Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R2 of 0.83) and hold-out (R2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer, and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.
Winter carbon loss in northern ecosystems is estimated to be greater than the average growing season carbon uptake and is primarily driven by microbial decomposers. Viruses modulate microbial carbon cycling via induced mortality and metabolic controls, but it is unknown whether viruses are active under winter conditions (anoxic and sub-freezing temperatures).
We used stable isotope probing (SIP) targeted metagenomics to reveal the genomic potential of active soil microbial populations under simulated winter conditions, with an emphasis on viruses and virus-host dynamics. Arctic peat soils from the Bonanza Creek Long-Term Ecological Research site in Alaska were incubated under sub-freezing anoxic conditions with H218O or natural abundance water for 184 and 370 days. We sequenced 23 SIP-metagenomes and measured carbon dioxide (CO2) efflux throughout the experiment. We identified 46 bacterial populations (spanning 9 phyla) and 243 viral populations that actively took up 18O in soil and respired CO2 throughout the incubation. Active bacterial populations represented only a small portion of the detected microbial community and were capable of fermentation and organic matter degradation. In contrast, active viral populations represented a large portion of the detected viral community and one third were linked to active bacterial populations. We identified 86 auxiliary metabolic genes and other environmentally relevant genes. The majority of these genes were carried by active viral populations and had diverse functions such as carbon utilization and scavenging that could provide their host with a fitness advantage for utilizing much-needed carbon sources or acquiring essential nutrients.
Overall, there was a stark difference in the identity and function of the active bacterial and viral community compared to the unlabeled community that would have been overlooked with a non-targeted standard metagenomic analysis. Our results illustrate that substantial active virus-host interactions occur in sub-freezing anoxic conditions and highlight viruses as a major community-structuring agent that likely modulates carbon loss in peat soils during winter, which may be pivotal for understanding the future fate of arctic soils' vast carbon stocks.
","language":"English","publisher":"Springer","doi":"10.1186/s40168-021-01154-2","usgsCitation":"Trubl, G., Kimbrel, J.A., Liquet-Gonzalez, J., Nuccio, E.E., Weber, P.K., Pett-Ridge, J., Jansson, J.K., Waldrop, M., and Blazewicz, S., 2021, Active virus-host interactions at sub-freezing temperatures in Arctic peat soil: Microbiome, v. 9, 208, 15 p., https://doi.org/10.1186/s40168-021-01154-2.","productDescription":"208, 15 p.","ipdsId":"IP-128011","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40168-021-01154-2","text":"Publisher Index Page"},{"id":462901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Trubl, Gareth","contributorId":345156,"corporation":false,"usgs":false,"family":"Trubl","given":"Gareth","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimbrel, Jeffrey A","contributorId":345157,"corporation":false,"usgs":false,"family":"Kimbrel","given":"Jeffrey","email":"","middleInitial":"A","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liquet-Gonzalez, Jose","contributorId":345158,"corporation":false,"usgs":false,"family":"Liquet-Gonzalez","given":"Jose","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nuccio, Erin E.","contributorId":345159,"corporation":false,"usgs":false,"family":"Nuccio","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":915865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Peter K.","contributorId":345160,"corporation":false,"usgs":false,"family":"Weber","given":"Peter","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":915866,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":915867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansson, Janet K.","contributorId":345161,"corporation":false,"usgs":false,"family":"Jansson","given":"Janet","email":"","middleInitial":"K.","affiliations":[{"id":82503,"text":"Pacific Northwest National Labs","active":true,"usgs":false}],"preferred":false,"id":915868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":915869,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blazewicz, Steve 0000-0001-7517-1750","orcid":"https://orcid.org/0000-0001-7517-1750","contributorId":272100,"corporation":false,"usgs":false,"family":"Blazewicz","given":"Steve","email":"","affiliations":[{"id":13621,"text":"Lawrence Livermore National Laboratory","active":true,"usgs":false}],"preferred":false,"id":915870,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226823,"text":"70226823 - 2021 - Effects of hydrologic variability and remedial actions on first flush and metal loading from streams draining the Silverton caldera, 1992–2014","interactions":[],"lastModifiedDate":"2021-12-14T12:52:04.069102","indexId":"70226823","displayToPublicDate":"2021-10-18T06:45:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of hydrologic variability and remedial actions on first flush and metal loading from streams draining the Silverton caldera, 1992–2014","docAbstract":"This study examined water quality in the upper Animas River watershed, a mined watershed that gained notoriety following the 2015 Gold King mine release of acid mine drainage to downstream communities. Water-quality data were used to evaluate trends in metal concentrations and loads over a two-decade period. Selected sites included three sites on tributary streams and one main-stem site on the Animas River downstream from the tributary confluences. During the study period, metal concentrations and loads varied seasonally and annually because of hydrologic variability and remedial actions designed to ameliorate the effects of acid mine drainage. Water-quality data were divided into two periods based on the timing of remedial activities in the watershed. The first period includes active water treatment, surface reclamation and installation of bulkheads in adits; the second period includes the decade following these activities. Water-quality data were used to estimate annual and monthly zinc loads using the Adjusted Maximum Likelihood Method (using LOADEST software) and U.S. Geological Survey streamflow data. This study presents one of the first applications of LOADEST focused on metal loads. Monthly flow-weighted concentrations were analysed using a Mann-Kendall trend test to determine the direction, magnitude, and significance of temporal trends in zinc loading in any given month and using t-test comparisons between the two periods. Zinc loads estimated for the Animas River below the tributaries indicate decreased zinc loading during the rising limb of the hydrograph in the second period, perhaps reflecting a reduction of snowmelt-derived zinc load following surface reclamation activities. In contrast, base-flow zinc loading increased at the main-stem site, perhaps because of the cessation of water treatment in tributary streams. Flow weighting of monthly load estimates yielded increased statistical significance and enabled more nuanced differentiation between the effects of hydrologic variability and remedial activities on zinc loading.
Rano Raraku, the crater lake constrained by basaltic tuff that served as the primary quarry used to construct the moai statues on Rapa Nui (Easter Island), has experienced fluctuations in lake level over the past centuries. As one of the only freshwater sources on the island, understanding the present and past geochemical characteristics of the lake water is critical to understand if the lake could have been a viable freshwater source for Rapa Nui. At the time of sampling in September 2017, the maximum lake depth was ~1 m. The lake level has substantially declined in the subsequent years, with the lake drying almost completely in January 2018. The lake is currently characterized by highly anoxic conditions, with a predominance of ammonium ions on nitrates, a high concentration of organic carbon in the water-sediment interface and reducing conditions of the lake, as evidenced by Mn/Fe and Cr/V ratios. Our estimates of past salinity inferred from the chloride mass balance indicates that it was unlikely that Rano Raraku provided a viable freshwater source for early Rapa Nui people. The installation of an outlet pipe around 1950 that was active until the late 1970s, as well as grazing of horses on the lake margins appear to have significantly impacted the geochemical conditions of Rano Raraku sediments and lake water in recent decades. Such impacts are distinct from natural environmental changes and highlight the need to consider the sensitivity of the lake geochemistry to human activities.
