The Prairie Chub Macrhybopsis australis is a poorly studied minnow species endemic to the upper Red River basin and is of both state and federal conservation interest due to uncertainty about its life history and potential listing status. The upper Red River basin of Oklahoma and Texas is a harsh environment where drought and extreme flow events are exacerbated by human alterations. As an assumed pelagic-broadcast-spawning minnow, the Prairie Chub is capable of a protracted spawning season and larval fish survival is assumed to be linked to discharge and streamflow variability.
We systematically collected 2,017 age-0 Prairie Chub from seven sites (North Fork Red River, Salt Fork Red River, Pease River, Red River, Prairie Dog Town Fork Red River, North Wichita River, and South Wichita River) with variable flow patterns during April–September 2019 and May–August 2020. We used otolith age estimates and back calculations to determine successful spawning dates. We used a hurdle model framework to examine relationships between hatch probability and hatch frequency.
Hatch probability had a negative relationship with calendar day and declined as calendar day increased. Hatch counts peaked in late June and early July and declined thereafter in 2019 but showed no discernible peak during the spawning season in 2020. Hatch probability during the spawning season increased with relative flow and air temperature. Increased hatch counts were also positively related to discharge variability (CV) for the 10 d prior to hatch dates.
Our findings indicated that successful hatches had considerable spatial and temporal variability, with some sites contributing minimally to the population during some years. Spatial and temporal variability of hatch probability and hatch frequencies pose a variety of considerations for future conservation and management efforts, particularly given the pending federal listing status of the species.
The Glen Torridon (GT) region is positioned in terrains with strong clay mineral signatures, as inferred from orbital spectroscopy. The GT campaign confirmed orbital distinctions with in situ measurements by the Mars Science Laboratory rover, Curiosity, and the CheMin X-ray diffraction instrument with of some of the highest clay mineral abundances to date. Additionally, GT is unique because of distinct phase identifications for the first time by CheMin, including: (i) Fe-carbonates, and (ii) a novel peak in the XRD patterns of some GT samples, with an interplanar spacing of at 9.2 Å. Fe-carbonates have been previously suggested from other instruments onboard, but this is the first definitive reporting by CheMin of multiple samples with Fe-carbonate. This new phase has never been observed in Gale crater with the CheMin instrument and may be a new mineral for Mars, but discrete identification still remains enigmatic because no single phase on Earth is able to account the mineralogical, geochemical, and sedimentological constraints in the GT region. Here, we modeled XRD profiles and propose an interstratified clay mineral, specifically greenalite-minnesotaite (G-M), as a reasonable candidate. The coexistence of Fe-carbonate and Fe-rich clay minerals in the GT samples, supports a conceptual model of a lacustrine groundwater mixing environment in the ancient Gale crater lake. Groundwater interaction with percolating lake waters in the sediments is common in terrestrial lacustrine settings, and the diffusion of two distinct water bodies within the subsurface can create a geochemical gradient and unique mineral front in the sediments. Ultimately, the proximity to this mixing zone controlled the secondary minerals preserved in the Jura, Knockfarril Hill, and Glasgow members of the Mt. Sharp group exposed in GT.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JE007099","usgsCitation":"Thorpe, M.T., Bristow, T.F., Rampe, E., Tosca, N., Grotzinger, J.P., Bennett, K.A., Achilles, C., Blake, D.F., Chipera, S.J., Downs, G., Downs, R.T., Morrison, S.M., Tu, V., Castle, N., Craig, P., Des Marais, D.J., Hazen, R.M., Ming, D.W., Morris, R.V., Treiman, A.H., Vaniman, D.T., Yen, A.S., Vasavada, A.R., Dehouck, E., Bridges, J., Berger, J., McAdam, A., Peretyazhko, T., Siebach, K., Bryk, A.B., Fox, V.F., and Fedo, C.M., 2023, Mars Science Laboratory CheMin data from the Glen Torridon region and the significance of lake-groundwater interactions in interpreting mineralogy and sedimentary history: Journal of Geophysical Research E: Planets, v. 127, e2021JE007099, 33 p., https://doi.org/10.1029/2021JE007099.","productDescription":"e2021JE007099, 33 p.","ipdsId":"IP-132887","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":445437,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021je007099","text":"Publisher Index Page"},{"id":421608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale crater, Mars","volume":"127","noUsgsAuthors":false,"publicationDate":"2022-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorpe, Michael T.","contributorId":261804,"corporation":false,"usgs":false,"family":"Thorpe","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":53022,"text":"Jacobs Technology","active":true,"usgs":false}],"preferred":false,"id":885104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bristow, T. 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B.","contributorId":265239,"corporation":false,"usgs":false,"family":"Bryk","given":"A.","email":"","middleInitial":"B.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":885134,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Fox, V. F.","contributorId":330485,"corporation":false,"usgs":false,"family":"Fox","given":"V.","email":"","middleInitial":"F.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":885135,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Fedo, Christopher M.","contributorId":229497,"corporation":false,"usgs":false,"family":"Fedo","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":885136,"contributorType":{"id":1,"text":"Authors"},"rank":32}]}} ,{"id":70238577,"text":"70238577 - 2023 - Improved method for simulating groundwater inundation using the MODFLOW 6 Lake Transport Package","interactions":[],"lastModifiedDate":"2023-05-25T15:31:45.649171","indexId":"70238577","displayToPublicDate":"2022-09-14T06:35:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Improved method for simulating groundwater inundation using the MODFLOW 6 Lake Transport Package","docAbstract":"Groundwater inundation due to sea level rise can affect island and coastal freshwater resources by exposing water tables to direct, continuous evaporation. Numerical simulations of groundwater inundation effects on coastal and island aquifers have been limited by an inability to simulate solute transport and variable density flow between the aquifer and lakes formed by groundwater inundation. Consequently, we contributed to the development of a new tool, the Lake Transport Package, for MODFLOW 6 that can calculate solute concentrations within lakes and allows for variable density flow between lakes and aquifers. Here we use groundwater inundation as an example application to showcase the functionality of the Lake Transport Package and the advantages of using this tool over past methods of representing groundwater inundation. We developed hypothetical island simulations based on hydrogeological characteristics of the Bahamas. Multiple sea level rise and lake evaporation rates were simulated to evaluate the effects of groundwater inundation on freshwater lens size for different climates. The results demonstrate the ability of the Lake Transport Package to calculate the solute concentration of the lake for transient simulations, including hypersaline concentrations. Higher sea level rise and greater lake evaporation rates lead to a greater loss of the freshwater lens and higher lake salinity. The formation of a lake and corresponding expansion due to groundwater inundation increases the loss of freshwater by 6–36%, depending on the lake evaporation rate. These simulations validate the performance and demonstrate usefulness of the Lake Transport Package as a tool in representing groundwater inundation.
