Geology topics

Research about Alaska

Source-linked reports with geographic coverage including Alaska.

At least 253 records · Page 14Linked to original sources

USGS2023

Hydrothermal monazite and xenotime chemistry as genetic discriminators for intrusion-related and orogenic gold deposits: Implications for an orogenic origin of the Pogo gold deposit, Alaska

Attempts to geochemically distinguish between metamorphic-hydrothermal systems that form orogenic gold deposits and both reduced and oxidized magmatic-hydrothermal systems using isotopes or metal associations have proven ambiguous, particularly for orogenic gold and reduced intrusion-related gold systems. The absence of conclusive geochemical discriminators and the overlap in geologic characteristics have led to gold deposit models being potentially incorrectly applied, which in turn negatively affect regional mineral exploration and mine planning. In this study, in situ electron microprobe geochemical analyses of hydrothermal monazite and xenotime crystals associated with different types of gold-bearing deposits are shown to be effective geochemical discriminators. There are notable differences in mineral chemistry such as rare earth element (REE) profiles, total light REE, Dy, Er, Pr, Y, Nd/Sm, and La/Sm that distinguish monazite precipitated from metamorphic-hydrothermal fluids that form orogenic gold deposits and those precipitated from magmatic-hydrothermal fluids that form both porphyry Cu-Mo-Au and reduced intrusion-related gold deposits. Notable differences in overall xenotime abundances and concentrations of heavy REEs, Ca, and Sc are distinctive between the different deposit classes for xenotime. The origin of the controversially classified Pogo gold deposit, Tintina gold province, Alaska, which has been characterized as both a reduced intrusion-related and an orogenic gold deposit, is tested based upon the noted chemical differences associated with these hydrothermal phosphates. The findings of this study have implications for exploration and mine development in the Tintina gold province and other areas that contain deposits that are controversially classified as either orogenic or as magmatic-hydrothermal gold deposits.

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USGS2023

Alaska's climate sensitive Yukon-Kuskokwim Delta supports seven million Arctic-breeding shorebirds, including the majority of six North American populations

Baseline information about declining North American shorebird populations is essential to determine the effects of global warming at low-lying coastal areas of the Arctic and subarctic, where numerous taxa breed, and to assess population recovery throughout their range. We estimated population sizes on the Yukon-Kuskokwim Delta in western Alaska on the eastern edge of the Bering Sea. We conducted ground-based surveys during 2015 and 2016 at 589 randomly selected plots from an area of 35,769 km 2 . We used stratified random sampling in 8 physiographic strata and corrected population estimates using detection ratios derived from double sampling on a subset of plots. We detected 11,110 breeding individuals of 21 taxa. Western Sandpiper ( Calidris mauri ), Red-necked Phalarope ( Phalaropus lobatus ), Dunlin (subspecies C. alpina pacifica ), and Wilson’s Snipe ( Gallinago delicata ) were the most abundant taxa. We estimated that ~6.9 million individual shorebirds were breeding on the entire Yukon-Kuskokwim Delta in 2015 and 2016. Our surveys of this region provided robust population estimates (CVs ≤ 0.35) for 14 species. Our results indicate that the Yukon-Kuskokwim Delta supports a large proportion of North America’s breeding populations of the Pacific Golden-Plover ( Pluvialis fulva ), the western population of a Whimbrel subspecies ( Numenius phaeopus hudsonicus ), a Bar-tailed Godwit subspecies ( Limosa lapponica baueri ), Black Turnstone ( Arenaria melanocephala ), a Dunlin subspecies ( Calidris alpina pacifica ), and Western Sandpiper. Our study highlights the importance to breeding shorebirds of this relatively pristine but climatically sensitive deltaic system. Estuaries and deltaic systems worldwide are rapidly being degraded by anthropogenic activities. Our population estimates can be used to refine prior North American population estimates, determine effects of global warming, and evaluate conservation success by measuring population change over time.

