Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
","language":"English","publisher":"MDPI","doi":"10.3390/s24072339","usgsCitation":"Thieme, A., Prabhakara, K., Jennewein, J., Lamb, B.T., McCarty, G.T., and Hively, W.D., 2024, Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits: Sensors, v. 24, no. 7, 2339, 25 p., https://doi.org/10.3390/s24072339.","productDescription":"2339, 25 p.","ipdsId":"IP-079899","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":439911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s24072339","text":"Publisher Index Page"},{"id":427561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Beltsville","otherGeospatial":"Beltsville Agricultural Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.93347699075227,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.029631686898625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prabhakara, Kusuma","contributorId":335445,"corporation":false,"usgs":false,"family":"Prabhakara","given":"Kusuma","affiliations":[{"id":80408,"text":"University of Maryland, Department of Geographic Sciences","active":true,"usgs":false}],"preferred":false,"id":898361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":335446,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","email":"","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarty, Gregory T.","contributorId":335447,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"T.","affiliations":[{"id":65190,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":898364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898365,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252825,"text":"70252825 - 2024 - Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US","interactions":[],"lastModifiedDate":"2024-04-08T23:51:25.344452","indexId":"70252825","displayToPublicDate":"2024-04-06T09:01:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US","docAbstract":"We developed an open source, extensible Python-based framework, that we call the Versatile Modeling of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi-source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non-linear least squares and Markov Chain Monte-Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel-1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm3/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm3/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi-source models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011341","usgsCitation":"Angarita, M., Grapenthin, R., Henderson, S., Christoffersen, M.S., and Anderson, K.R., 2024, Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US: Geochemistry, Geophysics, Geosystems, v. 25, e2023GC011341, 19 p., https://doi.org/10.1029/2023GC011341.","productDescription":"e2023GC011341, 19 p.","ipdsId":"IP-159327","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439914,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011341","text":"Publisher Index Page"},{"id":427557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Unimak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.24763757080336,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 55.437699893466515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":898366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grapenthin, Ronni","contributorId":257035,"corporation":false,"usgs":false,"family":"Grapenthin","given":"Ronni","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":898367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Scott","contributorId":206392,"corporation":false,"usgs":false,"family":"Henderson","given":"Scott","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":898368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christoffersen, Michael S","contributorId":237038,"corporation":false,"usgs":false,"family":"Christoffersen","given":"Michael","email":"","middleInitial":"S","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":898379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":898370,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255672,"text":"70255672 - 2024 - Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US","interactions":[],"lastModifiedDate":"2024-06-28T11:49:44.275525","indexId":"70255672","displayToPublicDate":"2024-04-06T06:48:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17987,"text":"Global Environmental Change Advances","active":true,"publicationSubtype":{"id":10}},"title":"Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US","docAbstract":"Potential changes in wildland fire regimes due to anthropogenic climate change can be projected using data from climate models, but directly applying these meteorological variables to long-term planning and adaptive management activities may be difficult for decision makers. Analog mapping, in contrast, creates more intuitive assessments of changing fire regimes that also recognize the complex, multivariate, and multi-scalar nature of ecosystems. Here, we use data from 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5), to identify and map future climate-fire analogs for 655 protected areas in the conterminous U.S. based on annual temperature, cumulative precipitation amount and seasonality, and fire regime potentials derived from a simple process-based fire frequency model. Patterns of analogs were heavily influenced by gradients in latitude and topography, with longer time frames (end-of-century conditions) and the more extreme climate forcing scenario resulting in greater analog distances and more ensemble entropy (i.e., less consensus among climate models regarding the closest analog for a given management unit). Finer scale analyses for three protected areas (Yellowstone and Great Smoky Mountains National Parks, White Mountain National Forest) illustrate how climate-fire analog mapping can improve insight into the types of ecosystem responses that might occur under similar management conditions. Federally protected areas such as national parks, forests, and wildlife refuges have long served as reference sites for the study of fire regimes, a role that is likely to continue because many of these units are managed to allow at least some ecosystem processes to operate independently. The results suggest that analog mapping approaches are well-suited as part of qualitative assessments within climate- and fire-aware adaptive management processes. The use of analogs to depict relatable, real-world depictions of possible ecosystem changes in a given place, can help managers make more strategic choices about when and where to resist, accept, or direct climate change-driven ecological change.
Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
He‘eia and ‘Ioleka‘a Streams in the He‘eia watershed on O‘ahu, Hawai‘i, receive substantial discharge from dike-impounded groundwater. Previous studies indicated that groundwater withdrawals from the watershed affect streamflow. Resource managers and users seek information that can be used to balance the needs of competing uses of groundwater and streamflow in the watershed.
In this study, analyses of historical streamflow and withdrawal data indicate that when groundwater withdrawals from Haiku Tunnel (a groundwater development tunnel built in the 1940s in the watershed) of 1.73–1.87 million gallons per day (Mgal/d) were introduced in the first few decades of the tunnel’s operation, base flow at a gage on He‘eia Stream decreased by 1.37–1.40 Mgal/d. Changes in rainfall during this period were not sufficient to account for the changes in base flow. The tunnel withdrawal also affected ‘Ioleka‘a Stream, but the effect was less. In the 1980s, average withdrawal from the tunnel decreased by 0.73–1.00 Mgal/d and base flow at the He‘eia streamgage increased by 0.15–0.21 Mgal/d; a concurrent rainfall increase may partly account for the base-flow increase. Withdrawal from another well (Haiku well) starting in the late 1980s had a much smaller effect than the tunnel did on flow at the He‘eia streamgage.
