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Recently, the subsoils of ephemeral stream (arroyos) floodplains in the northern Chihuahuan Desert were discovered to contain large naturally occurring NO3− reservoirs (floodplain: ~38,000 kg NO3-N/ha; background: ~60 kg NO3-N/ha). These reservoirs may be mobilized through land use change or natural stream channel migration which makes differentiating between anthropogenic and natural groundwater NO3− sources challenging. In this study, the fate and sources of NO3− were investigated in an area with multiple NO3− sources such as accidental sewer line releases and sewage lagoons as well as natural reservoirs of subsoil NO3−. To differentiate sources, this study used a large suite of geochemical tools including δ15N[NO3], δ18O[NO3], δ15N[N2], δ13C[DIC], 14C, tritium (3H), dissolved gas concentrations, major ion chemistry, and contaminants of emerging concern (CEC) including artificial sweeteners. NO3− at sites with the highest concentrations (25 to 229 mg/L NO3-N) were determined to be largely sourced from naturally occurring subsoil NO3− based on δ15N[NO3] (<8 ‰) and mass ratios of Cl−/Br− (〈100) and NO3−/Cl− (>1.5). Anthropogenic NO3− was deciphered using mass ratios of Cl−/Br− (>120) and NO3−/Cl− (<1), δ15N[NO3] (>8 ‰), and CEC detections. Nitrogen isotope analyses indicated that denitrification is fairly limited in the field area. CEC were detected at 67 % of sites including 3H dead sites (<1 pCi/L) with low percent modern carbon-14 (PMC; <30 %). Local supply wells are 3H dead with low PMC; as 3H does not re-equilibrate and 14C is very slow to re-equilibrate during recirculation through infrastructure, sites with low PMC, 3H < 1 pCi/L, and CEC detections were interpreted as locations with substantial anthropogenic groundwater recharge. Neotame was used to identify locations of very recent (<15 years before present) or ongoing wastewater influxes to the aquifer. This work shows the important influence of naturally occurring subsoil NO3− reservoirs on groundwater in arid regions and the major contribution of artificial recharge.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157345","usgsCitation":"Linhoff, B.S., 2022, Deciphering natural and anthropogenic nitrate and recharge sources in arid region groundwater: Science of the Total Environment, v. 848, 157345, 16 p., https://doi.org/10.1016/j.scitotenv.2022.157345.","productDescription":"157345, 16 p.","ipdsId":"IP-137249","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157345","text":"Publisher Index Page"},{"id":405070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Millennia-old coral holobiont DNA provides insight into future adaptive trajectories","docAbstract":"Ancient DNA (aDNA) has been applied to evolutionary questions across a wide variety of taxa. Here, for the first time, we leverage aDNA from millennia-old fossil coral fragments to gain new insights into a rapidly declining western Atlantic reef ecosystem. We sampled four Acropora palmata fragments (dated 4215 BCE - 1099 CE) obtained from two Florida Keys reef cores. From these samples, we established that it is possible both to sequence ancient DNA from reef cores and place the data in the context of modern-day genetic variation. We recovered varying amounts of nuclear DNA exhibiting the characteristic signatures of aDNA from the A. palmata fragments. To describe the holobiont sensu lato, which plays a crucial role in reef health, we utilized metagenome-assembled genomes as a reference to identify a large additional proportion of ancient microbial DNA from the samples. The samples shared many common microbes with modern-day coral holobionts from the same region, suggesting remarkable holobiont stability over time. Despite efforts, we were unable to recover ancient Symbiodiniaceae reads from the samples. Comparing the ancient A. palmata data to whole-genome sequencing data from living acroporids, we found that while slightly distinct, ancient samples were most closely related to individuals of their own species. Together, these results provide a proof-of-principle showing that it is possible to carry out direct analysis of coral holobiont change over time, which lays a foundation for studying the impacts of environmental stress and evolutionary constraints.","language":"English","publisher":"Wiley","doi":"10.1111/mec.16642","usgsCitation":"Scott, C.B., Cardenas, A., Mah, M., Narasimhan, V., Rohland, N., Toth, L., Voostra, C., Reich, D., and Matz, M.V., 2022, Millennia-old coral holobiont DNA provides insight into future adaptive trajectories: Molecular Ecology, v. 31, no. 19, p. 4979-4990, https://doi.org/10.1111/mec.16642.","productDescription":"12 p.","startPage":"4979","endPage":"4990","ipdsId":"IP-132992","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446852,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-jkfsqqrf91776","text":"External Repository"},{"id":407855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Carly B.","contributorId":297168,"corporation":false,"usgs":false,"family":"Scott","given":"Carly","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cardenas, Anny","contributorId":297169,"corporation":false,"usgs":false,"family":"Cardenas","given":"Anny","email":"","affiliations":[{"id":55536,"text":"University of Konstanz","active":true,"usgs":false}],"preferred":false,"id":853591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mah, Matthew","contributorId":297170,"corporation":false,"usgs":false,"family":"Mah","given":"Matthew","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Narasimhan, Vagheesh","contributorId":297171,"corporation":false,"usgs":false,"family":"Narasimhan","given":"Vagheesh","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohland, Nadin","contributorId":297173,"corporation":false,"usgs":false,"family":"Rohland","given":"Nadin","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Voostra, Christian","contributorId":297175,"corporation":false,"usgs":false,"family":"Voostra","given":"Christian","email":"","affiliations":[{"id":55536,"text":"University of Konstanz","active":true,"usgs":false}],"preferred":false,"id":853596,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reich, David","contributorId":297177,"corporation":false,"usgs":false,"family":"Reich","given":"David","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853597,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matz, Mikhail V","contributorId":243005,"corporation":false,"usgs":false,"family":"Matz","given":"Mikhail","email":"","middleInitial":"V","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":853598,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238066,"text":"70238066 - 2022 - Projecting flood frequency curves under near-term climate change","interactions":[],"lastModifiedDate":"2022-11-08T12:38:08.023903","indexId":"70238066","displayToPublicDate":"2022-08-09T06:35:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Projecting flood frequency curves under near-term climate change","docAbstract":"

Flood-frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate-informed methodology for estimating flood-frequency curves under non-stationary future climate conditions. The methodology develops an asynchronous, semiparametric local-likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log-Pearson Type-3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR-MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR-MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood-frequency quantiles resulting from the ASLLR-MM and GCM members compare well with the flood-frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land-surface conditions and a multi-level model structure that includes other basins in a region could further improve the model performance in arid basins.

","language":"English","publisher":"Wiley","doi":"10.1029/2021WR031246","usgsCitation":"Awasthi, C., Archfield, S.A., Ryberg, K.R., Kiang, J.E., and Sankarasubramanian, A., 2022, Projecting flood frequency curves under near-term climate change: Water Resources Research, v. 58, no. 8, e2021WR031246, 21 p., https://doi.org/10.1029/2021WR031246.","productDescription":"e2021WR031246, 21 p.","ipdsId":"IP-139361","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":409225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -128.3785240620699,\n 50.07881572127886\n ],\n [\n -128.3785240620699,\n 24.227669266731155\n ],\n [\n -65.80039906206952,\n 24.227669266731155\n ],\n [\n -65.80039906206952,\n 50.07881572127886\n ],\n [\n -128.3785240620699,\n 50.07881572127886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Awasthi, Chandramauli 0000-0001-9826-5231","orcid":"https://orcid.org/0000-0001-9826-5231","contributorId":298942,"corporation":false,"usgs":false,"family":"Awasthi","given":"Chandramauli","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":856738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":856740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":856741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sankarasubramanian, A. 0000-0002-7668-1311","orcid":"https://orcid.org/0000-0002-7668-1311","contributorId":241034,"corporation":false,"usgs":false,"family":"Sankarasubramanian","given":"A.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":856742,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235869,"text":"70235869 - 2022 - An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States","interactions":[],"lastModifiedDate":"2023-12-20T14:20:27.975397","indexId":"70235869","displayToPublicDate":"2022-08-08T09:08:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States","docAbstract":"

Early detection of dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.

