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Analyzing the Effectiveness of Maritime Policy for the Management of Increasing Rates of Vessel Traffic in the Bering Strait
Full text linkThe Arctic is changing at a rapid pace, affecting virtually every aspect of life in the
region, with major changes to sea ice, permafrost, and traditional lifestyles. These changes, in
conjunction with globalization, have led to a rise in interest in the Arctic and accessing its
resources. One considerable facet of accessing the Arctic is via maritime activity, with shorter
transport times between Asia and Europe, fishing valuable stocks, and access to offshore oil and
gas deposits. As countries such as Russia and China are intent on developing the Arctic and
commercializing maritime trade routes, many living in the region are contending with the
unintended impacts from increased maritime activity that may occur, threatening food and
environmental security.
While interest in the region is occurring across the Arctic, effects from increased
maritime activity are already being felt in the Bering Strait region of Western Alaska, with
decreases in multi-year sea ice important for subsistence and natural infrastructure, changes to
migratory patterns of several marine species, and occurrences of pollution from vessels (Tsujii et
al. 2021; NSIDC 2023; Raymond-Yakoubian and Daniel 2018; Hartsig et al. 2012). The impacts
on food and environmental security are likely to be amplified within the Bering Strait due to its
unique geography and ecosystem, serving as a migration corridor for many marine species, and
as a natural bottleneck for anthropogenic activity, with a mere 50 nautical miles at its narrowest
point and the Diomede Islands situated in the middle of the strait (Hartsig et al. 2012).
Furthermore, the Bering Strait is the only connection between the Arctic and Pacific Oceans.
With the heightened risk for negative impacts to the communities living within the
Bering Strait region—such as various types of pollution from maritime activity, ship strikes
(either to marine mammals or sea ice) and spread of invasive species—all affecting traditional
subsistence lifestyles, a new policy to protect the Bering Strait is needed. Thus, the main
objective of this study is to evaluate policy options that could mitigate the impacts of increasing
vessel traffic through the Bering Strait. A maritime traffic management plan can be determined
by analyzing how vessels behave in response to current policy. To best simulate the potential
traffic the Bering Strait could receive as a part of the Northern Sea Route (NSR) and the
Northwest Passage (NWP), as well as the theoretical Transpolar Route, policy applicable to the
Aleutian Archipelago (along the North Pacific Great Circle Route) is analyzed in addition to the
current conditions of the Bering Strait.
The policies analyzed within this study include the International Maritime Organization’s
‘Areas to be Avoided’(ATBA) and the United States Coast Guard ‘Alternative Planning Criteria’
(APC, through the Aleutian ATBAs). Analysis of vessel tracklines was performed with spatial
software and tabulated data with statistical software. To analyze the ATBA policy for both the
Aleutian Chain and Bering Strait, a spatio-temporal Hotspot Analysis was used to investigate
pattern shifts in vessel traffic over the 2015 to 2022 dataset. To further analyze the behavior of
vessels transiting through waters containing ATBAs, two additional analyses were conducted.
For the APC policy and the Aleutian Archipelago ATBAs, email communication between vessel
operators and the Marine Exchange of Alaska (MXAK) was analyzed, and information was
categorized, such as weather, the type of email exchange, and the overall receptiveness to being
contacted. To investigate whether port calls influenced adherence to the ATBAs, vessel traffic
along Western Alaska was tabulated by season for the traffic docking at a community.
From the analysis, three main findings emerged: (1) vessel behavior is impacted by the
ATBA implementation for each respective region, (2) various factors, including weather
and community access, continue to influence vessel behavior, and (3) communication from
a third-party organization (in this study, MXAK) impacts adherence to the ATBA policy.
As policy has influenced vessel behavior in both the Aleutian Archipelago and the Bering Strait
region in the past, policy alternatives to manage increased maritime activity within the Bering
Strait region can be useful to prevent negative impacts of vessel activity to Western Alaska
communities.
