{"pageNumber":"270","pageRowStart":"6725","pageSize":"25","recordCount":165605,"records":[{"id":70243807,"text":"70243807 - 2023 - Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","interactions":[],"lastModifiedDate":"2023-05-25T15:59:15.380077","indexId":"70243807","displayToPublicDate":"2023-05-22T08:29:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","docAbstract":"

In this study, we compare the Southern California Earthquake Center CyberShake platform against the Next Generation Attenuation‐West2 empirical datasets. Because the CyberShake and empirical datasets cover very different magnitude ranges and site conditions, we develop ground‐motion models (GMMs) for CyberShake datasets to compare trends with empirical GMMs and decompose the residuals for further analysis. We apply mixed effects regression to four CyberShake datasets in southern, central, and northern California at 2, 3, 5, and 10 s periods, and compare the results with the empirical datasets using the same approach. CyberShake captures the total variability of ground motions in the empirical datasets but tends to predict larger median ground motions relative to the empirical GMMs. We then calculate and compare the repeatable source‐specific location, site, and path effects between CyberShake and empirical datasets. We find that the correlations of site effects between the CyberShake and empirical datasets are generally satisfactory, but the variability of site effects is slightly smaller for CyberShake datasets. There is no apparent correlation of source‐specific location effects between the CyberShake and empirical datasets. Comparison of path effects shows a wide range of correlation coefficients. Finally, we investigate the source of observed differences between the CyberShake and empirical datasets. We attribute the larger median ground‐motion levels in CyberShake to a combination of the homogeneous slip patterns of the earthquake ruptures, the low resolution of near‐surface materials in the velocity models, and strong reflections at high‐contrast boundaries in the velocity models. These factors also impact the correlations of site and path effects between the CyberShake and empirical datasets. Moreover, the leakage from location effects into site and path terms further weakens the correlations. In summary, we find that CyberShake could be improved, but it is still very useful to supplement empirical datasets for ground‐motion studies, especially to inform their nonergodic components.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220049","usgsCitation":"Meng, X., Goulet, C., Milner, K.R., Graves, R., and Callaghan, S., 2023, Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1152-1175, https://doi.org/10.1785/0120220049.","productDescription":"24 p.","startPage":"1152","endPage":"1175","ipdsId":"IP-140662","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Meng, Xiaofeng","contributorId":304299,"corporation":false,"usgs":false,"family":"Meng","given":"Xiaofeng","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":873337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goulet, Christine","contributorId":304298,"corporation":false,"usgs":false,"family":"Goulet","given":"Christine","affiliations":[{"id":54387,"text":"SCEC","active":true,"usgs":false}],"preferred":false,"id":873338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":873339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X 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US","interactions":[],"lastModifiedDate":"2023-05-22T13:08:24.434575","indexId":"70243816","displayToPublicDate":"2023-05-22T07:57:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","docAbstract":"

This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.

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Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":873360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. Michael","affiliations":[{"id":66193,"text":"NOAA-NWS-OWP","active":true,"usgs":false}],"preferred":false,"id":873361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243804,"text":"70243804 - 2023 - User engagement testing with a pilot decision support tool aimed to support species managers","interactions":[],"lastModifiedDate":"2023-05-22T12:55:06.432596","indexId":"70243804","displayToPublicDate":"2023-05-22T07:46:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5829,"text":"Weather, Climate, and Society","printIssn":"1948-8327","active":true,"publicationSubtype":{"id":10}},"title":"User engagement testing with a pilot decision support tool aimed to support species managers","docAbstract":"Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/WCAS-D-22-0010.1","usgsCitation":"Cashwell, H.J., McNeal, K.S., Dello, K., Boyles, R., and Davis, C., 2023, User engagement testing with a pilot decision support tool aimed to support species managers: Weather, Climate, and Society, v. 15, no. 2, p. 327-338, https://doi.org/10.1175/WCAS-D-22-0010.1.","productDescription":"12 p.","startPage":"327","endPage":"338","ipdsId":"IP-137275","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":443439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1175/wcas-d-22-0010.1","text":"Publisher Index Page"},{"id":417289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cashwell, Haven J.","contributorId":305618,"corporation":false,"usgs":false,"family":"Cashwell","given":"Haven","email":"","middleInitial":"J.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":873329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNeal, Karen S.","contributorId":305619,"corporation":false,"usgs":false,"family":"McNeal","given":"Karen","email":"","middleInitial":"S.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":873330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dello, Kathie","contributorId":224723,"corporation":false,"usgs":false,"family":"Dello","given":"Kathie","email":"","affiliations":[{"id":25510,"text":"NC State University","active":true,"usgs":false}],"preferred":false,"id":873331,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":873332,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Corey","contributorId":221987,"corporation":false,"usgs":false,"family":"Davis","given":"Corey","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":873333,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248071,"text":"70248071 - 2023 - Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients","interactions":[],"lastModifiedDate":"2023-09-05T11:44:56.309185","indexId":"70248071","displayToPublicDate":"2023-05-22T06:41:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3563,"text":"The ISME Journal","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients","docAbstract":"

Permafrost underlies approximately one quarter of Northern Hemisphere terrestrial surfaces and contains 25–50% of the global soil carbon (C) pool. Permafrost soils and the C stocks within are vulnerable to ongoing and future projected climate warming. The biogeography of microbial communities inhabiting permafrost has not been examined beyond a small number of sites focused on local-scale variation. Permafrost is different from other soils. Perennially frozen conditions in permafrost dictate that microbial communities do not turn over quickly, thus possibly providing strong linkages to past environments. Thus, the factors structuring the composition and function of microbial communities may differ from patterns observed in other terrestrial environments. Here, we analyzed 133 permafrost metagenomes from North America, Europe, and Asia. Permafrost biodiversity and taxonomic distribution varied in relation to pH, latitude and soil depth. The distribution of genes differed by latitude, soil depth, age, and pH. Genes that were the most highly variable across all sites were associated with energy metabolism and C-assimilation. Specifically, methanogenesis, fermentation, nitrate reduction, and replenishment of citric acid cycle intermediates. This suggests that adaptations to energy acquisition and substrate availability are among some of the strongest selective pressures shaping permafrost microbial communities. The spatial variation in metabolic potential has primed communities for specific biogeochemical processes as soils thaw due to climate change, which could cause regional- to global- scale variation in C and nitrogen processing and greenhouse gas emissions.

","language":"English","publisher":"Nature","doi":"10.1038/s41396-023-01429-6","usgsCitation":"Waldrop, M., Chabot, C., Liebner, S., Holmes, S., Snyder, M., Dillon, M.L., Dudgeon, S., Douglas, T.A., Leewis, M., Walter- Anthony, K.M., McFarland, J., Arp, C.D., Bondurant, A.C., Tas, N., and Mackelprang, R., 2023, Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients: The ISME Journal, v. 17, p. 1224-1235, https://doi.org/10.1038/s41396-023-01429-6.","productDescription":"12 p.","startPage":"1224","endPage":"1235","ipdsId":"IP-137807","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":443448,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-023-01429-6","text":"Publisher Index 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Northridge","active":true,"usgs":false}],"preferred":false,"id":881752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liebner, Susanne 0000-0002-9389-7093","orcid":"https://orcid.org/0000-0002-9389-7093","contributorId":328923,"corporation":false,"usgs":false,"family":"Liebner","given":"Susanne","email":"","affiliations":[{"id":39797,"text":"GFZ German Research Centre for Geosciences","active":true,"usgs":false}],"preferred":false,"id":881753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holmes, Sheila","contributorId":236797,"corporation":false,"usgs":false,"family":"Holmes","given":"Sheila","email":"","affiliations":[],"preferred":false,"id":881754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Snyder, Marcia","contributorId":176290,"corporation":false,"usgs":false,"family":"Snyder","given":"Marcia","affiliations":[],"preferred":false,"id":881755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dillon, Martin L.","contributorId":316628,"corporation":false,"usgs":false,"family":"Dillon","given":"Martin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":881756,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dudgeon, S","contributorId":328943,"corporation":false,"usgs":false,"family":"Dudgeon","given":"S","email":"","affiliations":[{"id":78532,"text":"California State Northridge","active":true,"usgs":false}],"preferred":false,"id":881757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":881758,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leewis, Mary-Catherine","contributorId":328944,"corporation":false,"usgs":false,"family":"Leewis","given":"Mary-Catherine","email":"","affiliations":[{"id":24491,"text":"Agriculture and Agri-Food Canada","active":true,"usgs":false}],"preferred":false,"id":881759,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Walter- Anthony, Katie M","contributorId":328945,"corporation":false,"usgs":false,"family":"Walter- Anthony","given":"Katie","email":"","middleInitial":"M","affiliations":[{"id":78533,"text":"University Alaska Fairbanks, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":881760,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McFarland, Jack 0000-0001-9672-8597","orcid":"https://orcid.org/0000-0001-9672-8597","contributorId":214819,"corporation":false,"usgs":true,"family":"McFarland","given":"Jack","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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0000-0001-7525-2331","orcid":"https://orcid.org/0000-0001-7525-2331","contributorId":328928,"corporation":false,"usgs":false,"family":"Tas","given":"Neslihan","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":881764,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Mackelprang, Rachel","contributorId":200882,"corporation":false,"usgs":false,"family":"Mackelprang","given":"Rachel","email":"","affiliations":[{"id":7080,"text":"California State University, Northridge","active":true,"usgs":false}],"preferred":false,"id":881765,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70247515,"text":"70247515 - 2023 - High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2023-08-10T11:38:17.536908","indexId":"70247515","displayToPublicDate":"2023-05-22T06:36:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","docAbstract":"

Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki (Oreomystis bairdi) and 'akeke'e (Loxops caeruleirostris), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km2 for 'akikiki, compared to the current Kaua'i range of 13.09 km2. In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km2, respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe (Palmeria dolei), Maui 'alauahio (Paroreomyza montana), and kiwikiu (Pseudonestor xanthophrys). Weighted overlap areas between the species from both islands were moderate (<12 km2), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2889","usgsCitation":"Gallerani, E.M., Burgett, J., Vaughn, N.R., Fortini, L., Fricker, G.A., Mounce, H., Gillespie, T.W., Crampton, L.H., Knapp, D., Hite, J.M., and Gilb, R., 2023, High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands: Ecological Applications, v. 33, no. 5, e2889, 17 p., https://doi.org/10.1002/eap.2889.","productDescription":"e2889, 17 p.","ipdsId":"IP-146020","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2889","text":"Publisher Index Page"},{"id":419690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type","interactions":[],"lastModifiedDate":"2023-05-24T15:56:03.751044","indexId":"70243886","displayToPublicDate":"2023-05-20T10:50:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type","docAbstract":"

The total concentration of dissolved constituents in water is routinely quantified by measurements of salinity or total dissolved solids (TDS). However, salinity and TDS are operationally defined by their analytical methods and are not equivalent for most waters. Furthermore, multiple methods are available to determine salinity and TDS, and these methods have inherent differences. TDS is defined as the mass of anhydrous residue remaining in a sample vessel after evaporation and subsequent oven drying at a defined temperature. Salinity is a measure of the mass of dissolved salts in a given mass of solution. In addition, there are approaches that quantify the total solute (TS) concentration, including gases. The purpose of this study is to develop a proxy method using specific conductance and major-ion water type to reliably predict salinity, TDS, and/or TS. Thus, we compared several methods to calculate salinity, TDS, and TS for 6391 surface water samples and conclude the following: TDS measurements are best suited for studies of anhydrous residue (e.g., evaporites); salinity determined by summing the speciated ion concentrations, termed S∑spec, is the most comprehensive method to represent the concentration of dissolved constituents in natural waters; and TS determinations are useful if dissolved CO2 and other gases are of interest. Thus, we utilized S∑spec to compare differences between the various salinity, TDS, and TS methods, and to develop a new proxy method to predict salinity based on specific conductance (SC) and major ion water type, termed SSC_WT. For the surface waters used in this study, the median difference between TDS and S∑Spec was between −19% and −24%, depending on the method. The median difference between SSC_WT and S∑Spec was −2.4% for the samples in this study. The SSC_WT approach is cost effective, rapid, and capable of providing reliable real-time salinity determinations at surface water sites where SC data are available and water type is known.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2023.105684","usgsCitation":"McCleskey, R., Cravotta, C., Miller, M., Tillman, F.D., Stackelberg, P.E., Knierim, K.J., and Wise, D., 2023, Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type: Applied Geochemistry, v. 154, 105684, 13 p., https://doi.org/10.1016/j.apgeochem.2023.105684.","productDescription":"105684, 13 p.","ipdsId":"IP-148865","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":443454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2023.105684","text":"Publisher Index Page"},{"id":417398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":873614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":873618,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873619,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wise, Daniel R. 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873620,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70262259,"text":"70262259 - 2023 - Contemporary distribution of non-native Chinook salmon (Oncorhynchus tshawytscha) in South America","interactions":[],"lastModifiedDate":"2025-01-21T15:31:02.614835","indexId":"70262259","displayToPublicDate":"2023-05-20T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Contemporary distribution of non-native Chinook salmon (Oncorhynchus tshawytscha) in South America","docAbstract":"

Chinook salmon represent one of the most successful salmonid introductions in South America, and today multiple naturalized populations exist across Patagonia. Here, we present an updated regional distribution of Chinook salmon that includes new records of occurrences collected between 2006 and 2022. We found a significant range expansion in terms of occurrences for adult (18 new basins; 2,854,108 km2) and adult spawners or juvenile (12 new basins; 53,262 km2) salmon extending both to the north and south of the previously known colonized range in South America. This range expansion (38% of the area considering only occurrences indicating reproduction via adult spawners or juvenile salmon) included major basins draining to both the Pacific Ocean and Atlantic Ocean coasts of the continent. Adult Chinook salmon are currently reported from 48 large basins (33.62°–54.97° S) covering a total drainage area of 3,047,197 km2. The observed expansion we document here has been likely driven by the dispersal of straying adults from historically naturalized populations. Our findings provide evidence that the invasion of Chinook salmon in South America is ongoing and updated information relevant to the management of this invasive and socio-ecologically important fish.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-023-03083-7","usgsCitation":"Figueroa-Munoz, G., Olivos, J., Arismendi, I., Fabiano, G., Laporta, M., Silveira, S., González-Bergonzoni, I., Pavez, G., Ernst, B., Ciancio, J., Harrod, C., Di Prinzio, C., Chalde, T., Murphy, C.A., and Gomez-Uchida, D., 2023, Contemporary distribution of non-native Chinook salmon (Oncorhynchus tshawytscha) in South America: Biological Invasions, v. 25, p. 2727-2735, https://doi.org/10.1007/s10530-023-03083-7.","productDescription":"9 p.","startPage":"2727","endPage":"2735","ipdsId":"IP-144987","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South 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Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections. As an alternative, we present a long-recognized but underutilized Bayesian technique that uses limited local data and informative priors to make statistically valid predictions and forecasts about disease occurrence and spread. As a case study, we use limited local data that were available after the detection of chronic wasting disease in Michigan, U.S. along with information rich priors obtained from a previous study in a neighboring state. Using these limited local data and informative priors, we generate statistically valid predictions of disease occurrence and spread for the Michigan study area. This Bayesian technique is conceptually and computationally simple, relies on little to no local data, and is competitive with non-statistical distance-based metrics in all performance evaluations. Bayesian modeling has added benefits because it allows practitioners to generate immediate forecasts of future disease conditions and provides a principled framework to incorporate new data as they accumulate. We contend that the Bayesian technique offers broad-scale benefits and opportunities to make statistical inference across a diversity of data-deficient systems, not limited to disease.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-35177-6","usgsCitation":"Cook, J., Williams, D.M., Walsh, D.P., and Hefley, T.J., 2023, Bayesian forecasting of disease spread with little or no local data: Scientific Reports, v. 13, 8137, 8 p., https://doi.org/10.1038/s41598-023-35177-6.","productDescription":"8137, 8 p.","ipdsId":"IP-138124","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":443457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-35177-6","text":"Publisher Index Page"},{"id":430174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D","contributorId":256954,"corporation":false,"usgs":false,"family":"Cook","given":"Jonathan D","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":903759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, David M.","contributorId":205354,"corporation":false,"usgs":false,"family":"Williams","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":903760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":903761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":903762,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243785,"text":"ofr20211104D - 2023 - Potential effects of climate change on Emydoidea blandingii (Blanding’s turtle)","interactions":[{"subject":{"id":70243785,"text":"ofr20211104D - 2023 - Potential effects of climate change on Emydoidea blandingii (Blanding’s turtle)","indexId":"ofr20211104D","publicationYear":"2023","noYear":false,"chapter":"D","displayTitle":"Potential Effects of Climate Change on Emydoidea blandingii (Blanding’s Turtle)","title":"Potential effects of climate change on Emydoidea blandingii (Blanding’s turtle)"},"predicate":"IS_PART_OF","object":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"id":1}],"isPartOf":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"lastModifiedDate":"2023-05-22T13:50:13.574642","indexId":"ofr20211104D","displayToPublicDate":"2023-05-19T13:15:05","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1104","chapter":"D","displayTitle":"Potential Effects of Climate Change on Emydoidea blandingii (Blanding’s Turtle)","title":"Potential effects of climate change on Emydoidea blandingii (Blanding’s turtle)","docAbstract":"