","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2022.13.4.05","usgsCitation":"Winder, M., Sepulveda, A., and Hoegh, A., 2022, An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States: Management of Biological Invasions, v. 13, no. 4, p. 659-678, https://doi.org/10.3391/mbi.2022.13.4.05.","productDescription":"20 p.","startPage":"659","endPage":"678","ipdsId":"IP-137748","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446857,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2022.13.4.05","text":"Publisher Index Page"},{"id":405680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5703125,\n 34.016241889667015\n ],\n [\n -94.5703125,\n 37.09023980307208\n ],\n [\n -94.482421875,\n 39.639537564366684\n ],\n [\n -95.888671875,\n 40.84706035607122\n ],\n [\n -96.591796875,\n 42.94033923363181\n ],\n [\n -97.20703125,\n 49.15296965617042\n ],\n [\n -123.04687499999999,\n 49.15296965617042\n ],\n [\n -123.3984375,\n 48.16608541901253\n ],\n [\n -124.8046875,\n 48.22467264956519\n ],\n [\n -124.541015625,\n 40.245991504199026\n ],\n [\n -123.57421875,\n 38.34165619279595\n ],\n [\n -121.9921875,\n 35.60371874069731\n ],\n [\n -119.00390625,\n 33.358061612778876\n ],\n [\n -116.630859375,\n 32.69486597787505\n ],\n [\n -110.302734375,\n 31.203404950917395\n ],\n [\n -108.19335937499999,\n 31.42866311735861\n ],\n [\n -106.5234375,\n 31.80289258670676\n ],\n [\n -103.0078125,\n 32.39851580247402\n ],\n [\n -103.0078125,\n 36.38591277287651\n ],\n [\n -99.931640625,\n 36.4566360115962\n ],\n [\n -99.755859375,\n 34.30714385628804\n ],\n [\n -94.5703125,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Winder, Meaghan","contributorId":295487,"corporation":false,"usgs":false,"family":"Winder","given":"Meaghan","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":849584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoegh, Andrew","contributorId":265906,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849585,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239346,"text":"70239346 - 2022 - Electrical imaging for hydrogeology","interactions":[],"lastModifiedDate":"2023-01-10T14:46:39.606987","indexId":"70239346","displayToPublicDate":"2022-08-08T08:29:37","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Electrical imaging for hydrogeology","docAbstract":"

Geophysical methods offer hydrogeologists unprecedented access to understanding subsurface parameters and processes. In this book, we outline the theory and application of electrical imaging methods, which inject current into the ground and measure the resultant potentials. These data are sensitive to rock type, grain size, porosity, pore fluid electrical conductivity, saturation, and temperature. Here, we describe the physical basis for electrical imaging, parallels between electrical flow equations and the groundwater flow equation, practical considerations for field investigations, data processing and inverse modeling of field data, and how to QA/QC data. We additionally cover two case studies, including a 2-D waterborne survey and a 4-D dataset from a biostimulation experiment.

","language":"English","publisher":"The Groundwater Project","usgsCitation":"Singha, K., Johnson, T.C., Day-Lewis, F., and Slater, L., 2022, Electrical imaging for hydrogeology, xi, 74 p.","productDescription":"xi, 74 p.","ipdsId":"IP-127811","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":411626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411625,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://gw-project.org/books/electrical-imaging-for-hydrogeology/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":861207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Timothy C.","contributorId":199842,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":861209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":861208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":861210,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238480,"text":"70238480 - 2022 - Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","interactions":[],"lastModifiedDate":"2022-12-01T16:23:17.773293","indexId":"70238480","displayToPublicDate":"2022-08-08T07:24:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","title":"Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","docAbstract":"

The glossy snake (Arizona elegans) is a polytypic species broadly distributed across southwestern North America. The species occupies habitats ranging from California’s coastal chaparral to the shortgrass prairies of Texas and southeastern Nebraska, to the extensive arid scrublands of central México. Three subspecies are currently recognized in California, one of which is afforded state-level protection based on the extensive loss and modification of its preferred alluvial coastal scrub and inland desert habitat. We report the first genome assembly of A. elegans occidentalis as part of the California Conservation Genomics Project (CCGP). Consistent with the reference genome strategy of the CCGP, we used Pacific Biosciences HiFi long reads and Hi-C chromatin-proximity sequencing technologies to produce a de novo assembled genome. The assembly comprises a total of 140 scaffolds spanning 1,842,602,218 base pairs, has a contig NG50 of 61 Mb, a scaffold NG50 of 136 Mb, and a BUSCO complete score of 95.9%, and is one of the most complete snake genome assemblies. The A. e. occidentalis genome will be a key tool for understanding the genomic diversity and the basis of adaptations within this species and close relatives within the hyperdiverse snake family Colubridae.

","language":"English","publisher":"Oxford University Press","doi":"10.1093/jhered/esac040","usgsCitation":"Wood, D.A., Richmond, J.Q., Escalona, M., Marimuthu, M.P., Nguyen, O., Sacco, S., Beraut, E., Westphal, M.F., Fisher, R., Vandergast, A.G., Toffelmier, E., Wang, I., and Shaffer, H., 2022, Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern: Journal of Heredity, v. 113, no. 6, p. 632-640, https://doi.org/10.1093/jhered/esac040.","productDescription":"9 p.","startPage":"632","endPage":"640","ipdsId":"IP-143455","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446864,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9923794","text":"External Repository"},{"id":409680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.11935248282103,\n 32.53882373045977\n ],\n [\n -114.50243196034603,\n 32.72786950214554\n ],\n [\n -114.64233250350489,\n 33.1520790618809\n ],\n [\n -114.58470276618635,\n 33.51373515511381\n ],\n [\n -114.41452412993016,\n 34.10031267745222\n ],\n [\n -114.11907829718999,\n 34.32357161601179\n ],\n [\n -114.67741209652053,\n 35.09778131876418\n ],\n [\n -117.76088232794436,\n 37.33676457017539\n ],\n [\n -119.42981485345024,\n 35.62249205151011\n ],\n [\n -121.63638617466606,\n 38.725574279970715\n ],\n [\n -122.54328366670836,\n 38.42830074064648\n ],\n [\n -119.57711374079892,\n 34.87314120906966\n ],\n [\n -118.19555417338168,\n 34.27246184404002\n ],\n [\n -117.90704702548453,\n 33.82510987924552\n ],\n [\n -117.26765483662936,\n 32.80899171054767\n ],\n [\n -116.97534971889436,\n 32.510621812966775\n ],\n [\n -117.11935248282103,\n 32.53882373045977\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Escalona, Merly","contributorId":299346,"corporation":false,"usgs":false,"family":"Escalona","given":"Merly","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":857590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marimuthu, Mohan P. 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Coastal marsh within Mediterranean climate zones is exposed to episodic watershed runoff and sediment loads that occur during storm events. Simulating future marsh accretion under sea level rise calls for attention to: (a) physical processes acting over the time scale of storm events and (b) biophysical processes acting over time scales longer than storm events. Using the upper Newport Bay in Southern California as a case study, we examine the influence of event-scale processes on simulated change in marsh topography by comparing: (a) a biophysical model that integrates with an annual time step and neglects event-scale processes (BP-Annual), (b) a physical model that resolves event-scale processes but neglects biophysical interactions (P-Event), and (c) a biophysical model that resolves event-scale physical processes and biophysical processes at annual and longer time scales (BP-Event). A calibrated BP-Event model shows that large (>20-year return period) episodic storm events are major drivers of marsh accretion, depositing up to 30 cm of sediment in one event. Greater deposition is predicted near fluvial sources and tidal channels and less on marshes further from fluvial sources and tidal channels. In contrast, the BP-Annual model poorly resolves spatial structure in marsh accretion as a consequence of neglecting event-scale processes. Furthermore, the P-Event model significantly overestimates marsh accretion as a consequence of neglecting marsh surface compaction driven by annual scale biophysical processes. Differences between BP-Event and BP-Annual models translate up to 20 cm per century in marsh surface elevation.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JF006526","usgsCitation":"Brand, M.W., Buffington, K., Rogers, J.B., Thorne, K., Stein, E.D., and Sanders, B.F., 2022, Multi-decadal simulation of marsh topography evolution under sea level rise and episodic sediment loads: Journal of Geophysical Research: Earth Surface, v. 127, no. 9, e2021JF006526, 20 p., https://doi.org/10.1029/2021JF006526.","productDescription":"e2021JF006526, 20 p.","ipdsId":"IP-139798","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jf006526","text":"Publisher Index Page"},{"id":407208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Newport Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0316162109375,\n 33.52536850360117\n ],\n [\n -117.7679443359375,\n 33.52536850360117\n ],\n [\n -117.7679443359375,\n 33.735760815044635\n ],\n [\n -118.0316162109375,\n 33.735760815044635\n ],\n [\n -118.0316162109375,\n 33.52536850360117\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Brand, M W","contributorId":296909,"corporation":false,"usgs":false,"family":"Brand","given":"M","email":"","middleInitial":"W","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":852774,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":852775,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogers, J B","contributorId":296910,"corporation":false,"usgs":false,"family":"Rogers","given":"J","email":"","middleInitial":"B","affiliations":[{"id":64239,"text":"Southern California Coastal Water Research Project, Costa Mesa, CA","active":true,"usgs":false}],"preferred":false,"id":852776,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":852777,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stein, E D","contributorId":296911,"corporation":false,"usgs":false,"family":"Stein","given":"E","email":"","middleInitial":"D","affiliations":[{"id":64239,"text":"Southern California Coastal Water Research Project, Costa Mesa, CA","active":true,"usgs":false}],"preferred":false,"id":852778,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sanders, B F","contributorId":296912,"corporation":false,"usgs":false,"family":"Sanders","given":"B","email":"","middleInitial":"F","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":852779,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238346,"text":"70238346 - 2022 - Diverse tsunamigenesis triggered by the Hunga Tonga-Hunga Ha’apai eruption","interactions":[],"lastModifiedDate":"2022-11-17T12:44:49.762577","indexId":"70238346","displayToPublicDate":"2022-08-08T06:40:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Diverse tsunamigenesis triggered by the Hunga Tonga-Hunga Ha’apai eruption","docAbstract":"

