The late Pleistocene was a climatically dynamic period, with abrupt shifts between cool-wet and warm-dry conditions. Increased effective precipitation supported large pluvial lakes and long-lived spring ecosystems in valleys and basins throughout the western and southwestern U.S., but the source and seasonality of the increased precipitation are debated. Increases in the proportions of C4/(C4+ C3) grasses in the diets of large grazers have been ascribed both to increases in summer precipitation and lower atmospheric CO2 levels. Here we present stable carbon and oxygen isotope data from tooth enamel of late Pleistocene herbivores recovered from paleowetland deposits at Tule Spring Fossil Beds National Monument in the Las Vegas Valley of southern Nevada, as well as modern herbivores from the surrounding area. We use these data to investigate whether winter or summer precipitation was responsible for driving the relatively wet hydroclimate conditions that prevailed in the region during the late Pleistocene. We also evaluate whether late Pleistocene grass C4/(C4+ C3) was higher than today, and potential drivers of any changes.
Tooth enamel δ18O values for Pleistocene Equus, Bison, and Mammuthus are generally low (average 22.0 ± 0.7‰, 2 s.e., VSMOW) compared to modern equids (27.8 ± 1.5‰), and imply lower water δ18O values (−16.1 ± 0.8‰) than modern precipitation (−10.5‰) or in waters present in active springs and wells in the Las Vegas Valley (−12.9‰), an area dominated by winter precipitation. In contrast, tooth enamel of Camelops (a browser) generally yielded higher δ18O values (23.9 ± 1.1‰), possibly suggesting drought tolerance. Mean δ13C values for the Pleistocene grazers (−6.6 ± 0.7‰, 2 s.e., VPDB) are considerably higher than for modern equids (−9.6 ± 0.4‰) and indicate more consumption of C4 grass (17 ± 5%) than today (4 ± 4%). However, calculated C4 grass consumption in the late Pleistocene is strikingly lower than the proportion of C4 grass taxa currently present in the valley (55–60%). δ13C values in Camelops tooth enamel (−7.7 ± 1.0‰) are interpreted as reflecting moderate consumption (14 ± 8%) of Atriplex (saltbush), a C4 shrub that flourishes in regions with hot, dry summers.
Lower water δ18O values, lower abundance of C4 grasses, and the inferred presence of Atriplex are all consistent with general circulation models for the late Pleistocene that show enhanced delivery of winter precipitation, sourced from the north Pacific, into the interior western U.S. but do not support alternative models that infer enhanced delivery of summer precipitation, sourced from the tropics. In addition, we hypothesize that dietary competition among the diverse and abundant Pleistocene fauna may have driven the grazers analyzed here to feed preferentially on C4 grasses. Dietary partitioning, especially when combined with decreased pCO2 levels during the late Pleistocene, can explain the relatively high δ13C values observed in late Pleistocene grazers in the Las Vegas Valley and elsewhere in the southwestern U.S. without requiring additional summer precipitation. Pleistocene hydroclimate parameters derived from dietary and floral records may need to be reevaluated in the context of the potential effects of dietary preferences and lower pCO2 levels on the stability of C3 vs. C4 plants.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107784","usgsCitation":"Kohn, M.J., Springer, K.B., Pigati, J.S., Reynard, L., Drewicz, A.E., Crevier, J., and Scott, E., 2022, Seasonality of precipitation in the southwestern United States during the late Pleistocene inferred from stable isotopes in herbivore tooth enamel: Quaternary Science Reviews, v. 296, 107784, 21 p., https://doi.org/10.1016/j.quascirev.2022.107784.","productDescription":"107784, 21 p.","ipdsId":"IP-141465","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446131,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107784","text":"Publisher Index Page"},{"id":435657,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DBH6V7","text":"USGS data release","linkHelpText":"Data release for Seasonality of precipitation in 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E.","contributorId":297343,"corporation":false,"usgs":false,"family":"Drewicz","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854451,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crevier, Justin","contributorId":297344,"corporation":false,"usgs":false,"family":"Crevier","given":"Justin","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854452,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Eric","contributorId":127422,"corporation":false,"usgs":false,"family":"Scott","given":"Eric","email":"","affiliations":[],"preferred":false,"id":854453,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237575,"text":"70237575 - 2022 - Lower seismogenic depth model of western U.S. Earthquakes","interactions":[],"lastModifiedDate":"2022-10-31T14:52:24.02545","indexId":"70237575","displayToPublicDate":"2022-10-12T13:25:42","publicationYear":"2022","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":"Lower seismogenic depth model of western U.S. Earthquakes","docAbstract":"We present a model of the lower seismogenic depth of earthquakes in the western United States (WUS) estimated using the hypocentral depths of events M > 1, a crustal temperature model, and historical earthquake rupture depth models. Locations of earthquakes are from the Advanced National Seismic System Comprehensive Earthquake Catalog from 1980 to 2021 supplemented with seismicity in southern California for event hypocenters that were relocated by Hauksson et al. (2012) to obtain higher precision and better resolution in the model. We calculated the average depth of the deepest 10% of the merged catalog using an adaptive radius of 50 km or more. Along the San Andreas fault, the deepest seismogenic depths are located at 23 km around the Cholame segment, whereas the shallowest depths are located at about 10 km along the Rodgers Creek and Maacama faults. For the WUS outside California, the depth generally varies between 10 and 25 km with