Gopher tortoises (Gopherus polyphemus) are among the most commonly translocated reptiles. Waif tortoises are animals frequently of unknown origin that have been displaced from the wild and often held in human possession for various reasons and durations. Although there are risks associated with any translocation, waif tortoises are generally excluded from translocation projects because of heightened concerns of introducing pathogens and uncertainty about the post-release survival of these individuals. If these risks could be managed, waif tortoises could have conservation value because they can provide the needed numbers to stabilize populations. In the early 1990s, the discovery of an isolated population of gopher tortoises (≤15 individuals) near Aiken, South Carolina, USA, prioritized establishment of the Aiken Gopher Tortoise Heritage Preserve (AGTHP). Because of the population's need for augmentation and the site's isolation from other tortoise populations, the AGTHP provided the opportunity to evaluate the post-release survival of translocated waif tortoises without compromising a viable population. Since 2006, >260 waif tortoises have been introduced to the preserve. Using a Cormack-Jolly-Seber modeling framework to analyze release records and capture histories from trapping efforts in 2017 and 2018, we estimated the long-term apparent survival and site fidelity of this population composed largely of waif tortoises. We estimated annual apparent survival probabilities to be high (≥0.90) for subadult, adult male, and adult female tortoises, and these rates were similar to those reported for wild-to-wild translocated gopher tortoises and those from unmanipulated populations. Of the tortoises recaptured within the boundaries of the preserve, 75% were located within 400 m of their release location. These results suggest that waif tortoises could be an important resource in reducing the extirpation risk of isolated populations. © 2021 The Wildlife Society.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21998","usgsCitation":"McKee, R., Buhlmann, K., Moore, C.T., Hepinstall-Cymerman, J., and Tuberville, T., 2021, Waif gopher tortoise survival and site fidelity following translocation: Journal of Wildlife Management, v. 85, no. 4, p. 640-653, https://doi.org/10.1002/jwmg.21998.","productDescription":"14 p.","startPage":"640","endPage":"653","ipdsId":"IP-118119","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467254,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1817662","text":"External Repository"},{"id":395906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","county":"Aiken County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.5751,33.8751],[-81.5656,33.8697],[-81.5468,33.8593],[-81.5428,33.8548],[-81.5333,33.8348],[-81.5211,33.8258],[-81.5044,33.809],[-81.4872,33.8055],[-81.4601,33.7988],[-81.4318,33.7748],[-81.4323,33.7611],[-81.425,33.7439],[-81.4111,33.733],[-81.3758,33.7245],[-81.367,33.7295],[-81.3388,33.7214],[-81.31,33.7101],[-81.2978,33.7006],[-81.2851,33.6961],[-81.2724,33.6957],[-81.263,33.6889],[-81.2067,33.6617],[-81.1873,33.6526],[-81.373,33.4907],[-81.5165,33.3824],[-81.7575,33.1982],[-81.7577,33.1995],[-81.7588,33.2017],[-81.7605,33.2035],[-81.7621,33.2054],[-81.7628,33.2063],[-81.7644,33.2085],[-81.765,33.2095],[-81.7655,33.2103],[-81.7666,33.2131],[-81.7679,33.2159],[-81.7706,33.2182],[-81.7725,33.2198],[-81.7732,33.2203],[-81.776,33.2216],[-81.7777,33.2219],[-81.7788,33.2219],[-81.7809,33.2201],[-81.781,33.2188],[-81.7802,33.2167],[-81.78,33.216],[-81.7793,33.2146],[-81.7787,33.213],[-81.7792,33.2112],[-81.7814,33.2098],[-81.7858,33.2088],[-81.7939,33.2098],[-81.799,33.2115],[-81.8006,33.212],[-81.8017,33.2124],[-81.8039,33.2128],[-81.8056,33.2137],[-81.8072,33.2151],[-81.8084,33.2186],[-81.8084,33.2209],[-81.8101,33.2237],[-81.8127,33.2255],[-81.816,33.227],[-81.82,33.2282],[-81.8255,33.229],[-81.8263,33.2295],[-81.8286,33.2311],[-81.8321,33.235],[-81.8367,33.238],[-81.8421,33.2403],[-81.846,33.243],[-81.8466,33.2435],[-81.8487,33.2452],[-81.8494,33.2461],[-81.8503,33.2472],[-81.8511,33.2485],[-81.8515,33.2493],[-81.8519,33.2513],[-81.8518,33.2519],[-81.8515,33.2525],[-81.8499,33.2535],[-81.8477,33.2534],[-81.8453,33.2531],[-81.843,33.253],[-81.8416,33.2538],[-81.8414,33.2546],[-81.8414,33.2553],[-81.8419,33.2568],[-81.8435,33.2577],[-81.8458,33.2596],[-81.8473,33.2613],[-81.8481,33.2627],[-81.8483,33.2634],[-81.8477,33.2648],[-81.8467,33.2662],[-81.844,33.2661],[-81.8418,33.2654],[-81.8401,33.2635],[-81.8385,33.2617],[-81.8362,33.2603],[-81.8345,33.2596],[-81.8313,33.2594],[-81.8296,33.2603],[-81.8291,33.2613],[-81.8291,33.2634],[-81.8308,33.2643],[-81.833,33.2653],[-81.8352,33.2663],[-81.8369,33.2676],[-81.8363,33.2695],[-81.8358,33.2713],[-81.836,33.2735],[-81.8367,33.2744],[-81.8385,33.2754],[-81.8405,33.2763],[-81.8426,33.2777],[-81.8443,33.2799],[-81.8454,33.2826],[-81.8481,33.284],[-81.8491,33.2842],[-81.8513,33.2848],[-81.8557,33.2852],[-81.8572,33.2859],[-81.8596,33.2874],[-81.8617,33.2887],[-81.8622,33.2915],[-81.8622,33.2938],[-81.8622,33.296],[-81.8619,33.297],[-81.8613,33.2979],[-81.8608,33.2989],[-81.8597,33.2993],[-81.857,33.2993],[-81.8548,33.2989],[-81.8531,33.2985],[-81.8509,33.298],[-81.8495,33.2981],[-81.849,33.2986],[-81.8484,33.3],[-81.8478,33.3023],[-81.8467,33.3037],[-81.8474,33.3061],[-81.8494,33.3082],[-81.8512,33.3087],[-81.8527,33.3089],[-81.8549,33.3084],[-81.8568,33.3071],[-81.8576,33.3065],[-81.8593,33.3052],[-81.8608,33.3047],[-81.8631,33.3057],[-81.8642,33.3075],[-81.8642,33.3093],[-81.8647,33.3115],[-81.8669,33.3125],[-81.8697,33.312],[-81.8724,33.3107],[-81.8746,33.3083],[-81.8756,33.3073],[-81.8763,33.3066],[-81.8776,33.3062],[-81.879,33.306],[-81.8817,33.3064],[-81.8823,33.3073],[-81.8829,33.3085],[-81.8828,33.3108],[-81.8822,33.3135],[-81.8822,33.3158],[-81.8833,33.3167],[-81.8841,33.3169],[-81.8857,33.3169],[-81.889,33.3159],[-81.891,33.3159],[-81.8921,33.3173],[-81.891,33.3195],[-81.8912,33.3204],[-81.8914,33.321],[-81.894,33.3219],[-81.8946,33.322],[-81.8968,33.3227],[-81.8984,33.3236],[-81.899,33.326],[-81.8985,33.3282],[-81.898,33.331],[-81.8985,33.3331],[-81.8993,33.3339],[-81.9001,33.3346],[-81.9019,33.3346],[-81.9029,33.3345],[-81.9051,33.3313],[-81.9057,33.3279],[-81.9061,33.3253],[-81.9064,33.3239],[-81.9068,33.3233],[-81.9083,33.3213],[-81.9104,33.3208],[-81.9116,33.3218],[-81.9113,33.3229],[-81.9107,33.3243