Numerical groundwater-model simulations indicate that shutting down withdrawals from Haiku Tunnel and Haiku well would increase base flows in streams inside and outside of the He‘eia watershed. Simulated shutdown of 0.35 Mgal/d withdrawal from Haiku well caused base flow of streams in the He‘eia watershed to increase by 0.09 Mgal/d or 26 percent of the withdrawal reduction, and shutdown of 0.60 Mgal/d withdrawal from Haiku Tunnel caused base flow of streams within the watershed to increase by 0.12 Mgal/d or 20 percent of withdrawal reduction. Shutdown of a combined 0.95 Mgal/d withdrawal from the tunnel and well caused base flow of streams within the watershed to increase by 0.22 Mgal/d or 23 percent of the withdrawal reduction.
The model simulations and analyses of streamflow data demonstrate that, climate changes notwithstanding, reducing or shutting down withdrawal from Haiku Tunnel has not in the past, and will not in the future, restore base flow to predevelopment rates. The nearly pristine condition that existed prior to the construction of the Haiku Tunnel no longer exists because other large-producing tunnels and wells near the He‘eia watershed have since begun withdrawing water from the same dike-impounded aquifer. Reduction or shutdown of withdrawals from the wells and tunnel in the He‘eia watershed cannot restore streamflow to predevelopment rates if withdrawals from all other wells and tunnels continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245020","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Izuka, S.K., Kāne, H.L., and Rotzoll, K., 2024, Groundwater and surface-water interactions in the He‘eia watershed, O‘ahu, Hawai‘i—Insights from analysis of historical data and numerical groundwater-model simulations: U.S. Geological Survey Scientific Investigations Report 2024–5020, 22 p., https://doi.org/10.3133/sir20245020.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149791","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499449,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116223.htm","linkFileType":{"id":5,"text":"html"}},{"id":427359,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5020/sir20245020.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427358,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5020/covrthb.jpg"},{"id":427357,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91JM5FZ","text":"USGS Data Release","description":"Rotzoll, K., 2024, MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P91JM5FZ.","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii"}],"country":"United States","state":"Hawaii","otherGeospatial":"He‘eia Watershed, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.841667,\n 21.441667\n ],\n [\n -157.841667,\n 21.391667\n ],\n [\n -157.791667,\n 21.391667\n ],\n [\n -157.791667,\n 21.441667\n ],\n [\n -157.841667,\n 21.441667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America’s ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America.
We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature.
We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou (Rangifer tarandus), elk (Cervus canadensis), and white-tailed deer (Odocoileus virginianus) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat.
The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn (Antilocapra americana) and mountain goats (Oreamnos americanus), and on ungulates in the eastern U.S. and Mexico.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-024-00331-8","usgsCitation":"Malpeli, K., Endyke, S.C., Weiskopf, S.R., Thompson, L., Johnson, C.G., Kurth, K.A., and Carlin, M.A., 2024, Existing evidence on the effects of climate variability and climate change on ungulates in North America: A systematic map: Environmental Evidence, v. 13, 8, 21 p., https://doi.org/10.1186/s13750-024-00331-8.","productDescription":"8, 21 p.","ipdsId":"IP-159301","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439938,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-024-00331-8","text":"Publisher Index Page"},{"id":434994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EMV7HO","text":"USGS data release","linkHelpText":"Catalogue of the literature assessing climate effects on ungulates in North America 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Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":221497,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Ciara G.","contributorId":271273,"corporation":false,"usgs":false,"family":"Johnson","given":"Ciara","email":"","middleInitial":"G.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":898183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kurth, Katherine Anne 0000-0002-6883-8307","orcid":"https://orcid.org/0000-0002-6883-8307","contributorId":334177,"corporation":false,"usgs":true,"family":"Kurth","given":"Katherine","email":"","middleInitial":"Anne","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlin, Maxfield A.","contributorId":335367,"corporation":false,"usgs":false,"family":"Carlin","given":"Maxfield","email":"","middleInitial":"A.","affiliations":[{"id":37275,"text":"none","active":true,"usgs":false}],"preferred":false,"id":898185,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254886,"text":"70254886 - 2024 - Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","interactions":[],"lastModifiedDate":"2024-06-10T15:45:06.315386","indexId":"70254886","displayToPublicDate":"2024-04-04T10:36:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","title":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","docAbstract":"Ungulates commonly select habitat with higher forage biomass and or nutritional quality to improve body condition and fitness. However, predation risk can alter ungulate habitat selection and foraging behavior and may affect their nutritional condition. Ungulates often choose areas with lower predation risk, sometimes sacrificing higher quality forage. This forage–predation risk trade-off can be important for life history strategies and influences individual nutritional condition and population vital rates. We used GPS collar data from adult female mule deer (Odocoileus hemionus) and mountain lions (Puma concolor) to model mule deer habitat selection in relation to forage conditions, stalking cover and predation risk from mountain lions to determine if a forage-predation risk trade-off existed for mule deer in central New Mexico. We also examined mountain lion kill sites and mule deer foraging locations to assess trade-offs at a finer scale. Forage biomass and protein content were inversely correlated with horizontal visibility, hence associated with higher stalking cover for mountain lions, suggesting a forage-predation risk trade-off for mule deer. Mule deer habitat selection was influenced by forage biomass and protein content at the landscape and within home range spatial scales, with