Emydoidea blandingii (Holbrook, 1838; Blanding’s turtles) are a species of medium-sized, long-lived, semiaquatic, freshwater turtles with a wide distribution across the northern and eastern United States and southern Canada. They have an annual activity cycle consisting of late autumn and winter overwintering and spring emergence, spring movement and foraging, spring and summer nesting, and summer and autumn foraging and nonnesting movement. In response to changes in average and extreme temperatures, Blanding’s turtles are likely to experience increased physiological stress and reduced reproductive success. Variability in precipitation may affect the availability of freshwater habitats for overwintering, shelter, and feeding; however, projected changes in precipitation vary widely. This analysis presents anticipated climate conditions and effects on the species; the complex life history and expansive geographic range of this species require additional analysis at local scales.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104D","usgsCitation":"Lyons, M.P., Nikiel, C.A., LeDee, O.E., and Boyles, R., 2023, Potential effects of climate change on Emydoidea blandingii (Blanding’s turtle): U.S. Geological Survey Open-File Report 2021–1104–D, 46 p., https://doi.org/10.3133/ofr20211104D.","productDescription":"viii, 46 p.","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-149510","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":417246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/d/coverthb.jpg"},{"id":417247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/d/ofr20211104d.pdf","text":"Report","size":"27.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1104–D"},{"id":417248,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/d/ofr20211104d.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":417249,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/d/images"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101,\n 48.1\n ],\n [\n -101,\n 39\n ],\n [\n -71,\n 39\n ],\n [\n -71,\n 48.1\n ],\n [\n -101,\n 48.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Midwest Climate Adaptation Science Center
U.S. Geological Survey
1954 Buford Avenue
St. Paul, MN 55108

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-05-19","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, Marta P. 0000-0002-8117-8710 mlyons@usgs.gov","orcid":"https://orcid.org/0000-0002-8117-8710","contributorId":270223,"corporation":false,"usgs":true,"family":"Lyons","given":"Marta","email":"mlyons@usgs.gov","middleInitial":"P.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":873246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nikiel, Catherine A. 0000-0001-9785-7497","orcid":"https://orcid.org/0000-0001-9785-7497","contributorId":300807,"corporation":false,"usgs":false,"family":"Nikiel","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":30773,"text":"Oak Ridge Institute for Science and Education","active":true,"usgs":false}],"preferred":false,"id":873247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeDee, Olivia E. 0000-0002-7791-5829 oledee@usgs.gov","orcid":"https://orcid.org/0000-0002-7791-5829","contributorId":242820,"corporation":false,"usgs":true,"family":"LeDee","given":"Olivia","email":"oledee@usgs.gov","middleInitial":"E.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":873248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":873249,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263561,"text":"70263561 - 2023 - Rupture directivity of the 25 October 2022 Mw 5.1 Alum Rock earthquake","interactions":[],"lastModifiedDate":"2025-02-13T16:14:45.806153","indexId":"70263561","displayToPublicDate":"2023-05-19T10:09:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Rupture directivity of the 25 October 2022 Mw 5.1 Alum Rock earthquake","docAbstract":"

The 25 October 2022 Mw 5.1 Alum Rock earthquake shows strong evidence for southeast rupture directivity along the central Calaveras fault (CCF), as indicated by observed ground motions and simulated kinematic ruptures. Peak ground accelerations (PGAs) and peak ground velocities (PGVs) are notably higher to the southeast, with an order of magnitude difference for stations at the same distance but different azimuths. In addition, PGAs are lower than that predicted by ground‐motion models by a factor of 3 on average in all the directions, indicating a low stress drop (∼1.57 MPa). Directivity function modeling and ground‐motion simulations both indicate rupture propagation to the southeast with rupture velocity between 2.3 and 2.5 km/s. We suggest that the southward rupture propagation and relatively low stress drop may be typical of M ∼5 earthquakes on this portion of the CCF.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230013","usgsCitation":"Hirakawa, E.T., Parker, G.A., Baltay Sundstrom, A.S., and Hanks, T.C., 2023, Rupture directivity of the 25 October 2022 Mw 5.1 Alum Rock earthquake: The Seismic Record, v. 3, no. 2, p. 144-155, https://doi.org/10.1785/0320230013.","productDescription":"12 p.","startPage":"144","endPage":"155","ipdsId":"IP-151768","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230013","text":"Publisher Index Page"},{"id":482034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Califronia","otherGeospatial":"Alum Rock earthquake area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -122,\n 37\n ],\n [\n -121.4,\n 37\n ],\n [\n -121.4,\n 37.5\n ],\n [\n -122,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927342,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243845,"text":"70243845 - 2023 - Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","interactions":[],"lastModifiedDate":"2023-05-23T13:56:58.32871","indexId":"70243845","displayToPublicDate":"2023-05-19T08:52:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","docAbstract":"

In birds, parents must provide their eggs with a safe thermal environment suitable for embryonic development. Species with uniparental incubation must balance time spent incubating eggs with time spent away from the nest to satisfy self-maintenance needs. Patterns of nest attendance, therefore, influence embryonic development and the time it takes for eggs to hatch. We studied nest attendance (time on the nest), incubation constancy (time nests were at incubation temperatures), and variation in nest temperature of 1,414 dabbling duck nests of three species in northern California. Daily nest attendance increased from only 1–3% on the day the first egg was laid to 51–57% on the day of clutch completion, and 80–83% after clutch completion through hatch. Variation in nest temperature also decreased gradually during egg-laying, and then dropped sharply (33–38%) between the day of and the day after clutch completion because increased nest attendance, particularly at night, resulted in more consistent nest temperatures. During the egg-laying stage, nocturnal nest attendance was low (13–25%), whereas after clutch completion, nest attendance was greater at night (≥87%) than during the day (70–77%) because most incubation recesses occurred during the day. Moreover, during egg-laying, nest attendance and incubation constancy increased more slowly among nests with larger final clutch sizes, suggesting that the number of eggs remaining to be laid is a major driver of incubation effort during egg-laying. Although overall nest attendance after clutch completion was similar among species, the average length of individual incubation bouts was greatest among gadwall (Mareca strepera; 779 minutes), followed by mallard (Anas platyrhynchos; 636 minutes) and then cinnamon teal (Spatula cyanoptera; 347 minutes). These results demonstrate that dabbling ducks moderate their incubation behavior according to nest stage, nest age, time of day, and clutch size and this moderation likely has important implications for egg development and overall nest success.