],[-81.9095,33.3261],[-81.9095,33.3281],[-81.9111,33.3294],[-81.9128,33.3312],[-81.9155,33.3321],[-81.9182,33.3339],[-81.9198,33.3357],[-81.9193,33.3375],[-81.9187,33.3383],[-81.9173,33.3398],[-81.9163,33.3406],[-81.9148,33.3416],[-81.9124,33.3431],[-81.9102,33.3445],[-81.9097,33.3456],[-81.9106,33.3463],[-81.9113,33.3467],[-81.9148,33.3467],[-81.9175,33.3453],[-81.9197,33.3444],[-81.9212,33.3439],[-81.923,33.3444],[-81.9245,33.3452],[-81.9294,33.3447],[-81.9324,33.3441],[-81.9338,33.3438],[-81.9366,33.3442],[-81.9384,33.345],[-81.9395,33.3464],[-81.9383,33.3488],[-81.9372,33.351],[-81.9366,33.3545],[-81.9366,33.3567],[-81.9367,33.3574],[-81.94,33.3611],[-81.9431,33.3632],[-81.9458,33.3655],[-81.9466,33.3676],[-81.9467,33.3682],[-81.9457,33.371],[-81.9434,33.3725],[-81.9429,33.3728],[-81.9408,33.3728],[-81.9385,33.3719],[-81.9372,33.3711],[-81.9363,33.3706],[-81.9348,33.3698],[-81.9331,33.3691],[-81.9317,33.3688],[-81.9303,33.3688],[-81.9292,33.369],[-81.9282,33.3695],[-81.9265,33.3713],[-81.926,33.3724],[-81.9254,33.3737],[-81.926,33.3749],[-81.9265,33.3757],[-81.9287,33.3784],[-81.9314,33.3816],[-81.9334,33.3854],[-81.9338,33.3879],[-81.9338,33.3888],[-81.9334,33.3929],[-81.9345,33.3956],[-81.9349,33.3961],[-81.9361,33.3974],[-81.9389,33.3992],[-81.9416,33.4006],[-81.9443,33.4024],[-81.9454,33.4051],[-81.9447,33.4074],[-81.9426,33.4088],[-81.9406,33.4091],[-81.94,33.4092],[-81.9368,33.4088],[-81.9352,33.4088],[-81.934,33.4088],[-81.9331,33.4088],[-81.9315,33.4091],[-81.9293,33.41],[-81.927,33.4118],[-81.9254,33.4144],[-81.9254,33.4171],[-81.9258,33.419],[-81.928,33.4218],[-81.9294,33.4238],[-81.9302,33.425],[-81.9306,33.426],[-81.9309,33.4275],[-81.931,33.4284],[-81.9306,33.4297],[-81.9299,33.4302],[-81.9273,33.4317],[-81.9244,33.4326],[-81.9206,33.4336],[-81.9196,33.4338],[-81.9178,33.4344],[-81.9151,33.4363],[-81.9135,33.4386],[-81.9135,33.4408],[-81.914,33.4436],[-81.9156,33.4462],[-81.9188,33.4499],[-81.9199,33.4517],[-81.9208,33.454],[-81.9236,33.4595],[-81.9259,33.4626],[-81.9285,33.4651],[-81.9292,33.4656],[-81.9314,33.4671],[-81.9331,33.468],[-81.9351,33.4687],[-81.9392,33.4706],[-81.9419,33.4715],[-81.9437,33.4722],[-81.9458,33.4729],[-81.9464,33.4732],[-81.9488,33.4738],[-81.9509,33.4748],[-81.952,33.4752],[-81.9553,33.4771],[-81.9564,33.4776],[-81.9586,33.4785],[-81.9603,33.4793],[-81.9624,33.48],[-81.9668,33.4817],[-81.9709,33.4827],[-81.9757,33.4845],[-81.9801,33.4853],[-81.9834,33.4862],[-81.9862,33.4875],[-81.9889,33.4903],[-81.9912,33.493],[-81.9918,33.4975],[-81.9918,33.5016],[-81.9924,33.5043],[-81.9935,33.5071],[-81.994,33.5077],[-81.9967,33.5116],[-81.9989,33.5148],[-82.001,33.5175],[-82.0037,33.5207],[-82.0065,33.5235],[-82.0086,33.5253],[-82.0096,33.5263],[-82.0108,33.5276],[-82.0119,33.5294],[-82.0119,33.5313],[-82.0125,33.532],[-81.9062,33.6151],[-81.8608,33.6514],[-81.7595,33.7308],[-81.736,33.7505],[-81.652,33.8142],[-81.5751,33.8751]]]},\"properties\":{\"name\":\"Aiken\",\"state\":\"SC\"}}]}","volume":"85","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, R.K.","contributorId":276171,"corporation":false,"usgs":false,"family":"McKee","given":"R.K.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buhlmann, K.A.","contributorId":276172,"corporation":false,"usgs":false,"family":"Buhlmann","given":"K.A.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hepinstall-Cymerman, J.","contributorId":275628,"corporation":false,"usgs":false,"family":"Hepinstall-Cymerman","given":"J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tuberville, T.D.","contributorId":276175,"corporation":false,"usgs":false,"family":"Tuberville","given":"T.D.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834631,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226758,"text":"70226758 - 2021 - 2021 Computational Infrastructure for Geodynamics Developers Workshop","interactions":[],"lastModifiedDate":"2021-12-10T13:01:08.324661","indexId":"70226758","displayToPublicDate":"2021-02-28T07:00:43","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"2021 Computational Infrastructure for Geodynamics Developers Workshop","docAbstract":"The CIG Developers Workshop resulted in a number of recommendations that we think will help expand the CIG developer community, make software more accessible to new users, and increase developer productivity through use of common infrastructure and best practices for software development. This includes building a broad user base with sufficient support through documentation, tutorials, user forums, hackathons, scientific workshops, and mentoring to maintain a healthy suite of software developers and maintainers. Communities also need to offer opportunities, like this workshop, for developer teams to interact with each other to exchange ideas, identify common infrastructure, and interact with users to discuss modeling workflows and development priorities.","language":"English","publisher":"Computational Infrastructure for Geodynamics","usgsCitation":"Aagaard, B.T., Brown, J., Cooper, C., Gassmoeller, R., Hwang, L., and Spiegelman, M., 2021, 2021 Computational Infrastructure for Geodynamics Developers Workshop, 10 p.","productDescription":"10 p.","ipdsId":"IP-127608","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":392722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":392717,"type":{"id":15,"text":"Index Page"},"url":"https://geodynamics.org/cig/events/calendar/2021-cig-developers-workshop/?eID=1901"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":828172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jed","contributorId":269954,"corporation":false,"usgs":false,"family":"Brown","given":"Jed","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":828173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Catherin","contributorId":269955,"corporation":false,"usgs":false,"family":"Cooper","given":"Catherin","email":"","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":828174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gassmoeller, Rene","contributorId":269956,"corporation":false,"usgs":false,"family":"Gassmoeller","given":"Rene","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":828175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Lorraine","contributorId":269957,"corporation":false,"usgs":false,"family":"Hwang","given":"Lorraine","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":828176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spiegelman, Marc","contributorId":269958,"corporation":false,"usgs":false,"family":"Spiegelman","given":"Marc","email":"","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":828177,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227199,"text":"70227199 - 2021 - U–Pb zircon eruption age of the Old Crow tephra and review of extant age constraints","interactions":[],"lastModifiedDate":"2022-01-04T13:52:32.345028","indexId":"70227199","displayToPublicDate":"2021-02-27T07:48:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3216,"text":"Quaternary Geochronology","active":true,"publicationSubtype":{"id":10}},"title":"U–Pb zircon eruption age of the Old Crow tephra and review of extant age constraints","docAbstract":"Eruption of the Old Crow tephra deposited ~200 km3 of volcanic ash throughout Alaska and the northwestern Yukon (eastern Beringia), providing an isochronous marker across the region on a scale unique in the Pleistocene. The Old Crow tephra represents a critical temporal piercing point used extensively to link geographically disparate stratigraphic sections and the paleo-environmental records they contain. Although the canonical age of the Old Crow suggests eruption during the transition between the glacial and interglacial periods of Marine Isotope Stages (MIS) 5 and 6 at ~125 ka, recent U–Th–Pb and (U–Th)/He zircon dating of the tephra suggests eruption at ~200 ka, within MIS 7. If accurate, this revised eruption age begets significant change to existing models describing the geologic and biotic evolution of Beringia in the Pleistocene. Thus, confidently knowing the age of the tephra is critical to its time-stratigraphic utility and for past and future work in the region where the tephra has been found. With this contribution, we review existing Old Crow age constraints and present an eruption age for the tephra determined via high spatial resolution ion microprobe U–Pb surface analysis on zircon crystals isolated from source-proximal (<500 km from plausible source) pumiceous pyroclasts of the tephra. By dating only glass-mantled crystals isolated from discrete pumice clasts, we limit the potential for sample contamination from exotic crystals and resulting age bias. The young population of dates from this dataset corroborate previous radiometric dates and confirm Old Crow eruption within late MIS 7 at 207 ± 13 ka.
Despite abundant research documenting that land use/land cover (LULC) have substantial impacts on the hydrology of humid tropical systems, field-based evidence for the physical mechanisms behind these impacts are still lacking. In particular, our understanding of the hydrologic flowpaths that generate runoff in these systems, and how they vary with respect to LULC is insufficient to inform both physically-based hydrologic modeling and land-use decision-making. In this study, we use end-member mixing analysis (EMMA) of stream chemistry, and hydrometric characterizations of hillslope soil moisture to identify hydrologic flowpaths in humid tropical steep-land catchments of varying LULC: mature tropical forest, young secondary tropical forest, cattle pasture. EMMA was applied to data from 14 storm events (six at the mature forest, five at the young secondary forest, and three at the cattle pasture) that were intensively sampled during the 2017 wet season representing a wide range of rainfall magnitudes and intensities. Additionally, volumetric-soil-moisture responses at multiple depths were characterized during and after 74 storm events occurring from 2015 to 2017. EMMA results indicated that lateral preferential flow within the top 30 cm of the soil profile was a dominant source of runoff generation at the two forested catchments, with the contribution of this flow path increasing with rainfall magnitude and intensity. This was corroborated by volumetric-soil-moisture data, that showed that a perched zone of saturation developed at 30 cm at the time of peak storm runoff during the largest events and lasted for the remaining duration of the event. EMMA indicated that runoff was a combination of infiltration-excess overland flow and lateral subsurface flow in the actively grazed pastoral catchment. There, overland flow contributed 62 % of runoff during the highest runoff rate sampled (35.3 mm/hr) and this contribution increased substantially with storm magnitude. This flowpath identification was also supported by volumetric-soil-moisture data at the pasture, with peak saturation at all depths during the largest storm events occurring up to 30 min after peak runoff. These results provide a mechanistic explanation for previously observed hydrologic differences among tropical LULCs. Additionally, the wide range of hydrologic conditions during these storm events provide a basis for understanding how future changes to this, and similar humid tropical regions will impact hydrological processes and water availability.
Nitrogen (N) and phosphorus (P) inputs throughout the Mississippi/Atchafalaya River Basin (MARB) have been linked to the Gulf of Mexico hypoxia and water‐quality problems throughout the MARB. To describe N and P loading throughout the MARB, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were previously developed based on nutrient inputs and management similar to 1992 and 2002. In this study, refined SPARROW models were developed with higher resolution basin delineation, updated (2012) source inputs, improved calibration (load) targets, and additional statistical techniques than used in the previous SPARROW models. Based on the refined models, consistent with past models, N and P loads/yields were the highest from the central part of the MARB (Corn Belt) and along the Mississippi River. Agricultural activities remained the most important N and P source, but more so for N because its input, which could now be distinguished from atmospheric deposition, could be estimated. Natural loss of P from geologic material throughout the MARB was an important source, contributing about 23% of the total P from the MARB, and resulted in specific areas, such as Kentucky and Tennessee, being larger sources of P than previously estimated. This information can help managers decide where efforts will have the largest effects (highest ranked areas) on reducing nutrient loading to the Gulf hypoxia and what are the most important sources of N and P in these areas.