forage protein being related to habitat selection during spring and summer and forage biomass during winter. However, mule deer selection for areas with better foraging conditions was constrained by landscape-scale encounter risk for mountain lions, such that increasing encounter risk was associated with diminished selection for areas with better foraging conditions. Mule deer also selected for areas with higher visibility when mountain lion predation risk was higher. Mountain lion kill sites were best explained by decreasing horizontal visibility and available forage protein, suggesting that deer may be selecting for forage quality at the cost of predation risk. A site was 1.5 times more likely to be a kill site with each 1-meter decrease in visibility (i.e., increased stalking cover). Mule deer selection of foraging sites was related to increased forage biomass, further supporting the potential for a trade-off scenario. Mule deer utilized spatio-temporal strategies and risk-conditional behavior to reduce predation risk, and at times selected suboptimal foraging areas with lower predation risk.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1121439","usgsCitation":"Cain, J.W., Kay, J.H., Liley, S.G., and Gedir, J.V., 2024, Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk: Frontiers in Ecology and Evolution, v. 12, 1121439, 17 p., https://doi.org/10.3389/fevo.2024.1121439.","productDescription":"1121439, 17 p.","ipdsId":"IP-148026","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2024.1121439","text":"Publisher Index Page"},{"id":429764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cibola National Forest, Gallinas Mountains area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.59348062065897,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.340783560738174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kay, Jacob H.","contributorId":337909,"corporation":false,"usgs":false,"family":"Kay","given":"Jacob","email":"","middleInitial":"H.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liley, Stewart G.","contributorId":337910,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","email":"","middleInitial":"G.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gedir, Jay V.","contributorId":337911,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":902779,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252794,"text":"70252794 - 2024 - Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","interactions":[],"lastModifiedDate":"2024-04-05T15:19:37.198461","indexId":"70252794","displayToPublicDate":"2024-04-04T10:14:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","docAbstract":"Introduction: Information on spatial patterns of water depth in river channels is valuable for numerous applications, but such data can be difficult to obtain via traditional field methods. Ongoing developments in remote sensing technology have enabled various image-based approaches for mapping river bathymetry; this study evaluated the potential to retrieve depth from multispectral images acquired by an uncrewed aircraft system (UAS).
Methods: More specifically, we produced depth maps for a 4 km reach of a clear-flowing, relatively shallow river using an established spectrally based algorithm, Optimal Band Ratio Analysis. To assess accuracy, we compared image-derived estimates to direct measurements of water depth. The field data were collected by wading and from a boat equipped with an echo sounder and used to survey cross sections and a longitudinal profile. We partitioned our study area along the Sacramento River, California, USA, into three distinct sub-reaches and acquired a separate image for each one. In addition to the typical, self-contained, per-image depth retrieval workflow, we also explored the possibility of exporting a relationship between depth and reflectance calibrated using data from one site to the other two sub-reaches. Moreover, we evaluated whether sampling configurations progressively more sparse than our full field survey could still provide sufficient calibration data for developing robust depth retrieval models.
Results: Our results indicate that under favorable environmental conditions like those observed on the Sacramento River during low flow, accurate, precise depth maps can be derived from images acquired by UAS, not only within a sub-reach but also across multiple, adjacent sub-reaches of the same river.
Discussion: Moreover, our findings imply that the level of effort invested in obtaining field data for calibration could be significantly reduced. In aggregate, this investigation suggests that UAS-based remote sensing could facilitate highly efficient, cost-effective, operational mapping of river bathymetry at the reach scale in clear-flowing streams.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1305991","usgsCitation":"Legleiter, C.J., and Harrison, L.R., 2024, Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images: Frontiers in Remote Sensing, v. 5, 1305991, 16 p., https://doi.org/10.3389/frsen.2024.1305991.","productDescription":"1305991, 16 p.","ipdsId":"IP-156864","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1305991","text":"Publisher Index Page"},{"id":434995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEXVAR","text":"USGS data release","linkHelpText":"Multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, acquired September 14-16, 2021"},{"id":427518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 40\n ],\n [\n -122.5,\n 39\n ],\n [\n -121.75,\n 39\n ],\n [\n -121.75,\n 40\n ],\n [\n -122.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":898242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":898243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252628,"text":"sir20235133 - 2024 - Estimation and comparison of 1-percent annual exceedance probability flood flows at Federal Emergency Management Agency flood insurance study flow locations across Pennsylvania","interactions":[],"lastModifiedDate":"2026-01-30T19:32:57.548503","indexId":"sir20235133","displayToPublicDate":"2024-04-03T10:49:00","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":"2023-5133","displayTitle":"Estimation and Comparison of 1-Percent Annual Exceedance Probability Flood Flows At Federal Emergency Management Agency Flood Insurance Study Flow Locations Across Pennsylvania","title":"Estimation and comparison of 1-percent annual exceedance probability flood flows at Federal Emergency Management Agency flood insurance study flow locations across Pennsylvania","docAbstract":"Flood-flow estimates were computed at over 5,000 Federal Emergency Management Agency (FEMA) flood insurance study (FIS) flow locations across Pennsylvania for the 1-percent annual exceedance probability flood event (1-percent AEP). Depending on a point of interest’s proximity to a streamgage, weighting techniques may be