Using a modified Political, Economic, Social, Technological Analysis (in this analysis
Environmental and Legal components were utilized), in conjunction with a Comparative
Analysis, policy alternatives were examined for the best option given the current conditions
(geopolitical, legal) that the Bering Strait stakeholders face. From the policy analyses
conducted, the recommendation for managing increasing rates of maritime activity is to
create a voluntary vessel monitoring system with open enrollment by any vessel with the
Bering Strait that can provide weather advisories, warn of subsistence activities, and
inform of areas to be avoided and other existing policies. Due to this voluntary nature, the
vessel monitoring system can transcend both geopolitical tensions between Russia and the
United States. Additionally, this policy alternative provides a way for mariners to be aware of
any subsistence activities in the region. Upon further increases in maritime activity within the Bering Strait, it will be essential to revisit current policy for effectiveness, as well as include the
participation and concerns of the communities located within the Bering Strait Region.National Science FoundationAcknowledgements / Executive Summary / Introduction / Background / Policy Research Methods and Analysis / Policy Research Findings / Policy Analysis / Recommendations / References / Appendi
A quasi-one-dimensional ice mélange flow model based on continuum descriptions of granular materials
Full text linkField and remote sensing studies suggest that ice mélange influences glacier–fjord systems by exerting stresses on glacier termini and releasing large amounts of freshwater into fjords. The broader impacts of ice mélange over long timescales are unknown, in part due to a lack of suitable ice mélange flow models. Previous efforts have included modifying existing viscous ice shelf models, despite the fact that ice mélange is fundamentally a granular material, and running computationally expensive discrete element simulations. Here, we draw on laboratory studies of granular materials, which exhibit viscous flow when stresses greatly exceed the yield point, plug flow when the stresses approach the yield point, and exhibit stress transfer via force chains. By implementing the nonlocal granular fluidity rheology into a depth- and width-integrated stress balance equation, we produce a numerical model of ice mélange flow that is consistent with our understanding of well-packed granular materials and that is suitable for long-timescale simulations. For parallel-sided fjords, the model exhibits two possible steady-state solutions. When there is no calving of icebergs or melting of previously calved icebergs, the ice mélange is pushed down-fjord by the advancing glacier terminus, the velocity is constant along the length of the fjord, and the thickness profile is exponential. When calving and melting are included and treated as constants, the ice mélange evolves into another steady state in which its location is fixed relative to the fjord walls, the thickness profile is relatively steep, and the flow is extensional. For the latter case, the model predicts that the steady-state ice mélange buttressing force depends on the surface and basal melt rates through an inverse power-law relationship, decays roughly exponentially with both fjord width and gradient in fjord width, and increases with the iceberg calving flux. The buttressing force appears to increase with calving flux (i.e., glacier thickness) more rapidly than the force required to prevent the capsizing of full-glacier-thickness icebergs, suggesting that glaciers with high calving fluxes may be more strongly influenced by ice mélange than those with small fluxes.US National Science FoundationAbstract -- 1 Introduction -- 2 Model description -- 2.1 Depth-integrated flow equations -- 2.2 Width-integrated flow equations and boundary conditions -- 2.3 Numerical implementation and stability considerations -- 2.4 Ice mélange buttressing force -- 3 Model results -- 3.1 Steady-state and quasi-static profiles -- 3.1.1 Sensitivity to model parameters -- 3.1.2 Sensitivity of ice mélange flow, geometry, and buttressing force to external forcings and fjord geometry -- 3.2 Transient simulations -- 3.3 Buttressing forces in the steady-state and quasi-static regimes -- 4 Conclusions -- Appendix A: Coordinate streching -- Appendix B: Nondimensionalization -- Appendix C: Finite-difference discretization -- Appendix D: Description of model variables -- Code availability -- Data availability -- Author contributions -- Competing interests -- Disclaimer -- Acknowledgements -- Financial support -- Review statement -- ReferencesYe
Data Submission Package for Manuscript 'Progress on the world's primate hotspots and coldspots: Modeling ensemble Super SDMs in cloud-computers based on digital citizen-science Big Data and 200+ predictors for more sustainable conservation planning'2
No full textDescribing where distribution hotspots and coldspots are located with certainty is crucial for any science-based species management and governance. Thus, here we created the world’s first Super Species Distribution Models (SDMs) including all primate species and the best-available predictor set. These Super SDMs are conducted using modern Machine Learning ensembles like Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). For the global cold/ hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. We quantified the global primate hotspots for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. The global primate coldspots are Antarctica, the Arctic, most temperate regions, and Oceania past the Wallace line. We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world’s primates occur (or not). This shows us where the focus for most future research and conservation management efforts should be, using state-of-the-art digital data indication tools with reason. Those areas should be considered of the highest conservation priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival.Ye
Data Submission Package for Manuscript 'Using Machine Learning, the Cloud, Big Data, Citizen-science, and the world’s largest set of environmental predictors towards proposing modern add-ons to improve conservation management plans for squirrel species in Alaska and Indigenous lands'
No full textContext. Squirrel species in Alaska generally lack thorough conservation management plans, and they are actively hunted with no bag limits, closed seasons, or any other restrictions. This indicates a laissez-faire approach to Alaskan squirrel conservation management. Aims. In an attempt to improve this current situation, we employ an ensemble of machine-learning algorithms as proposed improvement add-ons to the traditional components of conservation management plans toward a more state-of-the-art approach to squirrel conservation. Methods. We combined the Machine Learning algorithms TreeNet, CART, Random Forest, and Maxent with over 200 environmental and socio-economic predictors for the ensemble Super Species Distribution Models. These ensemble models were carried out for all squirrel species individually occurring in Alaska and a 600 km buffer area and two assemblage models combined: a) all species currently occurring only in Alaska and b) all species occurring in Alaska and the 600km buffer area. Key results. Most predicted distribution hotspots for squirrels in Alaska and the 600 km buffer area were observed near road and river systems (close to human activities) and the last glacial maximum refugia. Conclusions & Implications. Applying a machine learning ensemble distribution modeling framework to conservation management plans can add valuable science-based insights to better understand the landscape and species to be managed. This can also be highly valuable for lands not directly managed by conventional agencies, e.g., land managed by the military or Native communities.Ye
School Travel Behaviors in Rural Communities: Pandemic-Related Impacts
Full text linkThe global pandemic, which started around early 2020, significantly disrupted life for many families, and the trip to and from school was not immune to these disruptions. Parents and children alike made travel adjustments depending on their preferences with regard to personal health and safety, social distancing, and aversion to risk. Each school district and individual school also made decisions with regard to in-person or remote learning during this period of uncertainty.