Climate change is occurring rapidly in the Arctic, and an improved understanding of the response of aquatic biota and ecosystems will be important for this data-limited region. Here, we applied biochronology techniques and mixed-effects modelling to assess relationships among growth increments found on lake trout (Salvelinus namaycush) otoliths (N = 49) captured from 13 lakes on the Arctic Coastal Plain of northern Alaska, observed and modelled climate patterns, and individual-level fish and lake characteristics. We found that annual growth varied by year, fish growth slowed significantly as individuals aged, and females grew faster than males. Lake trout had higher growth in flow-through lakes relative to lakes that were perennially or seasonally connected. Annual growth was positively correlated with observed air temperature measurements from a local weather station for the period 1998–2014, but no clear warming trend was evident for this period. Modelled August air temperatures from 1978–2014 predicted lake trout annual growth (root mean squared error = 0.045 mm) and indicated increasing temperatures and annual lake trout growth over the period 1950–2014. This study demonstrated that biochronology techniques can reconstruct recent climate patterns and provide a better understanding of trends in Arctic lake ecosystems under a changing climate.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12678","usgsCitation":"Torvinen, E., Falke, J.A., Arp, C.D., Jones, B.M., Whitman, M.S., and Zimmerman, C.E., 2023, Lake trout (Salvelinus namaycush) otoliths indicate effects of climate and lake morphology on growth patterns in Arctic lakes: Ecology of Freshwater Fish, v. 32, no. 1, p. 166-180, https://doi.org/10.1111/eff.12678.","productDescription":"15 p.","startPage":"166","endPage":"180","ipdsId":"IP-103948","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151,\n 70.5\n ],\n [\n -154,\n 70.5\n ],\n [\n -154,\n 69.5\n ],\n [\n -151,\n 69.5\n ],\n [\n -151,\n 70.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Torvinen, Eric","contributorId":191061,"corporation":false,"usgs":false,"family":"Torvinen","given":"Eric","email":"","affiliations":[],"preferred":false,"id":935556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":935557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M.","contributorId":305542,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":935558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Matthew S.","contributorId":338574,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":81170,"text":"Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":935559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935555,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240169,"text":"70240169 - 2023 - A novel origin for PGE reefs: A case study of the J-M Reef","interactions":[],"lastModifiedDate":"2023-02-22T15:54:25.079132","indexId":"70240169","displayToPublicDate":"2022-09-08T09:52:27","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A novel origin for PGE reefs: A case study of the J-M Reef","docAbstract":"The origin of meter scale stratiform layers of disseminated sulfides in enriched platinum group element (PGE) tenors and grades, called reef-type deposits, are the world’s most significant source of PGEs. Their origin in layered mafic intrusions remains debated, but in general, most researchers favor an orthomagmatic origin for reef-type deposits and agree that their formation requires the equilibration of an immiscible sulfide liquid with a significantly larger mass of silicate magma (i.e., silicate:sulfide mass ratios of 104 to 106). However, where, and how this chemical equilibration process takes place in the magmatic system is poorly constrained. In this contribution, we propose a new model for the origin of PGE reef deposits. We demonstrate that finely disseminated, resident sulfide liquid hosted within cumulate mush can be upgraded by incoming batches of S-undersaturated and PGE-undepleted silicate melt. We demonstrate this model through a case study of the J-M Reef deposit of the Stillwater Complex, the world’s highest-grade PGE deposit (14 ppm Pd over 1.8 m).","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Applied Earth Science","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"language":"English","publisher":"Taylor & Francis Online","doi":"10.1080/25726838.2022.2084233","usgsCitation":"Jenkins, M., Mungall, J.E., Zientek, M., Costin, G., and Yao, Z., 2023, A novel origin for PGE reefs: A case study of the J-M Reef, in Applied Earth Science, v. 131, no. 3, p. 147-148, https://doi.org/10.1080/25726838.2022.2084233.","productDescription":"2 p.","startPage":"147","endPage":"148","ipdsId":"IP-138074","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":413287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"131","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Michael 0000-0002-4261-409X mjenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-4261-409X","contributorId":172433,"corporation":false,"usgs":true,"family":"Jenkins","given":"Michael","email":"mjenkins@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mungall, James E. 0000-0001-9726-8545","orcid":"https://orcid.org/0000-0001-9726-8545","contributorId":269537,"corporation":false,"usgs":false,"family":"Mungall","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":862834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zientek, Michael L. 0000-0002-8522-9626","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":210763,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costin, Gelu 0000-0003-3054-7886","orcid":"https://orcid.org/0000-0003-3054-7886","contributorId":269538,"corporation":false,"usgs":false,"family":"Costin","given":"Gelu","email":"","affiliations":[{"id":7173,"text":"Rice University","active":true,"usgs":false}],"preferred":false,"id":862836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yao, Zhuo-sen 0000-0002-5075-0745","orcid":"https://orcid.org/0000-0002-5075-0745","contributorId":269539,"corporation":false,"usgs":false,"family":"Yao","given":"Zhuo-sen","email":"","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":862837,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250894,"text":"70250894 - 2023 - Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging","interactions":[],"lastModifiedDate":"2024-01-11T13:56:43.841598","indexId":"70250894","displayToPublicDate":"2022-09-08T07:53:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging","docAbstract":"The Southern San Andreas fault (SSAF) poses one of the largest seismic risks in California. Yet, there is much ambiguity regarding its deeper structural properties around Coachella Valley, in large part due to the relative paucity of everyday seismicity. Here, we image a multistranded section of the SSAF using a non-seismic method, namely magnetotelluric (MT) soundings, to help inform depth-dependent fault zone geometry, fluid content and porosity. The acquired MT data and resultant inversion models highlight a conductive column encompassing the SSAF zone that includes a 2–3 km wide vertical to steeply northeast dipping conductor down to ∼4 km depth (maximum of ∼1 Ω·m at 2 km depth) and another prominent conductor in the ductile crust (∼1 Ω·m at 12 km depth and slightly southwest of the surface SSAF). We estimate porosities of 18–44 per cent for the conductive uppermost 500 m, a 10–15 per cent porosity at 2 km depth and that small amounts (0.1–3 per cent) of interconnected hypersaline fluids produce the deeper conductor. Located northeast of this conductive region is mostly resistive crust indicating dry crystalline rock that extends down to ∼20 km in places. Most of the local seismicity is associated with this resistive region. Located farther northeast still is a conductive region at >13 km depth and separate from the one to the southwest. The imaged anomalies permit two interpretations. The SSAF zone is vertical to steeply northeast dipping in the upper crust and (1) is near vertical at greater depth creating mostly an impermeable barrier for northeast fluid migration or (2) continues to dip northeast but is relatively dry and resistive up to ∼13 km depth where it manifests as a secondary deep ductile crustal conductor. Taken together with existing knowledge, the first interpretation is more likely but more MT investigations are required.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac356","usgsCitation":"Share-MacParland, P., Peacock, J., Constable, S.C., Vernon, F.L., and Wang, S., 2023, Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging: Geophysical Journal International, v. 232, no. 1, p. 694-704, https://doi.org/10.1093/gji/ggac356.","productDescription":"11 p.","startPage":"694","endPage":"704","ipdsId":"IP-124157","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":445442,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11250/3051610","text":"External Repository"},{"id":424322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"northern Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.0,\n 34.25\n ],\n [\n -117.0,\n 33.5\n ],\n [\n -115.75,\n 33.5\n ],\n [\n -115.75,\n 34.25\n ],\n [\n -117.0,\n 34.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"232","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Share-MacParland, Pieter-Ewald 0000-0001-6674-1491","orcid":"https://orcid.org/0000-0001-6674-1491","contributorId":299108,"corporation":false,"usgs":false,"family":"Share-MacParland","given":"Pieter-Ewald","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":891962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":891963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Constable, Steve C. 0000-0001-6324-3470","orcid":"https://orcid.org/0000-0001-6324-3470","contributorId":333113,"corporation":false,"usgs":false,"family":"Constable","given":"Steve","email":"","middleInitial":"C.","affiliations":[{"id":79733,"text":"Institute of Geophysics and Planetary Physics, University of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":891964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vernon, Frank L. 0000-0002-9379-4000","orcid":"https://orcid.org/0000-0002-9379-4000","contributorId":333114,"corporation":false,"usgs":false,"family":"Vernon","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":79734,"text":"Institute of Geophysics and Planetary Science, University of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":891965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Shunguo","contributorId":333115,"corporation":false,"usgs":false,"family":"Wang","given":"Shunguo","email":"","affiliations":[{"id":79736,"text":"Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway","active":true,"usgs":false}],"preferred":false,"id":891966,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238426,"text":"70238426 - 2023 - Fires, floods and other extreme events – How watershed processes under climate change will shape our coastlines","interactions":[],"lastModifiedDate":"2022-11-22T12:48:30.808774","indexId":"70238426","displayToPublicDate":"2022-09-08T06:41:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12971,"text":"Cambridge Prisms: Coastal Futures","active":true,"publicationSubtype":{"id":10}},"title":"Fires, floods and other extreme events – How watershed processes under climate change will shape our coastlines","docAbstract":"Ongoing sea-level rise has brought renewed focus on terrestrial sediment supply to the coast because of its strong influence on whether and how long beaches, marshes and other coastal landforms may persist into the future. Here, we summarise findings of sediment discharge from several coastal rivers, revealing that infrequent, large-magnitude events have disproportionate influence on the morphodynamics of coastal landforms and littoral cells. These event-dominated effects are most pronounced for small, steep mountainous rivers that supply beach and wetland sediment along the world’s active tectonic margins, although infrequent events are important drivers of sediment discharge for rivers worldwide. Additionally, extreme events (recurrence intervals of decades to centuries) that follow wildfires, earthquakes, volcanic eruptions, extreme precipitation or – most notably – combinations of these factors can redefine coastal sediment budgets and morphology. Some of these extreme events (e.g., wildfires plus rainfall) are increasing in magnitude and frequency under modern climate warming, with the likely result of increasing sediment flux to affected coastlines. Climate change is also altering watershed processes in both high latitudes and high altitudes, resulting in increased sediment supply to downstream catchments. We conclude that sediment inputs to coastal systems are highly variable with time, and that the variability and trends in sediment input are as important to characterise as long-term averages.