applied to obtain flood-flow estimates for ungaged flow locations using observed peak-flow data from a nearby streamgage. Following the U.S. Geological Survey’s (USGS) published guidance, stream segments were identified where the drainage-area ratio method could be leveraged. Using updated regional regression equations and recently published flood-flow estimates at USGS streamgage locations following USGS Bulletin 17C guidelines, weighted and transferred flood flows were computed, where appropriate. For locations not applicable for the drainage-area ratio method, regression equations were used to compute flood-flow estimates. These flood-flow estimates were then compared to FEMA FIS 1-percent AEP flood-flow estimates. Percentage-difference values were computed for 3,599 FIS flow locations determined to be suitable for analysis, finding that USGS-derived flood-flow estimates were consistently lower than FEMA FIS flood-flow estimates with a statewide median percentage difference of −10.1 percent. The dataset was normally distributed with a standard deviation of 45.7 percent. Allegheny County was found to have 74 FIS flow locations with percentage-difference values greater than or equal to 67 percent or less than or equal to −67 percent. The flood-flow region in which Allegheny County is contained, Region 2, had a median percentage-difference value of −39 percent. Although removed from the final analysis, flow locations with drainage-area values above the recommended threshold for regression-based estimation (about 1,000 square miles [mi2]) were observed to have consistently higher percentage-difference values; a reminder of the limitations of use for regression-based flood-flow estimates. This report, the comparisons within, and a companion data release are intended to serve as tools to FEMA in assisting with the ongoing assessment of FIS flow locations across Pennsylvania.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235133","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Weaver, M.R., Stuckey, M.H., Colgin, J.E., and Roland, M.A., 2024, Estimation and comparison of 1-percent annual exceedance probability flood flows at Federal Emergency Management Agency flood insurance study flow locations across Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2023–5133, 33 p., https://doi.org/10.3133/sir20235133.","productDescription":"Report: viii, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151288","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":427275,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FG2MF2","text":"USGS data release","linkHelpText":"USGS-derived 1-percent 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road,
New Cumberland, PA 17070
Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16071264","usgsCitation":"Geisz, J.K., Wernette, P., and Esselman, P., 2024, Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning: Remote Sensing, v. 16, no. 7, 1264, 29 p., https://doi.org/10.3390/rs16071264.","productDescription":"1264, 29 p.","ipdsId":"IP-152592","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":439948,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16071264","text":"Publisher Index Page"},{"id":434996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N32CV7","text":"USGS data release","linkHelpText":"Autonomously Collected Benthic Imagery for Substrate Prediction, Lake Michigan 2020-2021"},{"id":433724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.90204822016032,\n 42.674502983776904\n ],\n [\n -87.65489307014272,\n 42.76669269166209\n ],\n [\n -86.26871023108912,\n 45.83189647310439\n ],\n [\n -86.71489901589742,\n 45.86861211740754\n ],\n [\n -87.71542062998205,\n 44.51147169699496\n ],\n [\n -88.08342598535049,\n 43.233909567544146\n ],\n [\n -87.90204822016032,\n 42.674502983776904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.61587060463187,\n 45.628545449737345\n ],\n [\n -84.87703701182424,\n 45.79764011104598\n ],\n [\n -85.13854737456055,\n 45.75496839185476\n ],\n [\n -85.7086983940175,\n 44.97892485981876\n ],\n [\n -85.62680073631381,\n 44.81109395544547\n ],\n [\n -84.61587060463187,\n 45.628545449737345\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Geisz, Joseph K. 0000-0001-6783-7057","orcid":"https://orcid.org/0000-0001-6783-7057","contributorId":342270,"corporation":false,"usgs":false,"family":"Geisz","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":912943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":912944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":912945,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252763,"text":"70252763 - 2024 - Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials","interactions":[],"lastModifiedDate":"2024-05-20T15:29:15.542173","indexId":"70252763","displayToPublicDate":"2024-04-03T09:13:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials","docAbstract":"While the recent incursion of highly pathogenic avian influenza into North America has resulted in notable losses to the commercial poultry industry, the mechanism by which virus enters commercial poultry houses is still not understood. One theorized mechanism is that waterfowl shed virus into the environment surrounding poultry farms, such as into retention ponds, and is then transmitted into poultry houses via bridge species. Little is known about if and when wild waterfowl use these retention ponds, leading to uncertainty regarding the potential significance of this interface. To quantify the use of retention ponds on commercial poultry farms by wild waterfowl, we surveyed 12 such ponds across Somerset and Dorchester counties, Maryland, USA. This region was chosen due to the high level of poultry production and its importance for migratory waterfowl. Surveys consisted of recording waterfowl visible on the retention ponds from public roadways at least once per week from 20 September 2022–31 March 2023. Throughout the course of this study, we observed a total of nine species of waterfowl using retention ponds on commercial poultry farms at nine of 12 sites. The number of waterfowl observed at retention ponds varied notably throughout the course of our survey period, with values generally following trends of fall migration within each species indicating that resident birds were not the only individuals to utilize these habitats. Additionally, waterfowl use was highest at sites with little vegetation immediately surrounding the pond, and lowest when ponds were surrounded by trees. Our data suggest that retention ponds on commercial poultry farms present a notable interface for waterfowl to introduce avian influenza viruses to farm sites. However, additional testing and surveys could provide further insight into whether it may be possible to reduce the use of these habitats by wild waterfowl through vegetative management as preliminarily reported here.
