The unprecedented 2018 summit collapse at Kīlauea and subsequent 2020–2021 eruption within the newly deepened Halema‘uma‘u Crater provide an unparalleled opportunity to understand how collapse events impact a volcano’s shallow reservoir system and magmatic processes. Glass and olivine from tephra ejected by lava fountains and several explosions on 20–21 December, within a few hours of the 2020 eruption onset, yield information about pre-eruptive magma storage and transport. The olivine population is bimodal with zoned and non-zoned phenocrysts. Normally zoned olivine crystals with core compositions around Fo88 have 30–50 μm wide Fo82 overgrowth rims that have skeletal textures. Two skeletal xenocrysts (cores Fo74 and Fo81) are also reversely zoned up to Fo82 rims. The crystal cores have trace element records of at least two cycles of growth and dissolution prior to the formation of the overgrowth rims. These rims and a separate population of non-zoned Fo82 crystals are in Fe–Mg equilibrium with their host glass (average MgO of 6.9 ± 0.4 wt% (1σ), Mg# [Mg / (Mg + Fe2+)] of 0.57), which suggests undercooling after intrusion of magma to shallow levels in the plumbing system. In the years prior to the 2018 collapse, non-zoned Fo81 olivine and slightly lower MgO glasses (6.8 wt%) reflected continuous mixing and compositional buffering of magma recharge into several km3 of stored magma in the Halema‘uma‘u reservoir (1–2 km depth). The 2020 olivine crystals lack evidence of an intrusion mixing with resident shallow magma, indicating that magma transport occurred in a disrupted system, and/or it may not have significantly mixed with stored magma remaining in the Halema‘uma‘u reservoir after the events of 2018. Diffusion modeling of Fe–Mg exchange in the zoned 2020 olivine crystals yield timescales that are mostly 60 days prior to the eruption or less, which aligns well with 22–24 October 2020 and subsequent seismic swarms at Nāmakani Paio ~ 5 km west of Kīlauea’s summit caldera. This correlation indicates that magma intruding beneath the summit (volume accommodation, recorded by the olivine crystals) was expressed by tectonic earthquakes along the Ka‘ōiki fault zone (stress accommodation). The absence of precursory SO2 within minutes prior to eruption also indicates that the 2020 December magma may have risen from 1 to 2 km depth to the surface in as little as 10 min.
The deep sea is the largest biome on earth, but one of the least studied despite its critical role in global carbon cycling and climate buffering. Deep-sea organisms largely rely on particulate organic matter from the surface ocean for energy – these organisms in turn play critical roles in energy transport, transformation, storage, and sequestration of carbon. Within the deep sea, submarine canyons are amongst the most complex and dynamic environments in our oceans, where varied morphology, powerful currents, and variable nutrient conditions influence the distribution of species and transport of organic material throughout the water column and the seafloor. Significant habitat heterogeneity provides ideal substrates for cold-water corals, making submarine canyons of interest to conservation and management. However, how these and other topographic features in the deep ocean influence energy flow and trophic pathways is poorly known. Thus, submarine canyons serve as model systems to track variability in organic material flux and consequential utilization and assimilation by the benthos. In this study, we used an extensive stable isotope dataset to examine food-web structure in Baltimore and Norfolk submarine canyons and compared them to their adjacent slopes located along the U.S. Atlantic margin. Linear models were used to construct geospatially-explicit consumer isoscapes that predicted variation in carbon and nitrogen isotopes across the canyon-slope seascape, providing a predictive map from which to test hypotheses on the distribution and flow of energy resources, relevant to understanding whole community function. Communities were composed of isotopically diverse feeding groups with photosynthetically-derived organic carbon providing the basal food resource. Canyon communities were distinct from the slope, with canyon consumers significantly 13C-depleted, indicating a greater supply and/or utilization of fresh organic matter compared to the slope. Isoscapes for benthic and suspension feeders were distinct, possibly due to the consumption of different quality organic matter sources (fresh = suspension feeders, old = benthic feeders), each with distinct isotope composition. To our knowledge, our modeled isoscapes represent the first spatially extensive isotopic maps of deep-sea consumers, providing insights into regional-scale variation in stable carbon and nitrogen isotopes for different consumer groups. They provide a baseline for tracking climate-change induced fluctuations in the quality and availability of surface primary production and the consequential impact to benthic communities, which play critical roles in carbon cycling in our world’s oceans.
Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c07576","usgsCitation":"Tesoriero, A.J., Wherry, S., Dupuy, D., and Johnson, T., 2024, Predicting redox conditions in groundwater at a national scale using random forest classification: Environmental Science and Technology, v. 58, no. 11, p. 5079-5092, https://doi.org/10.1021/acs.est.3c07576.","productDescription":"14 p.","startPage":"5079","endPage":"5092","ipdsId":"IP-154897","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":440191,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c07576","text":"Publisher Index Page"},{"id":435024,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DVPJIX","text":"USGS data release","linkHelpText":"Input and results from a random forest 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]\n}","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dupuy, Danielle 0000-0001-9007-641X","orcid":"https://orcid.org/0000-0001-9007-641X","contributorId":222277,"corporation":false,"usgs":true,"family":"Dupuy","given":"Danielle","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896394,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254184,"text":"70254184 - 2024 - Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","interactions":[],"lastModifiedDate":"2024-05-13T11:57:09.891301","indexId":"70254184","displayToPublicDate":"2024-03-07T06:53:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2321,"text":"Journal of Geophysical Research: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","docAbstract":"Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land-sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF-Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling.
Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature–dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24°C) temperatures, as predicted from an independent mechanistic model. Positive temperature–dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.
