Satellite telemetry (ST) has played a critical role in the management and conservation of polar bears (Ursus maritimus) over the last 50 years. ST data provide biological information relevant to subpopulation delineation, movements, habitat use, maternal denning, health, human-bear interactions, and accurate estimates of vital rates and abundance. Given that polar bears are distributed at low densities over vast and remote habitats, much of the information provided by ST data cannot be collected by other means. Obtaining ST data for polar bears requires chemical immobilization and application of a tracking device. Although immobilization has not been found to have negative effects beyond a several-day reduction in activity, over the last few decades opposition to immobilization and deployment of satellite-linked radio collars has resulted in a lack of current ST data in many of the 19 recognized polar bear subpopulations. Here, we review the uses of ST data for polar bears and evaluate its role in addressing 21st century conservation and management challenges, which include estimation of sustainable harvest rates, understanding the impacts of climate warming, delineating critical habitat, and assessing potential anthropogenic impacts from tourism, resource development and extraction. We found that in subpopulations where ST data have been consistently collected, information was available to estimate vital rates and subpopulation density, document the effects of sea-ice loss, and inform management related to subsistence harvest and regulatory requirements. In contrast, a lack of ST data in some subpopulations resulted in increased bias and uncertainty in ecological and demographic parameters, which has a range of negative consequences. As sea-ice loss due to climate warming continues, there is a greater need to monitor polar bear distribution, habitat use, abundance, and subpopulation connectivity. We conclude that continued collection of ST data will be critically important for polar bear management and conservation in the 21st century and that the benefits of immobilizing small numbers of individual polar bears in order to deploy ST devices significantly outweigh the risks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.816666","usgsCitation":"Laidre, K.L., Durner, G.M., Lunn, N.J., Regehr, E.V., Atwood, T.C., Rode, K.D., Aars, J., Routti, H., Wiig, O., Dyck, M., Richardson, E.S., Atkinson, S., Belikov, S., and Stirling, I., 2022, The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus): Frontiers in Marine Science, v. 9, 816666, 22 p., https://doi.org/10.3389/fmars.2022.816666.","productDescription":"816666, 22 p.","ipdsId":"IP-135225","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":448110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.816666","text":"Publisher Index Page"},{"id":399396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Greenland, Russia, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.9,\n 56.75272287205736\n ],\n [\n 179.9,\n 56.75272287205736\n ],\n [\n 179.9,\n 89\n ],\n [\n -179.9,\n 89\n ],\n [\n -179.9,\n 56.75272287205736\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":841130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":841131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lunn, Nicholas J","contributorId":198991,"corporation":false,"usgs":false,"family":"Lunn","given":"Nicholas","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":841132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":841133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":841134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":841135,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aars, Jon","contributorId":91338,"corporation":false,"usgs":false,"family":"Aars","given":"Jon","email":"","affiliations":[{"id":7238,"text":"Norwegian Polar Institute","active":true,"usgs":false}],"preferred":false,"id":841136,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Routti, Heli","contributorId":56879,"corporation":false,"usgs":false,"family":"Routti","given":"Heli","email":"","affiliations":[{"id":7238,"text":"Norwegian Polar Institute","active":true,"usgs":false}],"preferred":false,"id":841137,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wiig, Oystein","contributorId":192053,"corporation":false,"usgs":false,"family":"Wiig","given":"Oystein","email":"","affiliations":[],"preferred":false,"id":841138,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dyck, Markus","contributorId":173868,"corporation":false,"usgs":false,"family":"Dyck","given":"Markus","affiliations":[],"preferred":false,"id":841139,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Richardson, Evan S.","contributorId":139901,"corporation":false,"usgs":false,"family":"Richardson","given":"Evan","email":"","middleInitial":"S.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":841140,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Atkinson, Stephen D","contributorId":223225,"corporation":false,"usgs":false,"family":"Atkinson","given":"Stephen D","affiliations":[{"id":40688,"text":"Department of Microbiology, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":841141,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Belikov, Stanislav","contributorId":19513,"corporation":false,"usgs":false,"family":"Belikov","given":"Stanislav","email":"","affiliations":[],"preferred":false,"id":841142,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stirling, Ian","contributorId":72079,"corporation":false,"usgs":false,"family":"Stirling","given":"Ian","email":"","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":841143,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70230029,"text":"sir20215101 - 2022 - Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","interactions":[],"lastModifiedDate":"2026-04-02T19:40:54.308381","indexId":"sir20215101","displayToPublicDate":"2022-04-14T08:38:54","publicationYear":"2022","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":"2021-5101","displayTitle":"Aquatic-Life Criteria Compared to Concentrations of Cadmium, Copper, Lead, and Zinc in Streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","title":"Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","docAbstract":"The primary focus of this study was to document cadmium, copper, lead, and zinc concentrations in selected streams near the U.S. Army Joint Readiness Training Center (JRTC) and Fort Polk Military Reservation and to compare those values to Federal and State aquatic-life criteria guidelines. The acute aquatic-life criteria used for this study are as follows: the U.S. Environmental Protection Agency (EPA) aquatic-life criterion maximum concentration (CMC) based on hardness, the EPA CMC for copper based on the biotic ligand model (BLM), and the Louisiana Department of Environmental Quality (LDEQ) acute aquatic-life criteria based on hardness. The chronic aquatic-life criteria used for this study are as follows: the EPA aquatic-life criterion continuous concentration (CCC) based on hardness, the EPA CCC for copper based on the BLM, and the LDEQ chronic aquatic-life criteria based on hardness.
Cadmium was detected in one stream-water sample collected near the Peason Ridge training area, hereinafter referred to as Peason Ridge, and one stream-water sample collected near North and South Fort Polk, hereinafter referred to as the Main Post. A cadmium concentration of an estimated (E) 0.48 microgram per liter (μg/L) in a stream-water sample collected during high stage near Peason Ridge exceeded the EPA CMC of 0.10 μg/L. A second cadmium concentration of E0.33 μg/L in a stream-water sample collected during low stage exceeded the EPA CMC of 0.22 μg/L, and a 4-day average cadmium concentration of E0.16 μg/L exceeded the EPA CCC of 0.14 μg/L.