Prymnesium parvum is a toxin-producing harmful alga that has caused ecological and economic damage worldwide. Effective methods to control blooms of this species in the field, however, are unavailable. This study examined five natural compounds present in the invasive plant Arundo donax and one synthetic derivative (5,6-dichlorogramine) for their effect on P. parvum growth. All compounds except one inhibited growth in the following order of potency: ellipticine > > 5,6-dichlorogramine > 1 H-indole = 2,4,6-trimethyl-benzonitrile > gramine. Ellipticine was by far the most potent inhibitor, with full algicidal activity at concentrations as low as 0.04 mg L−1 and 3- and 9-day IC50 values of 0.012 and 0.007 mg L−1, respectively. A reduction in chlorophyll content and swimming activity and an increase in length and volume (swelling) were documented in algal cells exposed to 0.01–0.02 mg ellipticine L−1. These results show that ellipticine is among the most potent natural algicides identified to date. The sixth compound tested, oleamide, unexpectedly stimulated algal growth above control levels. Overall, these observations confirm the existence of highly potent anti-P. parvum allelochemicals in giant reed and demonstrate potential for using products derived from this plant in the development of natural, environmentally friendly methods to control harmful algal blooms.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10811-021-02545-6","usgsCitation":"Mary, M., Rashel, R.H., and Patino, R., 2021, Growth inhibition of the harmful alga Prymnesium parvum by plant-derived products and identification of ellipticine as highly potent allelochemical: Journal of Applied Phycology, v. 33, p. 3853-3860, https://doi.org/10.1007/s10811-021-02545-6.","productDescription":"8 p.","startPage":"3853","endPage":"3860","ipdsId":"IP-126020","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","noUsgsAuthors":false,"publicationDate":"2021-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mary, Mousumi","contributorId":288252,"corporation":false,"usgs":false,"family":"Mary","given":"Mousumi","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":837587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rashel, R. H.","contributorId":286960,"corporation":false,"usgs":false,"family":"Rashel","given":"R.","email":"","middleInitial":"H.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":837588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230315,"text":"70230315 - 2021 - Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach","interactions":[],"lastModifiedDate":"2023-06-09T13:58:22.728184","indexId":"70230315","displayToPublicDate":"2021-10-08T07:28:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach","docAbstract":"The Florida Everglades is a vast and iconic wetland ecosystem in the southern United States that has undergone dramatic changes from habitat degradation, development encroachment, and water impoundment. Starting in the past few decades, large restoration projects have been undertaken to restore the landscape, including improving conditions for threatened and imperiled taxa. One focus of restoration has been the marl prairie ecosystem, where the federally endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis; CSSS) resides. The CSSS is endemic to the Everglades where populations have been steadily declining, signaling the importance of decision support tools for natural resource managers for evaluating water management and restoration scenarios. Here we developed an ensemble logistic regression, combining a frequentist and Bayesian approach, to model CSSS presence and measure how environmental factors such as hydrometrics, fire occurrence, and vegetation structure impact CSSS habitat suitability. This is the first analysis to quantitatively assess the interdependent relationships between a broad range of environmental factors and CSSS presence across the landscape. Our results show that the probability of CSSS presence was highest in areas with dry conditions, hydroperiods between 80 and 120 days, percentages of canopy cover and woody vegetation less than 10%, and more than six years post-fire where 75% or more of the area was burned. Because the frequentist and Bayesian models had nearly identical spatial outputs with the Bayesian model having slightly higher validation metrics, we used the Bayesian approach as our final model (EverSparrow). The results from our analysis can provide a valuable decision support tool as natural resource managers work to restore the Everglades landscape.
Over the last several decades, the study of Earth surface processes has progressed from a descriptive science to an increasingly quantitative one due to advances in theoretical, experimental, and computational geosciences. The importance of geomorphic forecasts has never been greater, as technological development and global climate change threaten to reshape the landscapes that support human societies and natural ecosystems. Here we explore best practices for developing socially relevant forecasts of Earth surface change, a goal we are calling “earthcasting”. We suggest that earthcasts have the following features: they focus on temporal (∼1–∼100 years) and spatial (∼1 m–∼10 km) scales relevant to planning; they are designed with direct involvement of stakeholders and public beneficiaries through the evaluation of the socioeconomic impacts of geomorphic processes; and they generate forecasts that are clearly stated, testable, and include quantitative uncertainties. Earthcasts bridge the gap between Earth surface researchers and decision-makers, stakeholders, researchers from other disciplines, and the general public. We investigate the defining features of earthcasts and evaluate some specific examples. This paper builds on previous studies of prediction in geomorphology by recommending a roadmap for (a) generating earthcasts, especially those based on modeling; (b) transforming a subset of geomorphic research into earthcasts; and (c) communicating earthcasts beyond the geomorphology research community. Earthcasting exemplifies the social benefit of geomorphology research, and it calls for renewed research efforts toward further understanding the limits of predictability of Earth surface systems and processes, and the uncertainties associated with modeling geomorphic processes and their impacts.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EF002088","usgsCitation":"Ferdowsi, B., Gartner, J.D., Johnson, K.N., Kasprak, A., Miller, K.L., Nardin, W., Ortiz, A.C., and Tejedor, A., 2021, Earthcasting: Geomorphic forecasts for society: Earth's Future, v. 9, no. 11, e2021EF002088, 24 p., https://doi.org/10.1029/2021EF002088.","productDescription":"e2021EF002088, 24 p.","ipdsId":"IP-086529","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021ef002088","text":"Publisher Index Page"},{"id":498545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Ferdowsi, Behrooz","contributorId":365025,"corporation":false,"usgs":false,"family":"Ferdowsi","given":"Behrooz","affiliations":[{"id":16979,"text":"University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":953588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gartner, John D.","contributorId":365028,"corporation":false,"usgs":false,"family":"Gartner","given":"John","middleInitial":"D.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":953589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Kerri N.","contributorId":365029,"corporation":false,"usgs":false,"family":"Johnson","given":"Kerri","middleInitial":"N.