Oyster reefs across North America have declined precipitously over the past 140 years. In Washington State, Olympia oyster Ostrea lurida reefs historically provided water filtration and nearshore structural habitat for fishes and invertebrates, but this species is now functionally extinct across its historical range. In place of these naturally occurring reefs, shellfish farms consisting mainly of nonnative Pacific oysters Magallana gigas now occupy patches of nearshore habitat across Washington. These farms modify intertidal substrate by adding structural habitat via suspended oyster grow bags, predator exclusion nets, loose oyster beds, and other shellfish grow-out gear. As interest and investment in shellfish aquaculture have expanded both locally and globally, so has interest in how these farms modify intertidal habitat and whether the complex structure created by the shellfish and shellfish growing gear provides ecosystem services that are comparable to those of unfarmed areas, such as mudflats and eelgrass meadows.
In this study, we sought to quantify how shellfish farms are used as foraging habitat for several common nearshore species of fish and crabs in Puget Sound, Washington. We used direct observations of species-specific behaviors from underwater video to model how habitat type affected observed foraging rates.
We obtained a total of 393 crab observations, 431 demersal fish observations, and 1856 pelagic fish observations across all seven farm sites. Several common species of pelagic fish (e.g., surfperch [Embiotocidae]) used aquaculture-growing gear more frequently than unfarmed areas as foraging habitat, but Metacarcinus spp. crabs displayed higher foraging frequency in unfarmed mudflats. Species groups such as sculpins (Cottidae) and small flatfish (Pleuronectidae) clearly used specific aquaculture-growing gear and mudflats in roughly equal proportion.
Our results indicate that shellfish farms within a larger nearshore habitat mosaic of eelgrass meadows, mudflats, bivalve aquaculture gear, and edge habitat can provide foraging habitat for several species of nearshore fish.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10282","usgsCitation":"Veggerby, K., Scheuerell, M.D., Sanderson, B., Kiffney, P., and Ferriss, B., 2024, Shellfish aquaculture farms as foraging habitat for nearshore fishes and crabs: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 2, e10282, 14 p., https://doi.org/10.1002/mcf2.10282.","productDescription":"e10282, 14 p.","ipdsId":"IP-158997","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":440203,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10282","text":"Publisher Index Page"},{"id":430280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.227401475166,\n 48.993797248377604\n ],\n [\n -123.17273320541344,\n 48.993797248377604\n ],\n [\n -123.17273320541344,\n 46.919549204528664\n ],\n [\n -122.227401475166,\n 46.919549204528664\n ],\n [\n -122.227401475166,\n 48.993797248377604\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Veggerby, Karl","contributorId":338024,"corporation":false,"usgs":false,"family":"Veggerby","given":"Karl","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":903251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanderson, Beth","contributorId":338027,"corporation":false,"usgs":false,"family":"Sanderson","given":"Beth","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kiffney, Peter","contributorId":242881,"corporation":false,"usgs":false,"family":"Kiffney","given":"Peter","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferriss, Bridget","contributorId":338414,"corporation":false,"usgs":false,"family":"Ferriss","given":"Bridget","email":"","affiliations":[{"id":53980,"text":"NMFS","active":true,"usgs":false}],"preferred":false,"id":903255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263414,"text":"70263414 - 2024 - Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence","interactions":[],"lastModifiedDate":"2025-02-10T16:28:46.482152","indexId":"70263414","displayToPublicDate":"2024-03-06T09:21:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence","docAbstract":"The Mendocino triple junction, the intersection of the Pacific, North American, and Gorda plates, activates a collection of disparate faults that reconcile Cascadia subduction with San Andreas transform motion. The December 20, 2022, Mw 6.4 Ferndale, California earthquake occurred within this complex zone as strike-slip faulting within the subducting Gorda slab. Here, we analyze the seismic and geodetic signatures of the mainshock and aftershock sequence to illuminate its role within complex tectonic surroundings. We find aftershocks on varied fault structures within the uppermost Gorda slab, yet seismicity on the subduction interface itself was notably absent. Nevertheless, we identify small but coherent postseismic deformation that is well modeled by aseismic slip on this interface, likely triggered by stresses generated at the updip limit of coseismic rupture. This sequence demonstrates the potential for interactions between intra-slab earthquakes and slip on the subduction megathrust, highlighting the need to consider this and other subduction zones as coupled systems of interacting faults.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adl1226","usgsCitation":"Shelly, D.R., Goldberg, D.E., Materna, K.Z., Skoumal, R.J., Hardebeck, J.L., Yoon, C., Yeck, W.L., and Earle, P.S., 2024, Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence: Science Advances, v. 10, no. 10, eadl1226, 10 p., https://doi.org/10.1126/sciadv.adl1226.","productDescription":"eadl1226, 10 p.","ipdsId":"IP-157945","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":489932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adl1226","text":"Publisher Index Page"},{"id":481873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ferndale","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.32430634942911,\n 40.632387320582296\n ],\n [\n -124.32430634942911,\n 40.53136014005773\n ],\n [\n -124.21016659258673,\n 40.53136014005773\n ],\n [\n -124.21016659258673,\n 40.632387320582296\n ],\n [\n -124.32430634942911,\n 40.632387320582296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":926895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":926896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skoumal, Robert John 0000-0002-6960-481X rskoumal@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-481X","contributorId":299165,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","email":"rskoumal@usgs.gov","middleInitial":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926899,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926900,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":926901,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":926902,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252162,"text":"70252162 - 2024 - When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","interactions":[],"lastModifiedDate":"2024-05-20T15:27:10.978583","indexId":"70252162","displayToPublicDate":"2024-03-06T06:19:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","title":"When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","docAbstract":"In South Florida, the Argentine black and white tegu (Salvator merianae), a large, omnivorous lizard, has become a recent threat to the Everglades ecosystem. The increase in tegu observations, especially near ecologically sensitive areas such as Everglades National Park, makes informed management critical to contain the tegu population. Using Very High Frequency (VHF) and Global Positioning System (GPS) telemetry, we tracked 24 tegus in the Southern Glades Wildlife Management Area and Redland Agricultural Area in Homestead, Florida from March 2016 to November 2018 and March 2021 to January 2022. We used generalized additive models to determine factors that drive tegu movement to inform managers when traps and surveillance plots are most likely to be effective. Our top model included temporal (time of day and time of year), environmental (air temperature, relative humidity, rain, and wind speed from closest weather station), and biological (sex) variables. This model explained 34.6% of the deviance in tegu minimum rate of movement. We determined that tegus were most active between mid-March and mid-April, and tegu minimum rate of movement positively correlates with air temperature (i.e., highest activity during the hottest part of the day in the early afternoon). We observed a slight positive trend between tegu minimum rate of movement and relative humidity, and no clear trend between rate of movement and either rainfall or wind. Our results can inform natural resource management actions to target tegu removal and surveillance during high activity periods to maximize resource use.
Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people’s protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and people’s behavioral responses during the M7.1 2018 Anchorage earthquake. By using the fully annotated video data, we applied XGBoost, a widely-used machine learning method, to model and forecast people’s protective actions (e.g., drop and cover, hold on, and evacuate) during the earthquake. Then, explainable machine learning techniques were used to reveal the complex, nonlinear relationships between different factors and people’s choices of protective actions. Modeling results confirm that social and environmental cues played critical roles in affecting the probability of different protective actions. Certain factors, such as the earthquake shaking intensity and number of people shown in the environment, displayed evident nonlinear relationships with the probability of choosing to evacuate. These findings can help emergency managers and policymakers design more effective protective action recommendations during earthquakes.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-55584-7","usgsCitation":"Zhang, X., Zhao, X., Baldwin, D., McBride, S., Bellizzi, J., Cochran, E.S., Luco, N., Wood, M., and Cova, T.J., 2024, Modeling protective action decision-making in earthquakes by using explainable machine learning and video data: Scientific Reports, v. 14, 5480, 13 p., https://doi.org/10.1038/s41598-024-55584-7.","productDescription":"5480, 13 p.","ipdsId":"IP-162087","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-55584-7","text":"Publisher Index Page"},{"id":432865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Xiaojian","contributorId":214967,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaojian","email":"","affiliations":[{"id":39141,"text":"Department of Basic Science, College of Veterinary Medicine, Mississippi State University, 9 Mississippi, United States;","active":true,"usgs":false}],"preferred":false,"id":910521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Xilei","contributorId":342942,"corporation":false,"usgs":false,"family":"Zhao","given":"Xilei","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":910522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Dare","contributorId":269660,"corporation":false,"usgs":false,"family":"Baldwin","given":"Dare","email":"","affiliations":[],"preferred":false,"id":910523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bellizzi, Josephine","contributorId":342943,"corporation":false,"usgs":false,"family":"Bellizzi","given":"Josephine","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":910525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luco, Nicolas 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":140191,"corporation":false,"usgs":true,"family":"Luco","given":"Nicolas","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":910527,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Matthew","contributorId":342944,"corporation":false,"usgs":false,"family":"Wood","given":"Matthew","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910528,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cova, Thomas J.","contributorId":342946,"corporation":false,"usgs":false,"family":"Cova","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910529,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70273191,"text":"70273191 - 2024 - California community Earth Models for Seismic Hazard Assessments workshop report","interactions":[],"lastModifiedDate":"2025-12-18T15:39:21.401578","indexId":"70273191","displayToPublicDate":"2024-03-05T09:33:06","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"California community Earth Models for Seismic Hazard Assessments workshop report","docAbstract":"The California Community Earth Models for Seismic Hazard Assessments Workshop (https://www.scec.org/workshops/2024/california-community-models) was held online March 4–5, 2024, with more than 200 participants over the two days. In this report, we provide a summary of the key points from the presentations and discussions. We highlight three use cases that drive the development of community Earth models, present an inventory of existing community Earth models in California, summarize a few techniques for integrating and merging models, discuss potential connections with the Cascadia Region Earthquake Science Center, and discuss what “community” means in community Earth models.","language":"English","publisher":"Statewide California Earthquake Center","usgsCitation":"Aagaard, B.T., Marshall, S., Minson, S.E., Boyd, D., Denolle, M.A., Fielding, E.J., Gabriel, A., Goulet, C.A., Graymer, R., Hardebeck, J.L., Hatem, A.E., Hirakawa, E.T., Huynh, T., Hwang, L., Luttrell, K., Materna, K.Z., Montesi, L., Oskin, M., Rodgers, A., Pitarka, A., and Zachariasen, J., 2024, California community Earth Models for Seismic Hazard Assessments workshop report, 67 p.","productDescription":"67 p.","ipdsId":"IP-173281","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":497669,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":497662,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.scec.org/workshops/2024/california-community-models"}],"country":"United 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Washington","active":true,"usgs":false}],"preferred":false,"id":952658,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":952659,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gabriel, Alice-Agnes","contributorId":204611,"corporation":false,"usgs":false,"family":"Gabriel","given":"Alice-Agnes","email":"","affiliations":[{"id":36958,"text":"LMU Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":952660,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goulet, Christine A 0000-0002-7643-357X","orcid":"https://orcid.org/0000-0002-7643-357X","contributorId":336587,"corporation":false,"usgs":true,"family":"Goulet","given":"Christine","email":"","middleInitial":"A","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":952661,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graymer, Russell 0000-0003-4910-5682","orcid":"https://orcid.org/0000-0003-4910-5682","contributorId":207816,"corporation":false,"usgs":true,"family":"Graymer","given":"Russell","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":952662,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":952663,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":952664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":952665,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Huynh, Tran","contributorId":364458,"corporation":false,"usgs":false,"family":"Huynh","given":"Tran","affiliations":[{"id":86821,"text":"Statewide California Earthquake Center","active":true,"usgs":false}],"preferred":false,"id":952666,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hwang, Lorraine","contributorId":269957,"corporation":false,"usgs":false,"family":"Hwang","given":"Lorraine","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":952667,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Luttrell, Karen 