Spring viremia of carp virus (SVCV) ia a carp sprivivirus and a member of the genus Sprivivirus within the family Rhabdoviridae. The virus is the etiological agent of spring viremia of carp, a disease of cyprinid species including koi Cyprinus carpio L. and notifiable to the World Organisation for Animal Health. The goal of this study was to explore hypotheses regarding inter-genogroup (Ia to Id) SVCV infection dynamics in juvenile koi and contemporaneously create new reverse-transcription quantitative PCR (RT-qPCR) assays and validate their analytical sensitivity, specificity (ASp) and repeatability for diagnostic detection of SVCV. RT-qPCR diagnostic tests targeting the SVCV nucleoprotein (Q2N) or glycoprotein (Q1G) nucleotides were pan-specific for isolates typed to SVCV genogroups Ia to Id. The Q2N test had broader ASp than Q1G because Q1G did not detect SVCV isolate 20120450 and Q2N displayed occasional detection of pike fry sprivivirus isolate V76. Neither test cross-reacted with other rhabdoviruses, infectious pancreatic necrosis virus or co-localizing cyprinid herpesvirus 3. Both tests were sensitive with observed 50% limits of detection of 3 plasmid copies and high repeatability. Test analysis of koi immersed in SVCV showed that the virus could be detected for at least 167 d following exposure and that titer, prevalence, replicative rate and persistence in koi were correlated significantly with virus virulence. In this context, high virulence SVCV isolates were more prevalent, reached higher titers quicker and persisted in koi for longer periods of time relative to moderate and low virulence isolates.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03564","usgsCitation":"Clouthier, S.C., Schroeder, T., Bueren, E., Anderson, E., and Emmenegger, E., 2021, Analytical validation of two RT-qPCR tests and detection of spring viremia of carp virus (SVCV) in persistently infected koi Cyprinus carpio: Diseases of Aquatic Organisms, v. 143, p. 169-188, https://doi.org/10.3354/dao03564.","productDescription":"20 p.","startPage":"169","endPage":"188","ipdsId":"IP-119270","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":453304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao03564","text":"Publisher Index Page"},{"id":389150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clouthier, Sharon C","contributorId":265646,"corporation":false,"usgs":false,"family":"Clouthier","given":"Sharon","email":"","middleInitial":"C","affiliations":[{"id":54748,"text":"Fisheries & Oceans Canada, Freshwater Institute, Winnipeg, Manitoba R3T 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":823160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroeder, Tamara","contributorId":265647,"corporation":false,"usgs":false,"family":"Schroeder","given":"Tamara","email":"","affiliations":[{"id":54748,"text":"Fisheries & Oceans Canada, Freshwater Institute, Winnipeg, Manitoba R3T 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":823161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bueren, Emma K","contributorId":265648,"corporation":false,"usgs":false,"family":"Bueren","given":"Emma K","affiliations":[{"id":54749,"text":"Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, 24061 USA","active":true,"usgs":false}],"preferred":false,"id":823162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Eric D.","contributorId":213888,"corporation":false,"usgs":false,"family":"Anderson","given":"Eric D.","affiliations":[{"id":38922,"text":"Maine BioTek Inc., Winterport, ME 04496, USA","active":true,"usgs":false}],"preferred":false,"id":823163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emmenegger, Eveline 0000-0001-5217-6030","orcid":"https://orcid.org/0000-0001-5217-6030","contributorId":265649,"corporation":false,"usgs":false,"family":"Emmenegger","given":"Eveline","affiliations":[{"id":54750,"text":"Western Fisheries Research Center","active":true,"usgs":false}],"preferred":false,"id":823164,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234266,"text":"70234266 - 2021 - Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska","interactions":[],"lastModifiedDate":"2022-08-05T13:32:23.783492","indexId":"70234266","displayToPublicDate":"2021-02-25T08:25:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska","docAbstract":"In 1964, the Alaska margin ruptured in a giant Mw 9.2 megathrust earthquake, the 2nd largest during worldwide instrumental recording. The coseismic slip and aftershock region offshore Kodiak Island was surveyed in 1977 – 1981 to understand the region’s tectonics. We re-processed multichannel seismic (MCS) field data using current standard Kirchhoff depth migration and/or MCS traveltime tomography. Further surveys in 1994 added P-wave velocity structure from wide-angle seismic lines and multibeam bathymetry. Published regional gravity, backscatter, and earthquake compilations also became available at this time.
Beneath the trench, rough oceanic crust is covered by ~3 to 5 km thick sediment. Sediment on the subducting plate modulates the plate interface relief. The accreted prism’s imbricate thrust faults have a complex P-wave velocity structure. Landward, an accelerated increase in P-wave velocities is marked by a backstop splay fault zone (BSFZ) that marks a transition from the prism to the higher rigidity rock beneath the middle and upper slope. Structures associated with this feature may indicate fluid flow. Further upslope, another fault extends >100 km along-strike across the middle slope. Erosion from subducting seamounts leaves embayments in the frontal prism.
Plate interface roughness varies along the subduction zone. Beneath the lower and middle slope, 2.5 D plate interface images show modest relief whereas the oceanic basement image is rougher. The 1964 earthquake slip maximum coincides with the leading/landward flank of a subducting seamount and the BSFZ. The BSFZ is a potentially active structure and should be considered in tsunami hazard assessments.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02275.1","usgsCitation":"Krabbenhoeft, A., von Huene, R.E., Miller, J.J., and Klaeschen, D., 2021, Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska: Geosphere, v. 17, no. 2, p. 409-437, https://doi.org/10.1130/GES02275.1.","productDescription":"29 p.","startPage":"409","endPage":"437","ipdsId":"IP-118908","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":453307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02275.1","text":"Publisher Index Page"},{"id":404873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.96240234375,\n 55.29788360510556\n ],\n [\n -150.46875,\n 55.29788360510556\n ],\n [\n -150.46875,\n 56.49889156789072\n ],\n [\n -153.96240234375,\n 56.49889156789072\n ],\n [\n -153.96240234375,\n 55.29788360510556\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Krabbenhoeft, Anne","contributorId":208084,"corporation":false,"usgs":false,"family":"Krabbenhoeft","given":"Anne","email":"","affiliations":[{"id":37708,"text":"GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstr. 1-3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":848365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Huene, Roland E. 0000-0003-1301-3866 rvonhuene@usgs.gov","orcid":"https://orcid.org/0000-0003-1301-3866","contributorId":191070,"corporation":false,"usgs":true,"family":"von Huene","given":"Roland","email":"rvonhuene@usgs.gov","middleInitial":"E.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":848366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, John J. 0000-0002-9098-0967 jmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":3785,"corporation":false,"usgs":true,"family":"Miller","given":"John","email":"jmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":848367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaeschen, Dirk","contributorId":198022,"corporation":false,"usgs":false,"family":"Klaeschen","given":"Dirk","email":"","affiliations":[],"preferred":false,"id":848368,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223391,"text":"70223391 - 2021 - Accommodating the role of site memory in dynamic species distribution models","interactions":[],"lastModifiedDate":"2021-08-25T12:33:51.688114","indexId":"70223391","displayToPublicDate":"2021-02-25T07:30:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Accommodating the role of site memory in dynamic species distribution models","docAbstract":"First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.