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":953590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":204162,"corporation":false,"usgs":true,"family":"Kasprak","given":"Alan","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":953591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Kimberly L.","contributorId":365031,"corporation":false,"usgs":false,"family":"Miller","given":"Kimberly","middleInitial":"L.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":953592,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nardin, William","contributorId":365034,"corporation":false,"usgs":false,"family":"Nardin","given":"William","affiliations":[{"id":35259,"text":"Horn Point Laboratory, University of Maryland","active":true,"usgs":false}],"preferred":false,"id":953593,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ortiz, Alejandra C.","contributorId":365036,"corporation":false,"usgs":false,"family":"Ortiz","given":"Alejandra","middleInitial":"C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":953594,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tejedor, Alejandro","contributorId":365040,"corporation":false,"usgs":false,"family":"Tejedor","given":"Alejandro","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":953595,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225153,"text":"70225153 - 2021 - A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments","interactions":[],"lastModifiedDate":"2021-10-14T12:27:40.14122","indexId":"70225153","displayToPublicDate":"2021-10-06T07:22:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2929,"text":"Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments","docAbstract":"A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes. We summarize the process to develop the CSUs and derive summary descriptions for the seven MBON case study sites. We discuss the intended application of the new CSU data for research and management in coastal areas.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2021.219","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., Wright, D., Frye, C., Karagulle, D., Martin, M.T., Cress, J.J., Allen, T., Allee, R., Parsons, R., Nyberg, B., Costello, M., Harris, P., and Muller-Karger, F., 2021, A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments: Oceanography, v. 34, no. 2, 10 p., https://doi.org/10.5670/oceanog.2021.219.","productDescription":"10 p.","ipdsId":"IP-129029","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":450534,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2021.219","text":"Publisher Index Page"},{"id":436173,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWHSPU","text":"USGS data release","linkHelpText":"Global Ecological Classification of Coastal Segment Units"},{"id":390515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":213674,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":825178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Graafeiland, Keith","contributorId":267715,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean","contributorId":267716,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn","contributorId":267717,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frye, Charlie","contributorId":267718,"corporation":false,"usgs":false,"family":"Frye","given":"Charlie","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karagulle, Deniz","contributorId":267719,"corporation":false,"usgs":false,"family":"Karagulle","given":"Deniz","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Madeline T. 0000-0002-2704-1879","orcid":"https://orcid.org/0000-0002-2704-1879","contributorId":261694,"corporation":false,"usgs":true,"family":"Martin","given":"Madeline","email":"","middleInitial":"T.","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":825185,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cress, Jill Janene 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Rost","contributorId":267722,"corporation":false,"usgs":false,"family":"Parsons","given":"Rost","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":825189,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Nyberg, Bjorn","contributorId":267723,"corporation":false,"usgs":false,"family":"Nyberg","given":"Bjorn","affiliations":[{"id":28158,"text":"University of Bergen","active":true,"usgs":false}],"preferred":false,"id":825190,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Costello, Mark","contributorId":267725,"corporation":false,"usgs":false,"family":"Costello","given":"Mark","affiliations":[{"id":52959,"text":"Nord University","active":true,"usgs":false}],"preferred":false,"id":825191,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Harris, Peter","contributorId":267730,"corporation":false,"usgs":false,"family":"Harris","given":"Peter","affiliations":[{"id":52960,"text":"GRID Arendal","active":true,"usgs":false}],"preferred":false,"id":825193,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Muller-Karger, Frank","contributorId":267728,"corporation":false,"usgs":false,"family":"Muller-Karger","given":"Frank","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":825192,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70229716,"text":"70229716 - 2021 - Augmentation of natural prey reduces cattle predation by puma (Puma concolor) and jaguar (Panthera onca) on a ranch in Sonora, Mexico","interactions":[],"lastModifiedDate":"2022-03-16T16:54:16.116108","indexId":"70229716","displayToPublicDate":"2021-10-05T11:45:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3451,"text":"Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Augmentation of natural prey reduces cattle predation by puma (Puma concolor) and jaguar (Panthera onca) on a ranch in Sonora, Mexico","docAbstract":"Retaliatory killing of large carnivores due to livestock predation is one of the major threats for the conservation of many declining populations of predators. According to empirical observations, there is a higher incidence of livestock predation when native prey abundance is low. In this study, we applied a treatment consisting of augmentation of prey abundance by translocation of peccaries (Pecari tajacu) and placement of four feed stations for white-tailed deer (Odocoileus virginianus) on a cattle ranch in Sonora, Mexico, with verified calf predation by puma (Puma concolor) and jaguar (Panthera onca). We quantified and compared consumed prey over two periods—phase I (8 months before the augmentation of prey) and phase II (8 months after the augmentation of prey)—through investigation of kill sites from Global Positioning System–collared jaguar and puma, prey identification from analyzed scat using molecular DNA techniques, and opportunistic discoveries of recently killed animal remains by either predator. We calculated the relative abundance of species (17 mammals [one species with two distinct age classes] and 1 bird species) through camera traps and for the most relevant prey species for this study (deer, calf, and peccary), we also estimated prey use by the predator, based on their availability during each period (prey preference). In the prey composition analyses of scat, we observed a significant reduction in the consumption of bovids and a significant increase in the consumption of peccaries during phase II. In the analyses of prey use, during phase I, predators consumed peccaries and calves at a higher proportion in relation to their availability. During phase II, consumption of calves declined from being preferred, to being consumed at the same proportion as their availability. Application of these results can contribute to the decrease of livestock predation and therefore conservation of pumas and jaguars.