0000-0003-1405-1207","orcid":"https://orcid.org/0000-0003-1405-1207","contributorId":254967,"corporation":false,"usgs":false,"family":"Luttrell","given":"Karen","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":952668,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":952669,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Montesi, Laurent","contributorId":237939,"corporation":false,"usgs":false,"family":"Montesi","given":"Laurent","email":"","affiliations":[{"id":38092,"text":"UMD","active":true,"usgs":false}],"preferred":false,"id":952670,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Oskin, Michael","contributorId":140301,"corporation":false,"usgs":false,"family":"Oskin","given":"Michael","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":952671,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Rodgers, Arthur","contributorId":197124,"corporation":false,"usgs":false,"family":"Rodgers","given":"Arthur","affiliations":[],"preferred":false,"id":952672,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Pitarka, Arben","contributorId":184062,"corporation":false,"usgs":false,"family":"Pitarka","given":"Arben","email":"","affiliations":[],"preferred":false,"id":952673,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Zachariasen, Judy","contributorId":295384,"corporation":false,"usgs":false,"family":"Zachariasen","given":"Judy","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":952674,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70252474,"text":"70252474 - 2024 - Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA","interactions":[],"lastModifiedDate":"2024-03-26T12:14:35.348075","indexId":"70252474","displayToPublicDate":"2024-03-05T07:11:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA","docAbstract":"South Fork Quantico Creek (SFQ; 19.8 square kilometre (km2), forested) and Fourmile Run (4MR; 32.4-km2, urban) are small watersheds in northern Virginia, United States. Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation-adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long-term trend in annual peak flow was non-linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation-adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area-normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.
Both fine- and coarse-scale physicochemical conditions affect the quantity and quality of nursery habitats within riverine ecosystems. Nursery habitats in large, braided, and sandbed streams such as the lower Red River of Oklahoma, Texas, and Arkansas are not well described and likely vary among species. Identification of nursery habitats is important for developing proper conservation and management actions. We used an occupancy model framework to determine how hierarchical habitat factors related to the occupancy of 38 juvenile fish species. Our findings indicate that large river nursery habitats can generally be defined by reaches with off-channel slackwater habitat, having deep pools but shallow thalweg depths, typically located further away from dams, and with low percentages of limestone lithology. Species within the same genera often exhibited variable relationships with river slope, amount of large woody debris, channel shape, discharge, and position of reaches within the stream network. Our results indicate important species-specific relationships that define nursery habitats, indicating an important context dependency of nursery habitats even within fishes that are taxonomically similar. If the goal is to improve recruitment by native fishes, then consideration of the important species-specific differences would be beneficial if improvements are made to nursery habitats. Moreover, careful consideration of the effects of dam operations will help maintain proper connectivity to off-channel habitats important in downriver portions of the river network.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4259","usgsCitation":"Ramsey, P.Q., Dattilo, J., DeVries, D., and Brewer, S.K., 2024, Evaluating the context dependency of large river nursery habitats for fishes in the lower Red River catchment: River Research and Applications, v. 40, no. 5, p. 761-779, https://doi.org/10.1002/rra.4259.","productDescription":"19 p.","startPage":"761","endPage":"779","ipdsId":"IP-156597","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433069,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Oklahoma, Texas","otherGeospatial":"Red River catchment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.96811702095188,\n 34.17992556415783\n ],\n [\n -97.00241373400291,\n 33.62977289441275\n ],\n [\n -96.53385971970476,\n 33.38517573322382\n ],\n [\n -94.98278492229939,\n 33.31193918856651\n ],\n [\n -94.251085881531,\n 32.86168102177392\n ],\n [\n -93.15721184350213,\n 31.13251420455059\n ],\n [\n -92.10891096481797,\n 30.704108186888107\n ],\n [\n -90.99949105200645,\n 29.934066472273642\n ],\n [\n -90.36282805811372,\n 29.943977336999993\n ],\n [\n -90.36282805811372,\n 30.444831098333296\n ],\n [\n -91.41116362773315,\n 31.184598481944448\n ],\n [\n -92.6843235859769,\n 31.89282317843616\n ],\n [\n -93.15314867786464,\n 32.51528012073811\n ],\n [\n -93.46572663313837,\n 33.905619467393606\n ],\n [\n -95.11183304728418,\n 34.211830972816045\n ],\n [\n -96.63935240433628,\n 34.435111093305494\n ],\n [\n -96.96811702095188,\n 34.17992556415783\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramsey, Paul Q.","contributorId":341174,"corporation":false,"usgs":false,"family":"Ramsey","given":"Paul","email":"","middleInitial":"Q.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dattilo, John","contributorId":341175,"corporation":false,"usgs":false,"family":"Dattilo","given":"John","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeVries, Dennis","contributorId":341176,"corporation":false,"usgs":false,"family":"DeVries","given":"Dennis","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908045,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272817,"text":"70272817 - 2024 - Prefabricated Zero Ascend Omnispecies (ZAO) modular fish passage modules using advanced manufacturing techniques","interactions":[],"lastModifiedDate":"2025-12-10T16:04:04.409653","indexId":"70272817","displayToPublicDate":"2024-03-04T09:56:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":23097,"text":"Final Technical Report","active":true,"publicationSubtype":{"id":3}},"title":"Prefabricated Zero Ascend Omnispecies (ZAO) modular fish passage modules using advanced manufacturing techniques","docAbstract":"The Zero Ascend Omnispecies (ZAO) fish passage attraction module “ZAO Attractor” is a novel system designed to facilitate upstream passage for a broad spectrum of fish species, with a particular focus on American shad and river herring (alewife and/or blueback herring). Combining the use of prefabricated, modular components with advanced manufacturing, the ZAO aims to provide a cost-effective and scalable solution for fish passage attractions across various hydropower sites. The system features a surface level entry, so fish do not need to climb. In addition, it is designed to be adjusted and repositioned to create the most effective attraction flows by managing the outflows from hydropower turbines.