","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0286151","usgsCitation":"Hartman, C.A., Ackerman, J.T., Peterson, S.H., Fettig, B.L., Casazza, M.L., and Herzog, M.P., 2023, Nest attendance, incubation constancy, and onset of incubation in dabbling ducks: PLoS ONE, v. 18, no. 5, e0286151, 28 p., https://doi.org/10.1371/journal.pone.0286151.","productDescription":"e0286151, 28 p.","ipdsId":"IP-147141","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443460,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0286151","text":"Publisher Index Page"},{"id":435323,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSAKP8","text":"USGS data release","linkHelpText":"Nest Attendance, Incubation Constancy, and Onset of Incubation in Dabbling Ducks"},{"id":417335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Grizzly Island Wildlife Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.93120904361,\n 38.087026017844266\n ],\n [\n -121.92222832467957,\n 38.09123333537573\n ],\n [\n -121.90341158025386,\n 38.0839965992823\n ],\n [\n -121.88502248911058,\n 38.09022360125243\n ],\n [\n -121.89250642155254,\n 38.09846935507156\n ],\n [\n -121.88801606208753,\n 38.105031637082874\n ],\n [\n -121.88801606208753,\n 38.12353746197775\n ],\n [\n -121.89207876827012,\n 38.13463870601953\n ],\n [\n -121.90170096712419,\n 38.1351432679065\n ],\n [\n -121.9083295930017,\n 38.14170225498961\n ],\n [\n -121.93120904361,\n 38.1319476503748\n ],\n [\n -121.93698236292227,\n 38.13043388797155\n ],\n [\n -121.94788752162361,\n 38.14035686980549\n ],\n [\n -121.95344701429467,\n 38.13985234396577\n ],\n [\n -121.97376054520885,\n 38.156836128843764\n ],\n [\n -121.99236346299335,\n 38.1591900047855\n ],\n [\n -122.00391010161832,\n 38.15330517246224\n ],\n [\n -121.99835060894684,\n 38.142374938278834\n ],\n [\n -121.98209978421568,\n 38.13026569021102\n ],\n [\n -121.98209978421568,\n 38.1122662905006\n ],\n [\n -121.96371069307239,\n 38.105031637082874\n ],\n [\n -121.95644058727163,\n 38.09443073474577\n ],\n [\n -121.9457492552115,\n 38.09594524352207\n ],\n [\n -121.94296950887617,\n 38.08652112346789\n ],\n [\n -121.93120904361,\n 38.087026017844266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fettig, Brady Lynn 0000-0002-3124-2606","orcid":"https://orcid.org/0000-0002-3124-2606","contributorId":302106,"corporation":false,"usgs":true,"family":"Fettig","given":"Brady","email":"","middleInitial":"Lynn","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873484,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243759,"text":"70243759 - 2023 - Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","interactions":[],"lastModifiedDate":"2023-05-19T12:54:11.017578","indexId":"70243759","displayToPublicDate":"2023-05-19T07:29:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","docAbstract":"

Objective: The American Eel Anguilla rostrata historically was one of the most common fish species in Atlantic coast watersheds, but extensive dam construction and other factors caused a widespread population decline. One of the watersheds where American Eels have declined considerably is the Mohawk River in eastern and central New York. Recent attempts to characterize the distribution and abundance of American Eels in this watershed have been ineffective, and the extent to which a series of locks and dams on the Hudson River and lower Mohawk River limits use of the watershed is unclear.

Methods: We developed a model between environmental DNA (eDNA) quantity and American Eel abundance in the Hudson River watershed in which the DNA concentration in water samples explained up to 65% of the variability in eel density and 56% of the variability in eel biomass. We then used this relationship to interpret eDNA data collected twice from 36 sites across the Mohawk River watershed in 2021 and make inferences about the distribution and abundance of American Eels.

Result: American Eel DNA was detected almost exclusively in the downstream-most 4 km of the Mohawk River within a series of barriers. The concentration of DNA was reduced by approximately 80% across each successive upstream barrier before becoming too low to detect consistently. Our data suggest that eel population density was high in the Hudson River estuary and declined rapidly in the lower Mohawk River, and the species was nearly absent or undetectable in the Mohawk River and its tributaries upstream of the Crescent Dam and the Waterford Flight of Locks.

Conclusion: Barriers appear to be largely restricting American Eels from using over 99% of the Mohawk River watershed. Therefore, improvements in fish passage at dams and hydroelectric facilities in the region could help the American Eel to regain access to this part of its native range.

","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10404","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Wiley, J.J., Stich, D.S., Wells, S.M., and Winterhalter, D., 2023, Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system: Transactions of the American Fisheries Society, v. 152, no. 3, p. 310-326, https://doi.org/10.1002/tafs.10404.","productDescription":"17 p.","startPage":"310","endPage":"326","ipdsId":"IP-143996","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":443462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10404","text":"Publisher Index Page"},{"id":417238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.46545959281143,\n 43.193210843337965\n ],\n [\n -75.36068780837059,\n 43.1807103892566\n ],\n [\n -75.25020119932465,\n 43.095917619682325\n ],\n [\n -75.11304540878423,\n 43.06391605114166\n ],\n [\n -75.0406576304439,\n 43.00264908923049\n ],\n [\n -74.84063876923905,\n 43.01240019708166\n ],\n [\n -74.66347920645833,\n 42.963629178932905\n ],\n [\n -74.58156672044112,\n 42.881327587792015\n ],\n [\n -74.45774552064795,\n 42.879931698082856\n ],\n [\n -74.25772665944312,\n 42.84083396491155\n ],\n [\n -74.11866592736799,\n 42.86317855831385\n ],\n [\n -74.03865838288596,\n 42.83524655344701\n ],\n [\n -74.02532379213918,\n 42.75417231574653\n ],\n [\n -74.00627437678641,\n 42.707996466290325\n ],\n [\n -73.81976366766865,\n 42.62025113498041\n ],\n [\n -73.79690436924501,\n 42.550123385716574\n ],\n [\n -73.8254784922747,\n 42.384309188935845\n ],\n [\n -73.81404884306264,\n 42.31351753996279\n ],\n [\n -73.93977498439143,\n 42.2035468271101\n ],\n [\n -73.97025404895584,\n 42.10469190424271\n ],\n [\n -74.00454299659106,\n 41.89801206244189\n ],\n [\n -73.91310580289745,\n 41.86681033353875\n ],\n [\n -73.88262673833307,\n 42.11317123319691\n ],\n [\n -73.72642153243986,\n 42.28251957771869\n ],\n [\n -73.74547094779265,\n 42.37828214920688\n ],\n [\n -73.71880176629864,\n 42.619854148073244\n ],\n [\n -73.61212504032306,\n 42.82697116015191\n ],\n [\n -73.66165352024026,\n 42.88841312774437\n ],\n [\n -73.94442157442768,\n 42.88448522793766\n ],\n [\n -74.22444798011406,\n 42.97932677531503\n ],\n [\n -74.37874824447162,\n 42.982114021563405\n ],\n [\n -74.51971391808242,\n 42.93192425763374\n ],\n [\n -74.65305982555196,\n 43.01415827143066\n ],\n [\n -74.86679009610465,\n 43.05871377504309\n ],\n [\n -75.00966071125109,\n 43.05314610631808\n ],\n [\n -75.22491910473742,\n 43.151897073297846\n ],\n [\n -75.36207489527783,\n 43.21717980329879\n ],\n [\n -75.47065656278859,\n 43.221344415722996\n ],\n [\n -75.46545959281143,\n 43.193210843337965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":873175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false},{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false}],"preferred":false,"id":873176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiley, John J. Jr.","contributorId":305554,"corporation":false,"usgs":false,"family":"Wiley","given":"John","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":873178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wells, Scott M.","contributorId":305556,"corporation":false,"usgs":false,"family":"Wells","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better\nunderstanding and quantification of riverine carbon cycles.","language":"English","publisher":"Springer","doi":"10.1038/s41597-023-02162-7","usgsCitation":"Qiu, H., Zhang, X., Yang, A., Wickland, K., Stets, E.G., and Chen, M., 2023, Watershed carbon yield derived from gauge observations and river network connectivity in the United States: Scientific Data, v. 10, 278, 13 p., https://doi.org/10.1038/s41597-023-02162-7.","productDescription":"278, 13 p.","ipdsId":"IP-150043","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":443465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02162-7","text":"Publisher Index 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Background

Little is known about the transformer stage of the parasitic lampreys, a brief but critical period that encompasses juvenile out-migration from rivers to lakes or oceans to begin parasitic feeding. Information about this life stage could have significant conservation implications for both imperiled and invasive lampreys. We investigated tag retention, survival, wound healing, and swim performance of newly transformed sea lamprey (Petromyzon marinus) implanted with a new micro-acoustic transmitter, the eel–lamprey acoustic transmitter (ELAT), in a controlled laboratory environment.

