the seasonal snow zone across the western US. Yet, we do not understand the broad-scale variability of forest fire effects on snow-water storage and water resource availability. Using pre- and post-fire data from 78 burned SNOTEL stations, we evaluated post-fire shifts in snow accumulation (snow-water storage) and snowmelt across the West and Alaska. For a decade following fire, maximum snow-water storage decreased by over 30 mm, and the snow disappearance date advanced by 9 days, and in high severity burned forests snowmelt rate increased by 3 mm/day. Regionally, forest fires reduced snow-water storage in Alaska, Arizona, and the Pacific Northwest and advanced the snow disappearance date across the Rockies, Western Interior, Wasatch, and Uinta mountains. Broad-scale empirical results of forest fire effects on snow-water storage and snowmelt inform natural resource management and modeling of future snow-water resource availability in burned watersheds. Full article

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. Full article

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification. Full article

Central Interior Alaska is one of the most seismically active regions in North America, exhibiting a high concentration of intraplate earthquakes approximately 700 km away from the southern Alaska subduction zone. Seismological evidence suggests that intraplate seismicity in the region is not uniformly distributed, but concentrated in several discrete seismic zones, including the Nenana basin and the adjacent Tanana basin. Although the location and magnitude of the seismic activity in both basins are well defined by a network of seismic stations in the region, the tectonic controls on these intraplate earthquakes and the heterogeneous nature of Alaska’s continental interior remain poorly understood. We investigated the crustal structure of the Nenana and Tanana basins using available seismic reflection, aeromagnetic and gravity anomaly data, supplemented by geophysical well logs and outcrop data. We developed nine new two-dimensional forward models to delineate internal geometries and the crustal structure of Alaska’s interior. The results of our study demonstrates a strong crustal heterogeneity beneath both basins. The Tanana basin is a relatively shallow (up to 2 km) asymmetrical foreland basin with its southern, deeper side controlled by the northern foothills of the Central Alaska Range. Northeast-trending left lateral strike-slip faults within the Tanana basin are interpreted as a zone of clockwise crustal block rotation. The Nenana basin has a fundamentally different geometry. It is a deep (up to 8 km), narrow transtensional pull-apart basin that is deforming along the left-lateral Minto Fault. This study identifies two distinct modes of current tectonic deformation in Central Interior Alaska and provides a basis for modeling the interplay between intraplate stress fields and major structural features that potentially influence the generation of intraplate earthquakes in the region. Full article

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires. Full article

Monitoring antibiotic resistance genes (ARGs) across ecological niches is critical for assessing the impacts distinct microbial communities have on the global spread of resistance. In permafrost-associated soils, climate and human driven disturbances augment near-surface thaw shifting the predominant bacteria that shape the resistome in overlying active layer soils. This thaw is of concern in Alaska, because 85% of land is underlain by permafrost, making soils especially vulnerable to disturbances. The goal of this study is to assess how soil disturbance, and the subsequent shift in community composition, will affect the types, abundance, and mobility of ARGs that compose the active layer resistome. We address this goal through the following aims: (1) assess resistance phenotypes through antibiotic susceptibility testing, and (2) analyze types, abundance, and mobility of ARGs through whole genome analyses of bacteria isolated from a disturbance-induced thaw gradient in Interior Alaska. We found a high proportion of isolates resistant to at least one of the antibiotics tested with the highest prevalence of resistance to ampicillin. The abundance of ARGs and proportion of resistant isolates increased with disturbance; however, the number of ARGs per isolate was explained more by phylogeny than isolation site. When compared to a global database of soil bacteria, RefSoil+, our isolates from the same genera had distinct ARGs with a higher proportion on plasmids. These results emphasize the hypothesis that both phylogeny and ecology shape the resistome and suggest that a shift in community composition as a result of disturbance-induced thaw will be reflected in the predominant ARGs comprising the active layer resistome. Full article

Results of five climate model simulation studies on the Younger Dryas cold event (YD) are compared with a focus on temperature and precipitation. Relative to the Bølling-Allerød interstadial (BA), the simulations show consistent annual cooling in Europe, Greenland, Alaska, North Africa and over the North Atlantic Ocean and Nordic Seas with maximum reduction of temperatures being simulated over the oceans, ranging from −25 °C to −6 °C. Warmer conditions were simulated in the interior of North America. In two experiments, the mid-to-high latitudes of the Southern Hemisphere were also warmer, associated with a strong bi-polar seesaw mechanism in response to a collapse of the Atlantic meridional overturning circulation (AMOC). The modelled YD-BA temperature response was in general agreement with proxy-based evidence. The simulations reveal reduced YD-BA precipitation (up to 150 mm yr⁻¹) over all regions with major cooling, and over the northern equatorial region. South of the equator, modelled precipitation seemed to increase due to a southward shift of the InterTropical Convergence Zone (ITCZ). The largest uncertainty in the YD is the high-latitude response, where the models show diverging results. This disagreement is partly related to uncertainties in the freshwater forcing. Most model studies assume an AMOC shutdown, but this is incompatible with proxy evidence. Full article

The Drought Code (DC) is a moisture code of the Canadian Forest Fire Weather Index System underlain by a hydrological water balance model in which drying occurs in a negative exponential pattern with a relatively long timelag. The model derives from measurements from an evaporimeter and no soil parameters are specified, leaving its physical nature uncertain. One way to approximate the attributes of a “DC equivalent soil” is to compare its drying timelag with measurements of known soils. In situ measurements of timelag were made over the course of a fire season in a black spruce-feathermoss forest floor underlain by permafrost in Interior Alaska, USA. On a seasonally averaged basis, timelag was 28 d. The corresponding timelag of the DC water balance model was 60 d. Water storage capacity in a whole duff column 200 mm deep was 31 mm. Using these figures and a relationship between timelag, water storage capacity, and the potential evaporation rate, a “DC equivalent soil” was determined to be capable of storing 66 mm of water. This amount of water would require a soil 366 mm deep, suggesting a revision of the way fire managers in Alaska regard the correspondence between soil and the moisture codes of the FWI. Nearly half of the soil depth would be mineral rather than organic. Much of the soil water necessary to maintain a 60 d timelag characteristic of a “DC equivalent soil” is frozen until after the solstice. Unavailability of frozen water, coupled with a June peak in the potential evaporation rate, appears to shorten in situ timelags early in the season. Full article