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Arctic and subarctic rivers are warming rapidly, with unknown consequences for migratory fishes and the human communities dependent on them. To date, few studies have provided a comprehensive assessment of possible climate change impacts on the hydrology and temperature of Arctic rivers at the regional scale, and even fewer have connected those changes to multiple fish species with input and guidance from Indigenous communities. We used climate, hydrologic, and fish-growth simulations of historical (1990–2021) and future (2034–2065) young-of-year (YOY) growth potential of Chinook salmon (Oncorhynchus tshawytscha) and Dolly Varden (Salvelinus malma) for seven river basins in the Arctic-Yukon-Kuskokwim (AYK) region of Alaska, USA and Yukon Territory, Canada. Historically, summer water temperatures of all river basins remained below thresholds regarded as deleterious for Chinook salmon (14.6 °C) and Dolly Varden (16 °C), even in the warmest years. However, by the mid-century, Chinook salmon growth was limited, with declines in the warmest years in most river basins. Conversely, Dolly Varden are expected to benefit, with a near-doubling in growth projections in all river basins. This suggests that there may be an increase in suitable habitat for Dolly Varden by mid-century. The results highlight species-specific consequences of climate change and can guide future research on refugia for these species of cultural and subsistence importance to Indigenous communities in the AYK region and throughout the Arctic. 

Chinook salmon population declines span their geographic range with climate hypothesized as a major driver. Concerns of warming freshwater temperatures in their northern range gained urgency during 2019 when a heatwave coincided with premature mortality. This study examined heat stress during the 2019 heatwave compared to subsequent years and described water temperatures in western Alaska to understand the degree to which freshwater temperatures may be a stressor. Heat stress was prevalent among Chinook salmon captured in the 2019 heatwave (Kuskokwim tributaries: 90% in Kwethluk and 63% Takotna river), and variable in subsequent years (~8% to 60% across Kuskokwim tributaries and Norton Sound rivers). A review of water temperature data indicated potentially stressful temperatures (≥18°C) were most common and prolonged in the Yukon River, moderately common and prolonged in the Kuskokwim River, and relatively rare in the Norton Sound region. Water temperatures in 2019 broke several records for overall maximum and frequency of temperatures ≥18°C. Migration water temperatures and heat stress in northern Pacific salmon habitats varies more widely than previously recognized (up to 25°C). 

Within the dynamic world of Big Data, traditional systems typically operate in a passive mode, processing and responding to user queries by returning the requested data. However, this methodology falls short of meeting the evolving demands of users who not only wish to analyze data but also to receive proactive updates on topics of interest. To bridge this gap, Big Active Data (BAD) frameworks have been proposed to support extensive data subscriptions and analytics for millions of subscribers. As data volumes and the number of interested users continue to increase, it is imperative to optimize BAD systems for enhanced scalability, performance, and efficiency. To this end, this paper introduces three main optimizations, namely: strategic aggregation, intelligent modifications to the query plan, and early result filtering, all aimed at reinforcing a BAD platform’s capability to actively manage and efficiently process soaring rates of incoming data and distribute notifications to larger numbers of subscribers. 

Abstract Permafrost thaw alters groundwater flow, river hydrology, stream‐catchment interactions, and the availability of carbon and nutrients in headwater streams. The impact of permafrost on watershed hydrology and biogeochemistry of headwater streams has been demonstrated, but there is little understanding of how permafrost influences fish in these ecosystems. We examined relations among permafrost characteristics, the resulting changes in water temperature, stream hydrology (e.g., discharge flashiness), and macroinvertebrates, with the abundance, biomass, and energy density of juvenile Dolly Varden (Salvelinus malma) and Arctic Grayling (Thymallus arcticus) across 10 headwater streams in northwestern Alaska. Macroinvertebrate density was driven by concentrations of dissolved carbon and nutrients supporting stream food webs. Dolly Varden abundance was primarily related to water temperature with fewer fish in warmer streams, whereas Dolly Varden energy density decreased with the flashiness of the headwater streams. Dolly Varden biomass was related to both temperature and bottom‐up food web effects. The energy density of Arctic Grayling decreased with warmer temperatures and discharge flashiness. These relations demonstrate the importance of terrestrial–aquatic connections in permafrost landscapes and indicate the complexity of landscape effects on fish. Because permafrost thaw is one of the most impactful changes occurring as the Arctic warms, an improved understanding of how stream temperature, hydrology, and bottom‐up food web processes influence fish populations can aid forecasting of future conditions across the Arctic. 