an average around 14 km but could extend to 35 km along Cascadia subduction zone. We find good agreement between the small‐magnitude depths and rupture depths derived from coseismic slip of large earthquakes across the region. Our estimates are generally deeper than the previous seismogenic depths determined for the Uniform California Earthquake Rupture Forecast, Version 3 model based on work by Petersen et al. (1996) who used seismicity cross sections along major fault zones in California. Our new seismogenic depth distribution correlates closely with crustal temperature derived from WUS heat flow (Blackwell et al., 2011). This correlation allowed us to develop a map of the brittle–ductile transition that we use to replace seismogenic depths in the model east of the Intermountain West Seismic Belt where the seismicity rate is low. This updated depth model is useful for recalibrating the lower geologic fault rupture depths, and constraining deformation and seismicity source models in updates of the U.S. Geological Survey National Seismic Hazard Model.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220174","usgsCitation":"Zeng, Y., Petersen, M.D., and Boyd, O.S., 2022, Lower seismogenic depth model of western U.S. Earthquakes: Seismological Research Letters, v. 93, no. 6, p. 3186-3204, https://doi.org/10.1785/0220220174.","productDescription":"19 p.","startPage":"3186","endPage":"3204","ipdsId":"IP-142152","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":408265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203125,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 29.84064389983441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":854485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":854486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237388,"text":"70237388 - 2022 - Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","interactions":[],"lastModifiedDate":"2022-10-17T16:42:25.152014","indexId":"70237388","displayToPublicDate":"2022-10-12T09:07:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","docAbstract":"This study investigates the applicability of the Landsat Dynamic Surface Water Extent (DSWE) science product for waterbird habitat modeling in multiple non-canopied habitat types. We compare surface water distribution estimates derived from DSWE to two site-specific survey methods: visual surveys and digitized aerial imagery. These site-specific surveys were conducted on Poplar Island, a restoration island project in the Chesapeake Bay, USA. Visual surveys were collected bimonthly from 2006 – 2013, and digitized aerial imagery was collected annually from 2006 – 2015. As a restoration island, Poplar Island presents a unique opportunity to analyze DSWE in a rapidly changing site. We structure our analysis based on the procedural development of individual sub-island cells developed from unconsolidated dredge material into fully restored wetlands that have independent hydrologic connection to the surrounding bay. Each development status is analyzed using our three DSWE classifications: Open Water (OW), a conservative estimate; Wetland Inclusive (WI), an aggressive estimate; and Development Dependent (DD), a landcover adaptive estimate. The OW classification consistently underestimates surface water coverage especially in the more complex, fully developed cells. The WI classification is better able to capture the tidal channels in these cells, but marginally overestimates surface water coverage in more sparsely vegetated cells. The DD classification does not significantly improve upon the estimations of the WI classification. Our data indicate that DSWE can be a capable alternative to our site-specific survey methods. However, the product is limited by Landsat’s 30 m spatial resolution, especially in more structurally complex wetlands. A recommended classification method for characterizing waterbird habitats would depend on the goals and targeted scale of analysis, for which DSWE may be a viable option.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100845","usgsCitation":"Taylor, J., Sullivan, J.D., Teitelbaum, C.S., Reese, J.G., and Prosser, D., 2022, Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats: Remote Sensing Applications: Society and Environment, v. 28, 100845, 9 p., https://doi.org/10.1016/j.rsase.2022.100845.","productDescription":"100845, 9 p.","ipdsId":"IP-139932","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2022.100845","text":"Publisher Index Page"},{"id":435658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SW505K","text":"USGS data release","linkHelpText":"Surface water estimates for a complex study site derived from traditional and emerging methods"},{"id":408211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Poplar Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.36236190795898,\n 38.74631848708898\n ],\n [\n -76.36373519897461,\n 38.754886481591335\n ],\n [\n -76.36905670166014,\n 38.7564928660758\n ],\n [\n -76.37231826782227,\n 38.754886481591335\n ],\n [\n -76.37884140014648,\n 38.762114927054405\n ],\n [\n -76.37969970703125,\n 38.76519348705099\n ],\n [\n -76.37712478637695,\n 38.76827191422227\n ],\n [\n -76.36356353759766,\n 38.78165483591876\n ],\n [\n -76.35824203491211,\n 38.78580303175893\n ],\n [\n -76.35772705078125,\n 38.793295935073516\n ],\n [\n -76.36510848999022,\n 38.797577240505625\n ],\n [\n -76.36974334716797,\n 38.79650693825768\n ],\n [\n -76.37042999267578,\n 38.79102138696157\n ],\n [\n -76.37128829956055,\n 38.78807774637309\n ],\n [\n -76.37060165405273,\n 38.784331118923134\n ],\n [\n -76.37334823608398,\n 38.78272536117434\n ],\n [\n -76.37695312499999,\n 38.78339443129763\n ],\n [\n -76.38141632080078,\n 38.78138720209366\n ],\n [\n -76.39291763305664,\n 38.767201172010736\n ],\n [\n -76.39120101928711,\n 38.75582354360097\n ],\n [\n -76.38656616210938,\n 38.7500671112174\n ],\n [\n -76.38364791870117,\n 38.743239112884744\n ],\n [\n -76.37678146362305,\n 38.741766321754575\n ],\n [\n -76.36236190795898,\n 38.74631848708898\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, John B.","contributorId":296294,"corporation":false,"usgs":false,"family":"Taylor","given":"John B.","affiliations":[{"id":64012,"text":"Chesapeake Bay Trust","active":true,"usgs":false}],"preferred":false,"id":854370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire S. 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":255382,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reese, Jan G.","contributorId":296295,"corporation":false,"usgs":false,"family":"Reese","given":"Jan","email":"","middleInitial":"G.","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237375,"text":"70237375 - 2022 - Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","interactions":[],"lastModifiedDate":"2022-10-12T14:07:03.951041","indexId":"70237375","displayToPublicDate":"2022-10-12T08:55:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","docAbstract":"Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