Copper was detected in 34 stream-water samples collected near Peason Ridge and 22 stream-water samples collected near the Main Post. The EPA acute criteria for copper were exceeded 17 times in stream-water samples collected near Peason Ridge and 19 times in stream-water samples collected near the Main Post. The EPA chronic criteria for copper were exceeded five times in stream-water samples collected near Peason Ridge and seven times in stream-water samples collected near the Main Post.
Lead was detected in 31 stream-water samples collected near Peason Ridge and 16 stream-water samples collected near the Main Post. A concentration of 6.0 μg/L in a stream-water sample collected during high stage at site 2 near Peason Ridge exceeded the EPA CMC of 5.5 μg/L, and a concentration of 4.1 μg/L in a stream-water sample collected during high stage at site 4 near the Main Post exceeded the EPA CMC of 2.9 μg/L. The EPA chronic criteria for lead were exceeded nine times in stream-water samples collected near Peason Ridge and three times in stream-water samples collected near the Main Post. The LDEQ chronic criteria were exceeded two times in stream-water samples near Peason Ridge and none near the Main Post.
Zinc was detected in 35 stream-water samples collected near Peason Ridge and 17 stream-water samples collected near the Main Post. A concentration of 100 μg/L in a stream-water sample collected at site 3 near Peason Ridge exceeded the EPA CMC of 8.9 μg/L and the LDEQ acute aquatic-life criteria of 36 μg/L. One 4-day average zinc concentration, E28 μg/L for stream-water samples collected from site 3 near Peason Ridge, exceeded the EPA CCC of 8.2 μg/L; however, no concentrations of zinc exceeded the LDEQ chronic aquatic-life criteria near Peason Ridge or the Main Post.
The presence of copper, lead, and zinc at concentrations above the calculated acute or chronic aquatic-life criteria for some stream-water samples collected in relatively pristine streams near Peason Ridge and the Main Post indicates that these waters are susceptible to elevated trace element concentrations likely because of low ionic strength and hardness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215101","collaboration":"Prepared in cooperation with the U.S. Army Joint Readiness Training Center and the Fort Polk Military Reservation","usgsCitation":"Tollett, R.W., 2022, Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016: U.S. Geological Survey Scientific Investigations Report 2021–5101, 40 p., https://doi.org/10.3133/sir20215101.","productDescription":"Report: viii, 40 p.; Data Release; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-106720","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":397567,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5101/sir20215101.XML"},{"id":397566,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5101/sir20215101.pdf","text":"Report","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":502115,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112935.htm","linkFileType":{"id":5,"text":"html"}},{"id":397571,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397570,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74M93FJ","text":"USGS data release","linkHelpText":"Water-quality and grain-size data collected at three sites near the Peason Ridge training area and two sites near the Main Post at the Joint Readiness Training Center and Fort Polk, 2015–2016"},{"id":397568,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5101/images"},{"id":397565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5101/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Fort Polk Military Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.39340209960938,\n 30.9187201197222\n ],\n [\n -92.58865356445312,\n 30.9187201197222\n ],\n [\n -92.58865356445312,\n 31.431006719178512\n ],\n [\n -93.39340209960938,\n 31.431006719178512\n ],\n [\n -93.39340209960938,\n 30.9187201197222\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Global Seismographic Network (GSN)—a global network of ≈150 very broadband stations—is used by researchers to study the free oscillations of the Earth (≈0.3–10 mHz) following large earthquakes. Normal‐mode observations can provide information about the radial density and anisotropic velocity structure of the Earth (including near the core–mantle boundary), but only when signal‐to‐noise ratios at very low frequencies are sufficiently high. Most normal‐mode observations in the past three decades have been made using Streckeisen STS‐1 vault seismometers. However, these sensors are no longer being manufactured or serviced. Candidate replacement sensors, the Streckeisen STS‐6 and the Nanometrics T‐360GSN, have been recently installed in boreholes, postholes, and vaults at several GSN stations and GSN testbeds. In this study, we examine normal‐mode spectra following three
The importance of terrestrial coastal ecosystems for maintaining healthy coral reef ecosystems remains understudied. Sea kraits are amphibious snakes that require healthy coral reefs for foraging, but little is known about their requirements of terrestrial habitats, where they slough their skin, digest prey, and breed. Using concurrent microclimate measurements and behavior surveys, we show that a small, topographically flat atoll in Fiji with coastal forest provides many microhabitats that relate to the behaviors of Yellow Lipped Sea Kraits, Laticauda colubrina. Microclimates were significantly related to canopy cover, leaf litter depth, and distance from the high-water mark (HWM). Sea kraits were almost exclusively observed in coastal forest within 30 m of the HWM. Sloughing of skins only occurred within crevices of mature or dying trees. Resting L. colubrina were significantly more likely to occur at locations with higher mean diurnal temperatures, lower leaf litter depths, and shorter distances from the HWM. On Leleuvia, behavior of L. colubrina therefore relates to environmental heterogeneity created by old-growth coastal forests, particularly canopy cover and crevices in mature and dead tree trunks. The importance of healthy coastal habitats, both terrestrial and marine, for L. colubrina suggests it could be a good flagship species for advocating integrated land-sea management. Furthermore, our study highlights the importance of coastal forests and topographically flat atolls for biodiversity conservation. Effective conservation management of amphibious species that utilize land- and seascapes is therefore likely to require a holistic approach that incorporates connectivity among ecosystems and environmental heterogeneity at all relevant scales.
Ceratonova shasta is a myxozoan parasite endemic to the Pacific Northwest of North America that is linked to low survival rates of juvenile salmonids in some watersheds such as the Klamath River basin. The density of C. shasta actinospores in the water column is typically highest in the spring (March–June), and directly influences infection rates for outmigrating juvenile salmonids. Current management approaches require quantities of C. shasta density to assess disease risk and estimate survival of juvenile salmonids. Therefore, we developed a model to simulate the density of waterborne C. shasta actinospores using a mechanistic framework based on abiotic drivers and informed by empirical data. The model quantified factors that describe the key features of parasite abundance during the period of juvenile salmon outmigration, including the week of initial detection (onset), seasonal pattern of spore density, and peak density of C. shasta. Spore onset was simulated by a bio-physical degree-day model using the timing of adult salmon spawning and accumulation of thermal units for parasite development. Normalized spore density was simulated by a quadratic regression model based on a parabolic thermal response with river water temperature. Peak spore density was simulated based on retained explanatory variables in a generalized linear model that included the prevalence of infection in hatchery-origin Chinook juveniles the previous year and the occurrence of flushing flows (≥171 m3/s). The final model performed well, closely matched the initial detections (onset) of spores, and explained inter-annual variations for most water years. Our C. shasta model has direct applications as a management tool to assess the impact of proposed flow regimes on the parasite, and it can be used for projecting the effects of alternative water management scenarios on disease-induced mortality of juvenile salmonids such as with an altered water temperature regime or with dam removal.