","language":"English","publisher":"Southwestern Association of Naturalists","doi":"10.1894/0038-4909-65.2.123","usgsCitation":"Cassaigne, I., Thompson, R.W., Medellin, R., Culver, M., Ochoa, A., Vargas, K., Childs, J.L., Galaz, M., and Sanderson, J., 2021, Augmentation of natural prey reduces cattle predation by puma (Puma concolor) and jaguar (Panthera onca) on a ranch in Sonora, Mexico: Southwestern Naturalist, v. 65, no. 2, p. 123-130, https://doi.org/10.1894/0038-4909-65.2.123.","productDescription":"8 p.","startPage":"123","endPage":"130","ipdsId":"IP-130984","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.55429077148438,\n 29.545982511818\n ],\n [\n -109.42588806152342,\n 29.545982511818\n ],\n [\n -109.42588806152342,\n 29.656432596919352\n ],\n [\n -109.55429077148438,\n 29.656432596919352\n ],\n [\n -109.55429077148438,\n 29.545982511818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cassaigne, Ivonne","contributorId":287196,"corporation":false,"usgs":false,"family":"Cassaigne","given":"Ivonne","affiliations":[{"id":61499,"text":"pc","active":true,"usgs":false}],"preferred":false,"id":838082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Ron W.","contributorId":170001,"corporation":false,"usgs":false,"family":"Thompson","given":"Ron","email":"","middleInitial":"W.","affiliations":[{"id":24784,"text":"Arizona Game and Fish Department, 5000 West Carefree Highway, Phoenix, Arizona 85086, United States","active":true,"usgs":false}],"preferred":false,"id":838083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medellin, Rodrigo A.","contributorId":77456,"corporation":false,"usgs":true,"family":"Medellin","given":"Rodrigo A.","affiliations":[],"preferred":false,"id":838084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ochoa, Alexander","contributorId":169994,"corporation":false,"usgs":false,"family":"Ochoa","given":"Alexander","email":"","affiliations":[{"id":17653,"text":"School of Natural Resources & the Environment, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":838195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vargas, Karla","contributorId":173306,"corporation":false,"usgs":false,"family":"Vargas","given":"Karla","email":"","affiliations":[],"preferred":false,"id":838196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Childs, Jack L.","contributorId":147124,"corporation":false,"usgs":false,"family":"Childs","given":"Jack","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":838197,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Galaz, Manuel","contributorId":288670,"corporation":false,"usgs":false,"family":"Galaz","given":"Manuel","email":"","affiliations":[],"preferred":false,"id":838198,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sanderson, Jim","contributorId":173307,"corporation":false,"usgs":false,"family":"Sanderson","given":"Jim","email":"","affiliations":[],"preferred":false,"id":838199,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225553,"text":"70225553 - 2021 - Evaluating lava flow propagation models with a case study from the 2018 eruption of Kīlauea Volcano, Hawai'i","interactions":[],"lastModifiedDate":"2021-10-22T12:33:32.523635","indexId":"70225553","displayToPublicDate":"2021-10-05T07:31:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating lava flow propagation models with a case study from the 2018 eruption of Kīlauea Volcano, Hawai'i","docAbstract":"The 2018 lower East Rift Zone (LERZ) eruption of Kīlauea, Hawai’i, provides an excellent natural laboratory with which to test models of lava flow propagation. During early stages of eruption crises, the most useful lava flow propagation equations utilize readily determined parameters and require fewer a priori assumptions about future behavior of the flow. Here, we leverage the numerous observations of lava flows collected over the duration of the eruption crisis at Kīlauea in 2018 to test simple lava flow propagation models. These models track the one-dimensional propagation of the flows according to three main rheological restraining forces: bulk viscosity, yield strength, and growth of a surface crust. We calculate the predicted changes in length through time of three flows that vary in bulk composition, crystal content, and total flow length. Cooler flows that are more crystal-rich tend to be more dominated by crust growth, though early stages of propagation can be controlled by bulk viscosity. We find that variations in effusion rate significantly impact flows that are short-lived; flows that are produced during steady-state effusion are readily approximated by average values for the entire flow. Thus, accurate knowledge of variations in effusion rate are critical to accurate lava flow propagation forecasting.
Successful wildlife management depends upon coordination and consultation with local communities. However, much of the research used to inform management is often derived solely from data collected directly from wildlife. Indigenous people living in the Arctic have a close connection to their environment, which provides unique opportunities to observe their environment and the ecology of Arctic species. Further, most northern Arctic communities occur within the range of polar bears (nanuq, Ursus maritimus) and have experienced significant climatic changes. Here, we used semi-structured interviews from 2017 to 2019 to document Iñupiaq knowledge of polar bears observed over four decades in four Alaskan communities in the range of the Southern Beaufort Sea polar bear subpopulation: Wainwright, Utqiaġvik, Nuiqsut, and Kaktovik. All but one of 47 participants described directional and notable changes in sea ice, including earlier ice breakup, later ice return, thinner ice, and less multiyear pack ice. These changes corresponded with observations of bears spending more time on land during the late summer and early fall in recent decades—observations consistent with scientific and Indigenous knowledge studies in Alaska, Canada, and Greenland. Participants noted that polar bear and seal body condition and local abundance either varied geographically or exhibited no patterns. However, participants described a recent phenomenon of bears being exhausted and lethargic when arriving on shore in the summer and fall after extensive swims from the pack ice. Further, several participants suggested that maternal denning is occurring more often on land than sea ice. Participants indicated that village and regional governments are increasingly challenged to obtain resources needed to keep their communities safe as polar bears spend more time on land, an issue that is likely to be exacerbated both in this region and elsewhere as sea ice loss continues.
Aquatic-terrestrial contaminant transport via emerging aquatic insects has been studied across contaminant classes and aquatic ecosystems, but few studies have quantified the magnitude of these insect-mediated contaminant fluxes, limiting our understanding of their drivers. Using a recent conceptual model, we identified watershed mining extent, settling ponds, and network position as potential drivers of selenium (Se) fluxes from a mountaintop coal mining-impacted river network. Mining extent drove insect Se concentration (p = 0.008, R2 = 0.406), but ponding and network position were the principal drivers of Se flux through their impact on insect production. Se fluxes were 18 times higher from ponded, mined tributaries than from unponded ones and were comparable to fluxes from larger, productive mainstem sites. Thus, contaminant fluxes were highest in the river mainstem or below ponds, indicating that without considering controls on insect production, contaminant fluxes and their associated risks for predators like birds and bats can be misestimated.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2021.118257","usgsCitation":"Naslund, L.C., Gerson, J.R., Brooks, A.C., Rosemond, A.D., Walters, D., and Bernhardt, E., 2021, Ecosystem modification and network position impact insect-mediated contaminant fluxes from a mountaintop mining-impacted river network: Environmental Pollution, v. 291, 118257, 8 p., https://doi.org/10.1016/j.envpol.2021.118257.","productDescription":"118257, 8 p.","ipdsId":"IP-127990","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":450577,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2021.118257","text":"Publisher Index Page"},{"id":396095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Lincoln County","otherGeospatial":"Mud River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.19833374023438,\n 38\n ],\n [\n -81.90650939941406,\n 38\n ],\n [\n -81.90650939941406,\n 38.2\n ],\n [\n -82.19833374023438,\n 38.2\n ],\n [\n -82.19833374023438,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"291","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Naslund, Laura C.","contributorId":223770,"corporation":false,"usgs":false,"family":"Naslund","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":835232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gerson, Jacqueline R.","contributorId":198378,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","email":"","middleInitial":"R.","affiliations":[{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false},{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":835233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, Alexander C.","contributorId":223771,"corporation":false,"usgs":false,"family":"Brooks","given":"Alexander","email":"","middleInitial":"C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":835234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosemond, Amy D.","contributorId":279630,"corporation":false,"usgs":false,"family":"Rosemond","given":"Amy","email":"","middleInitial":"D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":203410,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bernhardt, Emily S.","contributorId":92143,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily S.","affiliations":[{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":835237,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225678,"text":"70225678 - 2021 - Making research relevant: Sharing climate change research with rangeland advisors to transform results into drought resilience","interactions":[],"lastModifiedDate":"2021-11-02T11:36:23.323969","indexId":"70225678","displayToPublicDate":"2021-10-01T06:32:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Making research relevant: Sharing climate change research with rangeland advisors to transform results into drought resilience","docAbstract":"On the Ground
• Public programs, strategies, and incentives to implement rangeland climate adaptation are more effective if they are tailored to local drought exposures, sensitivities, and adaptation opportunities. As such, local rangeland advisers who aid in climate adaptation are pivotal to the development of these resources.
• We hosted a virtual workshop with rangeland advisors to share results from our climate vulnerability assessment, gain their insight on finding usability, and discuss visions for resource creation.