On the evening of 15 January 2022, the Hunga Tonga-Hunga Ha’apai volcano1 unleashed a violent underwater eruption, blanketing the surrounding land masses in ash and debris. The eruption generated tsunamis observed around the world. An event of this type last occurred in 1883 during the eruption of Krakatau, and thus we have the first observations of a tsunami from a large emergent volcanic eruption captured with modern instrumentation. Here we show that the explosive eruption generated waves through multiple mechanisms, including: (1) air–sea coupling with the initial and powerful shock wave radiating out from the explosion in the immediate vicinity of the eruption; (2) collapse of the water cavity created by the underwater explosion; and (3) air–sea coupling with the air-pressure pulse that circled the Earth several times, leading to a global tsunami. In the near field, tsunami impacts are strongly controlled by the water-cavity source whereas the far-field tsunami, which was unusually persistent, can be largely described by the air-pressure pulse mechanism. Catastrophic damage in some harbours in the far field was averted by just tens of centimetres, implying that a modest sea level rise combined with a future, similar event would lead to a step-function increase in impacts on infrastructure. Piecing together the complexity of this event has broad implications for coastal hazards in similar geophysical settings, suggesting a currently neglected source of global tsunamis.

","language":"English","publisher":"Nature","doi":"10.1038/s41586-022-05170-6","usgsCitation":"Lynett, P., McCann, M., Zhou, Z., Renteria, W., Borrero, J., Greer, D., Fa’anunu, ’., Bosserelle, C., Jaffe, B.E., La Selle, S., Ritchie, A.C., Snyder, A.G., Nasr, B., Bott, J., Graehl, N., Synolakis, C., Ebrahimi, B., and Cinar, E., 2022, Diverse tsunamigenesis triggered by the Hunga Tonga-Hunga Ha’apai eruption: Nature, v. 609, p. 728-733, https://doi.org/10.1038/s41586-022-05170-6.","productDescription":"6 p.","startPage":"728","endPage":"733","ipdsId":"IP-138492","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446869,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41586-022-05170-6","text":"Publisher Index Page"},{"id":409413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hunga Tonga–Hunga Haʻapai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -175.27960338675533,\n -19.687559762945455\n ],\n [\n -175.27960338675533,\n -20.366959342757923\n ],\n [\n -174.4441245778288,\n -20.366959342757923\n ],\n [\n -174.4441245778288,\n -19.687559762945455\n ],\n [\n -175.27960338675533,\n -19.687559762945455\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"609","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynett, Patrick","contributorId":196027,"corporation":false,"usgs":false,"family":"Lynett","given":"Patrick","affiliations":[],"preferred":false,"id":857208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCann, 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Ezgi","contributorId":299192,"corporation":false,"usgs":false,"family":"Cinar","given":"Ezgi","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":857225,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70237590,"text":"70237590 - 2022 - Numbers and presence of guarding dogs affect wolf and leopard predation on livestock in northeastern Iran","interactions":[],"lastModifiedDate":"2022-10-17T13:23:03.379248","indexId":"70237590","displayToPublicDate":"2022-08-07T14:29:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":970,"text":"Basic and Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Numbers and presence of guarding dogs affect wolf and leopard predation on livestock in northeastern Iran","docAbstract":"

Livestock predation can pose socio-economic impacts on rural livelihoods and is the main cause of retaliatory killings of carnivores in many countries. Therefore, appropriate interventions to reduce livestock predation, lower conflict and promote coexistence are needed. Livestock guarding dogs have been traditionally used to reduce predation, yet details regarding the use of dogs, especially the number of dogs per herd effectively required, are rarely studied. In this study, we assessed how the number and presence of guarding dogs in a herd can reduce livestock losses to leopard and wolf in corrals at night and on grazing grounds in day-time. Using systematic interview surveys (2016-2019), we documented sheep/goat losses per attack (predation rates) from 139 shepherds across 32 villages around Golestan National Park, Iran. We analysed the effects of the number of dogs, presence of dogs, presence of shepherds, seasons, corral quality, livestock number, dog size, distance to villages and distance to reserve on predation rates using generalized linear models. For the leopard model, dog presence significantly decreased (β = –1.80, 95% confidence interval –2.61 to –0.81) predation rates during day-time to 1.41 individuals per attack. For wolf attacks in corrals at night, predation rates significantly decreased (β = –0.29, –0.54 to –0.04) with increasing dog numbers. Also, shepherd presence (β = –0.56, –1.10 to –0.10) and herd size (β = –0.36, –0.60 to –0.12) significantly reduced predation rates. In the wolf day-time model, shepherd presence significantly decreased (β = –0.93, –1.74 to –0.10) predation rates. Our study suggests that (1) using dogs can reduce, but not eliminate, predation by leopards during day-time; (2) with every additional dog, predation rates by wolves in corrals at night are likely to decrease on average by 25.2%; and (3) the presence of shepherds in corrals at night and during day-time can reduce predation rates.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.baae.2022.08.001","usgsCitation":"Soofi, M., Soufi, M., Royle, A., Waltert, M., and Khorozyan, I., 2022, Numbers and presence of guarding dogs affect wolf and leopard predation on livestock in northeastern Iran: Basic and Applied Ecology, v. 64, p. 147-156, https://doi.org/10.1016/j.baae.2022.08.001.","productDescription":"10 p.","startPage":"147","endPage":"156","ipdsId":"IP-136891","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.baae.2022.08.001","text":"Publisher Index Page"},{"id":408278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","otherGeospatial":"Azizabad No-Hunting Area, Golestan National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 55.52490234375,\n 37.18876668723709\n ],\n [\n 56.34063720703125,\n 37.18876668723709\n ],\n [\n 56.34063720703125,\n 37.694687703235914\n ],\n [\n 55.52490234375,\n 37.694687703235914\n ],\n [\n 55.52490234375,\n 37.18876668723709\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soofi, Mahmood","contributorId":297883,"corporation":false,"usgs":false,"family":"Soofi","given":"Mahmood","email":"","affiliations":[{"id":64430,"text":"Department of Conservation Biology, University of Goettingen,","active":true,"usgs":false}],"preferred":false,"id":854546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soufi, Mobin","contributorId":297884,"corporation":false,"usgs":false,"family":"Soufi","given":"Mobin","email":"","affiliations":[{"id":64431,"text":"Department of the Environment, Faculty of Fishery and Environment, Gorgan University of Agriculture and Natural Resources, Gorgan, Iran","active":true,"usgs":false}],"preferred":false,"id":854547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waltert, Matthias","contributorId":297885,"corporation":false,"usgs":false,"family":"Waltert","given":"Matthias","email":"","affiliations":[{"id":62110,"text":"Department of Conservation Biology, University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":854549,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Khorozyan, Igor","contributorId":297886,"corporation":false,"usgs":false,"family":"Khorozyan","given":"Igor","email":"","affiliations":[{"id":62110,"text":"Department of Conservation Biology, University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":854550,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243220,"text":"70243220 - 2022 - New projections of 21st century climate and hydrology for Alaska and Hawaiʻi","interactions":[],"lastModifiedDate":"2023-05-04T11:52:28.55815","indexId":"70243220","displayToPublicDate":"2022-08-07T06:50:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5567,"text":"Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"New projections of 21st century climate and hydrology for Alaska and Hawaiʻi","docAbstract":"