No abstract available.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.13250","usgsCitation":"Fienen, M., 2023, Book review: Analytical groundwater modeling: Theory and applications using Python: Groundwater, v. 61, no. 1, p. 4-5, https://doi.org/10.1111/gwat.13250.","productDescription":"2 p.","startPage":"4","endPage":"5","ipdsId":"IP-142834","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":406609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849932,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239910,"text":"70239910 - 2023 - Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination","interactions":[],"lastModifiedDate":"2023-01-25T12:41:17.885197","indexId":"70239910","displayToPublicDate":"2022-09-07T06:40:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination","docAbstract":"Earthquake focal mechanisms are traditionally produced using P‐wave first‐motion polarities and commonly require well‐recorded seismicity. A recent approach that is less dependent on high signal‐to‐noise exploits similar waveforms to produce relative polarity measurements between earthquake pairs. Utilizing these relative polarity measurements, it is possible to produce composite focal mechanisms for clusters within microseismic sequences using regional networks. However, missing or low‐confidence polarity measurements still limit our ability to calculate high‐quality composite focal mechanisms. Here, we replaced unreliable polarity measurements with estimates using iterative random forests, an unsupervised ensemble machine learning method. Using the imputed (“replaced”) polarity data, we then categorically clustered the events into families. As a case study, we applied this modified composite mechanism workflow to a multistation template matched catalog of an earthquake swarm that occurred during 2020 near the Maacama fault in northern California. We found that our modified methodology produced higher‐quality earthquake families and improved composite focal mechanisms, with fault‐plane uncertainties <35° for 94% of the families compared with 34% of families using the previous methodology.
Rarely are sufficient resources available to support the full suite of management actions to promote recovery of a species across their entire distribution. Decision support models are a tool that can inform natural resource management decisions with consideration of the perspectives from a variety of stakeholders who work across large geographic and jurisdictional extents. We offer an example of a decision support model that was developed by several Federal and State natural resource agencies to rank Bull Trout Salvelinus confluentus core areas for prioritizing conservation investment within Oregon, USA. We engaged State level decision makers to identify parameters believed to be influential in determining funding allocations for Bull Trout core areas. Parameters were linked in a model framework that was further refined with input from local Bull Trout experts with knowledge specific to the various core areas. The model produces a relative priority value that is a combination of the conservation risk to the species and the management capacity to address threats. A series of sensitivity analyses suggests that Bull Trout persistence and threat score are most influential in determining the relative priority of a core area, and life-history and genetic diversity are least influential. One of the more powerful products from this work is an interactive web-based application (https://das.ecosphere.fws.gov/public/obts/) that anyone can use to explore how their beliefs in parameter values will affect the relative priority of Bull Trout core areas across Oregon. Our modeling effort is an example of engaging stakeholders with different roles in species recovery and across a large geographic area to create a clearer path forward in allocating limited resources for species recovery. This approach can be employed to address a number of natural resource management situations across species and habitats.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10834","usgsCitation":"Brignon, W.R., Davis, M.B., Gunkel, S., Dunham, J.B., Meeuwig, M.H., Allen, C.S., and Clements, S., 2023, Engaging stakeholders to develop a decision support model of conservation risk and management capacity to prioritize investments in Bull Trout recovery: North American Journal of Fisheries Management, v. 43, no. 3, p. 821-838, https://doi.org/10.1002/nafm.10834.","productDescription":"18 p.","startPage":"821","endPage":"838","ipdsId":"IP-140598","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":407511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Brignon, William R.","contributorId":193087,"corporation":false,"usgs":false,"family":"Brignon","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":853154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, M. Brian","contributorId":297043,"corporation":false,"usgs":false,"family":"Davis","given":"M.","email":"","middleInitial":"Brian","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gunkel, Stephanie","contributorId":297045,"corporation":false,"usgs":false,"family":"Gunkel","given":"Stephanie","email":"","affiliations":[{"id":64284,"text":"Oregon Department of Fish and Wildlife, USFWS","active":true,"usgs":false}],"preferred":false,"id":853156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":853157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meeuwig, Michael H.","contributorId":198608,"corporation":false,"usgs":false,"family":"Meeuwig","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":853158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Chris S","contributorId":174031,"corporation":false,"usgs":false,"family":"Allen","given":"Chris","email":"","middleInitial":"S","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clements, Shaun","contributorId":171685,"corporation":false,"usgs":false,"family":"Clements","given":"Shaun","email":"","affiliations":[],"preferred":false,"id":853160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236378,"text":"70236378 - 2023 - From data to interpretable models: Machine learning for soil moisture forecasting","interactions":[],"lastModifiedDate":"2023-02-03T14:11:18.574287","indexId":"70236378","displayToPublicDate":"2022-09-05T09:13:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12569,"text":"International Journal of Data Science and Analytics","active":true,"publicationSubtype":{"id":10}},"title":"From data to interpretable models: Machine learning for soil moisture forecasting","docAbstract":"Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such forecasts, we introduce two data-driven models, the Naive Accumulative Representation (NAR) and the Additive Exponential Accumulative Representation (AEAR). Both of these models are rooted in deterministic, physically based hydrology, and we study their capabilities in forecasting soilmoisture over time periods longer than a fewhours. Learned\nmodel parameters represent the physically based unsaturated hydrological redistribution processes of gravity and suction. We validate our models using soil moisture and rainfall time series data collected from a steep gradient, post-wildfire site in southern California. Data analysis is complicated by rapid landscape change observed in steep, burned hillslopes in response to even small to moderate rain events. The proposed NAR and AEAR models are, in forecasting experiments, shown to be competitive with several established and state-of-the-art baselines. The AEAR model fits the data well for three distinct soil textures at variable depths below the ground surface (5, 15, and 30 cm). Similar robust results are demonstrated in controlled, laboratory-based experiments. Our AEAR model includes readily interpretable hydrologic parameters and provides more accurate forecasts than existing models for time horizons of 10–24 h. Such extended periods of warning for natural disasters, such as floods and landslides, provide actionable knowledge to reduce loss of life and property.","language":"English","publisher":"Springer","doi":"10.1007/s41060-022-00347-8","usgsCitation":"Basak, A., Schmidt, K.M., and Mengshoel, O., 2023, From data to interpretable models: Machine learning for soil moisture forecasting: International Journal of Data Science and Analytics, v. 15, p. 9-32, https://doi.org/10.1007/s41060-022-00347-8.","productDescription":"24 p.","startPage":"9","endPage":"32","ipdsId":"IP-073246","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":445452,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s41060-022-00347-8","text":"Publisher Index Page"},{"id":435577,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZB0Z7","text":"USGS data release","linkHelpText":"Field measurements of rainfall and soil moisture data used to support understanding of infiltration and runoff following the 2007 Canyon Fire, Malibu, CA, USA"},{"id":406219,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Basak, Aniruddha","contributorId":156329,"corporation":false,"usgs":false,"family":"Basak","given":"Aniruddha","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":850823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mengshoel, Ole","contributorId":156331,"corporation":false,"usgs":false,"family":"Mengshoel","given":"Ole","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":850825,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240893,"text":"70240893 - 2023 - GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning","interactions":[],"lastModifiedDate":"2023-07-24T16:32:28.230583","indexId":"70240893","displayToPublicDate":"2022-09-03T06:46:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1745,"text":"GeoInformatica","active":true,"publicationSubtype":{"id":10}},"title":"GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning","docAbstract":"The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.