In this study, the research team examines how the pandemic affected school transportation for hundreds of families across the Pacific Northwest. An online survey was developed and administered with the help of Qualtrics, an experience management company. Over 600 responses were gathered to assess school transportation-related travel decisions. In addition to collecting demographic data about the respondents, the survey also asked about travel mode choices and characteristics of the trip to and from school. The collective results were then analyzed to determine which factors directly contributed to pandemic-related changes in travel behavior.
The study concluded that the demographic factors of parent education level, household income, and age of child were all statistically significant variables that affected behavioral change, though the place of household residence, whether rural or urban, was determined to be an insignificant variable. Additionally, common travel assumptions associated with rural students, when compared with urban students, were confirmed. These factors included a greater reliance on a yellow school bus and lesser availability of critical infrastructure
Impact of the COVID-19 Pandemic on Travel Mode Choices and Fatal Crash Rates
Full text linkThe COVID-19 pandemic caused unprecedented disruptions to human mobility and transportation systems worldwide, significantly altering travel behavior and mode choices. This study investigates these changes within the Pacific Northwest region of the United States, encompassing a mix of urban and rural contexts with diverse socio- demographic characteristics. Using survey data from 807 respondents, we analyze transportation patterns before and during the pandemic, focusing on shifts in mode shares and probabilities of switching travel modes. The analysis incorporates McNemar’s test, logistic regression, and latent class analysis (LCA) to evaluate the extent of these shifts and identify key influencing factors. The results reveal a substantial reduction in public transport usage, reflecting heightened concerns over health risks and limited operational capacity during the pandemic. In contrast, there was a notable increase in the use of private vehicles and active transportation modes, such as walking and cycling. Demographic variables, including age, income, employment status, and gender, played significant roles in shaping travel behavior, with younger and lower-income individuals exhibiting higher probabilities of mode change. The latent class analysis highlighted distinct behavioral clusters, indicating that travel behavior responses were not uniform across populations. A logistic regression model further underscored the importance of pre-pandemic travel habits, socio-economic conditions, and pandemic-related concerns in influencing mode choice decisions. Additionally, traffic safety outcomes showed notable variations, with overall crash rates decreasing during the lockdowns but fatality rates rising due to riskier driving behaviors, such as speeding on roads. Crash patterns varied across urban and rural areas, with urban crashes experiencing a slight decline in proportion, while rural crashes increased
Data Submission Package for Manuscript 'Moving beyond the physical impervious surface impact and urban habitat fragmentation of Alaska: Quantitative Human Footprint Inference from the first large Scale 30m high-resolution Landscape Metrics Big Data Quantification in R and the Cloud'_2
No full textWith increased globalization, man-made climate change, and urbanization, the landscape – embedded within the Anthropocene - becomes increasingly fragmented. With habitats transitioning and getting lost, globally relevant regions considered ‘pristine', such as Alaska, are no exception. Alaska holds 60% of the U.S. National Park system’s area and is of national and international importance, considering the U.S. is one of the wealthiest nations on earth. These characteristics tie into densities and quantities of human features, e.g., roads, houses, mines, wind parks, agriculture, trails, etc., that can be summarized as ‘impervious surfaces.’ Those are physical impacts and actively affecting urban-driven landscape fragmentation. Using the remote sensing data of the National Land Cover Database (NLCD; https://www.mrlc.gov/data/nlcd-2016-land-cover-alaska ), here we attempt to create the first quantification of this physical human impact on the Alaskan landscape and its fragmentation. We quantified these impacts using the well-established landscape metrics tool ‘Fragstats’, implemented as the R package “landscapemetrics” in the desktop software and through the interface of a Linux Cloud-computing environment. This workflow allows for the first time to overcome the computational limitations of the conventional Fragstats software within a reasonably quick timeframe. Thereby, we are able to analyze a land area as large as approx. 