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Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269055,"text":"70269055 - 2022 - Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2025-07-15T15:43:20.638903","indexId":"70269055","displayToPublicDate":"2022-10-12T00:00:00","publicationYear":"2022","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":"Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","docAbstract":"It has been nearly a decade since updates to seismic and fault sources in the central and eastern United States (CEUS) were last assessed for the 2012 Central and Eastern United States Seismic Source Characterization for nuclear facilities (CEUS-SSCn) and 2014 United States Geological Survey National Seismic Hazard Model (NSHM) for the conterminous U.S. In advance of the 2023 NSHM update, we created 3 related geospatial databases to summarize and characterize new fault source information for the CEUS. These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220162","usgsCitation":"Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3100-3120, https://doi.org/10.1785/0220220162.","productDescription":"21 p.","startPage":"3100","endPage":"3120","ipdsId":"IP-138939","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.33762311995545,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 51.85857884546667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica A. 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Thompson Jobe","given":"Jessica","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher B. 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237376,"text":"70237376 - 2022 - Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","interactions":[],"lastModifiedDate":"2022-10-11T19:08:25.114099","indexId":"70237376","displayToPublicDate":"2022-10-11T14:04:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","title":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","docAbstract":"Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2022.102576","usgsCitation":"Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E., Siler, D.L., Karra, S., and Middleton, R.S., 2022, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering: Geothermics, v. 106, 102576, 15 p., https://doi.org/10.1016/j.geothermics.2022.102576.","productDescription":"102576, 15 p.","ipdsId":"IP-132590","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1890937","text":"Publisher Index Page"},{"id":408181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Colorado Plateau, Gila Hot Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 32.008075959291055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":854339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pepin, Jeffrey D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeffrey","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karra, Satish","contributorId":297512,"corporation":false,"usgs":false,"family":"Karra","given":"Satish","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middleton, Richard S.","contributorId":297513,"corporation":false,"usgs":false,"family":"Middleton","given":"Richard","email":"","middleInitial":"S.","affiliations":[{"id":64420,"text":"Carbon Solutions LLC","active":true,"usgs":false}],"preferred":false,"id":854344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237354,"text":"70237354 - 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It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. 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It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. 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Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-16","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2022, Physics-guided recurrent neural networks for predicting lake water temperature, chap. 16 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 373-398, https://doi.org/10.1201/9781003143376-16.","productDescription":"26 p.","startPage":"373","endPage":"398","ipdsId":"IP-132700","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854186,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237346,"text":"70237346 - 2022 - Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","interactions":[],"lastModifiedDate":"2022-10-11T16:08:12.135327","indexId":"70237346","displayToPublicDate":"2022-10-11T11:00:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12625,"text":"Limnology & Oceanography: Letters","active":true,"publicationSubtype":{"id":10}},"title":"Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","docAbstract":"The dataset described here includes estimates of historical (1980–2020) daily surface water temperature, lake metadata, and daily weather conditions for lakes bigger than 4 ha in the conterminous United States (n = 185,549), and also in situ temperature observations for a subset of lakes (n = 12,227). Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared error was 1.61°C. This dataset increases the number of lakes with daily temperature predictions when compared to existing datasets, as well as substantially improves predictive accuracy compared to a prior empirical model and a debiased process-based approach (2.01°C and 1.79°C median error, respectively).