Results

The 61-day survival of our tagged subjects was 71%, within the range reported in similar studies of juvenile lampreys. However, survival was significantly lower in the tagged animals (vs control), with no effect statistically attributable to measures of animal length, mass, condition, or population of origin (Great Lakes vs. Atlantic drainage). Mortality in tagged fish was concentrated in the first four days post-surgery, suggesting injury from the surgical process. An unusually long recovery time from anesthesia may have contributed to the increased mortality. In a simple burst swim assay, tagged animals swam significantly slower (− 22.5%) than untagged animals, but were not significantly different in endurance swim tests. A composite wound healing score at day four was a significant predictor of maximum burst swim speed at day 20, and wound condition was related to animal mass, but not length, at the time of tagging.

Conclusions

Impairments to survival and swim performance of juvenile sea lamprey implanted with the ELAT transmitter were within currently reported ranges for telemetry studies with small, difficult to observe fishes. Our results could be improved with more refined anesthesia and surgical techniques. The ability to track migratory movements of imperiled and pest populations of parasitic lampreys will improve our ability to estimate vital rates that underlie recruitment to the adult population (growth, survival) and to investigate the environmental factors that regulate the timing and rates of movement, in wild populations.

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The U.S. Geological Survey Precipitation Chemistry Quality Assurance project (PCQA) operated four distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2019–20. The NTN programs included (1) a field audit program to evaluate sample contamination and stability, and (2) an interlaboratory comparison program to evaluate analytical laboratory performance. The MDN programs included the (3) system blank program to evaluate sample contamination and stability, and (4) an interlaboratory comparison program. The results indicated increased levels of sample contamination compared to previous years for NTN samples and decreased contamination in MDN samples. Strong analytical laboratory performance with low overall variability and bias in concentration data were indicated for the NTN’s Central Analytical Laboratory. A positive bias in the hydrogen ion concentrations in NTN samples during 2019 was eliminated by correction of a pH calibration protocol during 2020. The MDN’s Mercury Analytical Laboratory performance declined in 2020 compared to 2019 as indicated by increased variability in analytical results and a negative bias of approximately -1 nanogram per liter in the concentrations of total mercury. Slight perturbations in contamination levels in NTN samples and in analytical performance for MDN are considered small. The PCQA results indicate that NADP data continue to be of sufficient quality for applications in independent research and NADP data products, including spatial interpolations and time trends for chemical constituents in wet deposition. Small shifts in data quality indicated by the 2019–20 PCQA results are intended to be used for interpretation of the NADP data products.

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Director, Observing Systems Division
Hydrologic Instrumentation Facility
U.S. Geological Survey
Buildings 2101 2204 HIF
Stennis Space Center
Bay St Louis, MS 39529

","tableOfContents":"","publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":215100,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":872996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, RoseAnn 0000-0002-2611-8395 ramartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2611-8395","contributorId":202920,"corporation":false,"usgs":true,"family":"Martin","given":"RoseAnn","email":"ramartin@usgs.gov","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":872997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liethen, Alexander 0000-0002-7301-7255","orcid":"https://orcid.org/0000-0002-7301-7255","contributorId":305495,"corporation":false,"usgs":false,"family":"Liethen","given":"Alexander","email":"","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":872998,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243696,"text":"sir20235027 - 2023 - Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","interactions":[],"lastModifiedDate":"2026-03-06T20:51:31.369117","indexId":"sir20235027","displayToPublicDate":"2023-05-18T10:56:00","publicationYear":"2023","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":"2023-5027","displayTitle":"Evaluating Drivers of Hydrology, Water Quality, and Benthic Macroinvertebrates in Streams of Fairfax County, Virginia, 2007–18","title":"Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","docAbstract":"

In 2007, the U.S. Geological Survey partnered with Fairfax County, Virginia, to establish a long-term water-resources monitoring program to evaluate the hydrology, water quality, and ecology of Fairfax County streams and the watershed-scale effects of management practices. Fairfax County uses a variety of management practices, policies, and programs to protect and restore its water resources, but the effects of such strategies are not well understood. This report used streamflow, water-quality, and ecological monitoring data collected from 20 Fairfax County watersheds from 2007 through 2018 to assess the effects of management practices, landscape factors, and climatic conditions on observed nutrient, sediment, salinity, and benthic-macroinvertebrate community responses.

Urbanization, climatic variability, and an increase in management practices occurred within Fairfax County during the study period. Impervious cover, housing units, wastewater infrastructure, and (or) stormwater infrastructure increased in most study watersheds. Climatic conditions varied among study years; countywide estimates of average-annual air temperature differed by about 3 degrees Celsius, and total precipitation ranged from about 34 to 63 inches per year. The effects of the management practices, implemented to reduce nitrogen, phosphorus, and (or) sediment loads, are considered in this study. These management practices primarily consist of stormwater retrofits and stream restorations; however, stream restorations account for most of the financial investment and expected load reductions. Management practices were implemented in half of the study watersheds, and most practices were installed and reductions credited late in the study period.

Changes in hydrologic response during storm events were evaluated over the study period because many management practices that were implemented were designed to achieve nutrient and sediment reductions by slowing or intercepting runoff. The average number and length of storm events was mostly unchanged throughout the monitoring network. Four watersheds with 10 years of streamflow data showed a mixture of trends in stormflow peak, volume, and rate-of-change. Event-mean nutrient and sediment concentrations from these watersheds were evaluated during storm events and generally showed increases in total phosphorus (TP) and suspended sediment and reductions or no changes in total nitrogen (TN).

Landscape inputs of nitrogen and phosphorus and the percentage of inputs delivered to streams were estimated for the study watersheds. Estimated phosphorus from fertilizer and nitrogen from atmospheric deposition represented large nutrient inputs in most watersheds; amounts of other nonpoint sources varied based on land use. Estimated nitrogen inputs declined throughout Fairfax County and in most study watersheds from 2008 through 2018; in comparison, phosphorus input changes were relatively small. Most nonpoint-nutrient inputs were retained on the landscape and did not reach streams, with slightly more nitrogen retention than phosphorus, on average. Retention rates were lower for years with more precipitation and streamflow. After adjusting for streamflow, TN and TP loads were generally higher for years with more nutrient inputs. Calculated as a function of flow-adjusted loads, TP retention declined at most stations from 2009 through 2018, in comparison, TN retention was relatively unchanged.

Landscape and climatic conditions affected spatial differences and changes in Fairfax County stream conditions from 2009 through 2018. TN concentrations were higher and increases over time were larger in watersheds with elevated septic-system density. TP concentrations were higher in watersheds with more turfgrass; concentrations were lower, but had larger increases over time, in watersheds with deeper soils. Suspended-sediment concentrations were higher in watersheds with greater stream densities. Specific conductance was higher in watersheds with more developed land use and shallower soils. Benthic-macroinvertebrate index of biotic integrity (IBI) scores were lower in watersheds with high road density and had larger increases over time in bigger, more developed watersheds. Annual variability in TN and TP concentrations and benthic-macroinvertebrate IBI scores was affected by precipitation; annual variability in suspended sediment concentrations and specific conductance was affected by air temperature.