In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.cliser.2022.100312","usgsCitation":"Mizukami, N., Newman, A.J., Littell, J., Giambelluca, T., Wood, A.W., Gutmann, E.D., Hamman, J.J., Gergel, D., Nijssen, B., Clark, M., and Arnold, J.R., 2022, New projections of 21st century climate and hydrology for Alaska and Hawaiʻi: Climate Services, v. 27, 100312, 15 p., https://doi.org/10.1016/j.cliser.2022.100312.","productDescription":"100312, 15 p.","ipdsId":"IP-141391","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":446875,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cliser.2022.100312","text":"Publisher Index Page"},{"id":416702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n 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Naoki","contributorId":178120,"corporation":false,"usgs":false,"family":"Mizukami","given":"Naoki","email":"","affiliations":[],"preferred":false,"id":871499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newman, Andrew J.","contributorId":194229,"corporation":false,"usgs":false,"family":"Newman","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":871500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giambelluca, Thomas W.","contributorId":304728,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas 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J.","contributorId":304729,"corporation":false,"usgs":false,"family":"Hamman","given":"Joseph","email":"","middleInitial":"J.","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":871505,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gergel, Diana R.","contributorId":304730,"corporation":false,"usgs":false,"family":"Gergel","given":"Diana R.","affiliations":[{"id":66153,"text":"Black Rock, USA","active":true,"usgs":false}],"preferred":false,"id":871506,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nijssen, Bart","contributorId":178123,"corporation":false,"usgs":false,"family":"Nijssen","given":"Bart","email":"","affiliations":[],"preferred":false,"id":871507,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Clark, Martyn .","contributorId":304731,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","email":"","middleInitial":".","affiliations":[{"id":66154,"text":"Centre for Hydrology, University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":871508,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Arnold, Jeffrey R.","contributorId":178125,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":871509,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240718,"text":"70240718 - 2022 - Root hemiparasitic plants are associated with more even communities across North America","interactions":[],"lastModifiedDate":"2023-02-16T12:46:52.994835","indexId":"70240718","displayToPublicDate":"2022-08-07T06:40:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Root hemiparasitic plants are associated with more even communities across North America","docAbstract":"

Root hemiparasitic plants both compete with and extract resources from host plants. By reducing the abundance of dominant plants and releasing subordinates from competitive exclusion, they can have an outsized impact on plant communities. Most research on the ecological role of hemiparasites is manipulative and focuses on a small number of hemiparasitic taxa. Here, we ask whether patterns in natural plant communities match the expectation that hemiparasites affect the structure of plant communities. Our data were collected on 129 national park units spanning the continental United States. The most common hemiparasite genera were Pedicularis, Castilleja, Krameria, and Comandra. We used null models and linear mixed models to determine whether hemiparasites were associated with changes in community richness and evenness. Hemiparasite presence did not affect community metrics. Hemiparasite abundance was positively associated with increasing evenness of herbaceous species, but not with species richness. The associations that we observed on a continental scale are consistent with evidence that the impacts of root hemiparasitic plants on evenness can be substantial and abundance dependent but that effects on richness are less pronounced. Hemiparasites mediate competitive exclusion in communities to facilitate species coexistence and merit consideration of inclusion in ecological theories of coexistence.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3837","usgsCitation":"Hodzic, J., Pearse, I.S., Beaury, E.M., Corbin, J., and Bakker, J., 2022, Root hemiparasitic plants are associated with more even communities across North America: Ecology, v. 103, no. 2, e3837, 13 p., https://doi.org/10.1002/ecy.3837.","productDescription":"e3837, 13 p.","ipdsId":"IP-132499","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446878,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3837","text":"Publisher Index Page"},{"id":413125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 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0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":864427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beaury, Evelyn M.","contributorId":236820,"corporation":false,"usgs":false,"family":"Beaury","given":"Evelyn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":864428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corbin, Jeff","contributorId":302406,"corporation":false,"usgs":false,"family":"Corbin","given":"Jeff","email":"","affiliations":[{"id":65470,"text":"Union College","active":true,"usgs":false}],"preferred":false,"id":864429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bakker, Jonathan D.","contributorId":229023,"corporation":false,"usgs":false,"family":"Bakker","given":"Jonathan D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":864430,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235718,"text":"70235718 - 2022 - Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales","interactions":[],"lastModifiedDate":"2022-09-15T15:17:51.351906","indexId":"70235718","displayToPublicDate":"2022-08-07T06:37:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5536,"text":"Deep Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales","docAbstract":"