Invasive species can dramatically alter ecosystems, but eradication is difficult, and suppression is expensive once they are established. Uncertainties in the potential for expansion and impacts by an invader can lead to delayed and inadequate suppression, allowing for establishment. Metapopulation viability models can aid in planning strategies to improve responses to invaders and lessen invasive species’ impacts, which may be particularly important under climate change. We used a spatially explicit metapopulation viability model to explore suppression strategies for ecologically damaging invasive brown trout (Salmo trutta), established in the Colorado River and a tributary in Grand Canyon National Park. Our goals were to estimate the effectiveness of strategies targeting different life stages and subpopulations within a metapopulation; quantify the effectiveness of a rapid response to a new invasion relative to delaying action until establishment; and estimate whether future hydrology and temperature regimes related to climate change and reservoir management affect metapopulation viability and alter the optimal management response. Our models included scenarios targeting different life stages with spatially varying intensities of electrofishing, redd destruction, incentivized angler harvest, piscicides, and a weir. Quasi-extinction (QE) was obtainable only with metapopulation-wide suppression targeting multiple life stages. Brown trout population growth rates were most sensitive to changes in age 0 and large adult mortality. The duration of suppression needed to reach QE for a large established subpopulation was 12 years compared with 4 with a rapid response to a new invasion. Isolated subpopulations were vulnerable to suppression; however, connected tributary subpopulations enhanced metapopulation persistence by serving as climate refuges. Water shortages driving changes in reservoir storage and subsequent warming would cause brown trout declines, but metapopulation QE was achieved only through refocusing and increasing suppression. Our modeling approach improves understanding of invasive brown trout metapopulation dynamics, which could lead to more focused and effective invasive species suppression strategies and, ultimately, maintenance of populations of endemic fishes.
Context: Small population sizes and no possibility of metapopulation rescue put narrowly distributed endemic species under elevated risk of extinction from anthropogenic change. Desert spring wetlands host many endemic species that require aquatic habitat and are isolated by the surrounding xeric terrestrial habitat.
Aims: We sought to model the occupancy dynamics of the Dixie Valley toad (Anaxyrus williamsi), a recently described species endemic to a small desert spring wetland complex in Nevada, USA.
Methods: We divided the species’ range into 20 m × 20 m cells and surveyed for Dixie Valley toads at 60 cells during six primary periods from 2018 to 2021, following an occupancy study design. We analysed our survey data by using a multi-state dynamic occupancy model to estimate the probability of adult occurrence, colonisation, site survival, and larval occurrence and the relationship of each to environmental covariates.
Key results: The detection probabilities of adult and larval toads were affected by survey length and time of day. Adult Dixie Valley toads were widely distributed, with detections in 75% of surveyed cells at some point during the 3-year study, whereas larvae were observed only in 20% of cells during the study. Dixie Valley toad larvae were more likely to occur in cells far from spring heads with a high coverage of surface water, low emergent vegetation cover, and water temperatures between 20°C and 28°C. Adult toads were more likely to occur in cells with a greater coverage of surface water and water depth >10 cm. Cells with more emergent vegetation cover and surface water were more likely to be colonised by adult toads.
Conclusions: Our results showed that Dixie Valley toads are highly dependent on surface water in both spring and autumn. Adults and larvae require different environmental conditions, with larvae occurring farther from spring heads and in fewer cells.
Implications: Disturbances to the hydrology of the desert spring wetlands in Dixie Valley could threaten the persistence of this narrowly distributed toad.
","language":"English","publisher":"CSIRO","doi":"10.1071/WR22029","usgsCitation":"Rose, J.P., Kleeman, P.M., and Halstead, B., 2023, Hot, wet and rare: Modelling the occupancy dynamics of the narrowly distributed Dixie Valley toad: Wildlife Research, v. 50, no. 7, p. 552-567, https://doi.org/10.1071/WR22029.","productDescription":"16 p.","startPage":"552","endPage":"567","ipdsId":"IP-136748","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445464,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr22029","text":"Publisher Index Page"},{"id":435581,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QCIC87","text":"USGS data release","linkHelpText":"USGS Occupancy Surveys for Dixie Valley Toads, Anaxyrus williamsi, in Churchill County, Nevada from April 2018 to May 2021"},{"id":435580,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97DSXJM","text":"USGS data release","linkHelpText":"Code to Analyze Occupancy Data for Dixie Valley Toads, Anaxyrus williamsi in Churchill County, Nevada from 2018 to 2021"},{"id":407214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":852766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":852767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":852768,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238028,"text":"70238028 - 2023 - Impact of sedimentary basins on Green’s functions for static slip inversion","interactions":[],"lastModifiedDate":"2022-11-04T12:06:15.770722","indexId":"70238028","displayToPublicDate":"2022-08-29T07:04:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Impact of sedimentary basins on Green’s functions for static slip inversion","docAbstract":"Earthquakes often occur in regions with complex material structure, such as sedimentary basins or mantle wedges. However, the majority of co-seismic modelling studies assume a simplified, often homogeneous elastic structure in order to expedite the process of model construction and speed up calculations. These co-seismic forward models are used to produce Green’s functions for finite-fault inversions, so any assumptions made in the forward model may introduce bias into estimated slip models. In this study, we use a synthetic model of a sedimentary basin to investigate the impact of 3-D elastic structure on forward models of co-seismic surface deformation. We find that 3-D elastic structure can cause changes in the shape of surface deformation patterns. The magnitude of this effect appears to be primarily controlled by the magnitude of contrast in material properties, rather than the sharpness of contrast, the fault orientation, the location of the fault, or the slip orientation. As examples of real-world cases, we explore the impact of 3-D elastic structure with a model of the Taipei basin in Taiwan and a simulated earthquake on the Sanchaio fault, and with a 3-D geologic model of the San Francisco Bay Area and a slip model of the 1984 Morgan Hill earthquake on the Calaveras fault. Once again, we find that the presence of the basin leads to differences in the shape and amplitude of the surface deformation pattern, but we observe that the primary differences are in the magnitude of surface deformation and can be accounted for with a layered elastic structure. Our results imply that the use of homogeneous Green’s functions may lead to bias in inferred slip models in regions with sedimentary basins, so, at a minimum, a layered velocity structure should be used.