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USGS2023

Rapid population decline in McKay's Bunting, an Alaskan endemic, highlights the species’ current status relative to international standards for vulnerable species

The McKay’s Bunting ( Plectrophenax hyperboreus ) is endemic to Alaska, breeds solely on the remote and uninhabited St. Matthew and Hall islands (332 km 2 ) in the central Bering Sea, and is designated as a species of high conservation concern due to its small population size and restricted range. A previous hypothesized population estimate (~2,800—6,000 individuals) was greatly increased (~31,200 individuals) after systematic surveys of the species’ entire breeding range in 2003, establishing McKay’s Bunting as one of the rarest passerines in North America. In 2018, we replicated the 2003 surveys and used density surface models to estimate breeding season densities, distributions, and population change over the intervening time period. Our results indicate that the McKay's Bunting population declined by 38% (95% CI: 27—48%) from ~31,560 to 19,481 individuals since 2003. Spatial model predictions showed no areas with an increase of birds on either St. Matthew or Hall islands but revealed declines across 13% (42 km 2 ) of St. Matthew Island. Declines disproportionately occurred both in marginal habitats with reduced rocky nesting substrate and in high-density hotspots along the coast of St. Matthew Island. The total area occupied by breeding adults decreased by 8%, and high-density hotspots shifted inland from the coast of St. Matthew Island to higher elevations on both islands, the latter potentially responses to exceptionally warm weather and reduced spring snow cover in 2018. Additionally, we observed low numbers of predators and interspecific competitors in 2018 suggesting these did not cause the decline. Our findings indicate that McKay’s Bunting meets international standards for elevating its conservation status from Least Concern to Endangered based on the International Union for Conservation of Nature Red List of Threatened Species ranking criteria. Additional population monitoring and studies to identify the causal mechanisms of the recent population decline of this rare species could assist future population assessments.

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USGS2023

Arctic-boreal lakes of interior Alaska dominated by contemporary carbon

Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.

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USGS2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697 µ molCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

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USGS2023

Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska

Our warming climate is adversely affecting cryospheric landscapes via glacial retreat, permafrost degradation, and associated slope destabilization. In Prince William Sound, Alaska, the rapid retreat of Barry Glacier has destabilized the slopes flanking the glacier, resulting in numerous landslides. The largest of these landslides (∼500 Mm 3 in volume) is more than 2 km wide and has the potential to generate a tsunami that could affect nearby recreationists, marine traffic, infrastructure, natural and cultural resources, and the community of Whittier, located 60 km from the landslide. Here, we combine landslide structural and kinematic element mapping with data acquired from bi-yearly airborne lidar, multi-week satellite-based synthetic aperture radar (SAR), sub-hourly ground-based SAR, and seismic monitoring from 2020 to 2022 to characterize this landslide and examine its evolution. While some methods serve as a snapshot in time that is a culmination of events, others emphasize the ever-evolving nature of the landslide and associated hazards. Four major kinematic elements define the overall structure of the landslide, which vary in deformation type and rate, from creep (5 mm per day over several months) to episodic movement (2 m in 30 days) and landslide-wide to localized events. In some areas of the landslide, short-term deformation deviates from structures formed by cumulative movement, implying structural and kinematic evolution associated with glacier retreat. These insights are important for assessing landslide hazards and hazard evolution for large, slow-moving bedrock landslides in actively deglaciating environments.

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USGS2023

Effects of vehicle traffic on space use and road crossings of caribou in the Arctic

Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou ( Rangifer tarandus granti ). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska. We first modeled spatiotemporal variation in hourly traffic volumes across the road system from traffic counter data using gradient-boosted regression trees. We then used generalized additive models to estimate nonlinear step selection functions and road-crossing probabilities from collared female caribou during the post-calving and insect harassment seasons, when they primarily interact with roads. Step selection analyses revealed that caribou selected areas further from roads (~1–3 km) during the post-calving and mosquito seasons and selected areas with lower traffic volumes during all seasons, with selection probabilities peaking when traffic was <5 vehicles/h. Using road-crossing models, we found that caribou were less likely to cross roads during the insect seasons as traffic increased, but that response dissipated as insect harassment became more severe. Past studies suggested that caribou exhibit behavioral responses when traffic exceeds 15 vehicles/h, but our results demonstrate behavioral responses at much lower traffic levels. Our results illustrate that vehicle activity mediates caribou responses to road infrastructure, information that can be used in future land-use planning to minimize the behavioral responses of caribou to industrial development in sensitive Arctic landscapes.