","language":"English","publisher":"Wiley","doi":"10.1002/lol2.10249","usgsCitation":"Willard, J.D., Read, J., Topp, S.N., Hansen, G., and Kumar, V., 2022, Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020): Limnology & Oceanography: Letters, v. 7, no. 4, p. 287-301, https://doi.org/10.1002/lol2.10249.","productDescription":"15 p.","startPage":"287","endPage":"301","ipdsId":"IP-127157","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":446163,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10249","text":"Publisher Index Page"},{"id":435660,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CEMS0M","text":"USGS data release","linkHelpText":"Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological 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0000-0003-4434-051X","orcid":"https://orcid.org/0000-0003-4434-051X","contributorId":297456,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":64397,"text":"University of Minnesota Department of Computer Science","active":true,"usgs":false}],"preferred":false,"id":854206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Topp, Simon Nemer 0000-0001-7741-5982","orcid":"https://orcid.org/0000-0001-7741-5982","contributorId":268229,"corporation":false,"usgs":true,"family":"Topp","given":"Simon","email":"","middleInitial":"Nemer","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gretchen J. A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":854209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854210,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240887,"text":"70240887 - 2022 - Decision support for aquatic restoration based on species-specific responses to disturbance","interactions":[],"lastModifiedDate":"2023-02-28T13:05:03.312497","indexId":"70240887","displayToPublicDate":"2022-10-11T07:02:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Decision support for aquatic restoration based on species-specific responses to disturbance","docAbstract":"Disturbances to aquatic habitats are not uniformly distributed within the Great Lakes and acute effects can be strongest in nearshore areas where both landscape and within lake effects can have strong influence. Furthermore, different fish species respond to disturbances in different ways. A means to identify and evaluate locations and extent of disturbances that affect fish is needed throughout the Great Lakes. We used partial Canonical Correspondence Analysis to separate “natural” effects on nearshore assemblages from disturbance effects. Species-specific quadratic models of fish abundance as functions of in-lake disturbance or watershed-derived disturbance were developed separately for each of 35 species and lakewide predictions mapped for Lake Erie. Most responses were unimodal and more species decreased in abundance with increasing watershed disturbance than increased. However, eight species increased in abundance with current in-lake disturbance conditions. Optimum Yellow Perch (Perca flavescens) abundance occurred at in-lake disturbance values less than the gradient mean, but decreased continuously from minimum watershed disturbance to higher values. Bands of optimum in-lake conditions occurred throughout the eastern and western portions of the Lake Erie nearshore zone; some areas were less disturbed than desirable. However, watershed-derived disturbance conditions were generally poor for Yellow Perch throughout the lake. In contrast, optimum Smallmouth Bass (Micropterus dolomieu) abundance occurred at in-lake disturbance values greater than the gradient mean and continuously increased with increasing watershed disturbance. Smallmouth Bass responses to disturbance indicated that most of the nearshore zone was less disturbed than is desirable and were most abundant in areas that the Yellow Perch response indicated were highly disturbed. Mapping counts of species response models that agreed on the disturbance level in each spatial unit of the nearshore zone showed a fine-scale mosaic of areas in which habitat restoration may benefit many or few species. This tool may assist managers in prioritizing conservation and restoration efforts and evaluating environmental conditions that may be improved.
In April 2022, a seismic swarm near Mt. Edgecumbe in southeast Alaska suggested renewed activity at this transform fault volcano, which was last active ≈800 years ago. Previously, thin rhyolitic tephras were deposited 5 and 4 ka. Satellite radar data from 2014 to 2022 resolves line-of-sight rapid inflation up to 7.1 cm/yr beginning in August 2018. Bayesian modeling suggests a transcrustal system of a deflating (−0.528 km3) dipping sill at 20 km depth recharging a magma chamber at 10 km (0.222 km3). A near-vertical conduit could capture the volume difference without noticeable surface deformation. Reanalyzed seismicity, recorded 25 km away, shows increases since July 2019. Magma ascent through ductile material and brittle strain release in a stressed overburden could explain the time delay. Cloud-native open data and workflows enabled discovery and analysis of this signal within days after going unnoticed for >3 years.
The phylogeography of the American black bear (Ursus americanus) is characterized by isolation into glacial refugia, followed by population expansion and genetic admixture. Anthropogenic activities, including overharvest, habitat loss, and transportation infrastructure, have also influenced their landscape genetic structure. We describe the genetic structure of the American black bear in the American Southwest and northern Mexico and investigate how prehistoric and contemporary forces shaped genetic structure and influenced gene flow. Using a suite of microsatellites and a sample of 550 bears, we identified 14 subpopulations organized hierarchically following the distribution of ecoregions and mountain ranges containing black bear habitat. The pattern of subdivision we observed is more likely a product of postglacial habitat fragmentation during the Pleistocene and Holocene, rather than a consequence of contemporary anthropogenic barriers to movement during the Anthropocene. We used linear mixed-effects models to quantify the relationship between landscape resistance and genetic distance among individuals, which indicated that both isolation by resistance and geographic distance govern gene flow. Gene flow was highest among subpopulations occupying large tracts of contiguous habitat, was reduced among subpopulations in the Madrean Sky Island Archipelago, where montane habitat exists within a lowland matrix of arid lands, and was essentially nonexistent between two isolated subpopulations. We found significant asymmetric gene flow supporting the hypothesis that bears expanded northward from a Pleistocene refugium located in the American Southwest and northern Mexico and that major highways were not yet affecting gene flow. The potential vulnerability of the species to climate change, transportation infrastructure, and the US–Mexico border wall highlights conservation challenges and opportunities for binational collaboration.
Offshore geological sequestration of CO2 offers a viable approach for reducing greenhouse gas emissions into the atmosphere. Strategies include injection of CO2 into the deep-ocean or ocean-floor sediments, whereby depending on pressure–temperature conditions, CO2 can be trapped physically, gravitationally, or converted to CO2 hydrate. Energy-driven research continues to also advance CO2-for-CH4 replacement strategies in the gas hydrate stability zone (GHSZ), producing methane for natural gas needs while sequestering CO2. In all cases, safe storage of CO2 requires reliable monitoring of the targeted CO2 injection sites and the integrity of the repository over time, including possible leakage. Electromagnetic technologies used for oil and gas exploration, sensitive to electrical conductivity, have long been considered an optimal monitoring method, as CO2, similar to hydrocarbons, typically exhibits lower conductivity than the surrounding medium. We apply 3D controlled-source electromagnetic (CSEM) forward modeling code to simulate an evolving CO2 reservoir in deep-ocean sediments, demonstrating sufficient sensitivity and resolution of CSEM data to detect reservoir changes even before sophisticated inversion of data. Laboratory measurements place further constraints on evaluating certain systems within the GHSZ; notably, CO2 hydrate is measurably weaker than methane hydrate, and >1 order of magnitude more conductive, properties that may affect site selection, stability, and modeling considerations.