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac344","usgsCitation":"Langer, L., Beller, S., Hirakawa, E.T., and Tromp, J., 2023, Impact of sedimentary basins on Green’s functions for static slip inversion: Geophysical Journal International, v. 232, no. 1, p. 569-580, https://doi.org/10.1093/gji/ggac344.","productDescription":"12 p.","startPage":"569","endPage":"580","ipdsId":"IP-133675","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499860,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-04920777","text":"External Repository"},{"id":409156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"232","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Langer, Leah 0000-0002-5384-0500","orcid":"https://orcid.org/0000-0002-5384-0500","contributorId":298853,"corporation":false,"usgs":true,"family":"Langer","given":"Leah","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":856606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beller, Stephen 0000-0002-5331-0788","orcid":"https://orcid.org/0000-0002-5331-0788","contributorId":298854,"corporation":false,"usgs":false,"family":"Beller","given":"Stephen","email":"","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":856607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":856608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tromp, Jeroen 0000-0002-2742-8299","orcid":"https://orcid.org/0000-0002-2742-8299","contributorId":298855,"corporation":false,"usgs":false,"family":"Tromp","given":"Jeroen","email":"","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":856609,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234260,"text":"70234260 - 2023 - Assessing population genomic structure and polyploidy: A crucial step for native plant restoration","interactions":[],"lastModifiedDate":"2023-03-15T14:19:26.847684","indexId":"70234260","displayToPublicDate":"2022-08-05T08:17:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing population genomic structure and polyploidy: A crucial step for native plant restoration","docAbstract":"Establishing an effective restoration program requires baseline genetic information to make sound decisions for seed increase and transfer. For many plants this information is lacking, especially among native forbs that are critical for pollinator health. Erigeron speciosus is a widespread, perennial forb occupying montane environments in the western United States and Canada. This species is important in fostering pollinator diversity. Our study examines the population genetic patterns across the species range using reduced-representation sequencing and surveys for genome duplication using flow cytometry and cytology. These genomic tools provide critical information for seed increase and seed transfer, necessary for restoration programs. Population genetic differentiation (FST) average was 0.13 and ranged from 0.05 to 0.24 among 23 collection sites. Model-based Bayesian clustering supported a model with collection sites grouped into two populations, occupying distinct geographic regions of this species range. A genetic distance-based neighbor-joining tree also supported this division. Flow cytometry of 53 samples from 17 populations had 2C values that ranged from 1.7 to 3.6 pg with a mean 2C value of 2.3 pg. Putative triploids were found in two individuals from one collection site. The spatial distribution of genetic structure supports regionally based taxonomic descriptions of two varieties: speciosus in the North and macranthus in the South. This assessment of genetic structure and genome duplication describes an effective approach in developing baseline genetic information for restoration species, especially those species that may harbor complex taxonomy and polyploidy.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13740","usgsCitation":"Richardson, B.A., Massatti, R., Islam-Faridi, N., Johnson, S., and Kilkenny, F.F., 2023, Assessing population genomic structure and polyploidy: A crucial step for native plant restoration: Restoration Ecology, v. 31, no. 3, e13740, 11 p., https://doi.org/10.1111/rec.13740.","productDescription":"e13740, 11 p.","ipdsId":"IP-133482","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445480,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13740","text":"Publisher Index Page"},{"id":404872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Montana, Oregon, South Dakota, Utah, Wyoming","otherGeospatial":"Idaho Batholith, Rocky Mountains, Snake River Plain, Uinta Mountains, Wasatch Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.61962890624999,\n 43.929549935614595\n ],\n [\n -117.70751953125,\n 41.983994270935625\n ],\n [\n -114.10400390625,\n 41.983994270935625\n ],\n [\n -114.08203125,\n 36.96744946416934\n ],\n [\n -107.16064453125,\n 37.00255267215955\n ],\n [\n -102.45849609375,\n 44.166444664458595\n ],\n [\n -111.533203125,\n 48.99463598353405\n ],\n [\n -117.61962890624999,\n 43.929549935614595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Bryce A.","contributorId":207820,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","middleInitial":"A.","affiliations":[{"id":37640,"text":"U.S.D.A. Forest Service Rocky Mountain Research Station, Provo, UT, 84606 USA","active":true,"usgs":false}],"preferred":false,"id":848356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":848357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Islam-Faridi, Nurul","contributorId":294566,"corporation":false,"usgs":false,"family":"Islam-Faridi","given":"Nurul","email":"","affiliations":[{"id":63605,"text":"USDA Forest Service, Southern Research Station, College Station, Texas","active":true,"usgs":false}],"preferred":false,"id":848358,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Skylar","contributorId":294567,"corporation":false,"usgs":false,"family":"Johnson","given":"Skylar","email":"","affiliations":[{"id":63608,"text":"USDA Forest Service, Rocky Mountain Research Station, Moscow, Idaho","active":true,"usgs":false}],"preferred":false,"id":848359,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kilkenny, Francis F.","contributorId":191031,"corporation":false,"usgs":false,"family":"Kilkenny","given":"Francis","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":848360,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241410,"text":"70241410 - 2023 - The global seismographic network reveals atmospherically coupled normal modes excited by the 2022 Hunga Tonga eruption","interactions":[],"lastModifiedDate":"2023-03-17T12:13:22.993684","indexId":"70241410","displayToPublicDate":"2022-07-26T07:09:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"The global seismographic network reveals atmospherically coupled normal modes excited by the 2022 Hunga Tonga eruption","docAbstract":"The eruption of the submarine Hunga Tonga-Hunga Haʻapai (Hunga Tonga) volcano on 15 January 2022, was one of the largest volcanic explosions recorded by modern geophysical instrumentation. The eruption was notable for the broad range of atmospheric wave phenomena it generated and for their unusual coupling with the oceans and solid Earth. The event was recorded worldwide across the Global Seismographic Network (GSN) by seismometers, microbarographs and infrasound sensors. The broad-band instrumentation in the GSN allows us to make high fidelity observations of spheroidal solid Earth normal modes from this event at frequencies near 3.7 and 4.4 mHz. Similar normal mode excitations were reported following the 1991 Pinatubo (Volcanic Explosivity Index of 6) eruption and were predicted, by theory, to arise from the excitation of mesosphere-scale acoustic modes of the atmosphere coupling with the solid Earth. Here, we compare observations for the Hunga Tonga and Pinatubo eruptions and find that both strongly excited the solid Earth normal mode 0S29 (3.72 mHz). However, the mean modal amplitude was roughly 11 times larger for the 2022 Hunga Tonga eruption. Estimates of attenuation (Q) for 0S29 across the GSN from temporal modal decay give Q = 332 ± 101, which is higher than estimates of Q for this mode using earthquake data (Q = 186.9 ± 5). Two microbarographs located at regional distances (<1000 km) to the volcano provide direct observations of the fundamental acoustic mode of the atmosphere. These pressure oscillations, first observed approximately 40 min after the onset of the eruption, are in phase with the seismic Rayleigh wave excitation and are recorded only by microbarographs in proximity (<1500 km) to the eruption. We infer that excitation of fundamental atmospheric modes occurs within a limited area close to the site of the eruption, where they excite select solid Earth fundamental spheroidal modes of similar frequencies that are globally recorded and have a higher apparent Q due to the extended duration of atmospheric oscillations.