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USGS2023

Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc

Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra-producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska-Aleutian arc, specifically its pre-Holocene spatiotemporal distribution.

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USGS2023

Stream hydrology and a pulse subsidy shape patterns of fish foraging

Pulsed subsidy events create ephemeral fluxes of hyper-abundant resources that can shape annual patterns of consumption and growth for recipient consumers. However, environmental conditions strongly affect local resource availability for much of the year, and can heavily impact consumer foraging and growth patterns prior to pulsed subsidy events. Thus, a consumer's capacity to exploit pulse subsidy resources may be influenced by antecedent environmental conditions, but this has rarely been shown in nature and is unknown in aquatic ecosystems. Here, we sought to understand the importance of hydrologic variation and a salmon pulse subsidy on the foraging and growth patterns of two stream salmonids in a coastal southeast Alaska drainage. To do this, we sampled fish stomach contents at a high temporal frequency (daily–weekly measurements) and analyzed fish consumption rates in relation to streamflow and pulse subsidy resource availability. We then explored the influence of interannual hydrologic variation on access to pulse subsidy resources (i.e. whether fish exceeded an egg consumption gape limit) in a bioenergetic simulation. Prior to Pink Salmon spawning, Dolly Varden and Coho Salmon displayed distinct and nonlinear flow-foraging relationships, where forage for both species consisted primarily of macroinvertebrates. During this time period, consumption maxima coincided with baseflow and the highest observed flow conditions, and consumption minima were observed at severe low-water and intermediate flow values. After salmon spawning began, forage was not significantly related to flow and consisted primarily of salmon eggs. Further, consumption rates increased overall, and foraging patterns did not appear to be affected by flow in either species. Bioenergetic simulations revealed that patterns of interannual hydrologic variation may shift Coho Salmon growth trajectories among years. Together, our results suggest that access to marine pulse subsidy resources may depend on whether antecedent hydrologic conditions are suitable for juvenile salmonids to grow large enough to consume salmon eggs by the onset of spawning.

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USGS2023

Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast

Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

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USGS2023

Late glacial–Younger Dryas climate in interior Alaska as inferred from the isotope values of land snail shells

The isotope values of fossil snail shells can be important archives of climate. Here, we present the first carbon (δ 13 C) and oxygen (δ 18 O) isotope values of snail shells in interior Alaska to explore changes in vegetation and humidity through the late-glacial period. Snail shell δ 13 C values were relatively consistent through the late glacial. However, late-glacial shell δ 13 C values are 2.8‰ higher than those of modern shells. This offset is best explained by the Suess effect and changes in the δ 13 C values of snail diet. Snail shell δ 18 O values varied through the late glacial, which can be partially explained by changes in relative humidity (RH). RH during the snail growing period was modeled based on a published flux balance model. Results suggest a dry period toward the beginning of the Bølling–Allerød (~14 ka) followed by two distinct stages of the Younger Dryas, a wetter stage in the early Younger Dryas from 12.9 to 12.3 ka, and subsequent drier stage in the late Younger Dryas between 12.3 and 11.7 ka. The results show that land snail isotopes in high-latitude regions may be used as a supplementary paleoclimate proxy to help clarify complex climate histories, such as those of interior Alaska during the Younger Dryas.