Steady-state numerical groundwater-flow models were constructed for the islands of Kaua‘i, O‘ahu, and Maui to enable quantification of the hydrologic consequences of withdrawals and other stresses that can place limits on groundwater availability. The volcanic aquifers of Hawai‘i supply nearly all drinking water for the islands’ residents, freshwater for diverse industries, and natural discharge to springs, streams, and nearshore areas that support ecosystems, cultural practices, aesthetics, and recreation. Increases in groundwater withdrawal and changes in climate can cause water-table depression, saltwater rise, and reduction of natural groundwater discharge—all of which can limit fresh groundwater availability. The numerical models described in this report are designed to quantify these consequences. Separate models were created for each island using MODFLOW-2005 with the Seawater Intrusion package, which allows simulation of freshwater and saltwater in ocean-island aquifers. Calibration resulted in models that generally replicate observed water-level, stream base-flow, and spring-flow data, and simulate groundwater-flow directions and fresh groundwater thicknesses that are consistent with conceptual models. The calibrated models use hydraulic properties that are consistent with the ranges reported in previous studies. The models show that the relative distribution of fresh groundwater discharge to the ocean, streams, and springs and withdrawals for human use differ substantially among the three islands studied here. These differences indicate that consequences that limit the availability of fresh groundwater for human use are likely to differ among the three islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205126","usgsCitation":"Izuka, S.K., Rotzoll, K., and Nishikawa, T., 2021, Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui: U.S. Geological Survey Scientific Investigations Report 2020-5126, 63 p., https://doi.org/10.3133/sir20205126.","productDescription":"Report: viii, 63 p.; Data Release","numberOfPages":"63","ipdsId":"IP-071367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5126/covrthb.jpg"},{"id":383612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5126/sir20205126.pdf","text":"Report","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4DK2P","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater availability in volcanic aquifers on Kaua‘i, O‘ahu, and Maui, Hawai‘i"},{"id":416444,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20155164","text":"Scientific Investigations Report 2015-5164","description":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models"},{"id":416445,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1876","text":"Professional Paper 1876","description":"Izuka, S.K., and Rotzoll, K., 2023, Volcanic aquifers of Hawaiʻi—Contributions to assessing groundwater availability on Kauaʻi, Oʻahu, and Maui: U.S. Geological Survey Professional Paper 1876, 100 p., https://doi.org/10.3133/pp1876.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Contributions to Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui"},{"id":417944,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233010","text":"Fact Sheet 2023-3010","description":"Izuka, S.K., and Rotzoll, K., 2023, Availability of groundwater from the volcanic aquifers of the Hawaiian Islands: U.S. Geological Survey Fact Sheet 2023-3010, 4 p., https://doi.org/10.3133/fs20233010.","linkHelpText":"- Availability of Groundwater from the Volcanic Aquifers of the Hawaiian Islands"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i, Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.73095703125,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 20.57365332356332\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33221435546875,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.235622362422877\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.85931396484375,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 21.830906665069758\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Linear dune gullies on poleward-facing Martian slopes are enigmatic. Formation by CO2-ice block or snow cornice falls has been proposed based on optical imagery of bright, high-albedo features inside gully channels. Because these features often resemble patchy frost residue rather than three-dimensional blocks, more evidence is needed to support the ice-block formation mechanism. Satellite imagery captured two simultaneous airborne plumes with in-channel sources at the Russell crater megadune, thrust up, and dispersed outward along the path of linear dune gullies. We use spectral data analyses, climatic analyses of bolometric temperatures, and thermal modeling to further develop the mechanistic framework for linear dune gully development. Basal sublimation and CO2 gas venting likely cause CO2-ice-block detachment and falls from gully alcoves in southern early spring, accompanied by ice-block off-gassing and saltation of sands and coarse silts that are redeposited around gully channels, and lofting of sublimation lag (coarse dust/silt) into airborne plumes.
","language":"English","publisher":"Wiley","doi":"10.1029/2020GL091920","usgsCitation":"Dinwiddie, C., and Titus, T.N., 2021, Airborne dust plumes lofted by dislodged ice blocks at Russell Crater, Mars: Geophysical Research Letters, v. 48, no. 6, e2020GL091920, 9 p., https://doi.org/10.1029/2020GL091920.","productDescription":"e2020GL091920, 9 p.","ipdsId":"IP-149099","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":453321,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl091920","text":"Publisher Index Page"},{"id":419474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars, Russell Crater","volume":"48","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dinwiddie, Cynthia L.","contributorId":38880,"corporation":false,"usgs":true,"family":"Dinwiddie","given":"Cynthia L.","affiliations":[],"preferred":false,"id":879399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":879400,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219148,"text":"70219148 - 2021 - Forecasting induced earthquake hazard using a hydromechanical earthquake nucleation model","interactions":[],"lastModifiedDate":"2021-06-30T17:55:05.055074","indexId":"70219148","displayToPublicDate":"2021-02-24T07:24:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting induced earthquake hazard using a hydromechanical earthquake nucleation model","docAbstract":"In response to the dramatic increase in earthquake rates in the central United States, the U.S Geological Survey began releasing 1 yr earthquake hazard models for induced earthquakes in 2016. Although these models have been shown to accurately forecast earthquake hazard, they rely purely on earthquake statistics because there was no precedent for forecasting induced earthquakes based upon wastewater injection data. Since the publication of these hazard models, multiple physics‐based methods have been proposed to forecast earthquake rates using injection data. Here, we use one of these methods to generate earthquake hazard forecasts. Our earthquake hazard forecasts are more accurate than statistics‐based hazard forecasts. These results imply that fluid injection data, where and when available, and the physical implications of fluid injection should be included in future induced earthquake hazard forecasts.
Linear dune gullies on poleward‐facing Martian slopes are enigmatic. Formation by CO2‐ice block or snow cornice falls has been proposed based on optical imagery of bright, high‐albedo features inside gully channels. Because these features often resemble patchy frost residue rather than three‐dimensional blocks, more evidence is needed to support the ice‐block formation mechanism. Satellite imagery captured two simultaneous airborne plumes with in‐channel sources at the Russell crater megadune, thrust up and dispersed outward along the path of linear dune gullies. We use spectral data analyses, climatic analyses of bolometric temperatures and thermal modeling to further develop the mechanistic framework for linear dune gully development. Basal sublimation and CO2 gas venting likely cause CO2‐ice‐block detachment and falls from gully alcoves in southern early spring, accompanied by ice‐block offgassing and saltation of sands and coarse silts that are redeposited around gully channels, and lofting of sublimation lag (coarse dust/silt) into airborne plumes.
Biogenic nano-hydroxyapatite (bio-nHAP) has recently gained great interest in many domains, especially in the remediation of heavy metal-contaminated soil, due to its high reactivity, low cost, and eco-friendly nature. The co-transport and reaction of bio-nHAP with Pb(II) in saturated porous media, however, are not well understood. This work investigated the effects of ionic strength (IS), ionic composition (IC), dissolved organic matter (DOM), and flow velocity on transport-reaction dynamics of Pb(II) and bio-nHAP by combining column breakthrough experiments and model simulations. Results showed that the mobility of Pb(II) was significantly enhanced with increasing IS/IC but less affected by flow velocity during the transport-reaction process of bio-nHAP and Pb(II) in the saturated sand column; while the transport of bio-nHAP was restricted by increasing IS/IC but facilitated by increasing velocity. IC, IS, and velocity only slightly affected the reaction kinetics between Pb(II) and bio-nHAP, likely due to the fast reaction rate between Pb(II) and bio-nHAP and precipitation of pyromorphite. The transport dynamics of bio-nHAP and Pb(II) were significantly changed by DOM, and this effect depended strongly on the type of DOM with different molecular weights. Breakthrough curves of Pb(II) and bio-nHAP exhibited apparent “anomalous”, sub-diffusive transport behaviors, which could be well quantified by a novel tempered fractional derivative bimolecular reaction equation (T-FBRE). Our findings highlighted the accurate simulation of the co-transport and reaction of bio-nHAP with Pb(II) using T-FBRE and had a great benefit for risk assessment and remediation strategy development for Pb(II) contaminated soil.