• Climate adaptation resources should not follow a one-size-fits-all approach. Accommodating variety in resource development and outreach must consider multiple factors: variation in the ranching community, instability in the environment beyond climate, and rancher/manager identified variables in climate vulnerability assessment analyses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.08.004","usgsCitation":"Dinan, M., Adler, P.B., Bradford, J., Brunson, M., Elias, E., Felton, A., Greene, C., James, J.J., Suding, K., and Thacker, E., 2021, Making research relevant: Sharing climate change research with rangeland advisors to transform results into drought resilience: Rangelands, v. 43, no. 5, p. 185-193, https://doi.org/10.1016/j.rala.2021.08.004.","productDescription":"9 p.","startPage":"185","endPage":"193","ipdsId":"IP-133021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450594,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.08.004","text":"Publisher Index Page"},{"id":391257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.91406249999999,\n 25.24469595130604\n ],\n [\n -93.1640625,\n 25.24469595130604\n ],\n [\n -93.1640625,\n 50.064191736659104\n ],\n [\n -126.91406249999999,\n 50.064191736659104\n ],\n [\n -126.91406249999999,\n 25.24469595130604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dinan, Maude","contributorId":268203,"corporation":false,"usgs":false,"family":"Dinan","given":"Maude","email":"","affiliations":[{"id":55592,"text":"USDA Southwest Climate Hub, Jornada Experimental Range, P.O. Box 30003, MSC 3JER, NMSU, Las Cruces, NM 88003-8003","active":true,"usgs":false}],"preferred":false,"id":826191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":826192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":826193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brunson, Mark","contributorId":178263,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","affiliations":[],"preferred":false,"id":826194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elias, Emile","contributorId":194484,"corporation":false,"usgs":false,"family":"Elias","given":"Emile","affiliations":[],"preferred":false,"id":826195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Felton, Andrew J","contributorId":264213,"corporation":false,"usgs":false,"family":"Felton","given":"Andrew J","affiliations":[{"id":54404,"text":"Department of Wildland Resources and The Ecology Center, Utah State University, Logan, Utah","active":true,"usgs":false}],"preferred":false,"id":826196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greene, Christina","contributorId":268204,"corporation":false,"usgs":false,"family":"Greene","given":"Christina","email":"","affiliations":[{"id":55593,"text":"Utah State University, Department of Wildland Resources, Logan, UT 84322","active":true,"usgs":false}],"preferred":false,"id":826197,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"James, Jeremy J.","contributorId":261601,"corporation":false,"usgs":false,"family":"James","given":"Jeremy","email":"","middleInitial":"J.","affiliations":[{"id":52912,"text":"Natural Resource Management and Environmental Sciences, Cal Poly State University, San Luis Obispo, CA, USA","active":true,"usgs":false}],"preferred":false,"id":826198,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Suding, Katharine","contributorId":172858,"corporation":false,"usgs":false,"family":"Suding","given":"Katharine","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":826199,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thacker, Eric","contributorId":268205,"corporation":false,"usgs":false,"family":"Thacker","given":"Eric","email":"","affiliations":[{"id":55594,"text":"Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, UT 84322","active":true,"usgs":false}],"preferred":false,"id":826200,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229735,"text":"70229735 - 2021 - Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream","interactions":[],"lastModifiedDate":"2022-03-16T16:22:23.529074","indexId":"70229735","displayToPublicDate":"2021-09-30T11:13:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream","docAbstract":"Stocking of Rainbow Trout Oncorhynchus mykiss commonly provides seasonal or mitigation fisheries; however, these fish are usually small and ecosystem effects are spatially or temporally limited. Yet agencies receive requests to stock Rainbow Trout in relatively natural settings (i.e., not tailwater or mitigation fisheries), where introductions may have greater ecosystem consequences. The size of introduced fish is an important factor in determining biotic interactions with native species; therefore, our objectives were to assess the seasonal feeding ecology and microhabitat use of large (265–530 mm TL) nonnative Emmerson strain Rainbow Trout in a relatively unaltered, groundwater-influenced, warmwater stream of the Ozark Highlands. Rainbow Trout consumed a variety of prey; however, diets differed between cool (winter and spring) and warm (summer) seasons. Cool-season Rainbow Trout exhibited a mixed feeding strategy, with individual specialization on crayfishes and fishes and generalist feeding on Ephemeroptera and Diptera, but Gastropoda were the dominant prey. Feeding strategy in the warm season switched to individual specialization on numerous prey types. Overall, larger prey resources were important components of Rainbow Trout diets. Piscivory was relatively high in both seasons, and crayfishes were one of the most important prey types across seasons. Selection of coarse substrates and deeper-water microhabitats (>0.95 m) was similar between seasons. Rainbow Trout selected the lowest-velocity microhabitats available during the warm season and moderate velocities in the cool season. Rainbow Trout were five times more likely to be associated with cover in the warm season. Due to their higher temperature tolerance, Emmerson strain Rainbow Trout may persist in Ozark Highland streams, where they disrupt local food webs and occupy habitat otherwise selected by native fish, such as Neosho Smallmouth Bass Micropterus dolomieu velox. If native species conservation is a priority for agencies, then caution regarding Rainbow Trout stockings may be warranted.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10694","usgsCitation":"Rodger, A.W., Wolf, S.L., Starks, T.A., Burroughs, J.P., and Brewer, S.K., 2021, Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream: North American Journal of Fisheries Management, v. 41, no. 6, p. 1764-1780, https://doi.org/10.1002/nafm.10694.","productDescription":"17 p.","startPage":"1764","endPage":"1780","ipdsId":"IP-129494","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Spavinaw Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.22987352301491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodger, A. W.","contributorId":288610,"corporation":false,"usgs":false,"family":"Rodger","given":"A.","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolf, S. L.","contributorId":288613,"corporation":false,"usgs":false,"family":"Wolf","given":"S.","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":838136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Starks, T. A.","contributorId":288616,"corporation":false,"usgs":false,"family":"Starks","given":"T.","email":"","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burroughs, J. P.","contributorId":288619,"corporation":false,"usgs":false,"family":"Burroughs","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838139,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224634,"text":"70224634 - 2021 - Machine learning can assign geologic basin to produced water samples using major ion geochemistry","interactions":[],"lastModifiedDate":"2021-11-16T15:48:00.581979","indexId":"70224634","displayToPublicDate":"2021-09-30T08:16:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning can assign geologic basin to produced water samples using major ion geochemistry","docAbstract":"Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11053-021-09949-8","usgsCitation":"Shelton, J., Jubb, A., Saxe, S., Attanasi, E., Milkov, A., Engle, M.A., Freeman, P., Shaffer, C., and