This study provides new stratigraphic data and identifications for fossil marine mammals from the Monterey Formation in the Capistrano syncline, Orange County, California, showing that there are two distinct marine mammal assemblages. Until now, marine mammals from the Monterey Formation of Orange County have been considered to represent a single assemblage that is 13.0–10.0 Ma in age. By combining data from diatoms with the geographic positions of sites, faunal analysis, and data from the literature, we can assign 59 sites to three main levels: the lower part (ca. 16–13 Ma), the middle part (ca. 13–10 Ma), and the upper part (ca. 10–8 Ma). We assigned 308 marine mammal specimens to 38 taxa, resulting in 97 occurrences (unique record of a taxon for a given site). Of the 38 taxa we identified within the study area, 15 taxa are restricted to the lower part of the Monterey Formation, 15 are restricted to the upper part of the Monterey Formation, eight were found in both, and none has yet been reported from the middle (possibly condensed) section. Six of the eight taxa that occur in both the lower and upper parts of the Monterey Formation are higher-level taxa, which accounts for their broad temporal range. The recognition of two distinct marine mammal assemblages in the Monterey Formation of Orange County is an important step toward a better-calibrated sequence of faunal evolution in the region while improving the utility of marine mammals for regional biostratigraphy.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding the Monterey Formation and similar biosiliceous units across space and time","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.2556(10)","usgsCitation":"Parham, J.F., Barron, J.A., and Velez-Juarbe, J., 2022, Middle and late Miocene marine mammal assemblages from the Monterey Formation of Orange County, California, chap. of Understanding the Monterey Formation and similar biosiliceous units across space and time, v. 556, p. 229-241, https://doi.org/10.1130/2021.2556(10).","productDescription":"13 p.","startPage":"229","endPage":"241","ipdsId":"IP-122364","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":448123,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/spe.s.19362497","text":"External Repository"},{"id":402269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Orange County","otherGeospatial":"Monterey Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76519775390626,\n 33.455505553269184\n ],\n [\n -117.49465942382811,\n 33.455505553269184\n ],\n [\n -117.49465942382811,\n 33.668354044590075\n ],\n [\n -117.76519775390626,\n 33.668354044590075\n ],\n [\n -117.76519775390626,\n 33.455505553269184\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"556","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Aiello, Ivano","contributorId":292488,"corporation":false,"usgs":false,"family":"Aiello","given":"Ivano","email":"","affiliations":[],"preferred":false,"id":854754,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":854755,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Ravelo, A. C.","contributorId":24778,"corporation":false,"usgs":false,"family":"Ravelo","given":"A.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":854756,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Parham, James F.","contributorId":147502,"corporation":false,"usgs":false,"family":"Parham","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":844736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":844737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Velez-Juarbe, Jorge","contributorId":292477,"corporation":false,"usgs":false,"family":"Velez-Juarbe","given":"Jorge","email":"","affiliations":[{"id":12725,"text":"Natural History Museum of Los Angeles County","active":true,"usgs":false}],"preferred":false,"id":844738,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250621,"text":"70250621 - 2022 - Water availability drives instream conditions and life-history of an imperiled desert fish: A case study to inform water management","interactions":[],"lastModifiedDate":"2023-12-20T13:08:51.15559","indexId":"70250621","displayToPublicDate":"2022-04-13T07:06:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Water availability drives instream conditions and life-history of an imperiled desert fish: A case study to inform water management","docAbstract":"In arid ecosystems, available water is a critical, yet limited resource for human consumption, agricultural use, and ecosystem processes—highlighting the importance of developing management strategies to meet the needs of multiple users. Here, we evaluated how water availability influences stream thermal regimes and life-history expressions of Lahontan cutthroat trout (Oncorhynchus clarkii henshawi) in the arid Truckee River basin in the western United States. We integrated air temperature and stream discharge data to quantify how water availability drives stream temperature during annual spawning and rearing of Lahontan cutthroat trout. We then determined how in situ stream discharge and temperature affected adult spawning migrations, juvenile growth opportunities, and duration of suitable thermal conditions. Air temperatures had significant, large effects (+) on stream temperature across months; the effects of discharge varied across months, with significant effects (−) during May through August, suggesting increased discharge can help mitigate temperatures during seasonally warm months. Two models explained adult Lahontan cutthroat trout migration, and both models indicated that adult Lahontan cutthroat trout avoid migration when temperatures are warmer (~ > 12 °C) and discharge is higher (~ > 50 m3*s−1). Juvenile size was best explained by a quadratic relationship with cumulative degree days (CDD; days>4 °C) as size increased with increasing CDDs but decreased at higher CDDs. We also found an interaction between CDDs and discharge explaining juvenile size: when CDDs were low, higher discharge was associated with larger size, but when CDDs were high, higher discharge was associated with smaller size. Stream temperatures also determined the duration of juvenile rearing, as all juvenile emigration ceased at temperatures >24.4 °C. Together, our results illustrated how stream discharge and temperature shape the life-history of Lahontan cutthroat trout at multiple stages and can inform management actions to offset warming temperatures and facilitate life-history diversity and population resilience.