Ensemble-tree machine learning (ML) regression models can be prone to systematic bias: small values are overestimated and large values are underestimated. Additional bias can be introduced if the dependent variable is a transform of the original data. Six methods were evaluated for their ability to correct systematic and introduced bias. Method performance was evaluated using four case studies of groundwater quality: the units of the dependent variable were pH in two and log-concentration in the others. When performance metrics (bias and RMSE for both points and the CDF) were computed using the same units as those in the ML model, empirical distribution matching (EDM) provided the best results. When the metrics were computed using retransformed concentration, EDM and a method incorporating Duan's smearing estimate were both effective. A method based on the Z-score transform approximates EDM if the correlation coefficient between rank-ordered ML estimates and rank-ordered observations approaches one.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105006","usgsCitation":"Belitz, K., and Stackelberg, P.E., 2021, Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression model: Environmental Modeling and Software, v. 139, 105006, 12 p., https://doi.org/10.1016/j.envsoft.2021.105006.","productDescription":"105006, 12 p.","ipdsId":"IP-122742","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453331,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105006","text":"Publisher Index Page"},{"id":436490,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LCTYI2","text":"USGS data release","linkHelpText":"Data Release for Evaluation of Six Methods for Correcting Bias in Estimates from Ensemble Tree Machine Learning Regression Models"},{"id":384773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":213728,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":813265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":813266,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225728,"text":"70225728 - 2021 - Long-term ecosystem and biogeochemical research in Loch Vale watershed, Rocky Mountain National Park, Colorado","interactions":[],"lastModifiedDate":"2021-11-05T11:44:16.72004","indexId":"70225728","displayToPublicDate":"2021-02-24T06:36:49","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":"Long-term ecosystem and biogeochemical research in Loch Vale watershed, Rocky Mountain National Park, Colorado","docAbstract":"Loch Vale watershed was instrumented in 1983 with initial support from the National Acid Precipitation Assessment Program to ask whether ecosystems of Rocky Mountain National Park (RMNP) were affected by acidic atmospheric deposition. Research and monitoring activities were expanded in 1991 by the U.S. Geological Survey Water, Energy, and Biogeochemical Budgets program to understand the processes, and their interactions, controlling water, energy, and biogeochemical fluxes. With help from many collaborators we have characterized trends and patterns in atmospheric deposition, climate, and hydrology, including glaciers and other ice features. Instead of acidic deposition, we documented high atmospheric inputs of reactive nitrogen (Nr), and have studied the ecological consequences in soils, surface water, and vegetation. Using paleolimnology, we documented the onset of human-caused change to lake primary producers ca. 1950 in response to increased Nr deposition and warming. Our results provided the basis for the Colorado Nitrogen Deposition Reduction Plan, a state policy that aims to reduce Nr emissions to protect resources in RMNP by 2032. Carbon cycle research revealed mountain wetlands now release more carbon than they store, and respiration and methane flux occurs even during winter through deep snow packs. Trend analyses found export of Nr to be closely tied to atmospheric inputs, but can lag in response to drought. Current research explores consequences of the combination of warming, changes in precipitation dynamics, and atmospheric deposition of Nr and dust on stream and lake CO2 dynamics, lake biology and trophic state, and soil carbon composition. Dramatic increases in park visitors have prompted studies on the effects of recreational use on water quality. New tools such as remote sensing and high frequency instream water quality sensors are being applied to lake and stream studies. Monitoring, combined with experiments, models, and spatial comparisons is an essential foundation for science-based resource management.
In Mono Basin, California, USA, a near-circular ring fracture 12 km in diameter was proposed by R.W. Kistler in 1966 to have originated as the protoclastic margin of the Cretaceous Aeolian Buttes pluton, to have been reactivated in the middle Pleistocene, and to have influenced the arcuate trend of the chain of 30 young (62−0.7 ka) rhyolite domes called the Mono Craters. In view of the frequency and recency of explosive eruptions along the Mono chain, and because many geophysicists accepted the ring fracture model, we assembled evidence to test its plausibility. The shear zone interpreted as the margin of the Aeolian Buttes pluton by Kistler is 50−400 m wide but is exposed only along a 7-km-long set of four southwesterly outcrops that subtend only a 70° sector of the proposed ring. The southeast end of the exposed shear zone is largely within the older June Lake pluton, and at its northwest end, the contact of the Aeolian Buttes pluton with a much older one crosses the shear zone obliquely. Conflicting attitudes of shear structures are hard to reconcile with intrusive protoclasis. Also inconsistent with the margin of the ovoid intrusion proposed by Kistler, unsheared salients of the pluton extend ∼1 km north of its postulated circular outline at Williams Butte, where there is no fault or other structure to define the northern half of the hypothetical ring. The shear zone may represent regional Cretaceous transpression rather than the margin of a single intrusion. There is no evidence for the Aeolian Buttes pluton along the aqueduct tunnel beneath the Mono chain, nor is there evidence for a fault that could have influenced its vent pattern. The apparently arcuate chain actually consists of three linear segments that reflect Quaternary tectonic influence and not Cretaceous inheritance. A rhyolitic magma reservoir under the central segment of the Mono chain has erupted many times in the late Holocene and as recently as 700 years ago. The ring fracture idea, however, prompted several geophysical investigations that sought a much broader magma body, but none identified a low-density or low-velocity anomaly beneath the purported 12-km-wide ring, which we conclude does not exist.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35747.1","usgsCitation":"Hildreth, E., Fierstein, J., and Ryan-Davis, J., 2021, No ring fracture in Mono Basin, California: Geological Society of America Bulletin, v. 133, no. 9-10, p. 2210-2225, https://doi.org/10.1130/B35747.1.","productDescription":"16 p.","startPage":"2210","endPage":"2225","ipdsId":"IP-118844","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":453334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b35747.1","text":"Publisher Index Page"},{"id":387731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mono Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.9215087890625,\n 37.86618078529668\n ],\n [\n -118.751220703125,\n 37.86618078529668\n ],\n [\n -118.751220703125,\n 38.039438891821746\n ],\n [\n -118.9215087890625,\n 38.039438891821746\n ],\n [\n -118.9215087890625,\n 37.86618078529668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2021-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hildreth, Edward 0000-0002-7925-4251 hildreth@usgs.gov","orcid":"https://orcid.org/0000-0002-7925-4251","contributorId":146999,"corporation":false,"usgs":true,"family":"Hildreth","given":"Edward","email":"hildreth@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fierstein, Judith 0000-0001-8024-1426 jfierstn@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-1426","contributorId":147000,"corporation":false,"usgs":true,"family":"Fierstein","given":"Judith","email":"jfierstn@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan-Davis, Juliet","contributorId":261795,"corporation":false,"usgs":false,"family":"Ryan-Davis","given":"Juliet","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":820666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220225,"text":"70220225 - 2021 - Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States","interactions":[],"lastModifiedDate":"2021-04-28T13:03:48.833583","indexId":"70220225","displayToPublicDate":"2021-02-23T08:00:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States","docAbstract":"Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aquifer system, northern United States (GLAC, 1.87 million km2), groundwater with high arsenic or manganese concentration is associated with reducing conditions and high nitrate with oxidizing conditions. This study uses machine learning to identify the relative influence of drivers of redox conditions (e.g., residence time vs. reactivity) across the glacial landscape. We developed three‐dimensional boosted regression tree models to predict redox conditions using the likelihood of low dissolved oxygen or high iron as indicators of anoxic conditions. Results indicate that variation in redox condition is controlled primarily by residence time (e.g., groundwater age and relative depth) and to a lesser extent by geochemical reactivity (e.g., subsurface contact time, soil carbon). Older water and deeper wells, along with more water storage or slower water movement was associated with higher probability of anoxic conditions. Mapped model results illustrate regions where anoxic redox conditions may mobilize geogenic contaminants or oxic conditions may limit denitrification potential. Results may also provide simplified redox input for process or predictive models of, for example, arsenic, manganese, or nitrate. Machine learning modeling methods can lead to improved understanding of contaminant occurrence and what drives redox conditions, and the methods may be transferable to other settings.