","language":"English","publisher":"MDPI","doi":"10.3390/en15197411","usgsCitation":"Constable, S., and Stern, L.A., 2022, Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies: Energies, v. 15, no. 19, 7411, 16 p., https://doi.org/10.3390/en15197411.","productDescription":"7411, 16 p.","ipdsId":"IP-142378","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en15197411","text":"Publisher Index Page"},{"id":408604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Constable, Steven","contributorId":9178,"corporation":false,"usgs":false,"family":"Constable","given":"Steven","email":"","affiliations":[{"id":16196,"text":"Scripps Institution of Oceanography, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":855447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":855448,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237298,"text":"ofr20221057 - 2022 - Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","interactions":[],"lastModifiedDate":"2026-03-27T20:28:39.65672","indexId":"ofr20221057","displayToPublicDate":"2022-10-07T11:57:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1057","displayTitle":"Channel Mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","title":"Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","docAbstract":"Bathymetric and topographic data were collected from May 2013 to February 2016 along the 15.84-mile reach of the Colorado River spanning from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona. Channel bathymetry was mapped using multibeam and singlebeam echo sounders; subaerial topography was mapped using a combination of ground-based total stations and aerial photogrammetry. These data were combined to produce a digital elevation model (DEM), spatially variable estimates of DEM uncertainty, and bed-substrate distribution maps. This project is part of a larger effort to monitor the status and trends of sand storage along the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. This report documents the study methodologies (survey methods and post-processing procedures, DEM production and uncertainty assessment, and bed-substrate classification) and presents the resulting datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221057","collaboration":"Prepared in cooperation with Northern Arizona University and Marda Science LLC","usgsCitation":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Gushue, T., Buscombe, D.D., and Kohl, K., 2022, Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona: U.S. Geological Survey Open-File Report 2022-1057, 20 p., https://doi.org/10.3133/ofr20221057.","productDescription":"Report: v, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-120853","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":408061,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1057/ofr20221057.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 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Although mangrove wetlands are sources of ESs worth billions of dollars, there is a lack of modeling tools. This is reflected in our lack of understanding of mangroves’ functional and structural attributes. Here, we discuss the “state of the art” of mangrove models used in the planning and monitoring of R/R projects during the last 30 years. The main objectives were to characterize the most frequent modeling approach, their spatiotemporal resolution, and their current utility/application in management decisions. We identified 281 studies in six broad model categories: conceptual, agent-based (ABM), process-based (PBM), spatial, statistical, and socioeconomic/management (ScoEco). The most widely used models are spatial and statistical, followed by PBM, ScoEco, and conceptual categories, while the ABMs were the least frequently used. Yet, the application of mangrove models in R/R projects since the early 1990s has been extremely limited, especially in the mechanistic model category. We discuss several approaches to help advance model development and applications, including the targeted allocation of potential revenue from global carbon markets to R/R projects using a multi-model and integrated approach.
","language":"English","publisher":"MDPI","doi":"10.3390/f13101638","usgsCitation":"Rivera-Monroy, V.H., Zhao, X., Wang, H., and Xue, Z.G., 2022, Are existing modeling tools useful to evaluate outcomes in mangrove restoration and rehabilitation projects? A minireview: Forests, v. 13, no. 10, 1638, 21 p., https://doi.org/10.3390/f13101638.","productDescription":"1638, 21 p.","ipdsId":"IP-144365","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f13101638","text":"Publisher Index Page"},{"id":408477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Xiaochen","contributorId":219696,"corporation":false,"usgs":false,"family":"Zhao","given":"Xiaochen","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":222813,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, Zuo G.","contributorId":298021,"corporation":false,"usgs":false,"family":"Xue","given":"Zuo","email":"","middleInitial":"G.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255211,"text":"70255211 - 2022 - Industrial energy development decouples ungulate migration from the green wave","interactions":[],"lastModifiedDate":"2024-06-13T16:04:59.632829","indexId":"70255211","displayToPublicDate":"2022-10-06T10:58:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Industrial energy development decouples ungulate migration from the green wave","docAbstract":"The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed ‘green-wave surfing’) is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (Odocoileus hemionus) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behaviour before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behaviour of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behaviour present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-022-01887-9","collaboration":"Western EcoSystems, INC","usgsCitation":"Aikens, E.O., Wyckoff, T., Sawyer, H., and Kauffman, M., 2022, Industrial energy development decouples ungulate migration from the green wave: Nature Ecology and Evolution, v. 6, p. 1733-1741, https://doi.org/10.1038/s41559-022-01887-9.","productDescription":"9 p.","startPage":"1733","endPage":"1741","ipdsId":"IP-136329","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.50057122965278,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 41.908308585415085\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":272241,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen","email":"","middleInitial":"O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":903738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wyckoff, Teal B.","contributorId":339010,"corporation":false,"usgs":false,"family":"Wyckoff","given":"Teal B.