Surface and subsurface moored buoy, ship-based, remotely sensed, and reanalysis datasets are used to investigate thermal variability of northern Gulf of Alaska (NGA) nearshore, coastal, and offshore waters over synoptic to century-long time scales. NGA sea surface temperature (SST) showed a larger positive trend of 0.22 ± 0.10 °C per decade over 1970–2021 compared to 0.10 ± 0.03 °C per decade over 1900–2021. Over synoptic time scales, SST covariance between two stations is small (<10%) when separation exceeds 100 km, while stations separated by 500 km retain 50% of their co-variability for seasonal and longer fluctuations. Relative to in situ sensor data, remotely sensed SST data has limited accuracy in some NGA settings, capturing 60–70% of the daily SST anomaly in coastal and offshore waters, but often <25% nearshore. North Pacific and NGA leading modes of SST variability leave 25–50% of monthly variance unresolved. Analysis of the 2014–2016 Pacific marine heatwave shows that NGA coastal surface temperatures warmed contemporaneously with offshore waters through 2013, but deep inner shelf waters (200–250 m) exhibited delayed warming. Offshore surface waters cooled from 2014 to 2016, while shelf waters continued to warm from the combined effects of local air-sea and advective heat fluxes. We find that annually averaged Sitka air temperature is a leading predictor (r2 = 0.37, p < 0.05) for following-year NGA coastal water column temperature. Our results can inform future environmental monitoring designs, assist forward-looking projections of marine conditions, and show the importance of in situ measurements for nearshore studies that require knowledge of thermal conditions over time scales of days and weeks.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2022.105155","usgsCitation":"Danielson, S.L., Hennon, T.D., Monson, D., Suryan, R.M., Cambell, R.W., Baird, S.J., Holderied, K., and Weingartner, T.J., 2022, Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales: Deep Sea Research Part II: Topical Studies in Oceanography, v. 203, 105155, 19 p., https://doi.org/10.1016/j.dsr2.2022.105155.","productDescription":"105155, 19 p.","ipdsId":"IP-140518","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":446880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2022.105155","text":"Publisher Index Page"},{"id":405177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.05859375,\n 52.696361078274485\n ],\n [\n -125.94726562499999,\n 52.696361078274485\n ],\n [\n -125.94726562499999,\n 63.54855223203644\n ],\n [\n -165.05859375,\n 63.54855223203644\n ],\n [\n -165.05859375,\n 52.696361078274485\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"203","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Danielson, Seth L.","contributorId":256682,"corporation":false,"usgs":false,"family":"Danielson","given":"Seth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":849078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hennon, Tyler D.","contributorId":291317,"corporation":false,"usgs":false,"family":"Hennon","given":"Tyler","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":849079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":849080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suryan, Robert M. 0000-0003-0755-8317","orcid":"https://orcid.org/0000-0003-0755-8317","contributorId":221852,"corporation":false,"usgs":false,"family":"Suryan","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40443,"text":"Oregon State University, NOAA","active":true,"usgs":false}],"preferred":false,"id":849081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cambell, Rob W.","contributorId":295302,"corporation":false,"usgs":false,"family":"Cambell","given":"Rob","email":"","middleInitial":"W.","affiliations":[{"id":13600,"text":"Prince William Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":849082,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baird, Steven J.","contributorId":12375,"corporation":false,"usgs":false,"family":"Baird","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":849083,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holderied, Kristine","contributorId":291319,"corporation":false,"usgs":false,"family":"Holderied","given":"Kristine","affiliations":[{"id":62686,"text":"Kasitsna Bay Laboratory, NOAA","active":true,"usgs":false}],"preferred":false,"id":849084,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weingartner, Thomas J.","contributorId":295303,"corporation":false,"usgs":false,"family":"Weingartner","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":849085,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239382,"text":"70239382 - 2022 - A geospatial knowledge graph prototype for national topographic mapping","interactions":[],"lastModifiedDate":"2023-01-11T15:03:10.21326","indexId":"70239382","displayToPublicDate":"2022-08-06T08:58:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12997,"text":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A geospatial knowledge graph prototype for national topographic mapping","docAbstract":"

Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attributes. This paper discusses research toward the development of a prototype GKG based on national topographic databases of geospatial feature instances, attributes, properties, metadata, and annotations. The challenges are to capture and represent geographic semantics inherent in the source data, to align such graph models with standards where possible, to test logical computations, and to visualize the data using a cartographic user interface. Data integration from outside sources was tested through SPARQL and GeoSPARQL queries. Called the MapKB, the approaches applied in this prototype use a number of software components to build a system architecture aligned with those objectives and are composed entirely of free and open-source software. The system and ontology design were validated through reasoning and competency questions. Technical aspects of the prototype software succeeded, but customization was found to be needed for user-based design.

","language":"English","publisher":"International Society of Photogrammetry and Remote Sensing","doi":"10.5194/isprs-archives-XLVIII-4-W1-2022-511-2022","usgsCitation":"Varanka, D.E., 2022, A geospatial knowledge graph prototype for national topographic mapping: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLVIII-4/W1-2022, p. 511-516, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-511-2022.","productDescription":"6 p.","startPage":"511","endPage":"516","ipdsId":"IP-120273","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":446887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-511-2022","text":"Publisher Index Page"},{"id":411719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"XLVIII-4/W1-2022","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":861370,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233932,"text":"70233932 - 2022 - Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","interactions":[],"lastModifiedDate":"2024-01-19T15:18:40.15331","indexId":"70233932","displayToPublicDate":"2022-08-05T11:36:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","docAbstract":"

Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation of RCMAP has been improved with more training data, regional-scale Landsat composites, and more robust change detection. We assess the temporal patterns in each component with a linear model and a structural change method that determines break points using an 8-year temporal moving window. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective. Although break point density within one year of fire and vegetation treatments was ~10× and ~4× that of unburned areas, respectively, break point detection in the correct year of fire was only moderately accurate. Climate responses in break points proved more robust, with strong spatiotemporal relation in break point density with both aridity index values and aridity index change. Break point density strongly responds to both increased and decreased aridity and is reflective of ecosystem resilience. Data provide spatiotemporal information on the occurrence of breaks, but even more importantly, attribute those change events to specific component(s).

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2104786","usgsCitation":"Shi, H., Rigge, M.B., Postma, K., and Bunde, B., 2022, Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020): GIScience & Remote Sensing, v. 59, no. 1, p. 1243-1265, https://doi.org/10.1080/15481603.2022.2104786.","productDescription":"23 p.","startPage":"1243","endPage":"1265","ipdsId":"IP-135462","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446897,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2104786","text":"Publisher Index Page"},{"id":424623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJXUI1","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Rangeland Condition Monitoring Assessment 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":847707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":293879,"corporation":false,"usgs":false,"family":"Postma","given":"Kory","affiliations":[{"id":63548,"text":"KBRwyle, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":847708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":847709,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234246,"text":"70234246 - 2022 - Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","interactions":[],"lastModifiedDate":"2022-08-05T13:15:34.056536","indexId":"70234246","displayToPublicDate":"2022-08-05T08:08:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","docAbstract":"In mountain headwater streams, the quality and resilience of summer cold-water habitat is generally regulated by stream discharge, longitudinal stream channel connectivity and groundwater exchange. These critical hydrologic processes are thought to be influenced by the stream corridor bedrock contact depth (sediment thickness), a parameter often inferred from sparse hillslope borehole information, piezometer refusal and remotely sensed data. To investigate how local bedrock depth might control summer stream temperature and channel disconnection (dewatering) patterns, we measured stream corridor bedrock depth by collecting and interpreting 191 passive seismic datasets along eight headwater streams in Shenandoah National Park (Virginia, USA). In addition, we used multi-year stream temperature and streamflow records to calculate several baseflow-related metrics along and among the study streams. Finally, comprehensive visual surveys of stream channel dewatering were conducted in 2016, 2019 and 2021 during summer low flow conditions (124 total km of stream length). We found that measured bedrock depths along the study streams were not well-characterized by soils maps or an existing global-scale geologic dataset where the latter overpredicted measured depths by 12.2 m (mean) or approximately four times the average bedrock depth of 2.9 m. Half of the eight study stream corridors had an average bedrock depth of less than 2 m. Of the eight study streams, Staunton River had the deepest average bedrock depth (3.4 m), the coldest summer temperature profiles and substantially higher summer baseflow indices compared to the other study steams. Staunton River also exhibited paired air and water annual temperature signals suggesting deeper groundwater influence, and the stream channel did not dewater in lower sections during any baseflow survey. In contrast, Paine Run and Piney River did show pronounced, patchy channel dewatering, with Paine Run having dozens of discrete dry channel sections ranging from 1 to greater than 300 m in length. Stream dewatering patterns were apparently influenced by a combination of discrete deep bedrock (20+ m) features and more subtle sediment thickness variation (1–4 m) depending on local stream valley hydrogeology. In combination, these unique datasets show the first large-scale empirical support for existing conceptual models of headwater stream disconnection based on spatially variable underflow capacity and shallow groundwater supply.","language":"English","publisher":"Copernicus","doi":"10.5194/hess-26-3989-2022","usgsCitation":"Briggs, M., Goodling, P.J., Johnson, Z., Rogers, K., Hitt, N.P., Fair, J.H., and Snyder, C.D., 2022, Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams: Hydrology and Earth System Sciences, v. 26, no. 15, p. 3989-4011, https://doi.org/10.5194/hess-26-3989-2022.","productDescription":"23 p.","startPage":"3989","endPage":"4011","ipdsId":"IP-132407","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":446904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-26-3989-2022","text":"Publisher Index Page"},{"id":404871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Blue Ridge Mountains, Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.85711669921875,\n 38.098901948321256\n ],\n [\n -78.8433837890625,\n 38.039438891821746\n ],\n [\n -78.71978759765625,\n 38.090255780611486\n ],\n [\n -78.69369506835938,\n 38.182068998322094\n ],\n [\n -78.64151000976562,\n 38.19718009396176\n ],\n [\n -78.60580444335938,\n 38.26945406815749\n ],\n [\n -78.50830078125,\n 38.312568460056966\n ],\n [\n -78.38333129882812,\n 38.33734763569314\n ],\n [\n -78.33663940429688,\n 38.43745529233546\n ],\n [\n -78.26385498046875,\n 38.53957267203905\n ],\n [\n -78.233642578125,\n 38.65119833229951\n ],\n [\n -78.2281494140625,\n 38.716590286734494\n ],\n [\n -78.1402587890625,\n 38.74551518488265\n ],\n [\n -78.13888549804686,\n 38.8407772667165\n ],\n [\n -78.15536499023438,\n 38.89423942194029\n ],\n [\n -78.2061767578125,\n 38.93698019310818\n ],\n [\n -78.23089599609375,\n 38.872859384572244\n ],\n [\n -78.22128295898438,\n 38.81296105899589\n ],\n [\n -78.25698852539062,\n 38.79476766282312\n ],\n [\n -78.26522827148438,\n 38.8225909761771\n ],\n [\n -78.31878662109375,\n 38.82901019751963\n ],\n [\n -78.34625244140625,\n 38.810820900566135\n ],\n [\n -78.41354370117188,\n 38.71980474264237\n ],\n [\n -78.40667724609375,\n 38.63081814300356\n ],\n [\n -78.49868774414062,\n 38.5213096674994\n ],\n [\n -78.59619140625,\n 38.541720956040386\n ],\n [\n -78.55636596679688,\n 38.43960662292255\n ],\n [\n -78.6181640625,\n 38.40302528453207\n ],\n [\n -78.82278442382812,\n 38.25543637637947\n ],\n [\n -78.85711669921875,\n 38.098901948321256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - 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2022 - Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest","interactions":[],"lastModifiedDate":"2022-08-04T14:33:52.831258","indexId":"70234236","displayToPublicDate":"2022-08-04T09:23:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest","docAbstract":"