After accounting for influences from landscape and climatic conditions, expected management-practice effects were not consistently observed in monitored stream responses. These effects were assessed by comparing expected management-practice load reductions with the timing, direction, and magnitude of changes in storm-event hydrology, nutrient and sediment loads, median-annual water-quality conditions, and benthic-macroinvertebrate IBI scores. An important consideration for future investigations of management-practice effects is how to control for water-quality and ecological variability caused by geologic properties, the urban environment, precipitation, and (or) air temperature. The interpretation of management-practice effects in this report was likely influenced by a combination of factors, including (1) the amount, timing, and location of management-practice implementation; (2) unmeasured landscape and climatic factors; (3) uncertain management-practice expectations; (4) hydrologic variability; and (5) analytical assumptions. Through continued data-collection efforts, particularly after management practices have been completed, many of these factors may become less influential in the future.

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U.S. Geological Survey
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","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Webber, James S. 0000-0001-6636-1368","orcid":"https://orcid.org/0000-0001-6636-1368","contributorId":222000,"corporation":false,"usgs":true,"family":"Webber","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chanat, Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, Aaron J. 0000-0002-0781-3309","orcid":"https://orcid.org/0000-0002-0781-3309","contributorId":239980,"corporation":false,"usgs":true,"family":"Porter","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jastram, John D. 0000-0002-9416-3358 jdjastra@usgs.gov","orcid":"https://orcid.org/0000-0002-9416-3358","contributorId":3531,"corporation":false,"usgs":true,"family":"Jastram","given":"John","email":"jdjastra@usgs.gov","middleInitial":"D.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872941,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243612,"text":"sir20225047 - 2023 - Simulation of flow and eutrophication in the central Salem River, New Jersey","interactions":[],"lastModifiedDate":"2026-02-23T19:12:37.435815","indexId":"sir20225047","displayToPublicDate":"2023-05-18T10:55:00","publicationYear":"2023","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":"2022-5047","displayTitle":"Simulation of Flow and Eutrophication in the Central Salem River, New Jersey","title":"Simulation of flow and eutrophication in the central Salem River, New Jersey","docAbstract":"

The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.

The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.

The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.

Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Spitz, Frederick J. 0000-0002-1391-2127 fspitz@usgs.gov","orcid":"https://orcid.org/0000-0002-1391-2127","contributorId":2777,"corporation":false,"usgs":true,"family":"Spitz","given":"Frederick","email":"fspitz@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":872613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DePaul, Vincent T. 0000-0002-7977-5217 vdepaul@usgs.gov","orcid":"https://orcid.org/0000-0002-7977-5217","contributorId":2778,"corporation":false,"usgs":true,"family":"DePaul","given":"Vincent","email":"vdepaul@usgs.gov","middleInitial":"T.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872614,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256484,"text":"70256484 - 2023 - Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan","interactions":[],"lastModifiedDate":"2024-08-07T15:35:27.569829","indexId":"70256484","displayToPublicDate":"2023-05-18T10:26:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan","docAbstract":"

Lake whitefish Coregonus clupeaformis are a native coldwater species supporting important recreational and commercial fisheries in the Laurentian Great Lakes. Climate-related changes in water temperature may have important implications for the future sustainability of these fisheries. However, projecting future habitat availability is difficult because limited information is available on lake whitefish thermal ecology in the region. In this study, archival temperature loggers were implanted into 400 lake whitefish from northwestern Lake Michigan, including Green Bay, during October–November 2017. Loggers recorded temperature for 11 months at 4-hr intervals. Thirteen recovered temperature loggers were used in analyses. In winter (1 December–31 March), temperatures occupied by lake whitefish ranged from 0 to 8.0 °C, while in spring (1 April–31 May) temperatures ranged from 0 to 20.0 °C. In summer (1 June–15 September) and fall (16 September–7 November), lake whitefish occupied temperatures of 4–21.5 and 4–21.0 °C, respectively. Average temperatures in summer (10.8 °C) were within the previously proposed optimal temperature range (10–14 °C) and broad thermal niche (7–17 °C); however, 58% of observations were outside the optimal temperature range and 11% of observations were outside the broad thermal niche. Our results suggest that lake whitefish from northwestern Lake Michigan inhabit temperatures both above and below previously reported expected temperature ranges. This study provides initial insights on lake whitefish thermal ecology in Lake Michigan and can be used as a baseline for future work aimed at determining how lake whitefish habitat availability may change in the future.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.03.002","usgsCitation":"Reed, K., Izzo, L.K., Binder, T., Hayden, T., Dembkowski, D., Hansen, S., Caroffino, D., Vandergoot, C., Krueger, C., and Isermann, D.A., 2023, Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan: Journal of Great Lakes Research, v. 49, no. 3, p. 757-766, https://doi.org/10.1016/j.jglr.2023.03.002.","productDescription":"10 p.","startPage":"757","endPage":"766","ipdsId":"IP-147146","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443470,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.03.002","text":"Publisher Index Page"},{"id":432341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.13743171358965,\n 45.614745689438536\n ],\n [\n -86.85031511100541,\n 46.103265453360024\n ],\n [\n -88.90338431195246,\n 43.9119981902781\n ],\n [\n -88.0470508747241,\n 43.978600122172\n ],\n [\n -86.13743171358965,\n 45.614745689438536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Kayla","contributorId":340827,"corporation":false,"usgs":false,"family":"Reed","given":"Kayla","email":"","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izzo, Lisa K.","contributorId":189241,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":907593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Tom","contributorId":166711,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayden, Todd","contributorId":340810,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dembkowski, Daniel","contributorId":340808,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hansen, Scott","contributorId":191464,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott","affiliations":[],"preferred":false,"id":907597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caroffino, David","contributorId":340835,"corporation":false,"usgs":false,"family":"Caroffino","given":"David","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vandergoot, Christopher","contributorId":340837,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907599,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles","contributorId":340820,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907600,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907601,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243717,"text":"70243717 - 2023 - Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","interactions":[],"lastModifiedDate":"2023-05-18T14:09:36.87445","indexId":"70243717","displayToPublicDate":"2023-05-18T09:05:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14427,"text":"BMJ Open","active":true,"publicationSubtype":{"id":10}},"title":"Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","docAbstract":"

Introduction: The burden of disease attributed to drinking water from private wells is not well characterised. The Wells and Enteric disease Transmission trial is the first randomised controlled trial to estimate the burden of disease that can be attributed to the consumption of untreated private well water. To estimate the attributable incidence of gastrointestinal illness (GI) associated with private well water, we will test if the household treatment of well water by ultraviolet light (active UV device) versus sham (inactive UV device) decreases the incidence of GI in children under 5 years of age.

Methods and analysis: The trial will enrol (on a rolling basis) 908 families in Pennsylvania, USA, that rely on private wells and have a child 3 years old or younger. Participating families are randomised to either an active whole-house UV device or a sham device. During follow-up, families will respond to weekly text messages to report the presence of signs and symptoms of gastrointestinal or respiratory illness and will be directed to an illness questionnaire when signs/symptoms are present. These data will be used to compare the incidence of waterborne illness between the two study groups. A randomly selected subcohort submits untreated well water samples and biological specimens (stool and saliva) from the participating child in both the presence and absence of signs/symptoms. Samples are analysed for the presence of common waterborne pathogens (stool and water) or immunoconversion to these pathogens (saliva).

Ethics: Approval has been obtained from Temple University’s Institutional Review Board (Protocol 25665). The results of the trial will be published in peer-reviewed journals.

Trial registration number: NCT04826991.