Endemic Hawaiian forest birds have experienced dramatic population declines. The Big Island National Wildlife Refuge Complex (BINWRC) was created for conservation of endangered Hawaiian forest birds and their habitats. Surveys have been conducted at two units of BINWRC to monitor forest bird populations and their response to management actions. We analyzed survey data from 1987 to 2019 at the Hakalau Forest Unit (HFU) and from 1995 to 2019 at the Kona Forest Unit (KFU). We analyzed three strata at HFU: open-forest, closed-forest, and pasture, and two strata at the KFU: upper (>1524 m elevation) and lower (<1524 m). In all years, ‘i‘iwi (Drepanis coccinea), ‘apapane (Himatione sanguinea), and Hawai‘i ‘amakihi (Chlorodrepanis virens virens) were the most abundant species at HFU. The three endangered forest bird species, Hawai‘i ‘ākepa (Loxops coccineus), ‘alawī (Loxops mana, also known as Hawai‘i creeper) and ‘akiapōlā‘au (Hemignathus wilsoni), had much lower densities. The most abundant species at KFU was ‘apapane, followed by Hawai‘i ‘amakihi and warbling white-eye (Zosterops japonicus) at much lower densities. At HFU we found a continuation of several trends observed in previous analyses from 1987–2012, with most species’ trends upward in pasture stratum, stable in the open-forest stratum, and downward in the closed-forest stratum. However, when we looked at the most recent decade at HFU, more species were showing downward trends in all three strata. At KFU results were mixed, with more species’ trends downward in the upper stratum and more species’ trends upward in the lower stratum. Populations of endangered forest species were either locally extirpated at KFU or in numbers too low to reliably estimate population densities. Both units in the BINWRC are important for conservation of forest birds on Hawai‘i Island, and our results show that HFU supports the majority of the three endangered forest bird species found on Hawai‘i Island. Our analysis also shows the importance of continuous monitoring and timely analysis to track forest bird populations. With the additional data provided by continued surveys, we determined conclusive population trends for species whose trends were previously inconclusive. Knowing current population densities, abundances, and trends allows managers to evaluate and adapt management actions to support forest bird conservation at the BINWRC.
Manipulating plant microbiomes may provide control of invasive species. Invasive Phragmites australis has spread rapidly in North American wetlands, causing significant declines in native biodiversity. To test microbiome effects on host growth, we inoculated four common fungal endophytes into replicated Phragmites genotypes and monitored their growth in field and growth chamber environments. Inoculations were highly successful in the growth chamber but inoculated plants in the field were rapidly colonized by diverse endophytes from the local environment. There were significant genotype effects and minimal inoculation effects in both experiments with a significant inoculation × genotype interaction on tiller height in the field. Our results demonstrate that endophyte inoculation treatments are feasible, but repeated inoculations may be required to maintain high titer in plants subject to endophyte colonization from the local environment. Future studies should investigate a wider range of fungal endophytes to identify taxa that inhibit Phragmites and other invaders.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.funeco.2022.101153","usgsCitation":"Quach, Q.N., Thrasher, T.T., Kowalski, K., and Clay, K., 2022, Fungal endophyte effects on invasive Phragmites australis performance in field and growth chamber environments: Fungal Ecology, v. 57-58, 101153, 9 p., https://doi.org/10.1016/j.funeco.2022.101153.","productDescription":"101153, 9 p.","ipdsId":"IP-132750","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448127,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.funeco.2022.101153","text":"Publisher Index Page"},{"id":435876,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96G9TV8","text":"USGS data release","linkHelpText":"Effects of fungal endophytes on invasive Phragmites australis (ssp. australis) performance in growth chamber and field experiments at the Indiana University Research and Teaching Preserve (N 39.217, W &minus;86.540) (2018)"},{"id":399880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57-58","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Quach, Quynh N 0000-0001-6504-6358","orcid":"https://orcid.org/0000-0001-6504-6358","contributorId":290314,"corporation":false,"usgs":false,"family":"Quach","given":"Quynh","email":"","middleInitial":"N","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":841756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thrasher, Thomas T","contributorId":290772,"corporation":false,"usgs":false,"family":"Thrasher","given":"Thomas","email":"","middleInitial":"T","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":841757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":841758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clay, Keith","contributorId":140472,"corporation":false,"usgs":false,"family":"Clay","given":"Keith","email":"","affiliations":[{"id":12645,"text":"Indiana University - Northwest","active":true,"usgs":false}],"preferred":false,"id":841759,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230500,"text":"70230500 - 2022 - Ecological consequences of neonicotinoid mixtures in streams","interactions":[],"lastModifiedDate":"2022-04-14T11:46:11.626748","indexId":"70230500","displayToPublicDate":"2022-04-13T06:44:41","publicationYear":"2022","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":"Ecological consequences of neonicotinoid mixtures in streams","docAbstract":"Dynamic rupture models are physics‐based simulations that couple fracture mechanics to wave propagation and are used to explain specific earthquake observations or to generate a suite of predictions to understand the influence of frictional, geometrical, stress, and material parameters. These simulations can model single earthquakes or multiple earthquake cycles. The objective of this article is to provide a self‐contained and practical guide for students starting in the field of earthquake dynamics. Senior researchers who are interested in learning the first‐order constraints and general approaches to dynamic rupture problems will also benefit. We believe this guide is timely given the recent growth of computational resources and the range of sophisticated modeling software that are now available. We start with a succinct discussion of the essential physics of earthquake rupture propagation and walk the reader through the main concepts in dynamic rupture model design. We briefly touch on fully dynamic earthquake cycle models but leave the details of this topic for other publications. We also highlight examples throughout that demonstrate the use of dynamic rupture models to investigate various aspects of the faulting process.