Global climate change has led to rising temperatures and to more frequent and intense climatic events, such as storms and droughts. Changes in climate and disturbance regimes can have non-additive effects on plant communities and result in complicated legacies we have yet to understand. This is especially true for tropical forests, which play a significant role in regulating global climate. We used understory vegetation data from the Tropical Responses to Altered Climate Experiment (TRACE) in Puerto Rico to evaluate how plant communities responded to climate warming and disturbance. The TRACE understory vegetation was exposed to a severe drought (2015), 2 years of experimental warming (4°C above ambient in half of the plots, 2016–2017 and 2018–2019), and two major hurricanes (Irma and María, September 2017). Woody seedlings and saplings were censused yearly from 2015 to 2019, with an additional census in 2015 after the drought ended. We evaluated disturbance-driven changes in species richness, diversity, and composition across ontogeny. We then used Bayesian predictive trait modeling to assess how species responded to disturbance and how this might influence the functional structure of the plant community. Our results show decreased seedling richness after hurricane disturbance, as well as increased sapling richness and diversity after warming. We found a shift in species composition through time for both seedlings and saplings, yet the individual effects of each disturbance were not significant. At both ontogenetic stages, we observed about twice as many species responding to experimental warming as those responding to drought and hurricanes. Predicted changes in functional structure point to disturbance-driven functional shifts toward a mixture of fast-growing and drought-tolerant species. Our findings demonstrate that the tropical forest understory community is more resistant to climatic stressors than expected, especially at the sapling stage. However, early signs of changes in species composition suggest that, in a warming climate with frequent droughts and hurricanes, plant communities might shift over time toward fast-growing or drought-tolerant species.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2022.733967","usgsCitation":"Alonso-Rodriguez, A.M., Wood, T.E., Torres-Diaz, J., Cavaleri, M.A., Reed, S., and Bachelot, B., 2022, Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest: Frontiers in Forests and Global Change, v. 5, 733967, 16 p., https://doi.org/10.3389/ffgc.2022.733967.","productDescription":"733967, 16 p.","ipdsId":"IP-133340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446918,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2022.733967","text":"Publisher Index Page"},{"id":404824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Puerto Rico","otherGeospatial":"Bosque experimental de Luquillo, Luquillo Experimental Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.8681869506836,\n 18.240764185529784\n ],\n [\n -65.70304870605469,\n 18.240764185529784\n ],\n [\n -65.70304870605469,\n 18.34800827349917\n ],\n [\n -65.8681869506836,\n 18.34800827349917\n ],\n [\n -65.8681869506836,\n 18.240764185529784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2022-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Alonso-Rodriguez, Aura M.","contributorId":206281,"corporation":false,"usgs":false,"family":"Alonso-Rodriguez","given":"Aura","email":"","middleInitial":"M.","affiliations":[{"id":37300,"text":"International Institute of Tropical Forestry, USDA Forest Service, Sabana Field Research Station, Luquillo, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":848288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Tana E.","contributorId":33193,"corporation":false,"usgs":true,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":848289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torres-Diaz, Jamarys","contributorId":294541,"corporation":false,"usgs":false,"family":"Torres-Diaz","given":"Jamarys","email":"","affiliations":[{"id":63595,"text":"USDA Forest Service International Institute of Tropical Forestry, Jardín Botánico Sur, Río Piedras, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":848290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":848291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":848292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bachelot, Benedicte","contributorId":294542,"corporation":false,"usgs":false,"family":"Bachelot","given":"Benedicte","email":"","affiliations":[{"id":63597,"text":"Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, USA","active":true,"usgs":false}],"preferred":false,"id":848293,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234229,"text":"70234229 - 2022 - A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia","interactions":[],"lastModifiedDate":"2023-04-14T17:00:52.620563","indexId":"70234229","displayToPublicDate":"2022-08-04T09:07:39","publicationYear":"2022","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":"A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia","docAbstract":"

Mangrove forests are the most important ecosystems on Pohnpei Island, Federated States of Micronesia, as the island communities of the central Pacific rely on the forests for many essential services including protection from sea-level rise that is occurring at a greater pace than the global average. As part of a multi-component assessment to evaluate vulnerabilities of mangrove forests on Pohnpei, mangrove forests were mapped at two points in time: 1983 and 2018. In 2018, the island had 6,426 ha of mangrove forest. Change analysis indicated a slight (0.76%) increase of mangrove area between 1983 and 2018, contrasting with global mangrove area declines. Forest structure and aboveground carbon (AGC) stocks were inventoried using a systematic sampling of field survey plots and extrapolated to the island using k-nearest neighbor and random forest species models. A gridded or wall to wall approach is suggested when possible for defining carbon stocks of a large area due to high variability seen in our data. The k-nearest neighbor model performed better than random forest models to map species dominance in these forests. Mean AGC was 167 ± 11 MgC ha-1, which is greater than the global average of mangroves (115 ± 7 MgC ha-1) but within their global range (37–255 MgC ha-1) Kauffman et al. (2020). In 2018, Pohnpei mangroves contained over 1.07 million MgC in AGC pools. By assigning the mean AGC stock per species per area to the map, carbon stock distributions were visualized spatially, allowing future conservation efforts to be directed to carbon dense stands.