Budget Period 1 (BP1) focused on the design and modeling of the ZAO Attractor and demonstrated the engineering feasibility of a modular, prefabricated, hydrodynamically efficient structure with surface level entry and passage. During BP1, the hypothesis was that certain structural adaptations, such as the addition of long fins, could direct turbine outflows to create a desirable downstream flow. Simulations, however, were inconclusive, revealing the complex nature of fish preferences for hydraulic conditions. It was discovered that there were limited published studies on the direct correlation between quantified flow patterns and fish attraction, emphasizing the need for controlled studies to observe and measure these effects.
In Budget Period 2 (BP2), controlled studies with live fish were prioritized to address these complexities. A partially scaled ZAO Attractor was evaluated in laboratory conditions using native river water to assess its capability to attract and guide fish. Despite challenges with flume flow conditions and the impact of flume dimensions on flow directionality and velocity, the studies provided insights into fish behavior and preferences, particularly the requirement for a consistent flow stream for migration cues. Adjustments to the fin structure demonstrated potential effectiveness in guiding fish toward the module. However, the hydraulic conditions within the flume environment did not accurately reflect typical in-river scenarios, therefore the results were inconclusive with respect to the optimal system configuration for in-river use.
In the future, conducting comprehensive field studies can enhance understanding of the interactions between fish behavior and flow conditions created by the ZAO Attractor. Additionally, a design review is advised, particularly regarding the load scenarios throughout the seasons and positioning of the ZAO Attractor. These studies should continue to refine the design to achieve its intended benefits: economical and effective fish attraction and passage that can be adapted to specific site conditions while allowing for continued energy generation at sites up for re-licensure and adding hydropower generation to previously untapped resources in non-powered dams and new stream reaches.
In conclusion, the ZAO fish passage attraction module presents a promising approach to fish passage at hydropower stations, with the potential to balance environmental concerns with renewable energy development. However, further field validation and optimization are necessary to ensure its effectiveness and practicality in real-world applications.
","language":"English","publisher":"Littoral Power Systems, Inc.","doi":"10.2172/3001800","usgsCitation":"Leighton, K., Bryan, J., Mulligan, K., Stewart, K., Jacobson, P., Duquette, D., and Challa, R., 2024, Prefabricated Zero Ascend Omnispecies (ZAO) modular fish passage modules using advanced manufacturing techniques: Final Technical Report, 187 p., https://doi.org/10.2172/3001800.","productDescription":"187 p.","ipdsId":"IP-165273","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":497302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Leighton, Katherine","contributorId":363650,"corporation":false,"usgs":false,"family":"Leighton","given":"Katherine","affiliations":[],"preferred":false,"id":951898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bryan, Janine","contributorId":245811,"corporation":false,"usgs":false,"family":"Bryan","given":"Janine","email":"","affiliations":[{"id":49332,"text":"Whooshh Innovations","active":true,"usgs":false}],"preferred":false,"id":951871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mulligan, Kevin B. 0000-0002-3534-4239 kmulligan@usgs.gov","orcid":"https://orcid.org/0000-0002-3534-4239","contributorId":177024,"corporation":false,"usgs":true,"family":"Mulligan","given":"Kevin","email":"kmulligan@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":951872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Kevin G.","contributorId":352583,"corporation":false,"usgs":false,"family":"Stewart","given":"Kevin G.","affiliations":[{"id":84275,"text":"University of North Carolina-Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":951873,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jacobson, Paul","contributorId":363601,"corporation":false,"usgs":false,"family":"Jacobson","given":"Paul","affiliations":[{"id":80850,"text":"Electric Power Research Institute","active":true,"usgs":false}],"preferred":false,"id":951874,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duquette, David","contributorId":363651,"corporation":false,"usgs":false,"family":"Duquette","given":"David","affiliations":[],"preferred":false,"id":951899,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Challa, Ravi","contributorId":363603,"corporation":false,"usgs":false,"family":"Challa","given":"Ravi","affiliations":[{"id":86732,"text":"Littoral Power Systems, Inc.,","active":true,"usgs":false}],"preferred":false,"id":951875,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251818,"text":"sir20245002 - 2024 - Streamflow, base flow, and precipitation trends and simulated effects of Rush Springs aquifer groundwater withdrawals on base flows upgradient from Fort Cobb Reservoir, western Oklahoma","interactions":[],"lastModifiedDate":"2026-01-30T20:06:15.927647","indexId":"sir20245002","displayToPublicDate":"2024-03-04T08:04:05","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5002","displayTitle":"Streamflow, Base Flow, and Precipitation Trends and Simulated Effects of Rush Springs Aquifer Groundwater