1,517,733 km2 (state of Alaska) while maintaining a high assessment resolution of 30 meters. Based on this traditional methodology, we found that Alaska has a reported physical human impact of c. 0.067%. But when assessed, we additionally overlaid other features that were not included in the input data to highlight the overall true human impact (e.g., roads, trails, airports, governance boundaries in game management and park units, mines, etc.). We found that using remote sensing (human impact layers), Alaska’s human impact is considerably underestimated to a meaningless estimate (0.067%). The state is more seriously fragmented and affected by humans than commonly assumed. Very few areas are truly untouched and display a high patch density with corresponding low mean patch sizes throughout the study area. Instead, the true human impact is likely close to 100% throughout Alaska for several metrics. With these newly created insights, we provide the first state-wide landscape data and inference that are likely of considerable importance for land management entities in the state of Alaska, and for the U.S. National Park systems overall, especially in the changing climate. Likewise, the methodological framework presented here shows an Open Access workflow and can be used as a reference to be reproduced virtually anywhere else on the planet to assess more realistic large-scale landscape metrics. It can also be used to assess human impacts on the landscape for more sustainable landscape stewardship and mitigation in policy.Ye
Alaska Misdemeanor Assault Arrest Rates, by Place: 1985-2022
Full text linkThis fact sheet presents Alaska misdemeanor assault arrest rates per 100,000 Anchorage residents and 100,000 residents outside of Anchorage, from 1985-2022
EVALUATING DRONE TECHNOLOGY TO IDENTIFY ICE CHANGES THAT CAN CAUSE ICE-ROAD HAZARDS
Full text linkIce roads in Alaska, a form that connects people during the winter months, enable the importing of critical goods and accessibility to medical services. These ice roads span 100 miles or more and are subject to spatial and temporal safety variability during the shoulder seasons and unseasonal warm events of above-freezing temperatures. In this work, we explore using an unmanned aircraft system (UAS) coupled with a ground penetrating radar (GPR) to inspect ice thickness safety and the presence of subsnow liquid overflow, common during winter. We compared our UAS-based GPR with ground-based GPR and nearby ice coring. We found the UAS-based GPR biased compared to the ice cores and the ground-based GPR. Nonetheless, when accounting for this bias, the UAS-based GPR had an RMSE of 5 cm for an ice thickness of 20 to 60 cm. More work is needed to understand the root cause of the UAS-based GPR for measuring ice thickness. The UAS-based GPR also effectively mapped subsnow liquid overflow by measuring the radar return amplitude, which is particularly strong when reflecting between the snow and water layers. Coupling UAS and GPR technology has great promise in conducting ice river safety assessments from a safe location. Still, more work must be done to understand the data’s bias
Using eDNA to supplement population genetic analyses for cryptic marine species: Identifying population boundaries for Alaska harbour porpoises
Full text linkIsolation by distance and biogeographical boundaries define patterns of population genetic structure for harbour porpoise along the Pacific coast from California to British Columbia. Until recently, inadequate sample sizes in many regions constrained efforts to characterise population genetic structure throughout the coastal waters of Alaska. Here, tissue samples from beachcast strandings and fisheries bycatch were supplemented with targeted environmental DNA (eDNA) samples in key regions of Alaska coastal and inland waters. Using a geographically explicit, hierarchical approach, we examined the genetic structure of Alaska harbour porpoises, using both mitochondrial DNA (mtDNA) sequence data and multilocus SNP genotypes. Despite a lack of evidence of genetic differentiation from nuclear SNP loci, patterns of relatedness and genetic differentiation from mtDNA suggest natal philopatry at multiple geographic scales, with limited gene flow among sites possibly mediated by male dispersal. A priori clustering of sampled areas at an intermediate scale (eastern and western Bering Sea, Gulf of Alaska and Southeast Alaska) best explained the genetic variance (12.37%) among regions. In addition, mtDNA differentiation between the Gulf of Alaska and eastern Bering Sea, and among regions within the Gulf of Alaska, indicated significant genetic structuring of harbour porpoise populations in Southeast Alaska. The targeted collection of eDNA samples from strata within Southeast Alaska was key for elevating the statistical power of our mtDNA dataset, and findings indicate limited dispersal between neighbouring strata within coastal and inland waters. These results provide evidence supporting a population boundary within the currently recognised Southeast Alaska Stock. Together, these findings will prove useful for ongoing management efforts to reduce fisheries conflict and conserve genetic diversity in this iconic coastal species.NOAA Office of Protected Resources.Keywords -- Abstract -- 1. Introduction -- 2. Methods -- 3. Results -- 4. Discussion -- Author contributions -- Acknowledgements -- Conflicts of interest -- Data availability statement -- References -- Supporting informationYe