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,{"id":70234233,"text":"70234233 - 2022 - Reestablishing a foundational species: limitations on post-wildfire sagebrush seedling establishment","interactions":[],"lastModifiedDate":"2022-08-04T13:30:49.237075","indexId":"70234233","displayToPublicDate":"2022-08-04T08:22:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Reestablishing a foundational species: limitations on post-wildfire sagebrush seedling establishment","docAbstract":"

Improving post-wildfire restoration of foundational plant species is crucial for conserving imperiled ecosystems. We sought to better understand the initial establishment of sagebrush (Artemisia sp.), a foundational shrubland species over a vast area of western North America, in the first 1–2 years post-wildfire, a critical time period for population recovery. Field data from 460 sagebrush populations sampled across the Great Basin revealed several patterns. Sagebrush seedlings were uncommon in the first 1–2 years after fire, with none detected in 69% of plots, largely because most fires occurred in areas of low resistance to invasive species and resilience to disturbance (hereafter, R&R). Post-fire aerial seeding of sagebrush dramatically increased seedling occupancy, especially in low R&R areas, which exhibited a 3.4-fold increase in occupancy over similar unseeded locations. However, occupancy models and repeat surveys suggested exceptionally high mortality, as occupancy rates declined by as much as 50% between the first and second years after fire. We found the prevalence of “fertile island” microsites (patches beneath fire-consumed sagebrush) to be the best predictor of seedling occupancy, followed by aerial seeding status, native perennial grass cover, and years since fire. In populations where no sagebrush seeding occurred, seedlings were most likely to occur in locations with a combination of high fertile island microsite cover and close proximity to a remnant sagebrush plant. These important attributes were only present in 13% of post-fire locations, making them rare across the Great Basin. Finally, in the absence of fertile islands and remnant plants, seedling establishment was not observed in any unseeded areas, and rarely in seeded locations. Thus, local extirpation of sagebrush could have important, long-term implications for sagebrush reestablishment following future fires if there are no mature individuals to leave behind fertile islands or serve as remnant individuals. These findings highlight the importance of landscape legacy effects and could help guide where and how big sagebrush restoration is conducted in the future.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4195","usgsCitation":"Arkle, R., Pilliod, D.S., Germino, M., Jeffries, M.I., and Welty, J.L., 2022, Reestablishing a foundational species: limitations on post-wildfire sagebrush seedling establishment: Ecosphere, v. 13, no. 8, e4195, 16 p., https://doi.org/10.1002/ecs2.4195.","productDescription":"e4195, 16 p.","ipdsId":"IP-136209","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":488326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4195","text":"Publisher Index Page"},{"id":435742,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCJ3A9","text":"USGS data release","linkHelpText":"Reestablishing a foundational species: limitations on post-wildfire sagebrush seedling 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Less than 30% of all species reintroductions have been successful and it is important that factors associated with success or failure be identified. Officials experimentally translocated 14 adult female American black bears (Ursus americanus) from Great Smoky Mountains National Park, North Carolina and Tennessee, USA, to Big South Fork National River and Recreation Area in the Cumberland Plateau of Kentucky and Tennessee, USA, in 1996–1997. Since that time, the reintroduced bear population has continued to expand in size and range so our study objective was to use spatially explicit capture-recapture methods across a wide spatial extent to estimate bear population abundance and growth. We constructed 440 (223 in KY, 217 in TN) hair traps in our primary sampling area in 2019 arranged in clusters of 4–9 traps/cluster, which we augmented with data from 138 hair traps in a secondary sampling area in Tennessee collected in 2018. We extracted and genotyped DNA from hair samples to construct spatially explicit capture histories, using spatial covariates to model inhomogeneous densities. Population abundance estimates across our 36,035-km2 study area were 411 males and 406 females excluding cubs. Based on an initial standing population of 18 adult and subadult bears, the mean annual growth rate (λ) from 1998 to 2019 was 1.199. The mean annual harvest rate in Kentucky from 2013 to 2019 was 5.1% and in Tennessee from 2014 to 2019 was 13.2%. Based on simulations, the hunting seasons reduced mean λ from 1.217 to 1.199, but growth was rapid despite harvest. Genetic diversity was retained, with similar expected heterozygosity as in the source population. The lack of conspecifics, highly productive habitat, and an initial age and sex distribution that was skewed toward the most fecund members of the population likely contributed to the rapid growth and high levels of gene retention in this bear population.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22298","usgsCitation":"Alston, J.D., Clark, J.D., Gibbs, D.B., and Hast, J.T., 2022, Density, harvest rates, and growth of a reintroduced American black bear population: Journal of Wildlife Management, v. 86, no. 8, e22298, 24 p., https://doi.org/10.1002/jwmg.22298.","productDescription":"e22298, 24 p.","ipdsId":"IP-133035","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":432852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.03209966391893,\n 36.885959664218205\n ],\n [\n -85.03209966391893,\n 36.256669997227874\n ],\n [\n -83.92202060964118,\n 36.256669997227874\n ],\n [\n -83.92202060964118,\n 36.885959664218205\n ],\n [\n -85.03209966391893,\n 36.885959664218205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Alston, Joshua D","contributorId":342919,"corporation":false,"usgs":false,"family":"Alston","given":"Joshua","email":"","middleInitial":"D","affiliations":[{"id":81953,"text":"Univ. TN","active":true,"usgs":false}],"preferred":false,"id":910480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":910481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, Daniel B.","contributorId":342920,"corporation":false,"usgs":false,"family":"Gibbs","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":910482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hast, John T.","contributorId":140197,"corporation":false,"usgs":false,"family":"Hast","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":910483,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250192,"text":"70250192 - 2022 - Outgassing through magmatic fractures enables effusive eruption of silicic magma","interactions":[],"lastModifiedDate":"2023-11-28T12:45:51.339818","indexId":"70250192","displayToPublicDate":"2022-08-04T06:40:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Outgassing through magmatic fractures enables effusive eruption of silicic magma","docAbstract":"

Several mechanisms have been proposed to allow highly viscous silicic magma to outgas efficiently enough to erupt effusively. There is increasing evidence that challenges the classic foam-collapse model in which gas escapes through permeable bubble networks, and instead suggests that magmatic fracturing and/or accompanying localized fragmentation and welding within the conduit play an important role in outgassing. The 2011–2012 eruption at Cordón Caulle volcano, Chile, provides direct observations of the role of magmatic fractures. This eruption exhibited a months-long hybrid phase, in which rhyolitic lava extrusion was accompanied by vigorous gas-and-tephra venting through fractures in the lava dome surface. Some of these fractures were preserved as tuffisites (tephra-filled veins) in erupted lava and bombs. We integrate constraints from petrologic analyses of erupted products and video analyses of gas-and-tephra venting to construct a model for magma ascent in a conduit. The one-dimensional, two-phase, steady-state model considers outgassing through deforming permeable bubble networks, magmatic fractures, and adjacent wall rock. Simulations for a range of plausible magma ascent conditions indicate that the eruption of low-porosity lava observed at Cordón Caulle volcano occurs because of significant gas flux through fracture networks in the upper conduit. This modeling emphasizes the important role that outgassing through magmatic fractures plays in sustaining effusive or hybrid eruptions of silicic magma and in facilitating explosive-effusive transitions.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107617","usgsCitation":"Crozier, J.A., Tramontano, S., Forte, P., Oliva, S., Gonnermann, H.M., Lev, E., Manga, M., Myers, M., Rader, E., Ruprecht, P., Tuffen, H., Paisley, R., Houghton, B.F., Shea, T., Schipper, I., and Castro, J., 2022, Outgassing through magmatic fractures enables effusive eruption of silicic magma: Journal of Volcanology and Geothermal Research, v. 430, 107617, 20 p., https://doi.org/10.1016/j.jvolgeores.2022.107617.","productDescription":"107617, 20 p.","ipdsId":"IP-138218","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107617","text":"Publisher Index Page"},{"id":423005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Puyehue 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The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite‐fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure assessments. Because large earthquakes can involve slip over tens to hundreds of kilometers, point‐source approximations are insufficient, and it is vital to rapidly assess the amount, timing, and location of slip along the fault. Initially, the USGS finite‐fault products were computed in the first several hours after a significant earthquake, using teleseismic body wave and surface wave observations. With only teleseismic waveforms, it is generally possible to obtain a reliable model for earthquakes of magnitude 7 and larger. Here, we detail newly implemented updates to NEIC’s modeling capabilities, specifically to allow joint modeling of local‐to‐regional strong‐motion accelerometer, Global Navigation Satellite System (GNSS), and Interferometric Synthetic Aperture Radar (InSAR) observations in addition to teleseismic waveforms. We present joint inversion results for the 2015 Mw\">