Withdrawals on Base Flows Upgradient From Fort Cobb Reservoir, Western Oklahoma","title":"Streamflow, base flow, and precipitation trends and simulated effects of Rush Springs aquifer groundwater withdrawals on base flows upgradient from Fort Cobb Reservoir, western Oklahoma","docAbstract":"To better understand the relation between groundwater use in the Rush Springs aquifer and inflows to the Fort Cobb Reservoir, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, used a previously published numerical groundwater-flow model and historical streamflow records to evaluate four scenarios to investigate how changing groundwater withdrawals could affect base flows in streams that flow into Fort Cobb Reservoir. These scenarios consisted of observing simulated base-flow response by (1) scaling the 20-year equal-proportionate-share groundwater-withdrawal rate by various percentages over a 50-year period; (2) scaling the historical groundwater-withdrawal rates by various percentages across the entire Rush Springs aquifer; (3) scaling the historical groundwater-withdrawal rates within various subareas (zones) of the Fort Cobb Reservoir surface watershed; and (4) simulating a base-flow-depletion scenario. Cobb, Lake, and Willow Creeks are the major streams upgradient from the Fort Cobb Reservoir (listed from highest to lowest mean annual base flow). The results of scenarios 1 and 2 indicated that Willow Creek is the most susceptible to drying, but Cobb Creek was the most likely to have reduced base flow. Scenarios 3 and 4 indicated that groundwater withdrawals affect Cobb Creek base flows over a broader watershed area compared to Lake and Willow Creeks. In scenario 4, Cobb Creek base-flow depletion was higher across a larger area than Lake Creek and Willow Creek. Groundwater withdrawals in the Cobb Creek watershed tended to affect total inflows into Fort Cobb Reservoir more than other areas in the extent of the Rush Springs aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.
We evaluated the Virgin, Verde, Salt, and Gila Rivers in the Lower Colorado River Basin. The watersheds have extents in Arizona, Utah, New Mexico, US and Sonora, MX.
We calculated trends in total dissolved solids (TDS) concentrations and fluxes with the Weighted Regressions on Time, Discharge and Season model. The modeling framework leverages daily streamflow and discrete water quality observations at specific monitoring sites. We evaluated trends for a common period (1976–2008) and the whole period of record at each monitoring site in terms of climate and anthropogenic controls.
Three rivers had persistent TDS concentrations exceeding the EPA secondary drinking water standard. All were associated with a geologic source of TDS. We observed increases and decreases in TDS concentrations at our monitoring sites, contrasting with global freshwater salinization and declining TDS concentrations in the Upper Colorado River Basin (UCRB). We attributed concentration variability to wintertime hydroclimatic forcing, with secondary influences of human water use. Reservoirs may decrease TDS concentrations by 50%. Efficiency improvements in irrigation and mining water uses may decrease TDS concentrations, while municipal growth increases TDS concentrations. We observed TDS flux declines at most monitoring sites. We attributed up to 85% of the TDS flux trend to changes in streamflow arising from drought and groundwater use. This study informs salinity dynamics in arid and aridifying locations, including the UCRB.
Wildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre-epidemic, early outbreak, mid-epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.
Fluvial landscape analysis is an essential part of geomorphology, hydrology, ecology, and cartography. It is traditionally focused on the transition between hillslopes and channel domain, in which the network drainage is represented by static flow lines. However, the natural fluctuations of the processes occurring in the watershed induce lateral and longitudinal expansions and contractions in the drainage patterns and variations of stream surface area. These dynamics can be better understood by introducing a two-dimensional (2D) view of catchment hydrography, in which river width and floodplain are included in the analysis.
The novelty introduced in this work is the development of a hydrodynamic hierarchical framework (HHF) to analyse the transitions among geomorphic and hydrographic features of the fluvial landscape, distinguishing hillslope, unchanneled valleys, floodplains, and single/multithreads channels. HHF is based on the estimation of nested inundation pattern domains (IPDs) from digital elevation models and 2D hydrodynamic modeling. IPDs are defined by scaling laws that characterize log–log relations between watershed drainage density and unit discharge thresholds extracted from a 2D direct rainfall method (DRM) under steady state solutions.
The physical significance of the IPDs is analysed within the context of both the physiographic features of the fluvial landscape and the rainfall rates employed as input for the modeling approach. Initially, the spatial heterogeneity of the IPDs is used to derive stream width metrics as a function of the rainfall rate. Then, a spatial index, representative of the IPDs' heterogeneity, is introduced as a measure of the susceptibility of the drainage network surface area to expansion and contraction. Finally, the consistency of the results is assessed in comparison to another hydrodynamic-based method for fluvial landscape analysis recently proposed in the literature.