","language":"English","publisher":"Hindawi","doi":"10.1155/2024/3022927","usgsCitation":"Sullivan, J.D., McDonough, A., Lescure, L., and Prosser, D., 2024, Identifying an understudied interface: Preliminary evaluation of the use of retention ponds on commercial poultry farms by wild waterfowl: Transboundary and Emerging Diseases, v. 2024, 3022927, 9 p., https://doi.org/10.1155/2024/3022927.","productDescription":"3022927, 9 p.","ipdsId":"IP-156554","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":439953,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2024/3022927","text":"Publisher Index Page"},{"id":434998,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U7QISZ","text":"USGS data release","linkHelpText":"Data describing the use of retention ponds on commercial poultry facilities on Delmarva by wild waterfowl"},{"id":427619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","noUsgsAuthors":false,"publicationDate":"2024-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":898427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonough, Ayla","contributorId":332811,"corporation":false,"usgs":false,"family":"McDonough","given":"Ayla","email":"","affiliations":[{"id":78934,"text":"Akima","active":true,"usgs":false}],"preferred":false,"id":898428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lescure, Lauren","contributorId":335066,"corporation":false,"usgs":false,"family":"Lescure","given":"Lauren","affiliations":[{"id":27609,"text":"Contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":898429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":898430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256184,"text":"70256184 - 2024 - The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population","interactions":[],"lastModifiedDate":"2024-07-26T00:07:03.307419","indexId":"70256184","displayToPublicDate":"2024-04-02T19:05:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population","docAbstract":"Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO = 0.65) was higher in the recipient population than predicted had no augmentation occurred (HO = 0.58) but less than what was predicted by our model (HO = 0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype–environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near nine bridges at eight highway crossings of the Missouri River between Kansas City and St. Louis, Missouri, from May 19 to 26, 2021. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 to 1,840 feet (ft) longitudinally and generally extending laterally across the active channel from bank to bank during low to moderate flood-flow conditions. These surveys provided channel geometry and hydraulic conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing or verifying predictive guidelines or equations for computing potential scour depth. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every in-channel pier. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. Occasionally, scour holes were minor and difficult to discern from nearby dunes and ripples. All the bridge sites in this study were previously surveyed and documented in previous studies. Comparisons between bathymetric surfaces from the previous surveys and those of the current (2021) study do not indicate any consistent correlation between channel-bed elevations and streamflow conditions. The average difference between the bathymetric surfaces varied from 1.59 ft higher to 0.95 ft lower in 2021 than 2017, which corresponds to a gain of 100,200 cubic yards and a loss of 55,800 cubic yards, respectively. The average difference between the bathymetric surfaces varied from 2.74 ft higher to 3.05 ft lower in 2021 than 2013, which corresponds to a gain of 111,500 cubic yards and a loss of 169,200 cubic yards, respectively. The average difference between the bathymetric surfaces varied from 4.52 ft higher to 1.38 ft lower in 2021 than 2011, which corresponds to a gain of 221,100 cubic yards and a loss of 90,300 cubic yards, respectively. The most substantial overall net gain was 221,100 cubic yards between 2011 and 2021 at structures L0550 and A4497 at Jefferson City (site 20). The large net gain likely results from a combination of the mitigation of the scour holes near pier 4 of both bridges and the substantially lower flow in 2021 than in 2011. Alternatively, the most substantial overall net loss was 169,200 cubic yards between 2013 and 2021 at structure A6288 at Hermann (site 21), despite comparable streamflows.
Pier size, nose shape, and skew to approach flow had a substantial effect on the size of the scour hole observed at a given pier. Larger and deeper scour holes were present at piers with wide or blunt noses caused by exposed footings or caissons. When a pier was skewed to primary approach flow, the scour hole was generally deeper and larger than at a similar pier without skew; furthermore, the shape of the scour hole near skewed piers in this study generally was longer and deeper on the side with impinging flow. At structure A6288 at Hermann (site 21), the scour hole near pier 5 was difficult to discern from nearby dunes and ripples, whereas the upstream edge of the footing was visible at pier 4, which likely contributes to the larger scour hole near that pier; the top of the footing may blunt the horseshoe vortex at pier 5, but the exposed front of the footing may exacerbate the vortex at pier 4.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245021","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2024, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 19–26, 2021: U.S. Geological Survey Scientific Investigations Report 2024–5021, 101 p., https://doi.org/10.3133/sir20245021.","productDescription":"Report: xi, 101 p.; Data Release; Dataset","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137677","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492017,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116213.htm","linkFileType":{"id":5,"text":"html"}},{"id":427309,"rank":7,"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":427308,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ULGQ4W","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 19–26, 2021 (ver. 2.0, August 2023)"},{"id":427306,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245021/full"},{"id":427302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5021/coverthb.jpg"},{"id":427303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5021/sir20245021.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5021"},{"id":427304,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5021/sir20245021.XML"},{"id":427305,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5021/images/"}],"country":"United States","state":"Missouri","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.99927657337038,\n 39.617338148546736\n ],\n [\n -94.99927657337038,\n 38.11188192313625\n ],\n [\n -89.92359297962084,\n 38.11188192313625\n ],\n [\n -89.92359297962084,\n 39.617338148546736\n ],\n [\n -94.99927657337038,\n 39.617338148546736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
With the decline of bee populations worldwide, studies determining current wild bee distributions and diversity are increasingly important. Wild bee identification is often completed by experienced taxonomists or by genetic analysis. The current study was designed to compare two methods of identification including: (1) morphological identification by experienced taxonomists using images of field-collected wild bees and (2) genetic analysis of composite bee legs (multiple taxa) using metabarcoding. Bees were collected from conservation grasslands in eastern Iowa in summer 2019 and identified to the lowest taxonomic unit using both methods. Sanger sequencing of individual wild bee legs was used as a positive control for metabarcoding. Morphological identification of bees using images resulted in 36 unique taxa among 22 genera, and >80% of Bombus specimens were identified to species. Metabarcoding was limited to genus-level assignments among 18 genera but resolved some morphologically similar genera. Metabarcoding did not consistently detect all genera in the composite samples, including kleptoparasitic bees. Sanger sequencing showed similar presence or absence detection results as metabarcoding but provided species-level identifications for cryptic species (i.e., Lasioglossum). Genus-specific detections were more frequent with morphological identification than metabarcoding, but certain genera such as Ceratina and Halictus were identified equally well with metabarcoding and morphology. Genera with proportionately less tissue in a composite sample were less likely to be detected using metabarcoding. Image-based methods were limited by image quality and visible morphological features, while genetic methods were limited by databases, primers, and amplification at target loci. This study shows how an image-based identification method compares with genetic techniques, and how in combination, the methods provide valuable genus- and species-level information for wild bees while preserving tissue for other analyses. These methods could be improved and transferred to a field setting to advance our understanding of wild bee distributions and to expedite conservation research.