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Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

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Decadal-long consecutive LWW resulted in a new equilibrium in low-flow and seasonal runoff after the third year of LWW that included a disconnected stream network, a reduced contributing area (54% of the watershed area) and limited fish passage throughout summer. Our results highlight that LWW is not offset by same-year snowmelt for lake water levels and streamflow as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial lake water withdrawals. \n\n ","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022wr032119","usgsCitation":"Gädeke, A., Arp, C., Liljedahl, A., Daanen, R., Cai, L., Alexeev, V., Jones, B., Wipfli, M.S., and Schulla, J., 2022, Modeled streamflow response to scenarios of Tundra Lake water withdrawal and seasonal climate extremes, Arctic Coastal Plain, Alaska: Water Resources Research, v. 58, no. 8, e2022WR032119, 19 p., https://doi.org/10.1029/2022wr032119.","productDescription":"e2022WR032119, 19 p.","ipdsId":"IP-126871","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr032119","text":"Publisher Index Page"},{"id":486512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.42580071682042,\n 70.81109388282138\n ],\n [\n -155.42580071682042,\n 69.72800714139643\n ],\n [\n -150.42311039636033,\n 69.72800714139643\n ],\n [\n -150.42311039636033,\n 70.81109388282138\n ],\n [\n -155.42580071682042,\n 70.81109388282138\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gädeke, Anne","contributorId":355846,"corporation":false,"usgs":false,"family":"Gädeke","given":"Anne","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher","contributorId":355847,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liljedahl, Anna K.","contributorId":355848,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna K.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daanen, Ronald P.","contributorId":355849,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald P.","affiliations":[{"id":84845,"text":"Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":938267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cai, Lei","contributorId":355850,"corporation":false,"usgs":false,"family":"Cai","given":"Lei","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alexeev, Vladimir","contributorId":355851,"corporation":false,"usgs":false,"family":"Alexeev","given":"Vladimir","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938269,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Benjamin","contributorId":355852,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938270,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938263,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schulla, Jörg","contributorId":355853,"corporation":false,"usgs":false,"family":"Schulla","given":"Jörg","affiliations":[{"id":84846,"text":"Hydrology Software Consulting","active":true,"usgs":false}],"preferred":false,"id":938271,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256656,"text":"70256656 - 2022 - Are we falling short on restoring oysters at a regional scale?","interactions":[],"lastModifiedDate":"2024-08-29T15:26:43.155388","indexId":"70256656","displayToPublicDate":"2022-08-03T10:22:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Are we falling short on restoring oysters at a regional scale?","docAbstract":"

Across coastal areas of the northern Gulf of Mexico, the Deepwater Horizon oil spill resulted in significant ecological injury, and over 8 billion USD directed to restoration activities. Oyster restoration projects were implemented with regional goals of restoring oyster abundance, spawning stock, and population resilience. Measuring regional or large-scale ecosystem restoration outcomes challenges traditional project-specific monitoring and outcome reporting. We examine the outcomes of oyster restoration at the project-level and discuss potential pathways to measure progress toward region-level goals. An estimated 15 km2 of oyster habitat was restored across 11 different estuaries with 62 individual reef footprints created, ranging in size from ~0.2 to 1.45 km2. Individual sites were distributed across the salinity gradient, and all reefs were subtidal. One-year post-restoration, mean total oyster density across all sites was 53.0 ± 60.7 ind m−2 of which 38.4 ± 42.2 ind m−2 were adult (>25 mm shell height) oysters. Recent data (2018/2019) available for all sites indicates reduced densities of total oysters (44.6 ± 70.9 ind m−2) and adult oysters (14.6 ± 21.6 ind m−2). These data provide insight into project specific outcomes, suggesting an overall enhancement in oyster abundance compared to pre-restoration, but fall short of informing outcomes at the regional-level that incorporate cumulative effects on adjacent and connected reef populations, or inform overall resiliency of the regional oyster resource. Developing regional outcome benchmarks that enable assessment of cumulative and synergistic impacts of individual projects may benefit from broader spatial and temporal monitoring requirements that can better inform development of regional tools or models. Such tools would enable cumulative effects analyses examining net resource change, resilience and assess impacts of restoration activities on regional resource status.

","language":"English","publisher":"Springer","doi":"10.1007/s00267-022-01691-y","usgsCitation":"La Peyre, M., Marshall, D.A., Buie, S.C., Hijuelos, A., and Steyer, G., 2022, Are we falling short on restoring oysters at a regional scale?: Environmental Management, v. 70, p. 581-592, https://doi.org/10.1007/s00267-022-01691-y.","productDescription":"12 p.","startPage":"581","endPage":"592","ipdsId":"IP-138828","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.36958808470514,\n 24.1126519217827\n ],\n [\n -80.85940900302997,\n 24.714635678707992\n ],\n [\n -81.10116830711196,\n 25.46706673170469\n ],\n [\n -82.39537707405206,\n 27.091011945078975\n ],\n [\n -82.65786309241751,\n 28.308364041898628\n ],\n [\n -82.5772062119935,\n 28.999162563752748\n ],\n [\n -83.9904926193391,\n 30.19347675768212\n ],\n [\n -85.07008045187222,\n 29.677396598341033\n ],\n [\n -86.43938452544526,\n 30.518697342417497\n ],\n [\n -87.63633030899686,\n 30.340763113128546\n ],\n [\n -88.02876199353206,\n 30.727492702231586\n ],\n [\n -88.68357561069969,\n 30.436884709045927\n ],\n [\n -89.8845804986809,\n 30.130244087006545\n ],\n [\n -89.88022780179678,\n 29.65775338151731\n ],\n [\n -94.0525922925487,\n 29.622956144774975\n ],\n [\n -96.92033720263936,\n 28.066156015973654\n ],\n [\n -97.46297846111713,\n 26.978138281620108\n ],\n [\n -97.21131853158765,\n 25.94538903066173\n ],\n [\n -83.36958808470514,\n 24.1126519217827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Danielle Aguilar","contributorId":341509,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle","email":"","middleInitial":"Aguilar","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buie, Sarah Catherine Leblanc","contributorId":341510,"corporation":false,"usgs":false,"family":"Buie","given":"Sarah","email":"","middleInitial":"Catherine Leblanc","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hijuelos, Ann","contributorId":341511,"corporation":false,"usgs":false,"family":"Hijuelos","given":"Ann","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":908526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":218813,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":908527,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236526,"text":"70236526 - 2022 - Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","interactions":[],"lastModifiedDate":"2022-11-16T17:03:55.108551","indexId":"70236526","displayToPublicDate":"2022-08-03T07:21:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","docAbstract":"

Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.4028","usgsCitation":"Barnhart, T., Farmer, W., Hammond, J., Sexstone, G., Curran, J.H., Koch, J.C., and Driscoll, J.M., 2022, Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA: River Research and Applications, v. 38, no. 9, p. 1569-1584, https://doi.org/10.1002/rra.4028.","productDescription":"16 p.","startPage":"1569","endPage":"1584","ipdsId":"IP-132476","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":435746,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TUAO5G","text":"USGS data release","linkHelpText":"Basin Characteristics and Streamflow Statistics for Selected Gages, Alaska, USA (ver. 2.0, September, 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