The proposed approach is analysed using challenging mountain and low-relief environments, characterized by multithread channels, meander cut-offs, oxbow lakes, and extreme landscapes that feature glacial outwash, permafrost, and peatlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.130728","usgsCitation":"Costabile, P., Costanzo, C., Lombardo, M., Shavers, E.J., and Stanislawski, L., 2024, Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks: Journal of Hydrology, v. 632, 130728, 24, https://doi.org/10.1016/j.jhydrol.2024.130728.","productDescription":"130728, 24","ipdsId":"IP-155522","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":489912,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.130728","text":"Publisher Index Page"},{"id":481446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Bari","active":true,"usgs":false}],"preferred":false,"id":925352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":925353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":925354,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252589,"text":"70252589 - 2024 - Seasonal and decadal subsurface thaw dynamics of an Aufeis feature investigated through numerical simulations","interactions":[],"lastModifiedDate":"2024-03-29T11:55:55.292733","indexId":"70252589","displayToPublicDate":"2024-03-03T06:54:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and decadal subsurface thaw dynamics of an Aufeis feature investigated through numerical simulations","docAbstract":"Aufeis (also known as icings) are large sheet-like masses of layered ice that form in river channels in arctic environments in the winter as groundwater discharges to the land surface and subsequently freezes. Aufeis are important sources of water for Arctic river ecosystems, bolstering late summer river discharge and providing habitat for caribou escaping insect harassment. The aim of this research is to use numerical simulations to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis. We used a conceptual model based on geophysical data from the Kuparuk aufeis field on the North Slope of Alaska to develop a two-dimensional heterogeneous vertical profile model of groundwater flow, heat transport, and freeze/thaw dynamics. Modelling results showed that groundwater can flow to the land surface through subvertical high permeability pathways during winter months when the lower permeability soils near the land surface are frozen. The groundwater discharge can freeze on the surface, contributing to aufeis formation throughout the winter. We performed sensitivity analyses on subsurface properties and surface temperature and found that aufeis formation is most sensitive to the volume of unfrozen water available in the subsurface and the rate at which the subsurface water travels to the land surface. Although a trend of warming air temperatures will lead to a greater volume of unfrozen subsurface water, the aufeis volume can be reduced under warming conditions if the period of time for which air temperatures are below freezing is reduced.
As part of a multi-year project, funded by the California Energy Commission, annual magnetotelluric (MT) surveys have been collected at The Geysers geothermal field in northern California with the goal of measuring temporal changes within the steam field. The repeat surveys started in 2021 and repeated a 2017 survey (Peacock et al., 2020) with further extension to the southern part of the geothermal field. Temporal variations in the MT transfer functions are observed to be spatially coherent and compartmentalized. Mapping residual phase tensor ellipses demonstrates the direction of maximum change is often aligned with existing fracture orientations. Three dimensional inversion of the MT data, using the inversion results from previous years as the starting model, indicates that the steam reservoir has generally become more resistive over time (~10%), suggesting more steam in the field. A few pockets within the steam field have become more electrically conductive over time and are collocated with injection wells, suggesting either more fluid content in those zones, less steam, or more saline fluids.
","conferenceTitle":"49th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 12-14, 2024","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J.R., Alumbaugh, D., Mitchell, M.A., and Hartline, C., 2024, Summary of annual repeat magnetotelluric surveys of the Geysers geothermal field, 49th Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 12-14, 2024, 6 p.","productDescription":"6 p.","ipdsId":"IP-161861","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501540,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=36417","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Geysers geothermal field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.9167,\n 38.9\n ],\n [\n -122.9167,\n 38.7333\n ],\n [\n -122.667,\n 38.7333\n ],\n [\n -122.667,\n 38.9\n ],\n [\n -122.9167,\n 38.9\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224 jpeacock@usgs.gov","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":4996,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared","email":"jpeacock@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":899089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alumbaugh, David 0000-0002-6975-7197","orcid":"https://orcid.org/0000-0002-6975-7197","contributorId":299109,"corporation":false,"usgs":false,"family":"Alumbaugh","given":"David","email":"","affiliations":[{"id":64775,"text":"Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":899090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":899091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartline, C.","contributorId":335661,"corporation":false,"usgs":false,"family":"Hartline","given":"C.","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":899092,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263100,"text":"70263100 - 2024 - Southern fox squirrel and eastern gray squirrel interactions in a fire-maintained ecosystem","interactions":[],"lastModifiedDate":"2025-01-29T16:39:26.167514","indexId":"70263100","displayToPublicDate":"2024-03-01T10:33:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Southern fox squirrel and eastern gray squirrel interactions in a fire-maintained ecosystem","docAbstract":"Southern fox squirrels (Sciurus niger niger) have been declining due to habitat fragmentation, cover type conversion, and fire suppression in the Southeast. A decrease in growing season burns has led to hardwood encroachment and forest mesophication that benefit the competing eastern gray squirrels (S. carolinensis). In the southern Coastal Plain and Piedmont of Virginia, these pattern raises the question of whether gray squirrels are competitively excluding southern fox squirrels in these altered landscapes. From October 2019 to October 2020, we conducted continual camera trapping for southern fox squirrels and gray squirrels on the Big Woods/Piney Grove Complex (BWPGC) and at Fort Barfoot (FB) in the Coastal Plain and lower Piedmont of Virginia, respectively. Both sites are among the few areas that still contain large, intact pine savanna and mixed-pine hardwood forests in southeastern Virginia. We used two-species occupancy modeling to investigate occupancy estimates of southern fox squirrels and possible competition with gray squirrels, based on detection histories collected from camera traps on BWPGC and FB. We then conducted informed single-species occupancy modeling to estimate the necessary level-of-effort (LOE) required to determine the probable absence of southern fox squirrels at sampling sites in the region. No fox squirrels were observed at FB. Our top, two-species occupancy model showed that gray squirrel occupancy increased with increasing time since last burn. However, southern fox squirrel occupancy, in the absence of gray squirrels, decreased with increasing time since last burn. Gray squirrels typically inhabited hardwood-dominant closed canopy areas whereas southern fox squirrels did so at BWPGC only in the absence of gray squirrels. This suggests that southern fox squirrels are selecting areas on BWPGC based on resource needs and possibly competition with gray squirrels. A single-season occupancy model confirmed that southern fox squirrel occupancy decreased with time since the last burn. Our LOE analysis indicated that seven consecutive days of camera trapping without a detection would provide 90% confidence of the subspecies’ absence in areas burned two or more years prior to sampling. Southern fox squirrels may benefit from increased short-rotation burns to maintain or enhance pine-hardwood savannas and pine-hardwood savanna ecotones in southeastern Virginia.