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac284","usgsCitation":"Ringler, A.T., Anthony, R.E., Aster, R., Taira, T., Shiro, B., Wilson, D.C., De Angelis, S.H., Ebeling, C., Haney, M.M., Matoza, R., and Ortiz, H., 2023, The global seismographic network reveals atmospherically coupled normal modes excited by the 2022 Hunga Tonga eruption: Geophysical Journal International, v. 232, no. 3, p. 2160-2174, https://doi.org/10.1093/gji/ggac284.","productDescription":"15 p.","startPage":"2160","endPage":"2174","ipdsId":"IP-139300","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":414335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hunga Tonga","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.0319755204097,\n -19.34999539921209\n ],\n [\n -176.0319755204097,\n -21.715107936512553\n ],\n [\n -174.05527592325726,\n -21.715107936512553\n ],\n [\n -174.05527592325726,\n -19.34999539921209\n ],\n [\n -176.0319755204097,\n -19.34999539921209\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"232","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866774,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aster, Rick","contributorId":303207,"corporation":false,"usgs":false,"family":"Aster","given":"Rick","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":866775,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taira, T.","contributorId":303208,"corporation":false,"usgs":false,"family":"Taira","given":"T.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":866776,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shiro, Brian 0000-0001-8756-288X","orcid":"https://orcid.org/0000-0001-8756-288X","contributorId":204040,"corporation":false,"usgs":true,"family":"Shiro","given":"Brian","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":866777,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866778,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"De Angelis, S. H.","contributorId":196732,"corporation":false,"usgs":false,"family":"De Angelis","given":"S.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":866779,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebeling, C.","contributorId":297933,"corporation":false,"usgs":false,"family":"Ebeling","given":"C.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":866780,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":866781,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Matoza, R.","contributorId":303211,"corporation":false,"usgs":false,"family":"Matoza","given":"R.","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":866782,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ortiz, H.","contributorId":303213,"corporation":false,"usgs":false,"family":"Ortiz","given":"H.","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":866783,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236621,"text":"70236621 - 2023 - Relationship of greater sage-grouse to natural and assisted recovery of key vegetation types following wildfire: Insights from scat","interactions":[],"lastModifiedDate":"2023-03-24T16:47:37.729215","indexId":"70236621","displayToPublicDate":"2022-07-07T06:41:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Relationship of greater sage-grouse to natural and assisted recovery of key vegetation types following wildfire: Insights from scat","docAbstract":"Megafires are creating severe conservation problems worldwide for wildlife that have obligate dependencies on plant species that are foundational but fire-intolerant. Wildfire-induced loss of native perennials and increases in exotic annual grasses threaten greater sage-grouse (GRSG, Centrocercus urophasianus) in its sagebrush steppe habitat in western North America. Post-fire restoration using herbicides, seeding, and planting of native perennials such as sagebrush are common, but there are few assessments of GRSG response to the treatments. We measured the presence of GRSG scat and modeled the probability of GRSG presence (PrGRSG-scat) in relation to variation in plot-level and landscape-level predictors, and land treatments, in an intensive, repeat sampling from 2017-2020 of 113,000-ha area burned in 2015 in the Soda Megafire (Oregon and Idaho, USA). GRSG scat was present in <200 of >8000 observations, as would be expected for a philopatric species (i.e., high fidelity to home site) returning to degraded habitat. PrGRSG-scat was positively associated with sagebrush presence at the plot-level and was positively related to elevation, lower-angle slopes, and proximity to sagebrush seedling outplant islands. The statistical significance of relationships of PrGRSG-scat to restoration treatments was marginal at best, with the largest effect being a positive response of PrGRSG-scat to pre-emergent herbicide sprayed to reduce exotic annual grasses. More time may be required for restored sagebrush steppe to meet GRSG needs or for GRSG to “adopt” the restored vegetation. Moreover, whereas scat is a convenient and non-invasive method to monitor GRSG, its post-fire scarcity weakens the strength of statistical inference on GRSG recovery patterns and response to restoration.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13758","usgsCitation":"Germino, M., Anthony, C.R., Kluender, C.R., Ellsworth, E.A., Moser, A.M., Applestein, C., and Fisk, M., 2023, Relationship of greater sage-grouse to natural and assisted recovery of key vegetation types following wildfire: Insights from scat: Restoration Ecology, v. 31, no. 3, e13758, 11 p., https://doi.org/10.1111/rec.13758.","productDescription":"e13758, 11 p.","ipdsId":"IP-133074","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":406584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Christopher R. 0000-0003-0968-224X","orcid":"https://orcid.org/0000-0003-0968-224X","contributorId":296314,"corporation":false,"usgs":true,"family":"Anthony","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluender, Chad Raymond 0000-0002-4108-4437","orcid":"https://orcid.org/0000-0002-4108-4437","contributorId":296077,"corporation":false,"usgs":true,"family":"Kluender","given":"Chad","email":"","middleInitial":"Raymond","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellsworth, Ethan A.","contributorId":201653,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Ethan","email":"","middleInitial":"A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":851523,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moser, Ann M.","contributorId":206592,"corporation":false,"usgs":false,"family":"Moser","given":"Ann","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":851524,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851525,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fisk, Matthew 0000-0002-2250-0116","orcid":"https://orcid.org/0000-0002-2250-0116","contributorId":205749,"corporation":false,"usgs":true,"family":"Fisk","given":"Matthew","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851526,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233411,"text":"70233411 - 2023 - Beyond glacier-wide mass balances: Parsing seasonal elevation change into spatially resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska","interactions":[],"lastModifiedDate":"2023-03-01T16:34:51.177631","indexId":"70233411","displayToPublicDate":"2022-06-24T07:41:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2328,"text":"Journal of Glaciology","active":true,"publicationSubtype":{"id":10}},"title":"Beyond glacier-wide mass balances: Parsing seasonal elevation change into spatially resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska","docAbstract":"We present spatially distributed seasonal and annual surface mass balances of Wolverine Glacier, Alaska, from 2016 to 2020. Our approach accounts for the effects of ice emergence and firn compaction on surface elevation changes to resolve the spatial patterns in mass balance at 10 m scale. We present and compare three methods for estimating emergence velocities. Firn compaction was constrained by optimizing a firn model to fit three firn cores. Distributed mass balances showed good agreement with mass-balance stakes (RMSE = 0.67 m w.e., r = 0.99, n = 41) and ground-penetrating radar surveys (RMSE = 0.36 m w.e., r = 0.85, n = 9024). Fundamental differences in the distributions of seasonal balances highlight the importance of disparate physical processes, with anomalously high ablation rates observed in icefalls. Winter balances were found to be positively skewed when controlling for elevation, while summer and annual balances were negatively skewed. We show that only a small percent of the glacier surface represents ideal locations for mass-balance stake placement. Importantly, no suitable areas are found near the terminus or in elevation bands dominated by icefalls. These findings offer explanations for the often-needed geodetic calibrations of glaciological time series.
