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At least 163 records · Page 9Linked to original sources

Caddisfly dives for oviposition: Record-shattering depths and poor life choices in a dammed river system

Oviposition is a critical step in the life cycles of aquatic insects. Adult caddisflies exhibit a variety of oviposition methods. In some species, females enter freshwaters to oviposit on submerged substrates. Here, we compile information on North American caddisflies that are known to dive and swim to oviposit and have sexually dimorphic leg characteristics that may be adaptations for swimming, diving, or both. We also report unexpected underwater captures of adult females of 3 caddisfly species in Willamette Basin reservoirs in Oregon, USA, including the deepest dive depths ever recorded for adult female caddisflies. From these captures, we note sexually dimorphic leg widening in the species Hydropsyche centra Ross, 1938 for the first time, confirm widened mesothoracic leg segments of Hydropsyche occidentalis Banks, 1900 adult females, and note fringes of long hairs on meso- and metathoracic tibiae and basal tarsal segments of Hydroptila argosa Ross, 1938 females. We also note fringes of long hairs on the meso- and metathoracic legs of Hydroptila ajax Ross, 1938 females from the banks of the Willamette River. The presumed oviposition attempts of caddisflies underwater in large, deep reservoirs suggest that these caddisflies may misinterpret oviposition cues in altered habitats and waste reproductive efforts. Greater understanding of caddisfly oviposition methods and abilities may be important for long-term conservation and restoration efforts supporting biodiversity in freshwater habitats.

Oregon

Two-dimensional habitat modeling in the Yellowstone/Upper Missouri River system

This study is being conducted to provide the aquatic biology component of a decision support system being developed by the U.S. Bureau of Reclamation. In an attempt to capture the habitat needs of Great Plains fish communities we are looking beyond previous habitat modeling methods. Traditional habitat modeling approaches have relied on one-dimensional hydraulic models and lumped compositional habitat metrics to describe aquatic habitat. A broader range of habitat descriptors is available when both composition and configuration of habitats is considered. Habitat metrics that consider both composition and configuration can be adapted from terrestrial biology. These metrics are most conveniently accessed with spatially explicit descriptors of the physical variables driving habitat composition. Two-dimensional hydrodynamic models have advanced to the point that they may provide the spatially explicit description of physical parameters needed to address this problem. This paper reports progress to date on applying two-dimensional hydraulic and habitat models on the Yellowstone and Missouri Rivers and uses examples from the Yellowstone River to illustrate the configurational metrics as a new tool for assessing riverine habitats.

Conference Paper

Upstream migration and spawning success of Chinook salmon in a highly developed, seasonally warm river system

This review summarizes what is known about the influence of water temperature and velocity on the migration and spawning success of an inland population of Chinook salmon Oncorhynchus tshawytscha . Models are then developed and used to illustrate how migration and spawning success might change if temperatures and velocities increase under a future climate. The illustration shows the potential for moderate increases in temperature and velocity to reduce homing and increase energy expenditure. Those two outcomes would reduce the abundance, productivity, and diversity of the population studied. Under the future scenario illustrated, it would become difficult for fish management actions alone to recover conservation-reliant populations of inland Chinook salmon.

Columbia River basin

Physics-guided machine learning from simulation data: An application in modeling lake and river systems

This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, these models often produce biased simulations due to inaccurate parameterizations or approximations used to represent the true physics. In this paper, we aim to build a new data-driven framework to monitor dynamical systems by extracting general scientific knowledge embodied in simulation data generated by the physics-based model. To handle the bias in simulation data caused by imperfect parameterization, we propose to extract general physical relations jointly from multiple sets of simulations generated by a physics-based model under different physical parameters. In particular, we develop a spatio-temporal network architecture that uses its gating variables to capture the variation of physical parameters. We initialize this model using a pre-training strategy that helps discover common physical patterns shared by different sets of simulation data. Then we fine-tune it using limited observation data via a contrastive learning process. By leveraging the complementary strength of machine learning and domain knowledge, our method has been shown to produce accurate predictions, use less training samples and generalize to out-of-sample scenarios. We further show that the method can provide insights about the variation of physical parameters over space and time in two domain applications: predicting temperature in streams and predicting temperature in lakes.

Conference Paper

Seasonal meso- and microhabitat selection by the northern snakehead ( Channa argus ) in the Potomac river system

The northern snakehead ( Channa argus ) is a large piscivorous fish that is invasive in eastern Europe and has recently been introduced in North America. We examined the seasonal habitat selection at meso- and microhabitat scales using radio-telemetry to increase understanding of the ecology of this species, which will help to inform management decisions. After the spawning season (postspawn season, September–November), northern snakeheads preferred offshore Eurasian water-milfoil ( Myriophyllum spicatum ) beds with shallow water (∼115 cm) and soft substrate. In the winter (November–April), these fish moved to deeper water (∼135 cm) with warmer temperatures, but habitat selection was weak at both scales. Northern snakeheads returned to shallower water (∼95 cm) in the prespawn season (April–June) and used milfoil and other cover. Habitat selection was the strongest at both meso- and microhabitat scales during the spawning season (June–September), when fish preferred macrophytes and cover in shallow water (∼88 cm). Our results help to identify habitats at the risk of invasion by northern snakeheads. We suggest that control efforts and future research focus on shallow waters, and take into consideration the seasonal habitat preferences.

Maryland, Virginia

Postglacial lahars and potential hazards in the White Salmon River system on the southwest flank of Mount Adams, Washington

Mount Adams, a stratovolcano in southwestern Washington State, formed during Pleistocene time and reached roughly its present size before the end of the Fraser glaciation, which occurred between 25,000 and 12,000 years ago. Since the last glaciation, Mount Adams has erupted at least nine times, producing eight peripheral lava flows and a cinder cone at the summit but no pyroclastic flows. No stratigraphic evidence was found for the occurrence of lahars concurrently with eruptions; in fact, at least one lahar and one debris avalanche occurred during apparently dormant intervals.

Bulletin