Efficient multi-join query processing is crucial but remains a complex, ongoing challenge for high-performance data management systems (DBMSs). This paper studies the impact of different memory distribution techniques among join operators on different classes of multi-join query plans under different assumptions regarding memory availability and storage devices such as HDD and SSD on Amazon Web Services (AWS). We re-evaluate the results of one of the early impactful studies from the 1990s that was originally done using a simulator for the Gamma database system. The main goal of our study is to scientifically re-evaluate and build upon previous studies whose results have become the basis for the design of past and modern database systems, and to provide a solid foundation for understanding basic "join physics", which is essential for eventually designing a resource-based scheduler for concurrent complex workloads. 

The increasing prevalence of large graph data has produced a variety of research and applications tailored toward graph data management. Users aiming to perform graph analytics will typically start by importing existing data into a separate graph-purposed storage engine. The cost of maintaining a separate system (e.g., the data copy, the associated queries, etc …) just for graph analytics may be prohibitive for users with Big Data. In this paper, we introduce Graphix and show how it enables property graph views of existing document data in AsterixDB, a Big Data management system boasting a partitioned-parallel query execution engine. We explain a) the graph view user model of Graphix, b) gSQL++ , a novel query language extension for synergistic document-based navigational pattern matching, and c) how edge hops are evaluated in a parallel fashion. We then compare queries authored in gSQL++ against versions in other leading query languages. Finally, we evaluate our approach against a leading native graph database, Neo4j, and show that Graphix is appropriate for operational and analytical workloads, especially at scale. 

Abstract Climate change in the Arctic is altering watershed hydrologic processes and biogeochemistry. Here, we present an emergent threat to Arctic watersheds based on observations from 75 streams in Alaska’s Brooks Range that recently turned orange, reflecting increased loading of iron and toxic metals. Using remote sensing, we constrain the timing of stream discoloration to the last 10 years, a period of rapid warming and snowfall, suggesting impairment is likely due to permafrost thaw. Thawing permafrost can foster chemical weathering of minerals, microbial reduction of soil iron, and groundwater transport of metals to streams. Compared to clear reference streams, orange streams have lower pH, higher turbidity, and higher sulfate, iron, and trace metal concentrations, supporting sulfide mineral weathering as a primary mobilization process. Stream discoloration was associated with dramatic declines in macroinvertebrate diversity and fish abundance. These findings have considerable implications for drinking water supplies and subsistence fisheries in rural Alaska. 

Join operations are crucial in data analysis, but can suffer inefficiency with large datasets and complex non- equality-based conditions. Optimized join algorithms have gained traction in database research to address these challenges. One popular choice for implementing join algorithms is distributed data processing frameworks, e.g., Hadoop and Spark, but each implementation is highly tailored for specific query types. As a result, they do not address join queries that involve diverse and complex conditions since they are not integrated into a holistic query optimization engine like in DBMSs. On the other hand, implementing new join algorithms on a DBMS from scratch requires substantial effort and expertise. This paper introduces FUDJ, Flexible User-defined Distributed Joins, a framework for complex distributed join algorithms. The key idea of FUDJ is to allow developers to realize new distributed join algorithms into the database without delving into the database internals. As shown, an algorithm implemented in FUDJ is up to an order of magnitude faster than existing user-defined implementations with an order of magnitude fewer lines of code. 

Join operations are crucial in data analysis, but can suffer inefficiency with large datasets and complex non-equality-based conditions. Optimized join algorithms have gained traction in database research to address these challenges. One popular choice for implementing join algorithms is distributed data processing frameworks, e.g., Hadoop and Spark, but each implementation is highly tailored for specific query types. As a result, they do not address join queries that involve diverse and complex conditions since they are not integrated into a holistic query optimization engine like in DBMSs. On the other hand, implementing new join algorithms on a DBMS from scratch requires substantial effort and expertise. This paper introduces FUDJ, Flexible User-defined Distributed Joins, a framework for complex distributed join algorithms. The key idea of FUDJ is to allow developers to realize new distributed join algorithms into the database without delving into the database internals. As shown, an algorithm implemented in FUDJ is up to an order of magnitude faster than existing user-defined implementations with an order of magnitude fewer lines of code.