Blondes, M., 2021, Machine learning can assign geologic basin to produced water samples using major ion geochemistry: Natural Resources Research, v. 30, p. 4147-4163, https://doi.org/10.1007/s11053-021-09949-8.","productDescription":"17 p.","startPage":"4147","endPage":"4163","ipdsId":"IP-126045","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-021-09949-8","text":"Publisher Index Page"},{"id":390110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milkov, Alexei","contributorId":266160,"corporation":false,"usgs":false,"family":"Milkov","given":"Alexei","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":824458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":824459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824460,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Christopher","contributorId":266161,"corporation":false,"usgs":false,"family":"Shaffer","given":"Christopher","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":824461,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824462,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230321,"text":"70230321 - 2021 - The Louisiana Amphibian Monitoring Program from 1997 to 2017: Results, analyses, and lessons learned","interactions":[],"lastModifiedDate":"2023-06-09T13:59:27.597559","indexId":"70230321","displayToPublicDate":"2021-09-30T07:24:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The Louisiana Amphibian Monitoring Program from 1997 to 2017: Results, analyses, and lessons learned","docAbstract":"To determine trends in either frog distribution or abundance in the State of Louisiana, we reviewed and analyzed frog call data from the Louisiana Amphibian Monitoring Program (LAMP). The data were collected between 1997 and 2017 using North American Amphibian Monitoring Program protocols. Louisiana was divided into three survey regions for administration and analysis: the Florida Parishes, and 2 areas west of the Florida parishes called North and South. Fifty-four routes were surveyed with over 12,792 stops and 1,066 hours of observation. Observers heard 26 species of the 31 species reported to be in Louisiana. Three of the species not heard were natives with ranges that did not overlap with survey routes. The other two species were introduced species, the Rio Grande Chirping Frog (Eleutherodactylus cystignathoides) and the Cuban Treefrog (Osteopilus septentrionalis). Both seem to be limited to urban areas with little to no route coverage. The 15 most commonly occurring species were examined in detail using the percentage of stops at which they observed along a given survey and their call indices. Most species exhibited a multimodal, concave, or convex pattern of abundance over a 15-year period. Among LAMP survey regions, none of the species had synchronous population trends. Only one group of species, winter callers, regularly co-occur. Based on the species lists, the North region could be seen as a subset of the South. However, based on relative abundance, the North was more similar to Florida parishes for both the winter and summer survey runs. Our analyses demonstrate that long-term monitoring (10 years or more) may be necessary to determine population and occupancy trends, and that frog species may have different local demographic patterns across large geographic areas.
Biogenic methane is estimated to account for one-fifth of the natural gas worldwide and there is great interest in controlling methane from different sources. Biogenic coalbed methane (CBM) production relies on syntrophic associations between fermentative bacteria and methanogenic archaea to anaerobically degrade recalcitrant coal and produce methanogenic substrates. However, very little is known about how differences in geochemistry, hydrology, and microbial community composition influence subsurface carbon utilization and CBM production. The addition of an amendment consisting of microalgal biomass has previously been shown to increase CBM production while providing the possibility of a closed-loop fossil system where waste (production water) is used to grow algae to ultimately produce energy (methane). However, the efficiency of enhancing CBM production under different redox conditions remains unresolved. In this study, we focused on the U.S. Geological Survey's Birney test site (Montana, USA) that has nine wells vertically accessing four coal seams with varying geochemistry (low and high sulfate (SO42−)) and methane production rates. We used organic matter (OM) in the form of algal biomass to discern the effect of this amendment on OM degradation and microbially enhanced CBM production potential under different geochemical constraints. We tracked changes in community composition, OM composition, organic carbon (OC) concentration, methane production, and nutrients in batch systems over six months. Methane production was detected only in microcosms from low SO42− wells (168 to 800 μg methane per gram of coal). The OC consumption varied across time for all wells and the variation was greatest for the low SO42− wells. Different groups of syntrophic bacteria were associated with net‑carbon consuming microcosms, and specifically Syntrophorhabdus was identified with several different statistical methods as a potentially important coal degrader. Results from this study provide insight into potential coal-degraders, the compositional changes in some of the different OM fractions, and trends in carbon consumption related to methane production across coal seams along the vertical SO42− gradient.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2021.103860","usgsCitation":"Smith, H.J., Schweitzer, H.S., Barnhart, E.P., Orem, W.H., Gerlach, R., and Fields, M.W., 2021, Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA: International Journal of Coal Geology, v. 248, 103860, 16 p., https://doi.org/10.1016/j.coal.2021.103860.","productDescription":"103860, 16 p.","ipdsId":"IP-106713","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":450630,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10037/24259","text":"External Repository"},{"id":391509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.75439453125,\n 41.409775832009565\n ],\n [\n -104.765625,\n 41.83682786072714\n ],\n [\n -104.23828125,\n 44.59046718130883\n ],\n [\n -104.9853515625,\n 46.649436163350245\n ],\n [\n -106.58935546875,\n 46.7549166192819\n ],\n [\n -108.1494140625,\n 46.51351558059737\n ],\n [\n -108.12744140625,\n 45.38301927899065\n ],\n [\n -106.41357421875,\n 43.6599240747891\n ],\n [\n -105.99609375,\n 41.83682786072714\n ],\n [\n -105.75439453125,\n 41.409775832009565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826469,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":826470,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227486,"text":"70227486 - 2021 - Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology","interactions":[],"lastModifiedDate":"2022-01-19T14:41:31.246868","indexId":"70227486","displayToPublicDate":"2021-09-27T08:40:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1726,"text":"GSA Memoirs","active":true,"publicationSubtype":{"id":10}},"title":"Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology","docAbstract":"The Neogene stratigraphic and tectonic history of the Mount Diablo area is a consequence of the passage of the Mendocino Triple Junction (MTJ) by the San Francisco Bay area between 12 and 6 Ma, volcanism above a slab-window trailing the MTJ, and crustal transpression beginning ~8-6 Ma, when the Pacific Plate and Sierra Nevada microplate began to converge obliquely. Between ~12-6 Ma, parts of the Sierra Nevada microplate were displaced by faults splaying from the main trace of the San Andreas Fault and incorporated into the Pacific Plate. The Mount Diablo anticlinorium was formed by crustal compression within a left-stepping, restraining bend of the eastern San Andreas Fault system (SAF), with southwest-verging thrusting beneath, and with possible clockwise rotation