Increasing stream metal concentrations apparently caused by climate warming have been reported for a small number of mountain watersheds containing hydrothermally altered bedrock with abundant sulfide minerals (mineralized watersheds). Such increases are concerning and could negatively impact downstream ecosystem health, water resources, and mine-site remediation efforts. However, the pervasiveness and typical magnitude of these trends remain uncertain. We aggregated available streamwater chemistry data collected from late summer and fall over the past 40 years for 22 mineralized watersheds throughout the Colorado Rocky Mountains. Temporal trend analysis performed using the Regional Kendall Test indicates significant regional upward trends of ∼2% of the site median per year for sulfate, zinc, and copper concentrations in the 17 streams affected by acid rock drainage (ARD; median pH ≤ 5.5), equivalent to concentrations roughly doubling over the past 30 years. An examination of potential load trends utilizing streamflow data from eight “index gages” located near the sample sites provides strong support for regionally increasing sulfate and metal loads in ARD-affected streams, particularly at higher elevations. Declining streamflows are likely contributing to regionally increasing concentrations, but increasing loads appear to be on average an equal or greater contributor. Comparison of selected site characteristics with site concentration trend magnitudes shows the highest correlation for mean annual air temperature and mean elevation (R2 of 0.42 and 0.35, respectively, with all others being ≤0.14). Future research on climate-driven controlling mechanisms should therefore focus on processes such as melting of frozen ground directly linked to site mean temperature and elevation.
Urban growth and other land-use changes were examined in the Lower Rio Grande Valley and Alluvial Floodplain ecoregions in Texas, along the U.S.-Mexico border. The analysis focused on understanding the types and causes of land change as well as the recovery of natural land-cover types between years 2001 and 2011. The purpose was to develop improved capabilities for understanding land change dynamics in urbanizing ecoregions and to provide data for further analyses. The spatial data, including metadata, allows further exploration and characterization of changes affecting this dynamic region.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
This study examined the abundance and distribution of nutrients and phytoplankton in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) and Suisun Bay, comprising three spatial surveys conducted in May, July, and October of 2018 that used continuous underway high frequency sampling and measurements onboard a high-speed boat to characterize spatial variation across the extent of the Delta. The method used involves simultaneously collecting information about the concentration and spatial distribution of all major nutrient forms with analogous information about the major classes of phytoplankton and associated water-quality conditions. The results showed substantial variation across space and time, providing an unprecedented snapshot of the dynamic environmental processes that shape the ways nutrients interact with and affect aquatic habitats in the Delta.
The purposes of this study were to improve our understanding of how hydrodynamics, landscape features, and aquatic primary productivity interact to drive nutrient cycling and transport in the Delta and to provide insights into the underlying processes most directly responsible for the conditions at the time of this study, and thus into the range of conditions that may be expected following the wide array of prospective future changes to the Delta. One major anticipated change at the time of this study was the planned upgrade to the Sacramento Regional Wastewater Treatment Plant, but the study also informs our understanding of potential effects from other changes to the Delta, such as those caused by other nutrient-management actions, flow actions, large-scale wetland restoration, drought, flood, levee failure, and changes to water management.
Nutrient loading is the primary driver of nutrient concentrations in the Delta, but several other major drivers interact to shape their distribution and effects: geomorphology, hydrodynamics, landscape features, and aquatic productivity. Hydrodynamics affect timescales of transport and dilution of nutrient loads in the Delta. During transit through the system, channel geometry, tidal mixing, and water exports affect hydrodynamics in diverse ways that influence water-residence and transport times, thereby markedly affecting the range of times during which natural internal cycling can alter nutrient concentrations and forms. Channel geometry and location shape tidal energy and river currents into these observed dynamics. Interactions with Delta aquatic landscapes such as herbaceous tidal marsh, submerged aquatic vegetation, and large expanses of intertidal or subtidal sediments (all highly productive landscapes) exert demand on available nutrient supplies but can also simultaneously transform and generate nutrients. Finally, while phytoplankton require nutrients to sustain production and thus are a potential nutrient sink, the amount and form of nutrients also can influence the occurrence of harmful algal blooms (HABs) that adversely affect aquatic organisms as well as affect the occurrence of beneficial algal blooms that result in production of algae that are favorable for imperiled Delta pelagic aquatic food webs.
The surveys revealed a complex mosaic of spatial variation, with nutrient concentrations varying from near zero to well above concentrations considered eutrophic; nutrient concentrations were more often related to the extent of hydrologic transport and mixing than to specific geographic locations or to specific landscape features. Similarly, the surveys identified phytoplankton abundance ranging from near detection to the level of large phytoplankton blooms, with large variation in phytoplankton community composition. Although the study occurred during a period of low bloom activity, phytoplankton productivity appeared to be the strongest potential sink for inorganic nutrients in the Delta, indicating that it is a larger control on nutrient concentrations and distribution than previously understood. Cycling and transformation within the water column only appeared to substantially lower total nutrient concentrations at the longest estimated transport timescales. Contrary to expectations, we did not observe substantial nutrient depletion near landscape-scale features such as open-water habitats, submerged aquatic vegetation beds, extensive wetlands, or exposed sediments, indicating that these habitat types did not act as major sinks for nutrients in the Delta during these surveys. These results indicated that nutrient reduction efforts may have the greatest effect on pelagic phytoplankton productivity in the more productive reaches of the Delta and estuary, but these effects are unlikely to be magnified by changes to nutrient loss within the Delta over conceivable changes in flow conditions, Delta water management actions, or large-scale wetland restoration activities. Nevertheless, local processes were shown to cause substantial loss, and thus integrating of nutrient effects with other indicators of aquatic habitat conditions will help inform planning future actions at specific sites.