In drylands, there is a need for controlled experiments over multiple planting years to examine how woody seedlings respond to soil texture and the potentially interactive effects of soil depth and precipitation. Understanding how multiple environmental factors interactively influence plant establishment is critical to restoration ecology and in this case to broad-scale restoration efforts in western US drylands dominated by big sagebrush (Artemisia tridentata). We planted sagebrush seedlings across a range of soil textures and depths in the southern portion of the species' range, on the Colorado Plateau. We evaluated survival of repeated plantings of caged and uncaged seedlings over two years across 20 plots in wet vs. average precipitation years at one site, and examined broader patterns of sagebrush seedling survival during an average precipitation year in 56 plots across four sites. First-year survival was >9x higher under wet than average precipitation. Under favorable (wet) conditions, early sagebrush seedling survival was highest on coarser soils, especially those that also had a shallower restrictive layer (e.g., 50-100 cm). Under average precipitation, soil texture and depth effects on survival of newly-planted seedlings were much weaker, but older (>1 yr) seedlings benefitted from growing on coarser textured soils. It may be possible to increase survival by sheltering seedlings with small mesh cages, which likely improve moisture availability. Our results provide new insights into environmental factors that limit woody seedling survival in drylands and illustrate that planting in wet years and incorporating detailed soil setting information could increase survival of sagebrush seedlings in restoration projects.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13700","usgsCitation":"Veblen, K.E., Nehring, K.C., Duniway, M.C., Knight, A.C., Monaco, T.A., Schupp, E.W., Boettinger, J., Villalba, J.J., Fick, S., Brungard, C.C., and Thacker, E., 2022, Soil depth and precipitation moderate soil textural effects on seedling survival of a foundation shrub species: Restoration Ecology, v. 30, no. 6, e13700, 11 p., https://doi.org/10.1111/rec.13700.","productDescription":"e13700, 11 p.","ipdsId":"IP-133946","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":399734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Veblen, Kari E.","contributorId":76872,"corporation":false,"usgs":false,"family":"Veblen","given":"Kari","email":"","middleInitial":"E.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":841510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nehring, Kyle C.","contributorId":210415,"corporation":false,"usgs":false,"family":"Nehring","given":"Kyle","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":841511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":841512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":841513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monaco, Thomas A.","contributorId":150564,"corporation":false,"usgs":false,"family":"Monaco","given":"Thomas","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":841514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schupp, Eugene W.","contributorId":178262,"corporation":false,"usgs":false,"family":"Schupp","given":"Eugene","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":841515,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boettinger, Janis L","contributorId":290670,"corporation":false,"usgs":false,"family":"Boettinger","given":"Janis L","affiliations":[{"id":62471,"text":"Ecology Center, Utah State University, 5205 Old Main Hill, Logan, UT, 84322; Dept. of Plants, Soils & Climate Department, Utah State University, Logan, UT 84322,","active":true,"usgs":false}],"preferred":false,"id":841516,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Villalba, Juan J","contributorId":290671,"corporation":false,"usgs":false,"family":"Villalba","given":"Juan","email":"","middleInitial":"J","affiliations":[{"id":62472,"text":"Dept. of Wildland Resources, 5230 Old Main Hill, Utah State University, Logan, UT, 84322; Ecology Center, Utah State University, 5205 Old Main Hill, Logan, UT, 84322","active":true,"usgs":false}],"preferred":false,"id":841517,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fick, Steven 0000-0002-3548-6966","orcid":"https://orcid.org/0000-0002-3548-6966","contributorId":265517,"corporation":false,"usgs":false,"family":"Fick","given":"Steven","email":"","affiliations":[{"id":54712,"text":"Former US Geological Survey, Southwest Biological Science Center, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":841518,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brungard, Colby C.","contributorId":248822,"corporation":false,"usgs":false,"family":"Brungard","given":"Colby","email":"","middleInitial":"C.","affiliations":[{"id":50029,"text":"New Mexico State University, Department of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":841519,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thacker, Eric","contributorId":268205,"corporation":false,"usgs":false,"family":"Thacker","given":"Eric","email":"","affiliations":[{"id":55594,"text":"Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, UT 84322","active":true,"usgs":false}],"preferred":false,"id":841520,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70234222,"text":"70234222 - 2022 - Summer/fall diet and macronutrient assimilation in an Arctic predator","interactions":[],"lastModifiedDate":"2022-08-03T22:03:27.188384","indexId":"70234222","displayToPublicDate":"2022-04-12T15:17:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Summer/fall diet and macronutrient assimilation in an Arctic predator","docAbstract":"Free-ranging predator diet estimation is commonly achieved by applying molecular-based tracers because direct observation is not logistically feasible or robust. However, tracers typically do not represent all dietary macronutrients, which likely obscures resource use as prey proximate composition varies and tissue consumption can be specific. For example, polar bears (Ursus maritimus) preferentially consume blubber, yet diets have been estimated using fatty acids based on prey blubber or stable isotopes of lipid-extracted prey muscle, neither of which represent both protein and lipid macronutrient contributions. Further, additional bias can be introduced because dietary fat is known to be flexibly routed beyond short-term energy production and storage. We address this problem by simultaneously accounting for protein and lipid assimilation using carbon and nitrogen isotope compositions of lipid-containing prey muscle and blubber to infer summer/fall diet composition and macronutrient proportions from Chukchi Sea polar bear guard hair (n = 229) sampled each spring between 2008 and 2017. Inclusion of blubber (85–95% lipid by dry mass) expanded the isotope mixing space and improved separation among prey species. Ice-associated seals, including nutritionally dependent pups, were the primary prey in summer/fall diets with lower contributions by Pacific walruses (Odobenus rosmarus) and whales. Percent blubber estimates confirmed preferential selection of this tissue and represented the highest documented lipid assimilation for any animal species. Our results offer an improved understanding of summer/fall prey macronutrient usage by Chukchi Sea polar bears which likely coincides with a nutritional bottleneck as the sea ice minimum is approached.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-022-05155-2","usgsCitation":"Stricker, C.A., Rode, K.D., Taras, B.D., Bromaghin, J.F., Horstmann, L., and Quakenbush, L.T., 2022, Summer/fall diet and macronutrient assimilation in an Arctic predator: Oecologia, v. 198, p. 917-931, https://doi.org/10.1007/s00442-022-05155-2.","productDescription":"25 p.","startPage":"917","endPage":"931","ipdsId":"IP-129765","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":404770,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.0244140625,\n 65.9016533861307\n ],\n [\n -162.00439453125,\n 65.9016533861307\n ],\n [\n -162.00439453125,\n 69.53451763078358\n ],\n [\n -170.0244140625,\n 69.53451763078358\n ],\n [\n -170.0244140625,\n 65.9016533861307\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"198","noUsgsAuthors":false,"publicationDate":"2022-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":848230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taras, Brian D.","contributorId":207216,"corporation":false,"usgs":false,"family":"Taras","given":"Brian","email":"","middleInitial":"D.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":848231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":848232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horstmann, Lara","contributorId":294522,"corporation":false,"usgs":false,"family":"Horstmann","given":"Lara","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":848233,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quakenbush, Lori T.","contributorId":192737,"corporation":false,"usgs":false,"family":"Quakenbush","given":"Lori","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":848234,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228749,"text":"sir20215144 - 2022 - Surface-water-quality data to support implementation of revised freshwater aluminum water-quality criteria in Massachusetts, 2018–19","interactions":[],"lastModifiedDate":"2026-02-23T18:31:13.191429","indexId":"sir20215144","displayToPublicDate":"2022-04-12T13:00:00","publicationYear":"2022","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":"2021-5144","displayTitle":"Surface-Water-Quality Data to Support Implementation of Revised Freshwater Aluminum Water-Quality Criteria in Massachusetts, 2018–19","title":"Surface-water-quality data to support implementation of revised freshwater aluminum water-quality criteria in Massachusetts, 2018–19","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, performed a study to inform the development of the department’s guidelines for the collection and use of water-chemistry data to support calculation of site-dependent aluminum criteria values. The U.S. Geological Survey collected and analyzed discrete water-quality samples at four wastewater-treatment facilities and seven water-treatment facilities in eastern and central Massachusetts from April 2018 through May 2019.