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawyer, Hall","contributorId":287880,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","affiliations":[{"id":61660,"text":"Western Ecosystems Technology, Inc., Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":903740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903741,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237277,"text":"70237277 - 2022 - Absolute accuracy assessment of lidar point cloud using amorphous objects","interactions":[],"lastModifiedDate":"2022-10-06T14:30:04.492861","indexId":"70237277","displayToPublicDate":"2022-10-06T09:26:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Absolute accuracy assessment of lidar point cloud using amorphous objects","docAbstract":"The accuracy assessment of airborne lidar point cloud typically estimates vertical accuracy by computing RMSEz (root mean square error of the z coordinate) from ground check points (GCPs). Due to the low point density of the airborne lidar point cloud, there is often not enough accurate semantic context to find an accurate conjugate point. To advance the accuracy assessment in full three-dimensional (3D) context, geometric features, such as the three-plane intersection point or two-line intersection point, are often used. Although the point density is still low, geometric features are mathematically modeled from many points. Thus, geometric features provide a robust determination of the intersection point, and the point is considered as a GCP. When no regular built objects are available, we describe the process of utilizing features of irregular shape called amorphous natural objects, such as a tree or a rock. When scanned to a high-density point cloud, an amorphous natural object can be used as ground truth reference data to estimate 3D georeferencing errors of the airborne lidar point cloud. The algorithm to estimate 3D accuracy is the optimization that minimizes the sum of the distance between the airborne lidar points to the ground scanned data. The search volume partitioning was the most important procedure to improve the computational efficiency. We also performed an extensive study to address the external uncertainty associated with the amorphous object method. We describe an accuracy assessment using amorphous objects (108 trees) spread over the project area. The accuracy results for ∆x, ∆y, and ∆z obtained using the amorphous object method were 3.1 cm, 3.6 cm, and 1.7 cm RMSE, along with a mean error of 0.1 cm, 0.1 cm, and 4.5 cm, respectively, satisfying the accuracy requirement of U.S. Geological Survey lidar base specification. This approach shows strong promise as an alternative to geometric feature methods when artificial targets are scarce. The relative convenience and advantages of using amorphous targets, along with its good performance shown here, make this amorphous object method a practical way to perform 3D accuracy assessment.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14194767","usgsCitation":"Kim, M., Stoker, J.M., Irwin, J., Danielson, J.J., and Park, S., 2022, Absolute accuracy assessment of lidar point cloud using amorphous objects: Remote Sensing, v. 14, no. 19, 4767, 18 p., https://doi.org/10.3390/rs14194767.","productDescription":"4767, 18 p.","ipdsId":"IP-145321","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":446201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14194767","text":"Publisher Index Page"},{"id":408035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":853945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":853946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Park, Seonkyung 0000-0003-3203-1998 seonkyungpark@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":222488,"corporation":false,"usgs":false,"family":"Park","given":"Seonkyung","email":"seonkyungpark@contractor.usgs.gov","affiliations":[{"id":40547,"text":"United Support Services, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":853949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237290,"text":"70237290 - 2022 - Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","interactions":[],"lastModifiedDate":"2022-10-06T14:25:24.776873","indexId":"70237290","displayToPublicDate":"2022-10-06T09:19:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","docAbstract":"Reliable quantitative information on sediment sources to rivers is critical to mitigate contamination and target conservation and restoration actions. However, the determination of the relative importance of sediment sources is complicated at the scale of large river basins by immense variability in erosional processes and sediment sources over space and time, heterogeneity in sediment transport and deposition, and a paucity of sediment monitoring data. Sediment source fingerprinting is an increasingly adopted field-based technique that identifies the nature and relative source contribution of sediment transported in waterways. Notably, sediment source fingerprinting provides information that is independent of other field, modeling, or remotely sensed techniques. However, the diversity in sediment fingerprinting sampling, analytical, and interpretive methods has been recognized as a problem in terms of developing standardized procedures for its application at the scale of large river basins. Accordingly, this review focuses on established sediment source fingerprinting studies conducted within the Mississippi River Basin (MRB), summarizes unique information provided by sediment source fingerprinting that is distinct from traditional monitoring techniques, evaluates consistency and reliability of methodological approaches among MRB studies, and provides prospects for the use of the sediment source fingerprinting technique as an aid to large-scale landscape conservation and restoration under current management frameworks. Most established MRB studies got creditable fingerprinting results and considered near-channel sources as the dominant sediment sources in most cases, while the comparability of their results suffers from a lack of standardization in procedural steps. Findings from MRB studies demonstrate that sediment source fingerprinting is a highly valuable and reliable sediment source assessment approach to assist land and water resource management under current management frameworks, but efforts are still needed to make this technique ready to be used in a more predominant way in large-scale landscape conservation and restoration efforts. We summarized research needs and suggested the best fingerprinting practices for management purposes with the aim of ensuring that this technique is as robust and reliable as it moves forward.