between faults on its southeast and northwest. At ~10,5 Ma, a drainage divide formed between the northern Great Central Valley (GCV) and the ocean. Regional uplift accelerated at ~6 Ma with onset of transpression between the Pacific and North American plates. Marine deposition ceased in the eastern Coast Range basins as a consequence of the regional uplift accompanying passage of the MTJ, and trailing slab-window volcanism. From ~11 to ~5 Ma, andesitic volcanic intrusive rocks and lavas were erupted along the northwest crest of the central to northern Sierra Nevada and were deposited on its western slope, providing abundant sediment to northern Great Central Valley (GCV) and the northeastern Coast Ranges. Sediment filled the GCV, overtopped the Stockton fault and arch forming one large, south-draining system that flowed into a marine embayment at its southwestern end, the ancestral San Joaquin Sea. This marine embayment shrunk with time and by ~2.3 Ma was eventually cut off from the ocean. Fluvial drainage continued southwest in GCV until it was cut off in turn, probably by some combination of sea level fluctuations and transpression along the SAF that uplifted, lengthened and narrowed the outlet channel. As a consequence, a great lake, Lake Clyde, formed in the GCV at ~1.4 Ma, occupying all of the ancestral San Joaquin Valley and part of ancestral Sacramento Valley. The lake rose and fell with global glacial and interglacial cycles. After a long, extreme glacial period, Marine Oxygen Isotope Stage (MOIS) 16, it overtopped Carquinez sill at 0.63 Ma and drained via San Francisco valley (now Bay) and the Colma gap, into the Merced marine embayment of the Pacific Ocean. Later, a new outlet for GCV drainage formed between ~75 and ~130 ka ago., when the Colma gap closed due to transpression and right-slip on the SAF, and Duxbury Point at the south end of Pt. Reyes Peninsula moved sufficiently northwest along the SAF to unblock a bedrock notch, the feature we now call the Golden Gate.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary: Geological Society of America memoir 217","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.1217(17)","usgsCitation":"Sarna-Wojcicki, A., 2021, Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology: GSA Memoirs, v. 217, p. 443-472, https://doi.org/10.1130/2021.1217(17).","productDescription":"30 p.","startPage":"443","endPage":"472","ipdsId":"IP-129812","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":394516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mount Diablo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.94103240966797,\n 37.84164803953047\n ],\n [\n -121.87957763671874,\n 37.84164803953047\n ],\n [\n -121.87957763671874,\n 37.90289686954944\n ],\n [\n -121.94103240966797,\n 37.90289686954944\n ],\n [\n -121.94103240966797,\n 37.84164803953047\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"217","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sarna-Wojcicki, Andrei 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":267781,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei","affiliations":[{"id":55498,"text":"U.S. Geological Survey, Emeritus","active":true,"usgs":false}],"preferred":false,"id":831153,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224563,"text":"70224563 - 2021 - Shifting correlations among multiple aspects of weather complicate predicting future demography of a threatened species","interactions":[],"lastModifiedDate":"2021-09-28T12:41:49.27722","indexId":"70224563","displayToPublicDate":"2021-09-26T07:40:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Shifting correlations among multiple aspects of weather complicate predicting future demography of a threatened species","docAbstract":"Most studies of the ecological effects of climate change consider only a limited number of weather drivers that could affect populations, though we know that multiple weather drivers can simultaneously affect population growth rate. Multiple drivers could simultaneously increase/decrease one vital rate, or one may increase a vital rate while another decreases the same vital rate. Considering the impact of multiple weather drivers on vital rates is particularly important in a changing climate, in which correlations among drivers may not be preserved in the future. We used a long-term dataset on the endangered red-cockaded woodpecker (Dryobates borealis) to understand how multiple weather drivers jointly affect survival and reproductive vital rates and then assessed the contributions of individual weather drivers to historical trends in vital rates over time. We found that vital rates were often influenced by more than one weather driver and that weather drivers most commonly exerted opposing effects. For instance, some weather drivers increased vital rates over time, while others acted in the opposite direction, decreasing vital rates over time. Importantly, the historical correlations among weather drivers are almost always projected to change in the future climate, such that future trends in vital rates may not match historical trends. For example, we do not find historical trends in adult survival, but changing correlations among weather drivers could generate future trends in this vital rate. Our work provides an example of how multiple weather drivers can control a variety of vital rates and also illustrates how changes in the correlation structure of weather drivers through time might substantially affect future trends in individual and population performance.
Alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis) are iteroparous anadromous fish found throughout the East Coast of North America. The phenology of anadromous fish migrations is important for fitness, and the duration of spawning migrations has been compressed in recent years in response to climate change. Anthropogenic barriers to movement, such as dams and culverts at road-stream crossings, can further disrupt migration phenology by delaying movement and increasing predation risk. We used passive integrated transponder (PIT) telemetry to quantify upstream and downstream migratory delay at five road-stream-crossing culverts on the Herring River (MA, USA). Groundspeeds were reduced at all culverts in both directions, confirming that the culverts impede movement despite high passage proportions. The cumulative delay of the culverts on the upstream migration was sufficient to more than double the amount of time required to traverse the river if the culverts had been absent. Furthermore, the presence of snapping turtles (Chelydra serpentina) ambushing river herring within one of the culverts resulted in reduced passage rates beyond the reduction in movement caused by the physical structure itself. This highlights that physical barriers can create cascading ecological consequences and the importance of taking a holistic approach to understanding barrier effects.
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with and without major dams, and studied how to assemble suitable training datasets for predictions in basins with or without temperature monitoring. For prediction in unmonitored basins (PUB), LSTM produced a root-mean-square error (RMSE) of 1.129°C and an R2 of 0.983. While these metrics declined from LSTM's temporal prediction performance, they far surpassed traditional models' PUB values, and were competitive with traditional models' temporal prediction on calibrated sites. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.202°C and an R2 of 0.984. For temporal prediction, the most suitable training set was the matching DAG that the basin could be grouped into (for example, the 60% DAG was most suitable for a basin with 61% data availability). However, for PUB, a training dataset including all basins with data was consistently preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results indicate there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations can still be predicted well, and LSTM appears to be a highly accurate Ts modelling tool even for spatial extrapolation.