For each of the 11 facilities considered, water-quality samples were collected from treatment-plant effluent and receiving-water bodies. Samples were collected for laboratory analysis of major ions (calcium and magnesium ions are used to calculate total hardness), dissolved organic carbon (DOC), total organic carbon (TOC), and total recoverable aluminum. Field parameters for pH, temperature, and specific conductance were measured in situ concurrently with sample collection.
Water-quality conditions differed among monitoring stations. The highest pH values were observed for stations on the Assabet River that receive effluent discharges from wastewater-treatment facilities (the Westborough, Marlborough, Hudson, and Maynard wastewater-treatment facilities). High DOC concentrations (greater than 10 mg/L) were measured in water bodies associated with large areas of riparian wetlands—Lily Pond (Cohasset) and Third Herring Brook (Hanover), and low DOC concentrations (less than 2.5 mg/L) were measured at three water bodies in central Massachusetts—Hocomonco Pond (Westborough), Wyman Pond (Fitchburg), and Monoosnoc Brook (Leominster). Wyman Pond (Fitchburg), Monoosnoc Brook (Leominster), and Lily Pond (Cohasset) also had low pH values and low total hardness concentrations.
The monthly discrete pH, DOC, and total hardness data for selected stations on receiving-water bodies were used in the U.S. Environmental Protection Agency Aluminum Criteria Calculator Version 2.0 to estimate site-dependent total recoverable aluminum concentrations that—if not exceeded—would be expected to protect fish, invertebrates, and other aquatic life from adverse effects associated with acute and chronic aluminum exposures. The U.S. Environmental Protection Agency Calculator output provides values for the acute criterion, defined as the criterion maximum concentration (CMC), an estimate of the highest aluminum concentration in surface water to which an aquatic community can be exposed briefly without resulting in an unacceptable effect. This output also provides values for the chronic criterion, defined as the criterion continuous concentration (CCC), an estimate of the highest concentration of aluminum in surface water to which an aquatic community can be exposed indefinitely without resulting in an unacceptable effect. To determine aluminum criteria values typically evaluated for use as protective water-quality criteria, the monthly instantaneous CMC and CCC values were used to calculate the minimum, 5th percentile, and 10th percentile CMC and CCC values for selected monitoring stations.
The monthly instantaneous aluminum CMC and CCC values generated using the EPA Calculator varied among stations. Aluminum CMC and CCC values were highest for four ambient (upstream) stations on the Assabet River associated with wastewater-treatment facilities (Westborough, Marlboro, Hudson, and Maynard). Aluminum CMC and CCC values were lower for stations associated with water-treatment facilities, and lowest for selected ambient stations on Lily Pond, Monoosnoc Brook, and Wyman Pond associated with water-treatment facilities in Cohasset, Leominster, and Fitchburg, respectively. For many stations, the highest CMC and CCC instantaneous aluminum criteria values generated using the U.S. Environmental Protection Agency Calculator were for months during the growing season for algae and aquatic macrophytes (April or May through September or October) and the lowest values were for months during the nongrowing season (October or November through March or April), indicating the importance of collecting water-quality data during the nongrowing season.
Aluminum CMC and CCC values generated by the U.S. Environmental Protection Agency Calculator are sensitive to variations in the input parameters (pH, DOC, and total hardness). Aluminum solubility is particularly affected by pH. To characterize diel and seasonal variations in pH, multiparameter water-quality monitors recording continuous (15-minute interval) water temperature and pH were installed in the receiving-water body for one station near each facility upstream from the effluent discharge (in rivers) or at a station outside the immediate effect of effluent discharge (in ponds). Continuous water temperature and pH data were collected from April or May 2018 through November or December 2018. Continuous pH data indicated that the pond stations and Assabet River stations had large diel variations in pH during the growing season. Continuous pH data were used together with discrete DOC and total hardness data to evaluate the potential effect of diel variations in pH on calculated site-dependent aluminum criteria values. For the 11 stations, diel variations in pH were determined to correspond to differences in the 10th percentile of CMC values by a median of 160 μg/L, ranging from 0 to 610 μg/L, and differences in the 10th percentile of CCC values by a median of 40 μg/L, ranging from 15 to 210 μg/L. The low monthly instantaneous CMC and CCC values that have the greatest effect on the minimum, 5th percentile, and 10th percentile aluminum values tend to result during the nongrowing season (October or November through March or April) when the range of diel variations in pH is small, thus minimizing the effect of diel variations in pH on the lowest CMC and CCC values.
Historical water-quality data on organic carbon in Massachusetts streams were investigated using data retrieved from the USGS National Water Information System database. An assessment of the availability of historical pH, DOC, and hardness data indicated that more data were available for TOC than for DOC. A linear regression equation was developed for the relation between DOC and TOC concentrations to inform the potential use of available data to evaluate water-quality conditions at additional sites across Massachusetts where only pH, hardness, and TOC data are available. DOC and TOC concentrations were well correlated in the 223 samples in which both constituents were analyzed, and the equation had a coefficient of determination (R2) equal to 0.93.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215144","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Armstrong, D.S., Savoie, J.G., DeSimone, L.A., Laabs, K.L., and Carey, R.O., 2022, Surface-water-quality data to support implementation of revised freshwater aluminum water-quality criteria in Massachusetts, 2018–19 (ver. 1.1, February 2023): U.S. Geological Survey Scientific Investigations Report 2021–5144, 85 p., https://doi.org/10.3133/sir20215144.","productDescription":"Report: x, 85 p.; 2 Data Releases","numberOfPages":"85","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114770","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":500448,"rank":9,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (Ne). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.