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.116260","usgsCitation":"Xu, Z., Belmont, P., Brahney, J., and Gellis, A.C., 2022, Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin: Journal of Environmental Management, v. 324, 116260, 20 p., https://doi.org/10.1016/j.jenvman.2022.116260.","productDescription":"116260, 20 p.","ipdsId":"IP-141762","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":446204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2022.116260","text":"Publisher Index Page"},{"id":408033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.0869140625,\n 29.57345707301757\n ],\n [\n -89.7802734375,\n 28.729130483430154\n ],\n [\n -89.20898437499999,\n 29.34387539941801\n ],\n [\n -89.5166015625,\n 30.107117887092357\n ],\n [\n -89.384765625,\n 33.65120829920497\n ],\n [\n -82.8369140625,\n 34.77771580360469\n ],\n [\n -79.40917968749999,\n 36.59788913307022\n ],\n [\n -77.6953125,\n 42.391008609205045\n ],\n [\n -79.365234375,\n 42.4234565179383\n ],\n [\n -81.9580078125,\n 41.31082388091818\n ],\n [\n -87.099609375,\n 41.0130657870063\n ],\n [\n -87.62695312499999,\n 41.672911819602085\n ],\n [\n -88.2861328125,\n 43.229195113965005\n ],\n [\n -90.1318359375,\n 46.49839225859763\n ],\n [\n -92.021484375,\n 46.558860303117164\n ],\n [\n -93.8232421875,\n 47.54687159892238\n ],\n [\n -95.00976562499999,\n 47.54687159892238\n ],\n [\n -98.61328125,\n 47.100044694025215\n ],\n [\n -104.0185546875,\n 49.009050809382046\n ],\n [\n -115.00488281250001,\n 49.03786794532644\n ],\n [\n -114.60937499999999,\n 46.92025531537451\n ],\n [\n -114.521484375,\n 45.85941212790755\n ],\n [\n -112.8076171875,\n 44.62175409623324\n ],\n [\n -111.1376953125,\n 44.99588261816546\n ],\n [\n -109.2919921875,\n 44.715513732021336\n ],\n [\n -108.896484375,\n 42.65012181368022\n ],\n [\n -106.12792968749999,\n 40.88029480552824\n ],\n [\n -105.3369140625,\n 39.26628442213066\n ],\n [\n -105.29296874999999,\n 37.16031654673677\n ],\n [\n -105.29296874999999,\n 34.92197103616377\n ],\n [\n -103.0517578125,\n 34.23451236236987\n ],\n [\n -97.646484375,\n 33.46810795527896\n ],\n [\n -93.955078125,\n 31.353636941500987\n ],\n [\n -94.0869140625,\n 29.57345707301757\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Zhen","contributorId":297389,"corporation":false,"usgs":false,"family":"Xu","given":"Zhen","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":853997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belmont, Patrick","contributorId":275033,"corporation":false,"usgs":false,"family":"Belmont","given":"Patrick","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":853998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":853999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237272,"text":"70237272 - 2022 - Simple statistical models can be sufficient for testing hypotheses with population time series data","interactions":[],"lastModifiedDate":"2022-10-06T14:10:39.16442","indexId":"70237272","displayToPublicDate":"2022-10-06T08:52:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Simple statistical models can be sufficient for testing hypotheses with population time series data","docAbstract":"Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9339","usgsCitation":"Wenger, S., Stowe, E.S., Gido, K.B., Freeman, M., Kanno, Y., Franssen, N.R., Olden, J., Poff, N.L., Walters, A.W., Bumpers, P.M., Mims, M.C., Hooten, M.B., and Lu, X., 2022, Simple statistical models can be sufficient for testing hypotheses with population time series data: Ecology and Evolution, v. 12, no. 9, e9339, 13 p., https://doi.org/10.1002/ece3.9339.","productDescription":"e9339, 13 p.","ipdsId":"IP-133439","costCenters":[{"id":683,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446206,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.9339","text":"External Repository"},{"id":408029,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":853925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stowe, Edward S.","contributorId":273256,"corporation":false,"usgs":false,"family":"Stowe","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gido, Keith B.","contributorId":198487,"corporation":false,"usgs":false,"family":"Gido","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":853927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Franssen, Nathan R.","contributorId":273252,"corporation":false,"usgs":false,"family":"Franssen","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olden, Julian 0000-0003-2143-1187","orcid":"https://orcid.org/0000-0003-2143-1187","contributorId":296007,"corporation":false,"usgs":false,"family":"Olden","given":"Julian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":853931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poff, N. LeRoy","contributorId":261271,"corporation":false,"usgs":false,"family":"Poff","given":"N.","email":"","middleInitial":"LeRoy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":853933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bumpers, Phillip M.","contributorId":203871,"corporation":false,"usgs":false,"family":"Bumpers","given":"Phillip","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853934,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mims, Meryl C. 0000-0003-0570-988X","orcid":"https://orcid.org/0000-0003-0570-988X","contributorId":209951,"corporation":false,"usgs":false,"family":"Mims","given":"Meryl","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":853935,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hooten, Mevin B. 0000-0002-1614-723X","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":292295,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853936,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lu, Xinyi","contributorId":279368,"corporation":false,"usgs":false,"family":"Lu","given":"Xinyi","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":853937,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70237276,"text":"70237276 - 2022 - Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris)","interactions":[],"lastModifiedDate":"2022-10-06T13:47:45.013432","indexId":"70237276","displayToPublicDate":"2022-10-06T08:35:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris)","title":"Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris)","docAbstract":"Northern Red-Bellied Cooters (Pseudemys rubriventris) have a disjunct distribution with a relictual population in southeastern Massachusetts and a larger range across the mid-Atlantic United States. The relictual population is currently listed with protections under the U.S. Endangered Species Act but the status of the population in the remainder of the species' range has not been assessed, and there is concern that it may be at risk of extinction without protection. The U.S. Fish and Wildlife Service requires scientific information of the species' status to inform conservation decisions. There is little empirical information available from P. rubriventris populations and, furthermore, the majority of what exists comes from the disjunct northern subpopulation. To fill data gaps in the species' life history and reduce geographic bias, we supplement available data from P. rubriventris with demographic rate estimates from other Pseudemys species to parameterize an age-structured population projection model. Our estimate of mean population growth rate was 0.987 (0.92–1.04), indicating that P. rubriventris populations may be in decline. However, there was considerable uncertainty in our results, with 35% of projections resulting in stable or increasing populations. Additional uncertainty about parameter values, geographic variation, and current threats limit the assessment. We discuss the merits and limitations of our population projection modeling (PPM) approach where other analytical methods are precluded by lack of available data.