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USGS2023

Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system ( https://sitkalandslide.org/ , last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

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USGS2023

Disparate data streams together yield novel survival estimates of Alaska-breeding Whimbrels

Survival estimates are critical components of avian ecology. In well-intentioned efforts to maximize the utility of one's research, survival estimates often derive from data that were not originally collected for survival assessments, and such post hoc analyses may include unintentional biases. We estimated the survival of Whimbrels captured and marked at two breeding sites in Alaska using divergent data streams that in isolation were subject to methodological biases. Although both capture sites were chosen to study the migration ecology of Alaska-breeding Whimbrels, maximizing the conservation value of the data we collected was obviously desirable. We used multi-year telemetry information to infer survival from one site (Colville River) and mark-resight techniques to estimate survival from a second site (Kanuti River). At the Colville River, we could not feasibly include a control group of birds to assess potential survival effects of externally mounted transmitters, while at Kanuti River we were unable to accurately account for potential emigration events because we used resightings alone. We integrated these datasets in a Bayesian hierarchical framework, an approach that permitted insights across sites that moderated methodological biases within sites. Using telemetry enabled us to detect permanent emigration events from breeding sites in two of ten birds; results that informed estimates for birds without tracking devices. These datasets yielded point estimates of true survival of Whimbrels from Colville River equipped with solar-powered satellite transmitters that were higher (0.83) than true survival estimates of Whimbrels from Kanuti River marked with leg flags alone (0.74) or equipped with surgically implanted satellite transmitters (0.50), but the 95% credible intervals on these estimates overlapped across groups. For species like Whimbrels that are difficult and costly to study, combining information from disparate data streams allowed us to derive novel demographic estimates, an approach with clear application to other similar studies.

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USGS2023

Informing management of recovering predators and their prey with ecological diffusion models

The reintroduction and recovery of predators can be ecologically beneficial as well as socially and economically controversial. However, the growth and expansion of predator populations, and thus their ecological, social, and economic impacts, are not static but rather they vary in space and time. We propose a spatiotemporal statistical modeling framework based on ecological diffusion to better inform the ecology and management of recovering predators and their prey. We demonstrate its utility by applying it to a recovering sea otter ( Enhydra lutris ) population in Southeast Alaska, where sea otters were reintroduced in the late 1960s and have exhibited unprecedented population growth. Estimated parameters yield inferences about movement and population ecology, and our approach provides useful derived quantities, such as local abundance and carrying capacity as well as a quantity we term the equilibrium differential. We used our model to examine how density dependence and carrying capacity of sea otters vary spatially across a region. The diffusion modeling approach we present can be generalized for use in other instances of (re)colonization across taxa to inform management and conservation efforts.

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USGS2023

Wind-modulated groundwater discharge along a microtidal Arctic coastline

Groundwater discharge transports dissolved constituents to the ocean, affecting coastal carbon budgets and water quality. However, the magnitude and mechanisms of groundwater exchange along rapidly transitioning Arctic coastlines are largely unknown due to limited observations. Here, using first-of-its-kind coastal Arctic groundwater timeseries data, we evaluate the magnitude and drivers of groundwater discharge to Alaska's Beaufort Sea coast. Darcy flux calculations reveal temporally variable groundwater fluxes, ranging from −6.5 cm d −1 (recharge) to 14.1 cm d −1 (discharge), with fluctuations in groundwater discharge or aquifer recharge over diurnal and multiday timescales during the open-water season. The average flux during the monitoring period of 4.9 cm d −1 is in line with previous estimates, but the maximum discharge exceeds previous estimates by over an order-of-magnitude. While the diurnal fluctuations are small due to the microtidal conditions, multiday variability is large and drives sustained periods of aquifer recharge and groundwater discharge. Results show that wind-driven lagoon water level changes are the dominant mechanism of fluctuations in land–sea hydraulic head gradients and, in turn, groundwater discharge. Given the microtidal conditions, low topographic relief, and limited rainfall along the Beaufort Sea coast, we identify wind as an important forcing mechanism of coastal groundwater discharge and aquifer recharge with implications for nearshore biogeochemistry. This study provides insights into groundwater flux dynamics along this coastline over time and highlights an oft overlooked discharge and circulation mechanism with implications towards refining solute export estimates to coastal Arctic waters.