Consumptive and non-consumptive interactions of predators and prey can have strong direct and indirect effects on primary producers, such as stream algae. Increasing water temperatures may alter these interactions and thus influence productivity in streams. For each of 3 temperature treatments (‘ambient’, +2°C and +4°C), we measured the amount of algal biomass removed by grazing mayflies from 91 mesocosms after a 24-hour test period under 3 grazing treatments: lethal predators, non-lethal predators, and no predators. At all temperatures, grazers reduced algal biomass (p < 0.01), and the presence of lethal predators effectively dampened mayfly consumption of algae (p < 0.01). However, differences in algal biomass between lethal and non-lethal predator treatments were not significant, indicating that predators had no indirect behaviorally mediated effects on grazer consumption. Grazer removal of algal biomass marginally increased with increasing temperature (p = 0.051). We analyzed video data for changes in grazer foraging and drift behavior. Mayflies increased drift in the presence of lethal predators (p < 0.01) but not non-lethal predators, and no behavioral changes were seen with temperature increases. Mesocosms can help elucidate possible future shifts in trophic interactions due to climate warming. Yet, we found no evidence of indirect stonefly predator effects on grazing mayflies under these warming scenarios.
Bathymetric data were collected at 12 water-supply lakes in northwestern Missouri by the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources and in collaboration with various local agencies, as part of a multiyear effort to establish or update the surface area and capacity tables for the surveyed lakes. Ten of the lakes were surveyed from July to September 2019, one of the original 10 was resurveyed in March 2020, and two lakes of high interest near Maryville were surveyed in June 2020. Six of the lakes had been surveyed by the U.S. Geological Survey before, and the recent surveys were compared to the earlier surveys to document the changes in the bathymetric surface and capacity of the lake and to produce a bathymetric change map.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data were collected in shallow areas with an acoustic Doppler current profiler on a remote-controlled boat. At Hamilton Reservoir, a Global Navigation Satellite System survey receiver was used to collect additional bathymetric data at several points across four transects and around the perimeter of a substantial shallow area filled with aquatic vegetation upstream from a low-clearance bridge on the northern arm.
Data points from the various sources were exported at a gridded data resolution appropriate to each lake. Data outside the multibeam echosounder survey extent and greater than the surveyed water-surface elevation generally were obtained from data collected using aerial light detection and ranging point cloud data, 1/9 arc-second National Elevation Dataset data based on aerial light detection and ranging data, or both. A linear enforcement technique was used to add points to the dataset in areas of sparse data (the upper ends of coves where the water was shallow or aquatic vegetation precluded data acquisition) based on surrounding multibeam and upland data values. The various point datasets were used to produce a three-dimensional triangulated irregular network surface of the lake-bottom elevations for each lake. A surface area and capacity table was produced from the three-dimensional surface showing surface area and capacity at specified lake water-surface elevations. Various quality-assurance tests were conducted to ensure quality data were collected with the multibeam, including beam angle checks and patch tests. Additional quality-assurance tests were conducted on the gridded bathymetric data from the survey, the bathymetric surface created from the gridded data, and the contours created from the bathymetric survey.
If data from a previous bathymetric survey existed at a given lake, a bathymetric change map was generated from the elevation difference between the previous survey and the 2019 bathymetric survey data points. After applying any vertical elevation changes to the previous survey data to ensure a match to the 2019 survey datum, coincident points between the surveys were found, and a bathymetric change map was generated using the coincident point data.
A decrease in capacity was observed at all the lakes for which a previous survey existed. The decrease in capacity at the primary spillway or intake elevation ranged from 0.8 percent at Lake Viking to 21.4 percent at Middle Fork Grand River Reservoir. The mean bathymetric change ranged from 0.33 foot at Willow Brook Lake to 1.18 feet at Middle Fork Grand River Reservoir. The computed sedimentation rate generally ranged from 0.54 to 4.19 acre-feet per year at Maysville Lake and Middle Fork Grand River Reservoir, respectively; however, Lake Viking had the largest sedimentation rate of 14.9 acre-feet per year, despite having the smallest decrease in capacity at the spillway elevation of only 0.8 percent and a mean bathymetric change of only 0.4 foot. Evidence of dredging was observed in the bathymetric surface for Lake Viking. Some changes observed in some bathymetric change maps are hypothesized to result from the difference in data collection equipment and techniques between the previous and present bathymetric surveys. Certain erosional features around the perimeter of certain lakes may be the result of wave action during low-water years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3486","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Huizinga, R.J., Oyler, L.D., and Rivers, B.C., 2022, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in northwestern Missouri, 2019 and 2020: U.S. Geological Survey Scientific Investigations Map 3486, 12 sheets, includes 21-p. pamphlet, https://doi.org/10.3133/sim3486.","productDescription":"Pamphlet: vi, 21 p.; 13 Sheets: 44.00 x 34.00 inches or smaller; Data Release","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-127919","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501902,"rank":31,"type":{"id":36,"text":"NGMDB Index 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near Bethany, Missouri, 2019"},{"id":398194,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet04.pdf","text":"Sheet 4","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Bethany New City Lake near Bethany, Missouri, 2020"},{"id":398193,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet03.pdf","text":"Sheet 3","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Willow Brook Lake near Maysville, Missouri, 2019"},{"id":398200,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet09.pdf","text":"Sheet 9","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for King City Reservoir system near King City, 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Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112851.htm","text":"Hamilton Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":398831,"rank":19,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sim3486/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398206,"rank":18,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet12.pdf","text":"Sheet 12","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Maryville Reservoir near Maryville, Missouri, 2020"},{"id":398204,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet11.2.pdf","text":"Sheet 11.2","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Mozingo Lake near Maryville, Missouri, 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U.S. Geological Survey
1400 Independence Rd
Rolla, MO 65401
As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground‐motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain‐derived ground‐motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground‐motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground‐motion metrics from DAS data. To obtain high‐quality ground‐motion observations, EEW‐compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.