","language":"English","publisher":"British Medical Journals","doi":"10.1136/bmjopen-2022-068560","usgsCitation":"Lee, D., Denno, D., Tarr, P., Wu, J., Stokdyk, J.P., Borchardt, M.A., and Murphy, H., 2023, Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial: BMJ Open, v. 13, e068560, 12 p., https://doi.org/10.1136/bmjopen-2022-068560.","productDescription":"e068560, 12 p.","ipdsId":"IP-145434","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":443472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1136/bmjopen-2022-068560","text":"Publisher Index Page"},{"id":417212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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regensburgei","interactions":[],"lastModifiedDate":"2023-05-18T13:27:34.435517","indexId":"70243714","displayToPublicDate":"2023-05-18T08:13:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5020,"text":"Microorganisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A bacteriological comparison of the hemolymph from healthy and moribund unionid mussel populations in the upper Midwestern U.S.A. prompts the development of diagnostic assays to detect Yokenella regensburgei","title":"A bacteriological comparison of the hemolymph from healthy and moribund unionid mussel populations in the upper Midwestern U.S.A. prompts the development of diagnostic assays to detect Yokenella regensburgei","docAbstract":"

Recent bacteriological investigations of freshwater mussel mortality events in the southeastern United States have identified a variety of bacteria and differences in bacterial communities between sick and healthy mussels. In particular, Yokenella regensburgei and Aeromonas spp. have been shown to be associated with moribund mussels, although it remains unclear whether these bacteria are causes or consequences of disease. To further understand the role of bacteria in mussel epizootics, we investigated mortality events that occurred in the upper Midwest in the Embarrass River (Wisconsin) and the Huron River (Michigan). For comparison, we also studied mussels from an unaffected population in the St. Croix River (Wisconsin). Diverse bacterial genera were identified from these sites, including Y. regensburgei from moribund mussels in the Embarrass River (Wisconsin). This bacterium has also been consistently isolated during ongoing mortality events in the Clinch River (Virginia). Subsequently, we developed and validated molecular assays for the detection of Yokenella to use in future investigations of mussel mortality events and to identify environmental reservoirs of this bacterium.

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,{"id":70243719,"text":"70243719 - 2023 - Mapping landslide susceptibility over large regions with limited data","interactions":[],"lastModifiedDate":"2023-05-18T13:12:16.140007","indexId":"70243719","displayToPublicDate":"2023-05-18T08:04:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Mapping landslide susceptibility over large regions with limited data","docAbstract":"

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006810","usgsCitation":"Woodard, J.B., Mirus, B., Crawford, M., Or, D., Leshchinsky, B., Allstadt, K.E., and Wood, N.J., 2023, Mapping landslide susceptibility over large regions with limited data: Journal of Geophysical Research: Earth Surface, v. 128, no. 5, e2022JF006810, 21 p., https://doi.org/10.1029/2022JF006810.","productDescription":"e2022JF006810, 21 p.","ipdsId":"IP-142367","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443478,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006810","text":"Publisher Index Page"},{"id":435324,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P959G9JN","text":"USGS data 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]\n}","volume":"128","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Matthew","contributorId":224687,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Or, Dani","contributorId":267915,"corporation":false,"usgs":false,"family":"Or","given":"Dani","affiliations":[{"id":55530,"text":"ETH / DRI","active":true,"usgs":false}],"preferred":false,"id":873055,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":267910,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":873056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873058,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243710,"text":"70243710 - 2023 - Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?","interactions":[],"lastModifiedDate":"2023-05-18T13:04:03.365322","indexId":"70243710","displayToPublicDate":"2023-05-18T07:59:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?","docAbstract":"

Habitat restoration is an important tool used to conserve biodiversity and restore species, but its effects are notoriously difficult to predict. Although outcomes of restoration projects are usually assessed using indices of species abundance and diversity, phenotypic differences among individuals within species are likely associated with differing responses to restored habitats. Here, we use lake sturgeon (Acipenser fulvescens) as a case study to illustrate how responses to habitat restoration can differ between phenotypes and potentially lead to unanticipated effects on populations. North America’s St. Clair River supports one of the largest remaining populations of lake sturgeon but has lost much spawning habitat due to its role as a major industrial corridor between the Laurentian Great Lakes Erie and Huron. Two artificial reefs were recently built in the lower and middle segments of the river to increase the available sturgeon spawning habitat. Interestingly, lake sturgeon in the St. Clair River express different migratory phenotypes that may be associated with different likelihoods of colonizing artificial reefs. Acoustic telemetry revealed that artificial reefs were more likely to be used by sturgeon that migrated downstream to overwinter in Lake St. Clair than those that migrated upstream to overwinter in Lake Huron. Furthermore, increasing time spent at the artificial reefs by Lake St. Clair migrants was associated with later arrival to and shorter occupancy of the river’s only natural spawning site, the primary location where the two phenotypes have opportunity to interbreed. Additional research is necessary to determine the ultimate impacts of the artificial reefs on lake sturgeon populations; nevertheless, our study showed phenotype-specific opportunity to colonize restored habitat and a mechanism through which this could lead to changes in gene flow. Our results illustrate the importance of considering intra-specific diversity when planning restoration projects and assessing the effects on populations.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2023.110076","usgsCitation":"Buchinger, T.J., Hondorp, D.W., and Krueger, C.C., 2023, Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?: Ecological Indicators, v. 148, 110076, 7 p., https://doi.org/10.1016/j.ecolind.2023.110076.","productDescription":"110076, 7 p.","ipdsId":"IP-151337","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":443480,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2023.110076","text":"Publisher Index Page"},{"id":417203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Lake Huron, Lake St. Clair, St. Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.50885589470187,\n 42.48978302604715\n ],\n [\n -82.52588339888945,\n 42.56005734914967\n ],\n [\n -82.47650363674543,\n 42.63902146230933\n ],\n [\n -82.44925963004543,\n 42.772905765259964\n ],\n [\n -82.46288163339523,\n 42.81538798109838\n ],\n [\n -82.44415137878899,\n 42.90026491830179\n ],\n [\n -82.38285236371387,\n 42.970075962069245\n ],\n [\n -82.3794468628761,\n 43.024871973150255\n ],\n [\n -82.44925963004543,\n 43.02611676912798\n ],\n [\n -82.4509623804641,\n 42.9688300302966\n ],\n [\n -82.49523389135167,\n 42.895275386959554\n ],\n [\n -82.51055865258667,\n 42.805665154220605\n ],\n [\n -82.49863939965553,\n 42.77442615992763\n ],\n [\n -82.53780265928711,\n 42.64179770072707\n ],\n [\n -82.65529243818133,\n 42.648060128002214\n ],\n [\n -82.67231994236889,\n 42.572869396811484\n ],\n [\n -82.6716018993314,\n 42.52443050483427\n ],\n [\n -82.58305887755624,\n 42.485516224320605\n ],\n [\n -82.50885589470187,\n 42.48978302604715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buchinger, Tyler J. 0000-0002-4590-341X","orcid":"https://orcid.org/0000-0002-4590-341X","contributorId":290501,"corporation":false,"usgs":false,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":873016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":873017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krueger, Charles C. 0000-0002-6735-5012","orcid":"https://orcid.org/0000-0002-6735-5012","contributorId":274493,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":873018,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243706,"text":"70243706 - 2023 - Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community","interactions":[],"lastModifiedDate":"2023-07-11T16:04:06.943392","indexId":"70243706","displayToPublicDate":"2023-05-18T07:47:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community","docAbstract":"Coastal erosion is one of the foremost hazards that circumpolar communities face. Climate change and warming temperatures are anticipated to accelerate coastal change, increasing risk to coastal communities. Most erosion hazard studies for Alaska communities only consider linear erosion and do not anticipate coastal morphologic changes. This study showcases the possibility and consequence of accelerated erosion by examining a shift from stability to rapid erosion that forced the rural Alaska Native village of Meshik (now Port Heiden) to abandon the original town site and relocate inland. A combination of remote sensing, coastal surveys, and community-based monitoring are used to map coastal morphologic changes and identify erosion drivers. The community’s shoreline was stable until a protective barrier island eroded away. The exposure to open ocean waves, coupled with unconsolidated, low-density sediments, led to rapid erosion rates averaging of 5.8 ± 0.6 m/y from the 1970s to 2020s. The sudden and rapid erosion put great stress on Meshik and resulted in the loss of homes, erosion of a safe boat harbor, and pollution of the beach and bay. Erosion of the barrier island coincided with a period of greater storm activity and sea ice decline, but the exact cause for its erosion could not be determined. Many polar communities are built on or behind barriers and are on easily erodible soils such as sands and thawing permafrost. This study highlights the need to study, monitor, and predict morphologic change and regime shifts that can bring catastrophic impacts to coastal communities.","language":"English","publisher":"Allen Press","doi":"10.2112/JCOASTRES-D-22-00093.1","usgsCitation":"Buzard, R., Kinsman, N.E., Maio, C.V., Erikson, L.H., Jones, B.M., Anderson, S.K., Glenn, R., and Overbeck, J.R., 2023, Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community: Journal of Coastal Research, v. 39, no. 4, p. 625-642, https://doi.org/10.2112/JCOASTRES-D-22-00093.1.","productDescription":"18 p.","startPage":"625","endPage":"642","ipdsId":"IP-147862","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":417202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Meshik, Port Heiden","otherGeospatial":"Aniakchak Volcano, Bristol Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.82365753904958,\n 56.95251936361021\n ],\n [\n -158.82365753904958,\n 56.879736242479424\n ],\n [\n -158.64890766892418,\n 56.879736242479424\n ],\n [\n -158.64890766892418,\n 56.95251936361021\n ],\n [\n -158.82365753904958,\n 56.95251936361021\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buzard, Richard M.","contributorId":208627,"corporation":false,"usgs":false,"family":"Buzard","given":"Richard M.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":873006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinsman, Nicole E.M.","contributorId":305383,"corporation":false,"usgs":false,"family":"Kinsman","given":"Nicole","email":"","middleInitial":"E.M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":873007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maio, Christopher V.","contributorId":208635,"corporation":false,"usgs":false,"family":"Maio","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":873008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":873150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Scott K.","contributorId":71748,"corporation":false,"usgs":false,"family":"Anderson","given":"Scott","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":873011,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Glenn, Roberta","contributorId":305500,"corporation":false,"usgs":false,"family":"Glenn","given":"Roberta","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":873012,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Overbeck, Jacquelyn R.","contributorId":181813,"corporation":false,"usgs":false,"family":"Overbeck","given":"Jacquelyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":873013,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243709,"text":"70243709 - 2023 - Heavy: Software for forward-modeling gravity change from MODFLOW output","interactions":[],"lastModifiedDate":"2023-05-18T12:45:59.131321","indexId":"70243709","displayToPublicDate":"2023-05-18T07:43:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Heavy: Software for forward-modeling gravity change from MODFLOW output","docAbstract":"Fortran software, named Heavy, was developed to simulate gravity change due to water-storage change in MODFLOW groundwater models. Heavy is compatible with MODFLOW-2005 and MODFLOW-NWT models using the layer-property flow or upstream weighting packages. All of the necessary information for the gravity calculation—the geometry of the model cells, the storage coefficient, and head change—is present within the existing MODFLOW model files and no additional information is necessary. Gravity change is calculated at each time step, for each layer, at user specified locations or at a grid of hypothetical positions across the model. The software has been validated using analytical gravity solutions and three example MODFLOW models are included for demonstration. Heavy leverages the input/output routines from MODFLOW and is orders of magnitude faster than previous efforts using interpreted languages such as Python or MATLAB. The objective of the software is to facilitate repeat microgravity field measurements for groundwater-flow model calibration.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2023.105714","usgsCitation":"Kennedy, J.R., and Larsen, J., 2023, Heavy: Software for forward-modeling gravity change from MODFLOW output: Environmental Modelling and Software, v. 165, 105714, 7 p., https://doi.org/10.1016/j.envsoft.2023.105714.","productDescription":"105714, 7 p.","ipdsId":"IP-137200","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":435325,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IIHXN3","text":"USGS data release","linkHelpText":"Heavy"},{"id":417201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":873014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873015,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243712,"text":"70243712 - 2023 - Conspecific density and habitat quality affect breeding habitat selection: Support for the social attraction hypothesis","interactions":[],"lastModifiedDate":"2023-05-18T12:43:23.743339","indexId":"70243712","displayToPublicDate":"2023-05-18T07:37:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Conspecific density and habitat quality affect breeding habitat selection: Support for the social attraction hypothesis","docAbstract":"