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Guill, M., De La Cruz, J., Puckett, M., Klopfer, S., Martin, B., and Ford, W., 2024, Southern fox squirrel and eastern gray squirrel interactions in a fire-maintained ecosystem: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 11, p. 215-222.","productDescription":"8 p.","startPage":"215","endPage":"222","ipdsId":"IP-142691","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481441,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2024/southern-fox-squirrel-and-eastern-gray-squirrel-interactions-fire-maintained-ecosystem"},{"id":481465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78,\n 37.1\n ],\n [\n -78,\n 36.9\n ],\n [\n -77,\n 36.9\n ],\n [\n -77,\n 37.1\n ],\n [\n -78,\n 37.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Guill, M.H.","contributorId":350233,"corporation":false,"usgs":false,"family":"Guill","given":"M.H.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":925510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":925511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puckett, M.","contributorId":350236,"corporation":false,"usgs":false,"family":"Puckett","given":"M.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":925512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klopfer, S.D.","contributorId":350239,"corporation":false,"usgs":false,"family":"Klopfer","given":"S.D.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":925513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, B.","contributorId":350241,"corporation":false,"usgs":false,"family":"Martin","given":"B.","affiliations":[{"id":83699,"text":"Fort Pickett","active":true,"usgs":false}],"preferred":false,"id":925514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":925515,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257538,"text":"70257538 - 2024 - Seasonal activity patterns of northern long-eared bats on the coastal Mid-Atlantic","interactions":[],"lastModifiedDate":"2024-09-09T11:12:12.04529","indexId":"70257538","displayToPublicDate":"2024-03-01T10:09:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal activity patterns of northern long-eared bats on the coastal Mid-Atlantic","docAbstract":"Conservation of bats declining from white-nose syndrome (WNS) impacts requires an understanding of both temporal and landscape-level habitat relationships. Traditionally, much of the research on bat ecology has focused on behavior of summer maternity colonies within species’ distribution cores, including that of the endangered northern long-eared bat (Myotis septentrionalis). To further our knowledge of this species, we evaluated multi-season activity patterns in eastern North Carolina and Virginia, including areas where populations were recently discovered. We used passive acoustic monitoring to assess relative and probable activity of northern long-eared bats from October 2016 to August 2021. Northern long-eared bat relative activity was greatest in areas containing greater proportions of woody wetlands and upland pine-dominated evergreen forests. However, the likelihood of recording northern long-eared bats was associated with smaller proportions of woody wetlands and open water resources. Furthermore, we observed a higher probability of recording northern long-eared bats during non-winter seasons. Probable activity was greatest at temperatures between 10 and 25 C, potentially highlighting an optimal thermoneutral zone for the species regionally. Relative activity of northern long-eared bats on the Coastal Plain of Virginia and North Carolina was primarily driven by cover features, whereas probable activity was driven by a combination of cover features, seasonality, and temperature. Therefore, acoustical surveys for this species may be most effective when targeting woody wetlands adjacent to upland forests, particularly upland pine-dominated evergreen stands, during moderate temperatures of non-winter seasons (1 April–15 November). Moreover, conservation of a diverse mosaic of woody wetlands juxtaposed by upland forests may promote both roosting and overwintering habitat, thereby enhancing overwintering survival, maternity colony establishment, and ultimately, successful reproduction of northern long-eared bats.
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Resources","active":true,"usgs":false}],"preferred":false,"id":910654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":910655,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264589,"text":"70264589 - 2024 - Surface elevation trends in North Carolina's coastal wetlands","interactions":[],"lastModifiedDate":"2025-03-17T14:31:11.336488","indexId":"70264589","displayToPublicDate":"2024-03-01T09:22:04","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Surface elevation trends in North Carolina's coastal wetlands","docAbstract":"Coastal wetlands exist in the narrow fringe between uplands and open water and consequently, are uniquely vulnerable to the impacts of sea level rise (SLR). Predictive models suggest that in the coming decades sea level rise will lead to substantial losses of coastal wetland habitat in North Carolina. Empirical measurements of wetland response to SLR are vital for understanding which wetland complexes are most immediately threatened, so that mitigation, adaptation, and conservation efforts can be prioritized accordingly. Surface Elevation Tables (SETs) provide high resolution measures of wetland elevation change that can meet this need.
The North Carolina SET Community of Practice (NC SET COP) is a voluntary and unfunded partnership among stakeholders who have either installed Surface Elevation Tables (SETs) in North Carolina coastal wetlands, or who rely on SET data. The NC SET COP was formed in 2018 to identify and map the locations of all SETs installed within North Carolina, increase collaboration among SET users, and support efforts to identify regional trends in North Carolina coastal wetland response to sea level rise. The data presented here represent the first synoptic analysis of elevation trend data collected by NC SET COP partners.
Of 132 SETs installed across North Carolina (NC), 33 recorded net losses in elevation over the entire record of measurement. Among the 99 SETs that recorded positive elevation change, 79 (80%) did not build elevation fast enough to keep pace with the average rate of SLR over the past 30 years. The story these data tell is clear: the majority of NC’s coastal wetlands are not keeping pace with SLR. These data also provide a spatially explicit understanding of which wetlands are most at risk, and as a result, the SET data can help guide the use of restoration efforts for maximum effectiveness.
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