Groundwater development has increased substantially in southeastern Oregon’s Harney Basin since 2010, mainly for the purpose of large-scale irrigation. Concurrently, some areas of the basin experienced groundwater-level declines of more than 100 feet, and some shallow wells have gone dry. The Oregon Water Resources Department has limited new groundwater development in the basin until an improved understanding of the groundwater-flow system is available. This report describes the results of a hydrologic investigation undertaken to provide that understanding. The investigation encompasses the groundwater hydrology of the entire 5,240-square-mile Harney Basin.
Most of the precipitation in the Harney Basin falls in the higher-elevation areas of the Blue Mountains and Steens Mountain. Although considerable groundwater recharge occurs in these upland areas, most (83 percent) re-emerges as streams and springs in the uplands. Groundwater recharge in the lowlands is provided through infiltration of surface water flowing onto the lowlands from rivers and streams leaving the uplands and as groundwater flow from the surrounding upland rocks. Water-balance calculations indicate that the rate of groundwater recharge to the Harney Basin lowlands (where most groundwater is withdrawn) averages 173,000 acre-feet per year (acre-ft/yr).
Groundwater in the Harney Basin lowlands mainly discharges through evapotranspiration from groundwater-irrigated (supplied from wells) crops or from natural vegetation drawing groundwater from the shallow water table and capillary fringe. Groundwater discharge in the lowlands is estimated to be about 283,000 acre-ft/yr, which exceeds the estimated groundwater recharge to the lowlands by about 110,000 acre-ft/yr. This imbalance results in removal of groundwater from storage in the aquifer system and is evidenced by the large declines observed in groundwater levels in the areas of greatest groundwater pumpage.
To a large degree, the location and depth of pumpage dictate the timing and distribution of the effects of groundwater use in the Harney Basin. Pumpage is commonly greatest in the areas where higher-permeability geologic units allow for higher well yields. However, many of these higher-permeability units are bounded by lower-permeability units that cannot supply groundwater at a sufficient rate to replenish the areas of greatest pumpage, resulting in groundwater-level declines. Three Harney Basin areas with a combined area exceeding 140 square miles have experienced groundwater-level declines exceeding 40 feet compared to pre-development conditions: near the Weaver Spring/Dog Mountain area, in the northeastern floodplains along Highway 20, and near Crane. Areas of more modest groundwater-level decline (about 10 feet) were identified in the Virginia Valley area and the Silver Creek floodplain north of Riley. Smaller localized areas of groundwater-level depression have also formed around individual wells or groups of wells throughout the Harney Basin lowlands.
Most groundwater being pumped from the Harney Basin lowlands, including all three areas experiencing large groundwater-level declines, was recharged more than 12,000 years ago, near the end of the last glacial period when the climate in the basin was cooler and wetter than today. Geochemical evidence indicates that modern recharge generally circulates to a depth no greater than 100 feet below the floodplains of major rivers and streams in the lowlands. Away from the major river and stream corridors, pre-modern water commonly is found at the water table. Recharge to groundwater and recovery of groundwater levels in the most heavily pumped areas in the Harney Basin lowlands are restricted by the limited spatial extent and depth of modern recharge in the Harney Basin lowlands and the relatively fine-grained deposits underlying most of the lowland areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215103","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gingerich, S.B., Johnson, H.M., Boschmann, D.E., Grondin, G.H., and Garcia, C.A., 2022, Groundwater resources of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2021–5103, 118 p., https://doi.org/10.3133/sir20215103.","productDescription":"Report: xii, 118 p.; 3 Plates: 30.00 x 42.00 inches or smaller; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119872","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502118,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112850.htm","linkFileType":{"id":5,"text":"html"}},{"id":397922,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5103/"},{"id":397921,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5103/images"},{"id":397920,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate03.pdf","text":"Plate 3","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 3"},{"id":398172,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0FE5M","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Location information, discharge, and water-quality data for selected wells, springs, and streams in the Harney Basin, Oregon"},{"id":398171,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZJTZUV","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Contour data set of the potentiometric surfaces of shallow and deep groundwater-level altitudes in Harney Basin, Oregon, February–March 2018"},{"id":397917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103.pdf","text":"Report","size":"28.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103"},{"id":397918,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate01.pdf","text":"Plate 1","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 1"},{"id":397916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5103/coverthb.jpg"},{"id":397919,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate02.pdf","text":"Plate 2","size":"27.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 2"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.
The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.
The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.
Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.
Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).
During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Groundwater is an important source of water for New Mexico. An estimated 48 percent of the total water used comes from groundwater sources, and groundwater levels generally are declining over large areas of New Mexico. Groundwater levels are affected by local and regional recharge or discharge processes. Groundwater hydrographs show the history of groundwater-level changes at a well. A single hydrograph is not necessarily representative of the larger regional area; however, individual hydrographs from several wells can be combined into a composite hydrograph to show average groundwater changes for a regional area. The U.S. Geological Survey, in cooperation with the New Mexico Office of the State Engineer, has been measuring groundwater levels in a network of wells since about 1925. Although groundwater levels in the statewide well network have been measured at various frequencies, most wells have been measured in 5-year cycles since about 1980. The composite hydrographs in this report were developed to show groundwater-level changes for selected principal aquifers in New Mexico. Composite hydrographs were developed using wells in the Colorado Plateaus aquifers, the High Plains aquifer, the Pecos River Basin alluvial aquifer, the Rio Grande aquifer system, and the Roswell Basin aquifer system. Statewide, groundwater levels generally have declined or remained steady over the time period in aquifers analyzed for this study. The largest water-level declines occurred in the Colorado Plateaus and High Plains aquifers and in the Rio Grande aquifer system (north-central New Mexico), where median water-level declines ranged from 17 to 40 feet and mean water-level declines ranged from 3.8 to 32 feet. Groundwater-level declines (or rises) were generally smaller in other areas of New Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221008","collaboration":"Prepared in cooperation with the New Mexico Office of the State Engineer","usgsCitation":"Myers, N.C., 2022, Composite regional groundwater hydrographs for selected principal aquifers in New Mexico, 1980–2019: U.S. Geological Survey Open-File Report 2022–1008, 51 p., https://doi.org/10.3133/ofr20221008.","productDescription":"Report: vii, 51 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-128607","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501755,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112848.htm","linkFileType":{"id":5,"text":"html"}},{"id":397979,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System 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U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113