Breeding habitat selection is a critical component of the annual cycle because of its effect on fitness. Multiple theories of habitat selection can be differentiated by their responses to the quantity of habitat, conspecific density, and habitat quality. Here, we use network analysis to understand the characteristics of fine-scale breeding habitat selected by both immigrant and returning adult piping plovers (Charadrius melodus) to test five hypotheses of habitat selection. Between 2014 and 2019, we recorded 2034 uniquely marked adults breeding at 326 breeding locations with 1240 successive breeding events. Among adults, immigration events (i.e., individuals that moved to a new breeding location) were detected as often as fidelity to the same breeding location. We found support for the social attraction hypothesis for both immigrants and returners, indicating that adult plovers use social cues for settlement decisions. Adult plovers selected habitats with intermediate levels of conspecific density and high habitat quality, as assessed by con- and heterospecific nest survival, with no effect from the amount of available habitat. We also simulated the loss of breeding habitat and identified highly connected breeding locations, which occurred mostly on the riverine habitat type, which have important implications for habitat conservation for this listed species. Our results highlight the role of conspecifics at identifying high-quality breeding habitat regardless of whether individuals return to the same breeding site or immigrate to new areas.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4524","usgsCitation":"Swift, R.J., Anteau, M.J., Ellis, K.S., Ring, M., Sherfy, M.H., and Toy, D.L., 2023, Conspecific density and habitat quality affect breeding habitat selection: Support for the social attraction hypothesis: Ecosphere, v. 14, no. 5, e4524, 12 p., https://doi.org/10.1002/ecs2.4524.","productDescription":"e4524, 12 p.","ipdsId":"IP-140109","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":443485,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4524","text":"Publisher Index Page"},{"id":435326,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XB413Y","text":"USGS data release","linkHelpText":"Piping plover breeding habitat selection in the Northern Great Plains, USA, 2014&amp;amp;ndash;2019"},{"id":417200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Lake Oahe, Lake Sakakawea, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.18442118329881,\n 48.35463083958285\n ],\n [\n -104.09891917447544,\n 47.76471426471906\n ],\n [\n -103.1327109712617,\n 47.92572317027427\n ],\n [\n -102.78500641418202,\n 47.439051157349496\n ],\n [\n -101.45700618798827,\n 47.2855296804496\n ],\n [\n -100.87073239849553,\n 46.579058531385584\n ],\n [\n -100.73907291847854,\n 45.646308005736955\n ],\n [\n -101.22677175398002,\n 44.58089698238982\n ],\n [\n -100.37456474138085,\n 44.30127361509915\n ],\n [\n -100.10530614157318,\n 44.48996692830562\n ],\n [\n -100.06757401023947,\n 45.88942366016579\n ],\n [\n -99.1350326404962,\n 45.9059501063806\n ],\n [\n -98.99248103453064,\n 47.02045800069712\n ],\n [\n -99.98004118855073,\n 47.896252650449895\n ],\n [\n -101.92239012079415,\n 48.88838995054792\n ],\n [\n -104.2187984346267,\n 48.803498132546196\n ],\n [\n -104.18442118329881,\n 48.35463083958285\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellis, Kristen S. 0000-0003-2759-3670","orcid":"https://orcid.org/0000-0003-2759-3670","contributorId":251877,"corporation":false,"usgs":true,"family":"Ellis","given":"Kristen","email":"","middleInitial":"S.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ring, Megan M. 0000-0001-8331-8492","orcid":"https://orcid.org/0000-0001-8331-8492","contributorId":225026,"corporation":false,"usgs":true,"family":"Ring","given":"Megan M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherfy, Mark H. 0000-0003-3016-4105 msherfy@usgs.gov","orcid":"https://orcid.org/0000-0003-3016-4105","contributorId":125,"corporation":false,"usgs":true,"family":"Sherfy","given":"Mark","email":"msherfy@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873023,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Toy, Dustin L. 0000-0001-5390-5784 dtoy@usgs.gov","orcid":"https://orcid.org/0000-0001-5390-5784","contributorId":5150,"corporation":false,"usgs":true,"family":"Toy","given":"Dustin","email":"dtoy@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873024,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}