Many coastal states throughout the USA have observed negative effects in marine and estuarine environments caused by cyanotoxins produced in inland waterbodies that were transported downstream or produced in the estuaries. Estuaries and other downstream receiving waters now face the dual risk of impacts from harmful algal blooms (HABs) that occur in the coastal ocean as well as those originating in inland watersheds. Despite this risk, most HAB monitoring efforts do not account for hydrological connections in their monitoring strategies and designs. Monitoring efforts in California have revealed the persistent detection of cyanotoxins across the freshwater-to-marine continuum. These studies underscore the importance of inland waters as conduits for the transfer of cyanotoxins to the marine environment and highlight the importance of approaches that can monitor across hydrologically connected waterbodies. A HAB monitoring strategy is presented for the freshwater-to-marine continuum to inform HAB management and mitigation efforts and address the physical and hydrologic challenges encountered when monitoring in these systems. Three main recommendations are presented based on published studies, new datasets, and existing monitoring programs. First, HAB monitoring would benefit from coordinated and cohesive efforts across hydrologically interconnected waterbodies and across organizational and political boundaries and jurisdictions. Second, a combination of sampling modalities would provide the most effective monitoring for HAB toxin dynamics and transport across hydrologically connected waterbodies, from headwater sources to downstream receiving waterbodies. Third, routine monitoring is needed for toxin mixtures at the land–sea interface including algal toxins of marine origins as well as cyanotoxins that are sourced from inland freshwater or produced in estuaries. Case studies from California are presented to illustrate the implementation of these recommendations, but these recommendations can also be applied to inland states or regions where the downstream receiving waterbody is a freshwater lake, reservoir, or river.
Alluvial fans and sinuous ridges are both important records of the history of fluvial activity on Mars, and they often occur together. We present observations of alluvial fans, many of which exhibit inverted relief, in five craters in the region north of Hellas basin. The observed fans ranged in size from ~10 to 820 km2. We identified three primary fan surface morphology classes (chute, degraded, and Inverted) as well as many instances where the morphology transitions from proximal chutes (or, rarely, a cratered degraded surface) to distal ridges corresponding to increasing thermal inertia. Clear superposition relationships at contacts between adjacent fans are rarely observed, suggesting interfingered deposits and concurrent fan development across the region. Localized factors appear to influence fan development as there is no systematic trend in the azimuth range of fan location, size of fan or catchment, as well as the degree of crater filling. Water and sediment availability may be controlled by lithology differences and weather patterns. Many of the fans had a mismatch between catchment and fan volume, corresponding to significant amounts of erosion perhaps due to windblown stripping of fine sediment. However, several notable fans exhibited volumes greater than their corresponding catchments. This may reflect uncertainty in the accuracy of the estimated paleosurface, or it may indicate sediment contributions to the fan from outside the mapped catchment. Ridges, inferred to be the resistant remnants of fluvially transported deposits, were used to estimate flow magnitude in fan construction with computed discharges of 60–400 m3/s and corresponding supply rate runoff values ~1–20 mm/h. Acknowledging that width-derived discharge values may overestimate flow conditions due to the likelihood of amalgamated channel deposits, this quantification provides important climate constraints.
The upper range of runoff values and discharge rates are quite high, and would require either intense rain storms to generate immediate runoff, or longer-term snow accumulation and subsequent melt-runoff, potentially enhanced by rain-on-snow events. Minimum continuous formation time scales of less than a century are computed, but are incompatible with fan morphology (e.g., superposition relationships, embedded craters) and mechanisms to sustain flows. More realistic lower-limit fan construction times, accounting for modeled precipitation rates from the literature, are tens to hundreds of thousands of years. Fans were active in multiple events spanning the Hesperian to Amazonian periods, requiring transient climate conditions to support the fan aggradation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2022.115122","usgsCitation":"Anderson, R.B., Williams, R., Gullikson, A.L., and Nelson, W., 2023, Morphology and paleohydrology of intracrater alluvial fans north of Hellas Basin, Mars: Icarus, v. 394, 115122, 22 p., https://doi.org/10.1016/j.icarus.2022.115122.","productDescription":"115122, 22 p.","ipdsId":"IP-130189","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":445517,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.icarus.2022.115122","text":"Publisher Index Page"},{"id":410790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hellas Basin, Mars","volume":"394","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":859659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Rebecca","contributorId":195304,"corporation":false,"usgs":false,"family":"Williams","given":"Rebecca","affiliations":[],"preferred":false,"id":859660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gullikson, Amber L. 0000-0002-1505-3151","orcid":"https://orcid.org/0000-0002-1505-3151","contributorId":208679,"corporation":false,"usgs":true,"family":"Gullikson","given":"Amber","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":859661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, William","contributorId":300211,"corporation":false,"usgs":false,"family":"Nelson","given":"William","affiliations":[{"id":65046,"text":"U. of Hawaii","active":true,"usgs":false}],"preferred":false,"id":859662,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232195,"text":"70232195 - 2023 - Management and environmental factors associated with simulated restoration seeding barriers in sagebrush steppe","interactions":[],"lastModifiedDate":"2023-02-14T14:37:56.099474","indexId":"70232195","displayToPublicDate":"2022-06-13T10:30:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Management and environmental factors associated with simulated restoration seeding barriers in sagebrush steppe","docAbstract":"Adverse weather conditions, particularly freezing or drought, are often associated with poor seedling establishment following restoration seeding in drylands like the Great Basin sagebrush steppe (USA). Management decisions such as planting date or seed source could improve restoration outcomes by reducing seedling exposure to weather barriers. We simulated the effects of management and environmental factors on seedling exposure to post-germination barriers for bottlebrush squirreltail (Elymus elymoides), Sandberg bluegrass (Poa secunda), and bluebunch wheatgrass (Pseudoroegneria spicata). We combined germination timing models with daily soil moisture and temperature estimates to calculate yearly germination favorability and post-germination freezing and drought barriers for three planting dates (Oct. 15, Nov. 15, and Mar. 15) and three seed sources or cultivars per species for 5000 sites in each of 40 yrs (water years 1980-2019). We tested the effects of site environmental variables (elevation, mean annual precipitation, heat load, and clay content) and management choices (seed source and planting date) on germination favorability and barrier occurrence (mean) and variability (coefficient of variation). Seedling exposure to barriers was strongly linked to management decisions in addition to site mean precipitation and elevation. Later fall plantings and seed sources with slower germination (lower mean germination favorability) were less likely to encounter freezing and drought barriers. These results suggest that management actions can play a role comparable to site environmental variables in reducing exposure of vulnerable seedlings to adverse weather conditions and subsequent effects on restoration outcomes.