Finally, we note that the primary contribution of this study was intended to be the survey data themselves. Aside from the results highlighted in this report, the surveys are a benchmark against which future environmental change may be evaluated, including changes to nutrient management or water exports, drought, large-scale wetland restoration, and climate change. Further, although we highlight some of the main findings from the surveys in this report, the necessarily limited scope precludes examination of many topics for which these surveys may be highly informative. To facilitate the utility of these data to stakeholders, managers, and researchers, we have released the data online (Bergamaschi and others, 2020) and created an online data exploration portal (https://ca.water.usgs.gov/bay-delta/2018-delta-wide-mapping-surveys.html) where users may query the surveys in a variety of ways to test hypotheses, examine relationships, assess spatial trends, and download data. The data exploration portal is intended to be an immersive experience that allows users to gain greater understanding of the complex interactions that shape Delta aquatic environments. This report is intended as a companion to the portal, allowing the reader to challenge and further explore the highlighted findings.
This study was a collaboration between the U.S. Geological Survey and the Delta Regional Monitoring Program, with additional funding provided from U.S. Geological Survey Cooperative Matching Funds Program.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Summer Lake is located in south-central Oregon at the extreme northwestern extent of the Basin and Range Province, bordered by the Cascade Volcanic Province to the west and the High Lava Plains to the north. The valley hosts numerous hot springs and a small geothermal powerplant at the southeastern end of the valley in the town of Paisley. This tectonically active region has undergone significant ENE-directed extension producing highly faulted terrain with fault blocks tilting on average 60° from the maximum extension direction. Local geology consists of young volcanics which have been extensively dissected by predominantly NNW-trending normal faults. These same structures likely extend through the basin but are concealed by young basin fill sediments and volcanics. As a result, potential field geophysical methods are ideally suited for characterizing subsurface geology and structures in this region which are important for understanding basin evolution and tectonics within the valley. New ground-based gravity and magnetic data, as well as sUAS- (small uncrewed aerial systems) based magnetic data reveal a prevalent NNW-trending fabric beneath the basin fill in southern Summer Lake valley that likely plays an important role in controlling the flow of subsurface hydrothermal fluids. Additionally, measurements were performed on outcrops, hand samples and paleomagnetic cores to constrain the physical properties (density, magnetic susceptibility and magnetic remanence) of local geology. Together, these data help resolve basin geometry and delineate concealed faults and contacts, informing our understanding of the structural framework and geothermal resource potential of southern Summer Lake valley.
","conferenceTitle":"49th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 12-14, 2024","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Earney, T.E., and Glen, J.M., 2024, Characterizing structure in southern Summer Lake valley, Oregon using ground- and sUAS-based potential field geophysics, 49th Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 12-14, 2024, 16 p.","productDescription":"16 p.","ipdsId":"IP-161623","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":432320,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=36321","linkFileType":{"id":5,"text":"html"}},{"id":432336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Summer Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.96741614462996,\n 43.12495779691321\n ],\n [\n -120.96741614462996,\n 42.57546720866469\n ],\n [\n -120.36914716752312,\n 42.57546720866469\n ],\n [\n -120.36914716752312,\n 43.12495779691321\n ],\n [\n -120.96741614462996,\n 43.12495779691321\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":909204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":909205,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252666,"text":"70252666 - 2024 - A Robot Operating System (ROS) package for mapping flow fields in rivers via Particle Image Velocimetry (PIV)","interactions":[],"lastModifiedDate":"2024-04-03T13:47:52.663272","indexId":"70252666","displayToPublicDate":"2024-04-01T08:45:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17446,"text":"Software X","active":true,"publicationSubtype":{"id":10}},"title":"A Robot Operating System (ROS) package for mapping flow fields in rivers via Particle Image Velocimetry (PIV)","docAbstract":"Non-contact, remote sensing approaches to measuring flow velocities in river channels are widely used, but typical workflows involve acquiring images in the field and then processing data later in the office. To reduce latency between acquisition and output, with the ultimate goal of enabling real-time image velocimetry, we developed a Robot Operating System (ROS) package for Particle Image Velocimetry (PIV) that can be deployed on an embedded computer aboard an uncrewed aircraft system (UAS). The ROSPIV package consists of a series of nodes that can be run in parallel and comprise an end-to-end PIV workflow. Software development involved converting MATLAB code to C++, organizing files within a catkin workspace, and building nodes using catkin_make. The codebase is available via a repository that includes a user’s guide and demo script. This paper describes the nodes in the ROSPIV package as well as functions for preparing inputs, facilitating code generation, and visualizing PIV output. To illustrate the application of the software, we present two examples, one based on a simulated image sequence and the other based on data acquired from a UAS. For the simulated data, the velocity field derived via the ROSPIV package closely matched the known flow field used to generate the image sequence. Using real data as input demonstrated the ability of the ROSPIV package to ingest and pre-process raw images. Our initial results suggest that the ROSPIV package could become a viable approach for mapping river surface velocities in real time.