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USGS2023

Tree-ring derived avalanche frequency and climate associations in a high-latitude, maritime climate

Snow avalanches are a natural hazard in mountainous areas worldwide with severe impacts that include fatalities, damage to infrastructure, disruption to commerce, and landscape disturbance. Understanding long-term avalanche frequency patterns, and associated climate and weather influences, improves our understanding of how climate change may affect avalanche activity. We used dendrochronological techniques to evaluate the historical frequency of large magnitude avalanches (LMAs) in the high-latitude climate of southeast Alaska, United States. We collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between 1720 and 2018, which allowed us to reconstruct 82 years with LMA activity across three sub-regions. By combining this tree-ring-derived avalanche data set with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found that February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of LMA activity in the study area. Specifically, LMA activity occurred during winters with substantial February and March precipitation and neutral or negative (cold) ONI values, while years not characterized by LMAs occur more frequently during warm winters (positive ONI values). Our examination of the climate-avalanche relationship in southeast Alaska sheds light on important climate variables and physical processes associated with LMA years. These results can be used to inform long-term infrastructure planning and avalanche mitigation operations in an urban area, such as Juneau, where critical infrastructure is subject to substantial avalanche hazard.

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USGS2023

Comparing sediment microbial communities of Arctic beaver ponds to tundra lakes and streams

In recent decades the habitat of North American beaver ( Castor canadensis ) has expanded from boreal forests into Arctic tundra ecosystems. Beaver ponds in Arctic watersheds are known to alter stream biogeochemistry, which is likely coupled with changes in the activity and composition of microbial communities inhabiting beaver pond sediments. We investigated bacterial, archaeal, and fungal communities in beaver pond sediments along tundra streams in northwestern Alaska (AK), USA and compared them to those of tundra lakes and streams in north-central Alaska that are unimpacted by beavers. β -glucosidase activity assays indicated higher cellulose degradation potential in beaver ponds than in unimpacted streams and lakes within a watershed absent of beavers. Beta diversity analyses showed that dominant lineages of bacteria and archaea in beaver ponds differed from those in tundra lakes and streams, but dominant fungal lineages did not differ between these sample types. Beaver pond sediments displayed lower relative abundances of Crenarchaeota and Euryarchaeota archaea and of bacteria from typically anaerobic taxonomic groups, suggesting differences in rates of fermentative organic matter (OM) breakdown, syntrophy, and methane generation. Beaver ponds also displayed low relative abundances of Chytridiomycota (putative non-symbiotic) fungi and high relative abundances of ectomycorrhizal (plant symbionts) Basidiomycota fungi, suggesting differences in the occurrence of plant and fungi mutualistic interactions. Beaver ponds also featured microbes with taxonomic identities typically associated with the cycling of nitrogen and sulfur compounds in higher relative abundances than tundra lakes and streams. These findings help clarify the microbiological implications of beavers expanding into high latitude regions.

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USGS2023

Long-term assessment of relationships between changing environmental conditions and the physiology of southern Beaufort Sea polar bears (Ursus maritimus)

Climate change is influencing polar bear ( Ursus maritimus ) habitat, diet, and behavior but the effects of these changes on their physiology is not well understood. Blood-based biomarkers are used to assess the physiologic health of individuals but their usefulness for evaluating population health, especially as it relates to changing environmental conditions, has rarely been explored. We describe links between environmental conditions and physiologic functions of southern Beaufort Sea polar bears using data from blood samples collected from 1984 to 2018, a period marked by extensive environmental change. We evaluated associations between 13 physiologic biomarkers and circumpolar (Arctic oscillation index) and regional (wind patterns and ice-free days) environmental metrics and seasonal and demographic co-variates (age, sex, season, and year) known to affect polar bear ecology. We observed signs of dysregulation of water balance in polar bears following years with a lower annual Arctic oscillation index. In addition, liver enzyme values increased over time, which is suggestive of potential hepatocyte damage as the Arctic has warmed. Biomarkers of immune function increased with regional-scale wind patterns and the number of ice-free days over the Beaufort Sea continental shelf and were lower in years with a lower winter Arctic oscillation index, suggesting an increased allocation of energetic resources for immune processes under these conditions. We propose that the variation in polar bear immune and metabolic function is likely indicative of physiologic plasticity, a response that allows polar bears to remain in homeostasis even as they experience changes in nutrition and habitat in response to changing environments.

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