Nesting birds must provide a thermal environment sufficient for egg development while also meeting self‐maintenance needs. Many birds, particularly those with uniparental incubation, achieve this balance through periodic incubation recesses, during which foraging and other self‐maintenance activities can occur. However, incubating birds may experience disturbances such as predator or human activity which interrupt natural incubation patterns by compelling them to leave the nest. We characterized incubating mallard Anas platyrhynchos and gadwall Mareca strepera hens’ responses when flushed by predators and investigators in Suisun Marsh, California, USA. Diurnal incubation recesses initiated by investigators approaching nests were 63% longer than natural diurnal incubation recesses initiated by the hen (geometric mean: 226.77 min versus 142.04 min). Nocturnal incubation recesses, many of which were likely the result of predators flushing hens, were of similar duration regardless of whether the nest was partially depredated during the event (115.33 [101.01;131.68] minutes) or not (119.62 [111.96;127.82] minutes), yet were 16% shorter than natural diurnal incubation recesses. Hens moved further from the nest during natural diurnal recesses or investigator‐initiated recesses than during nocturnal recesses, and the proportion of hen locations recorded in wetland versus upland habitat during recesses varied with recess type (model‐predicted means: natural diurnal recess 0.77; investigator‐initiated recess 0.82; nocturnal recess 0.31). Hens were more likely to take a natural recess following an investigator‐initiated recess earlier that same day than following a natural recess earlier that same day, and natural recesses that followed an investigator‐initiated recess were longer than natural recesses that followed an earlier natural recess, suggesting that hens may not fulfill all of their physiological needs during investigator‐initiated recesses. We found no evidence that the duration of investigator‐initiated recesses was influenced by repeated visits to the nest, whether by predators or by investigators, and trapping and handling the hen did not affect investigator‐initiated recess duration unless the hen was also fitted with a backpack‐harness style GPS–GSM transmitter at the time of capture. Hens that were captured and fitted with GPS–GSM transmitters took recesses that were 26% longer than recesses during which a hen was captured but a GPS–GSM transmitter was not attached. Incubation interruptions had measurable but limited and specific effects on hen behavior.
Hydrologic and irrigation regimes mediate the timing of selenium (Se) mobilization to rivers, but the extent to which patterns in Se uptake and trophic transfer through recipient food webs reflect the temporal variation in Se delivery is unknown. We investigated Se mobilization, partitioning, and trophic transfer along approximately 60 river miles of the selenium-impaired segment of the Lower Gunnison River (Colorado, USA) during six sampling trips between June 2015 and October 2016. We found temporal patterns in Se partitioning and trophic transfer to be independent of those in dissolved Se concentrations and that the recipient food web sustained elevated Se concentrations from earlier periods of high Se mobilization. Using an ecosystem-scale Se accumulation model tailored to the Lower Gunnison River, we predicted that the endangered Razorback Sucker (Xyrauchen texanus) and Colorado Pikeminnow (Ptychocheilus lucius) achieve whole-body Se concentrations exceeding aquatic life protection criteria during periods of high runoff and irrigation activity (April–August) that coincide with susceptible phases of reproduction and early-life development. The results of this study challenge assumptions about Se trophodynamics in fast-flowing waters and introduce important considerations for the management of Se risks for biota in river ecosystems.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06582","usgsCitation":"Brandt, J., Roberts, J., Stricker, C.A., Rogers, H., Nease, P., and Schmidt, T., 2021, Temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river: Environmental Science and Technology, v. 55, no. 6, p. 3645-3656, https://doi.org/10.1021/acs.est.0c06582.","productDescription":"12 p.","startPage":"3645","endPage":"3656","ipdsId":"IP-122278","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":453343,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06582","text":"Publisher Index Page"},{"id":436495,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TD4THX","text":"USGS data release","linkHelpText":"Dataset for temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river"},{"id":389070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 38.0\n ],\n [\n -107.25,\n 38.0\n ],\n [\n -107.25,\n 39.16666667\n ],\n [\n -108.666,\n 39.16666667\n ],\n [\n -108.666,\n 38.0\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandt, Jessica E","contributorId":257351,"corporation":false,"usgs":false,"family":"Brandt","given":"Jessica E","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":823037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Holly hrogers@usgs.gov","contributorId":174358,"corporation":false,"usgs":true,"family":"Rogers","given":"Holly","email":"hrogers@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nease, Patricia","contributorId":265586,"corporation":false,"usgs":false,"family":"Nease","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":823041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823042,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218256,"text":"70218256 - 2021 - Evaluating fish rescue as a drought adaptation strategy using a life cycle modeling approach for imperiled coho salmon","interactions":[],"lastModifiedDate":"2021-02-22T14:36:57.130166","indexId":"70218256","displayToPublicDate":"2021-02-22T08:30:32","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":"Evaluating fish rescue as a drought adaptation strategy using a life cycle modeling approach for imperiled coho salmon","docAbstract":"Projected intensification of drought as a result of climate change may reduce the capacity of streams to rear fish, exacerbating the challenge of recovering salmonid populations listed under the Endangered Species Act. Without management intervention, some stocks will likely go extinct as stream drying and fragmentation reduce juvenile survival to unsustainable levels. To offset drought‐related mortality, fish rescue programs have proliferated, whereby juvenile salmonids are captured and transferred to off‐site rearing facilities. However, the efficacy of this potential conservation tool remains poorly understood. We developed a life cycle model to examine the implications of fish rescue on the abundance of Coho Salmon Oncorhynchus kisutch across serial life stages. The simulation model examines scenarios with varying quantities of rescued fish, time in captivity, drought severity, and reduced smolt‐to‐adult return rates. Our results indicate that fish rescue can increase the abundance of adults and lower extinction risk, particularly for fish held in captivity for a full year. However, fish rescue can also decrease the abundance of adults and increase extinction risk if fish are held only for summer and there is limited winter habitat. We found that when fish rescue did increase returns, it functioned more like a stock enhancement program than a drought mitigation tool and it would likely lead to consecutive generations of captive rearing, which has been shown to have negative effects on fitness. We translated our model into an R Shiny application (https://shiny.wdfw‐fish.us/CohoPopulationDynamics/) that allows users to explore how fish rescue affects Coho Salmon population dynamics through customized parameterization of the model to represent different systems or different assumptions about the effects of fish rescue.