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/21-065","usgsCitation":"Fleming, J.E., Moore, J.F., Waddle, H., Martin, J., and Campbell Grant, E.H., 2022, Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris): Journal of Herpetology, v. 56, no. 3, p. 362-369, https://doi.org/10.1670/21-065.","productDescription":"8 p.","startPage":"362","endPage":"369","ipdsId":"IP-130062","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":408028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Massachusetts, New Jersey, North Carolina, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.332763671875,\n 34.551811369170494\n ],\n [\n -76.607666015625,\n 34.6241677899049\n ],\n [\n -76.201171875,\n 34.831841149828655\n ],\n [\n -75.399169921875,\n 35.23664622093195\n ],\n [\n -75.322265625,\n 35.47856499535729\n ],\n [\n -75.377197265625,\n 35.84453450421662\n ],\n [\n -75.83862304687499,\n 37.046408899699564\n ],\n [\n -74.970703125,\n 38.26406296833961\n ],\n [\n -74.981689453125,\n 38.65119833229951\n ],\n [\n -74.827880859375,\n 38.8824811975508\n ],\n [\n -74.168701171875,\n 39.58029027440865\n ],\n [\n -73.80615234375,\n 40.33817045213394\n ],\n [\n -74.1357421875,\n 40.49709237269567\n ],\n [\n -77.750244140625,\n 40.75557964275589\n ],\n [\n -79.332275390625,\n 39.715638134796336\n ],\n [\n -79.595947265625,\n 38.59970036588819\n ],\n [\n -78.134765625,\n 35.71975793933433\n ],\n [\n -77.332763671875,\n 34.551811369170494\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.202392578125,\n 41.72213058512578\n ],\n [\n -70.499267578125,\n 41.72213058512578\n ],\n [\n -70.499267578125,\n 42.27730877423709\n ],\n [\n -71.202392578125,\n 42.27730877423709\n ],\n [\n -71.202392578125,\n 41.72213058512578\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fleming, Jillian Elizabeth 0000-0003-2570-914X","orcid":"https://orcid.org/0000-0003-2570-914X","contributorId":238931,"corporation":false,"usgs":true,"family":"Fleming","given":"Jillian","email":"","middleInitial":"Elizabeth","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":853941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waddle, Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":206866,"corporation":false,"usgs":true,"family":"Waddle","given":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":216734,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853944,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238817,"text":"70238817 - 2022 - Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","interactions":[],"lastModifiedDate":"2022-12-13T13:40:55.160716","indexId":"70238817","displayToPublicDate":"2022-10-06T07:10:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5538,"text":"AoB PLANTS","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","docAbstract":"Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/aobpla/plac045","usgsCitation":"Applestein, C., Caughlin, T., and Germino, M., 2022, Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation: AoB PLANTS, v. 14, no. 6, plac045, 13 p., https://doi.org/10.1093/aobpla/plac045.","productDescription":"plac045, 13 p.","ipdsId":"IP-127630","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aobpla/plac045","text":"Publisher Index Page"},{"id":410359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.2526670227266,\n 45.4\n ],\n [\n -117.2526670227266,\n 43.21761290801206\n ],\n [\n -113.74569616023115,\n 43.21761290801206\n ],\n [\n -113.74569616023115,\n 45.4\n ],\n [\n -117.2526670227266,\n 45.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"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":858780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":858781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":858782,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240117,"text":"70240117 - 2022 - Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","interactions":[],"lastModifiedDate":"2023-01-27T13:09:43.480884","indexId":"70240117","displayToPublicDate":"2022-10-06T07:02:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","docAbstract":"Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.
Dissolved organic matter (DOM) and particulate organic matter (POM) can influence biogeochemical processes in aquatic systems. An understanding, however, of the source, composition, and processes driving inland reservoir organic matter (OM) cycling at a regional scale over the long term is currently unexplored. Here, we quantify decadal patterns (> 20 yr) of DOM quantity and composition and POM in 40 reservoirs in the midcontinent United States. We built 184 Random Forest models to identify how the relative influence of watershed characteristics and limnological parameters on OM dynamics may vary over time and in synchrony with hydroclimatic anomalies. The reservoir OM quantity and composition varied nonmonotonically through time and in contrast to lake browning observed in the northern hemisphere. Reservoir DOM composition switched from humic and aromatic during wet summers to aliphatic, potentially autochthonous DOM during particularly prolonged dry summers in the mid-2000s. The shift in reservoir DOM quantity and composition could be attributed to the change in time-varying control of watershed and limnological factors mediated by the hydroclimatic conditions. Watershed control (e.g., percent crops) was predominant during wet summers, while the effect of reservoir morphology (e.g., maximum depth) and water quality parameters (e.g., Secchi depth, chlorophyll a) were evident during dry summers. Thus, future predictions of drier conditions may promote “greening” with negative implications for reservoir water quality and treated drinking water. Considering the nonlinear nature of reservoir OM dynamics and its controls will help to better mitigate water quality issues in these constructed systems increasingly impacted by global changes.