Freshwater mussels are one of the most imperiled groups of animals in the world and are among the most sensitive species to a variety of chemicals. However, little is known about the sensitivity of freshwater mussels to wastewater effluents. The objectives of the present study were to (1) assess the toxicity of a permitted effluent, which entered the Deep Fork River, Oklahoma (USA), to a unionid mussel (Lampsilis siliquoidea) and to two standard test species (cladoceran Ceriodaphnia dubia; and fathead minnow Pimephales promelas) in short-term 7-day effluent tests; (2) evaluate the relative sensitivities of the three species to potassium (K), an elevated major ion in the effluent, using 7-day toxicity tests with KCl spiked into a Deep Fork River upstream reference water; (3) determine the potential influences of background water characteristics on the acute K toxicity to the mussel (96-h exposures) and cladoceran (48-h exposure) in four reconstituted waters that mimicked the hardness and ionic composition ranges of the Deep Fork River; and (4) determine the potential influence of temperature on acute K toxicity to the mussel. The effluent was found to be toxic to mussels and cladocerans, and it contained elevated concentrations of major cations and anions relative to the upstream Deep Fork River reference water. The K concentration in the effluent was 48-fold greater than in the upstream water. Compared with the standard species, the mussel was more than 4-fold more sensitive to the effluent in the 7-day effluent tests and more than 8-fold more sensitive to K in the 7-day K toxicity tests. The acute K toxicity to the mussel decreased by a factor of 2 when the water hardness was increased from soft (42 mg/L as CaCO3) to very hard (314 mg/L as CaCO3), whereas the acute K toxicity to the cladoceran remained almost the same as hardness increased from 84 to 307 mg/L as CaCO3. Acute K toxicity to the mussel at 23 °C was similar to the toxicity at an elevated temperature of 28 °C. The overall results indicate that the two standard test species may not represent the sensitivity of the tested mussel to both the effluent and K, and the toxicity of K was influenced by the hardness in test waters, but by a limited magnitude.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry (SETAC)","doi":"10.1002/etc.5221","usgsCitation":"Kunz, J.L., Wang, N., Martinez, D., Dunn, S., Cleveland, D.M., and Steevens, J.A., 2021, The sensitivity of a unionid mussel (Lampsilis siliquoidea) to a permitted effluent and elevated potassium in the effluent: Environmental Toxicology and Chemistry, v. 40, no. 12, p. 3410-3420, https://doi.org/10.1002/etc.5221.","productDescription":"11 p.","startPage":"3410","endPage":"3420","ipdsId":"IP-129678","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":436187,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YZGDX","text":"USGS data release","linkHelpText":"Chemical and biological data from a study on sensitivity of a unionid mussel (Lampsilis siliquoidea) to a permitted effluent and elevated potassium"},{"id":396104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Okmulgee","otherGeospatial":"Deep Fork National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.60827636718749,\n 35.546753306701\n ],\n [\n -95.92849731445312,\n 35.546753306701\n ],\n [\n -95.92849731445312,\n 35.69968630125204\n ],\n [\n -96.60827636718749,\n 35.69968630125204\n ],\n [\n -96.60827636718749,\n 35.546753306701\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":835189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":835190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinez, David","contributorId":279598,"corporation":false,"usgs":false,"family":"Martinez","given":"David","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunn, Suzanne","contributorId":279599,"corporation":false,"usgs":false,"family":"Dunn","given":"Suzanne","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":835193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":835194,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224546,"text":"70224546 - 2021 - The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020","interactions":[],"lastModifiedDate":"2021-10-18T15:08:18.866454","indexId":"70224546","displayToPublicDate":"2021-09-24T08:50:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020","docAbstract":"This data note describes the Biscuit Brook and Neversink Reservoir watershed Long-Term Monitoring Data that includes: 1) stream discharge, (1983 – 2020 for Biscuit Brook and 1937 – 2020 for the Neversink Reservoir watershed), 2) stream water chemistry, 1983-2020, at 4 stations, 3) fish survey data from 16 locations in the watershed 1990-2019, 4) soil chemistry data from 2 headwater sub-watersheds, 1993-2012, and 5) periodic stream water chemistry sampling data from 364 locations throughout the watershed, 1983-2020. The Neversink Reservoir watershed in the Catskill Mountains of New York, USA drains an area of 172.5 km2. The watershed feeds one of 6 reservoirs in New York City's West of Hudson water supply, which accounts for about 90% of the city's water supply. Biscuit Brook is a 9.63 km2 tributary sub-watershed within the Neversink Reservoir watershed.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14394","usgsCitation":"Murdoch, P.S., Burns, D., McHale, M., Siemion, J., Baldigo, B., Lawrence, G.B., George, S.D., Antidormi, M.R., and Bonville, D.B., 2021, The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020: Hydrological Processes, v. 35, e14394, 12 p., https://doi.org/10.1002/hyp.14394.","productDescription":"e14394, 12 p.","ipdsId":"IP-126065","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":450676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14394","text":"Publisher Index Page"},{"id":389807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Biscuit Brook and Neversink Reservoir Watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62188720703125,\n 41.840920397579936\n ],\n [\n -74.60403442382812,\n 41.85319643776675\n ],\n [\n -74.53811645507812,\n 41.881831370505594\n ],\n [\n -74.44129943847656,\n 41.92629234083705\n ],\n [\n -74.28337097167969,\n 42.007978804701\n ],\n [\n -74.278564453125,\n 42.06509700139039\n ],\n [\n -74.30671691894531,\n 42.11095834849246\n ],\n [\n -74.3341827392578,\n 42.13133052651052\n ],\n [\n -74.4049072265625,\n 42.132858175814626\n ],\n [\n -74.44747924804688,\n 42.11707068963613\n ],\n [\n -74.48867797851562,\n 42.042153895364\n ],\n [\n -74.57725524902344,\n 41.984504674276074\n ],\n [\n -74.652099609375,\n 41.9528519300999\n ],\n [\n -74.73518371582031,\n 41.89869952106346\n ],\n [\n -74.74273681640625,\n 41.86291329896065\n ],\n [\n -74.70497131347656,\n 41.81636125072054\n ],\n [\n -74.64866638183594,\n 41.81175536180908\n ],\n [\n -74.62188720703125,\n 41.840920397579936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Murdoch, Peter S. 0000-0001-9243-505X pmurdoch@usgs.gov","orcid":"https://orcid.org/0000-0001-9243-505X","contributorId":2453,"corporation":false,"usgs":true,"family":"Murdoch","given":"Peter","email":"pmurdoch@usgs.gov","middleInitial":"S.","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":824014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824022,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70224611,"text":"70224611 - 2021 - Effects of variable-density thinning on non-native understory plants in coniferous forests of the Pacific Northwest","interactions":[],"lastModifiedDate":"2021-09-30T12:00:07.259372","indexId":"70224611","displayToPublicDate":"2021-09-24T06:58:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of variable-density thinning on non-native understory plants in coniferous forests of the Pacific Northwest","docAbstract":"Old-growth forests serve as critical habitat for many sensitive species, but management practices have diminished their prevalence, and former regions of old-growth are now dominated by second-growth stands lacking the structural heterogeneity, diversity, and species richness that these older forests possess. In western Washington state in the Pacific Northwest of the United States, the Olympic Habitat Development Study was designed to address this issue and hasten the development of specific old-growth features in second-growth stands using variable-density thinning. One concern with such methods, however, is the potential to introduce non-native plants, which can have negative ecological and economic impacts. Here, we examine how variable-density thinning influences desirable forest characteristics, such as increased plant species diversity, versus the less desirable effects of non-native plant species introduction. We test two hypotheses regarding plant invasions. First, thinning would promote establishment of non-native, shade-intolerant species, but their abundance would gradually decline over time. Second, thinning disturbance and increased heterogeneity of canopy cover would initially promote understory richness of all species, although richness would decline over time with canopy closure and increased cover of shrubs and regenerating trees. We found that the number and cover of non-native species initially increased after thinning, peaking at 16 species present in variable-density thinned treatments in year three. By year 17, 11 species remained throughout the seven 6.5 ha treatment plots sampled, and cover was negligible. As predicted, species richness increased following thinning, however, native species richness remained elevated through year 17, contrary to our hypothesis. Furthermore, native species diversity also increased following thinning and remained higher in thinned treatments than controls through year 17. Our results show that variable-density thinning in temperate coniferous forests can enhance native, but not exotic, plant richness and diversity in the long term.