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10532","usgsCitation":"Beebe, B.A., Bentley, K.T., Buehrens, T.W., Perry, R., and Armstrong, J.B., 2021, Evaluating fish rescue as a drought adaptation strategy using a life cycle modeling approach for imperiled coho salmon: North American Journal of Fisheries Management, v. 41, no. 1, p. 3-18, https://doi.org/10.1002/nafm.10532.","productDescription":"16 p.","startPage":"3","endPage":"18","ipdsId":"IP-119203","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":383418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Beebe, Brittany A","contributorId":251872,"corporation":false,"usgs":false,"family":"Beebe","given":"Brittany","email":"","middleInitial":"A","affiliations":[{"id":50408,"text":"Department of Fisheries and Wildlife Department, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":810740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bentley, Kale T","contributorId":251873,"corporation":false,"usgs":false,"family":"Bentley","given":"Kale","email":"","middleInitial":"T","affiliations":[{"id":50409,"text":"Fish Science Division, Washington Department of Fish & Wildlife, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":810741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buehrens, Thomas W.","contributorId":210018,"corporation":false,"usgs":false,"family":"Buehrens","given":"Thomas","email":"","middleInitial":"W.","affiliations":[{"id":38048,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195","active":true,"usgs":false}],"preferred":false,"id":810750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":223235,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":810742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Jonathan B.","contributorId":251874,"corporation":false,"usgs":false,"family":"Armstrong","given":"Jonathan","email":"","middleInitial":"B.","affiliations":[{"id":50408,"text":"Department of Fisheries and Wildlife Department, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":810743,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220237,"text":"70220237 - 2021 - Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination","interactions":[],"lastModifiedDate":"2021-06-30T18:46:44.051993","indexId":"70220237","displayToPublicDate":"2021-02-22T07:57:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination","docAbstract":"In an area of elevated nitrate (NO3) groundwater concentrations in the northern Chihuahuan Desert in central New Mexico (United States), a large reservoir of nitrate was found in the subsoil of an arroyo floodplain. Nitrate inventories in the floodplain subsoils ranged from 10,000 to 38,000 kg NO3-N/ha—over twice as high as any previously measured arid region. The floodplain subsoil NO3 reservoir was over 100 times higher than the adjacent desert (59–95 kg NO3-N/ha). Chloride mass balance calculations of subsoils indicate arroyo floodplain subsoils have undergone negative recharge since 2600–8600 yr ago, while the surrounding desert has had negative recharge since 13,000–17,000 yr ago. Compared to the adjacent desert, plant communities are larger and more abundant in the floodplain, though subsoil NO3 is apparently not utilized. We demonstrate that NO3 accumulates in the subsoil of the floodplain through evaporation of monsoon season precipitation funneled into the arroyo. Through a one-dimensional vadose zone model, we show that the NO3 inventories in the arroyo floodplain could be acquired 8 to 75 times faster than through atmospheric deposition through the lateral movement
Restoring and protecting “blue carbon” ecosystems - mangrove forests, tidal marshes, and seagrass meadows - are actions considered for increasing global carbon sequestration. To improve understanding of which management actions produce the greatest gains in sequestration, we used a spatially explicit model to compare carbon sequestration and its economic value over a broad spatial scale (2500 km of coastline in southeastern Australia) for four management scenarios: (1) Managed Retreat, (2) Managed Retreat Plus Levee Removal, (3) Erosion of High Risk Areas, (4) Erosion of Moderate to High Risk Areas. We found that carbon sequestration from avoiding erosion-related emissions (abatement) would far exceed sequestration from coastal restoration. If erosion were limited only to the areas with highest erosion risk, sequestration in the non-eroded area exceeded emissions by 4.2 million Mg CO2 by 2100. However, losing blue carbon ecosystems in both moderate and high erosion risk areas would result in net emissions of 23.0 million Mg CO2 by 2100. The removal of levees combined with managed retreat was the strategy that sequestered the most carbon. Across all time points, removal of levees increased sequestration by only an additional 1 to 3% compared to managed retreat alone. Compared to the baseline erosion scenario, the managed retreat scenario increased sequestration by 7.40 million Mg CO2 by 2030, 8.69 million Mg CO2 by 2050, and 16.6 million Mg CO2 by 2100. Associated economic value followed the same patterns, with large potential value loss from erosion greater than potential gains from conserving or restoring ecosystems. This study quantifies the potential benefits of managed retreat and preventing erosion in existing blue carbon ecosystems to help meet climate change mitigation goals by reducing carbon emissions.
Hierarchical stream habitat conditions influence patterns of fish abundance and population dynamics. The spawning period is important for stream fishes but coincides with unpredictable environmental conditions and stressors. Thus, identifying habitats that confer suitable spawning is crucial to managing vulnerable fish populations, including narrow-range endemics. Here, we evaluate reach- and catchment-scale habitat features related to Neosho Smallmouth Bass (Micropterus dolomieu velox) nest presence, abundance and aggregations (clusters) and quantify nest microhabitat.
Ozark Highlands ecoregion, USA.
We conducted snorkel and habitat surveys from 2016 to 2018 to quantify nest abundance, describe nest cluster characteristics and quantify nest microhabitat. We used field-collected and geospatial variables and developed generalized mixed models to evaluate the influence of multi-scale habitat features on nest cluster presence and nest abundance.
Nest clusters, scarcely known for other Smallmouth Bass populations, contained 25% of all documented nests. Presence of nests was more likely in warmer stream reaches with wide, shallow channels and more pool habitat. Nest cluster presence was more likely with greater nest densities and earlier in the spawning season. The abundance of Smallmouth Bass nests was related to several reach-scale habitat conditions, with greater nest counts in warmer reaches and reaches with deeper pool habitat. Regardless of cluster behaviour, nesting Smallmouth Bass used similar microhabitats, including a range of depths (0.26–1.85 m), low velocities (<0.1 m/s) and typically gravel substrates.
Our results indicate plasticity in nesting ecology within Neosho Smallmouth Bass populations and highlight the need to consider multiple aspects of stream habitat when developing conservation and management plans. The importance of reach-scale habitat features suggests it may be important to limit landscape and channel alterations. Nest clustering behaviour suggests these populations may be vulnerable to human influence during the nesting season, but also provides management opportunities for protection during critical time periods.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13250","usgsCitation":"Miller, A., and Brewer, S.K., 2021, Riverscape nesting dynamics of Neosho Smallmouth Bass: To cluster or not to cluster?: Diversity and Distributions, v. 27, no. 6, p. 1005-1018, https://doi.org/10.1111/ddi.13250.","productDescription":"14 p.","startPage":"1005","endPage":"1018","ipdsId":"IP-122996","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":453357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13250","text":"Publisher Index Page"},{"id":396671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.80078125,\n 35.88905007936091\n ],\n [\n -92.98828125,\n 35.88905007936091\n ],\n [\n -92.98828125,\n 37.3002752813443\n ],\n [\n -95.80078125,\n 37.3002752813443\n ],\n [\n -95.80078125,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Andrew D.","contributorId":287529,"corporation":false,"usgs":false,"family":"Miller","given":"Andrew D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":836856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":836857,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218456,"text":"70218456 - 2021 - Local explosion detection and infrasound localization by reverse time migration using 3-D finite-difference wave propagation","interactions":[],"lastModifiedDate":"2021-02-26T13:42:46.530587","indexId":"70218456","displayToPublicDate":"2021-02-21T07:32:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Local explosion detection and infrasound localization by reverse time migration using 3-D finite-difference wave propagation","docAbstract":"Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.