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13722","usgsCitation":"Copeland, S., Bradford, J., Hardegree, S.P., Schlaepfer, D.R., and Badik, K.J., 2023, Management and environmental factors associated with simulated restoration seeding barriers in sagebrush steppe: Restoration Ecology, v. 31, no. 2, e13722, https://doi.org/10.1111/rec.13722.","productDescription":"e13722","ipdsId":"IP-135148","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445519,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13722","text":"Publisher Index Page"},{"id":402087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah, Washington","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 36.4566360115962\n ],\n [\n -114.47753906249999,\n 36.73888412439431\n ],\n [\n -112.8515625,\n 37.43997405227057\n ],\n [\n -112.54394531249999,\n 40.27952566881291\n ],\n [\n -111.4453125,\n 42.61779143282346\n ],\n [\n -111.62109375,\n 44.24519901522129\n ],\n [\n -112.236328125,\n 44.43377984606822\n ],\n [\n -112.939453125,\n 44.43377984606822\n ],\n [\n -114.82910156249999,\n 44.213709909702054\n ],\n [\n -116.01562499999999,\n 44.43377984606822\n ],\n [\n -117.24609374999999,\n 46.558860303117164\n ],\n [\n -118.740234375,\n 46.22545288226939\n ],\n [\n -121.59667968749999,\n 45.213003555993964\n ],\n [\n -121.86035156249999,\n 43.54854811091286\n ],\n [\n -121.1572265625,\n 41.04621681452063\n ],\n [\n -120.1904296875,\n 38.51378825951165\n ],\n [\n -117.158203125,\n 36.4566360115962\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":844529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hardegree, Stuart P.","contributorId":195696,"corporation":false,"usgs":false,"family":"Hardegree","given":"Stuart","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":844531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844532,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Badik, Kevin J","contributorId":292423,"corporation":false,"usgs":false,"family":"Badik","given":"Kevin","email":"","middleInitial":"J","affiliations":[{"id":62901,"text":"The Nature Conservancy 1 E. 1st St. STE 1007, Reno, NV, 89501","active":true,"usgs":false}],"preferred":false,"id":844533,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231884,"text":"70231884 - 2023 - Late Cretaceous time-transgressive onset of Laramide arch exhumation and basin subsidence across northern Arizona−New Mexico, USA, and the role of a dehydrating Farallon flat slab","interactions":[],"lastModifiedDate":"2023-01-18T15:49:56.953328","indexId":"70231884","displayToPublicDate":"2022-05-13T08:21:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Late Cretaceous time-transgressive onset of Laramide arch exhumation and basin subsidence across northern Arizona−New Mexico, USA, and the role of a dehydrating Farallon flat slab","docAbstract":"Spatiotemporal constraints for Late Cretaceous tectonism across the Colorado Plateau and southern Rocky Mountains (northern Arizona−New Mexico, USA) are interpreted in regards to Laramide orogenic mechanisms. Onset of Laramide arch development is estimated from cooling recorded in representative thermochronologic samples in a three-step process of initial forward models, secondary HeFTy inverse models with informed constraint boxes, and a custom script to statistically estimate timing of rapid cooling from inverse model results. Onset of Laramide basin development is interpreted from increased rates of tectonic subsidence. Onset estimates are compared to published estimates for Laramide timing, and together suggest tectonism commenced ca. 90 Ma in northwestern Arizona and progressed eastward with later onset in north-central New Mexico by ca. 75−70 Ma. The interpreted sweep of onset progressed at a rate of ∼50 km/m.y. and was approximately half the 100−150 km/m.y. rate estimated for Late Cretaceous Farallon-North America convergence during the same timeframe. Previous suggestions that the Laramide tectonic front progressed at a rate similar to convergence via basal traction are not supported by our results. We thereby suggest that (1) a plate margin end load established far field compression and that (2) sequential Laramide-style strain was facilitated by progressive weakening of North American lithosphere from the dehydrating Farallon flat slab. Results are compared to models of sweeping tectonism and magmatism in other parts of the Laramide foreland. Discussions of the utility of the custom script and the potential for stratigraphic constraints to represent only minimum onset estimates are also presented.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36245.1","usgsCitation":"Thacker, J., Karlstrom, K., Kelley, S., Crow, R.S., and Kendall, J., 2023, Late Cretaceous time-transgressive onset of Laramide arch exhumation and basin subsidence across northern Arizona−New Mexico, USA, and the role of a dehydrating Farallon flat slab: GSA Bulletin, v. 135, no. 1-2, p. 389-406, https://doi.org/10.1130/B36245.1.","productDescription":"18 p.","startPage":"389","endPage":"406","ipdsId":"IP-122202","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":445525,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/gsab.s.19362371","text":"External Repository"},{"id":401533,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.818359375,\n 33.90689555128866\n ],\n [\n -103.447265625,\n 33.90689555128866\n ],\n [\n -103.447265625,\n 37.020098201368114\n ],\n [\n -113.818359375,\n 37.020098201368114\n ],\n [\n -113.818359375,\n 33.90689555128866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"135","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2022-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Thacker, Jacob","contributorId":292189,"corporation":false,"usgs":false,"family":"Thacker","given":"Jacob","affiliations":[{"id":62838,"text":"NMBG","active":true,"usgs":false}],"preferred":false,"id":844026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Karl","contributorId":292190,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":844027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Shari","contributorId":292191,"corporation":false,"usgs":false,"family":"Kelley","given":"Shari","affiliations":[{"id":62838,"text":"NMBG","active":true,"usgs":false}],"preferred":false,"id":844028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendall, Jerry","contributorId":292192,"corporation":false,"usgs":false,"family":"Kendall","given":"Jerry","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":844030,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236814,"text":"70236814 - 2023 - Spatially averaged stratigraphic data to inform watershed sediment routing: An example from the Mid-Atlantic United States","interactions":[],"lastModifiedDate":"2023-01-18T16:42:41.793618","indexId":"70236814","displayToPublicDate":"2022-05-05T08:41:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Spatially averaged stratigraphic data to inform watershed sediment routing: An example from the Mid-Atlantic United States","docAbstract":"New and previously published stratigraphic data define Holocene to present sediment storage time scales for Mid-Atlantic river corridors. Empirical distributions of deposit ages and thicknesses were randomly sampled to create synthetic age-depth records. Deposits predating European settlement accumulated at a (median) rate of 0.06 cm yr−1, range from ∼18,000 to 225 yr old, and represent 39% (median) of the total accumulation. Sediments deposited from 1750 to 1950 (“legacy sediments”) accumulated at a (median) rate of 0.39 cm yr−1 and comprise 47% (median) of the total, while “modern sediments” (1950−present) represent 11% of the total and accumulated at a (median) rate of 0.25 cm yr−1. Synthetic stratigraphic sequences, recast as age distributions for the presettlement period, in 1900 A.D., and at present, reflect rapid postsettlement alluviation, with enhanced preservation of younger sediments related to postsettlement watershed disturbance. An averaged present age distribution for vertically accreted sediment has modal, median, and mean ages of 190, 230, and 630 yr, reflecting the predominance of stored legacy sediments and the influence of relatively few, much older early Holocene deposits. The present age distribution, if represented by an exponential approximation (mean age ∼300 yr), and naively assumed to represent steady-state conditions, implies median sediment travel times on the order of centuries for travel distances greater than ∼100 km. The percentage of sediment reaching the watershed outlet in 30 yr (a reasonable time horizon to achieve watershed restoration efficacy) is ∼60% for a distance of 50 km, but this decreases to <20% for distances greater than 200 km. Age distributions, evaluated through time, not only encapsulate the history of sediment storage, but they also provide data for calibrating watershed-scale sediment-routing models over geological time scales.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36282.1","usgsCitation":"Pizzuto, J., Skalak, K., Benthem, A.J., Mahan, S.A., Sherif, M., and Pearson, A., 2023, Spatially averaged stratigraphic data to inform watershed sediment routing: An example from the Mid-Atlantic United States: GSA Bulletin, v. 135, no. 1-2, p. 249-270, https://doi.org/10.1130/B36282.1.","productDescription":"22 p.","startPage":"249","endPage":"270","ipdsId":"IP-132540","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":445529,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b36282.1","text":"Publisher Index Page"},{"id":406955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.57373046875,\n 36.57142382346277\n ],\n [\n -75.069580078125,\n 36.57142382346277\n ],\n [\n -75.069580078125,\n 40.01078714046552\n ],\n [\n -80.57373046875,\n 40.01078714046552\n ],\n [\n -80.57373046875,\n 36.57142382346277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"135","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pizzuto, James","contributorId":207115,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":852245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":852246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":852248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherif, Mahmoud 0000-0002-6504-0439","orcid":"https://orcid.org/0000-0002-6504-0439","contributorId":296698,"corporation":false,"usgs":false,"family":"Sherif","given":"Mahmoud","email":"","affiliations":[{"id":64145,"text":"Tanta University","active":true,"usgs":false}],"preferred":false,"id":852249,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Adam 0000-0002-6719-9750","orcid":"https://orcid.org/0000-0002-6719-9750","contributorId":296699,"corporation":false,"usgs":false,"family":"Pearson","given":"Adam","email":"","affiliations":[{"id":64146,"text":"SUNY, Postdam","active":true,"usgs":false}],"preferred":false,"id":852250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}