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.softx.2024.101711","usgsCitation":"Legleiter, C.J., and Dille, M., 2024, A Robot Operating System (ROS) package for mapping flow fields in rivers via Particle Image Velocimetry (PIV): Software X, v. 26, 101711, 7 p., https://doi.org/10.1016/j.softx.2024.101711.","productDescription":"101711, 7 p.","ipdsId":"IP-157370","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439989,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.softx.2024.101711","text":"Publisher Index Page"},{"id":435001,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96BQQQ6","text":"USGS data release","linkHelpText":"Remotely sensed data from a reach of the Sacramento River near Glenn, California, used to perform Particle Image Velocimetry (PIV) within the Robot Operating System (ROS)"},{"id":427352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":897859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dille, Michael","contributorId":331596,"corporation":false,"usgs":false,"family":"Dille","given":"Michael","email":"","affiliations":[{"id":79249,"text":"NASA Ames Research Center Intelligent Robotics Group","active":true,"usgs":false}],"preferred":false,"id":897860,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255026,"text":"70255026 - 2024 - Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures","interactions":[],"lastModifiedDate":"2024-07-15T15:13:06.675879","indexId":"70255026","displayToPublicDate":"2024-04-01T08:31:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures","docAbstract":"Surface-water supplies are important sources of drinking water for residents in the Triangle area of North Carolina, which is located within the upper Cape Fear and Neuse River Basins. Since 1988, the U.S. Geological Survey and a consortium of local governments have participated in a cooperative effort, known as the Triangle Area Water Supply Monitoring Project, to track water-quality and quantity conditions in several of the area’s water-supply reservoirs and streams. This report summarizes the hydrologic and water-quality monitoring activities through this cooperative effort, including an overview of previous and current data collection and quality-assurance and quality-control activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241003","issn":"2331-1258","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"Diaz, J.C., and Fanelli, R.M., 2024, Triangle Area Water Supply Monitoring Project, North Carolina—Overview of hydrologic and water-quality monitoring activities and data quality assurance: U.S. Geological Survey Open-File Report 2024–1003, 8 p., https://doi.org/10.3133/ofr20241003.","productDescription":"Report: vi, 8 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-140656","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":499203,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116211.htm","linkFileType":{"id":5,"text":"html"}},{"id":426743,"rank":1,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1003/images"},{"id":426744,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1003/coverthb.jpg"},{"id":426745,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1003/ofr20241003.pdf","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1003"},{"id":426747,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1003/ofr20241003.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1003 XML"},{"id":426746,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241003/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1003 HTML"},{"id":426748,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MU5BAZ","text":"USGS Data Release","linkHelpText":"Associated data for the Triangle Area Water Supply Monitoring Project, North Carolina, October 2019–September 2022"}],"country":"United States","state":"North Carolina","otherGeospatial":"Triangle area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.65,\n 36.25\n ],\n [\n -79.375,\n 36.25\n ],\n [\n -79.375,\n 35.5\n ],\n [\n -78.65,\n 35.5\n ],\n [\n -78.65,\n 36.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Prior to 2014, local utilities and State agencies monitored for cyanotoxins and taste-and-odor (T&O) compounds and reported occasional detections in three water-supply reservoirs in Wake County, North Carolina. Comparable data for cyanotoxins and T&O compounds were lacking for other water-supply reservoirs in the Triangle area of North Carolina. This report assesses whether cyanotoxins and T&O compounds occurred in four previously unmonitored North Carolina Triangle area water-supply reservoirs at levels that exceed existing North Carolina and U.S. Environmental Protection Agency recreational and drinking water health advisory, guidance, and criterion levels based on data collected during the peak phytoplankton growth period in 2014. Samples were collected from five sites across the study reservoirs (Cane Creek Reservoir, West Fork Eno River Reservoir, B. Everett Jordan Lake, and University Lake) between April and October 2014 and analyzed for physical characteristics, chemical constituents, phytoplankton communities, cyanotoxins, and T&O compounds.
Lake stratification during the sampling period in 2014 could indicate that the deep zones of the water column, during stratified anoxic conditions, may serve as possible sources of nutrients and metals for algal growth and other biogeochemical processes. Differences in phytoplankton communities were attributed to variability in environmental conditions across the sites and sampling events. Differences generally were greater among sites than among sampling events for phytoplankton communities and environmental conditions.
Phytoplankton community assemblages, within reservoirs, often were dominated by cyanobacteria that contained genera capable of producing T&O compounds and cyanotoxins during summer and fall months. The occurrence and associated biovolumes of potential producers of cyanotoxins and T&O compounds varied across the sites and sampling events. Of 20 samples collected during the study, the T&O compound geosmin and the cyanotoxin microcystin were present in 19 and 18 samples, respectively. While not harmful, the aesthetically displeasing geosmin concentrations periodically exceeded the human detection threshold of 15 nanograms per liter at most sites. The T&O compound 2-methylisoborneol (MIB) was detected in 11 of 20 samples, with concentrations below the human detection threshold of 15 nanograms per liter in all but one sample. The cyanotoxin anatoxin-a was detected in two of the samples. No other cyanotoxins were detected during the study.
In general, results did not indicate the biovolume of any given phytoplankton genera in the study was correlated with increased concentrations of MIB, geosmin, or microcystin. Results from this study indicated that microcystin concentrations in the water-supply reservoirs in the Triangle area were below EPA-recommended recreational level of 8 micrograms per liter, but periodically exceeded the EPA finished-water 10-day health advisory level of 0.3 microgram per liter for bottle-fed infants and preschool-age children. This suggests longer term data collection may be necessary to better understand the magnitude and frequency of cyanotoxin concentrations in these four water-supply reservoirs, particularly those with an elevated risk of exceeding the EPA 10-day health advisory levels in the finished drinking water or those with a higher frequency of T&O compound occurrence.
Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093