{"pageNumber":"318","pageRowStart":"7925","pageSize":"25","recordCount":184769,"records":[{"id":70240255,"text":"70240255 - 2023 - Evaluations of Lagrangian egg drift models: From a laboratory flume to large channelized rivers","interactions":[],"lastModifiedDate":"2023-02-02T16:20:30.401536","indexId":"70240255","displayToPublicDate":"2022-11-18T08:17:43","publicationYear":"2023","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":"Evaluations of Lagrangian egg drift models: From a laboratory flume to large channelized rivers","docAbstract":"

To help better interpret computational models in predicting drift of carp eggs in rivers, we present a series of model assessments for the longitudinal egg dispersion. Two three-dimensional Lagrangian particle tracking models, SDrift and FluEgg, are evaluated in a series of channels with increasing complexity. The model evaluation demonstrates that both models are able to accommodate channel complexity and provide a wide range of dispersion coefficients: Kl=0(1 − 100)Hu∗ with H being water depth and u∗ being shear velocity. In a straight channel with Kl=0(1)Hu∗ SDrift predicts weaker longitudinal dispersion than FluEgg in the early stage as a result of weak vertical mixing associated with smooth wall turbulence. With sufficient time, SDrift and FluEgg predict similar egg dispersion, accounting for the differential advection due to the vertical velocity profile. In an idealized curved channel with Kl=0(10)Hu∗, dispersion is driven by both vertical and transverse velocity profiles. SDrift yields slightly larger dispersion coefficients than FluEgg. In a real river with channel-training structures and having Kl=0(100)Hu∗ SDrift predicts a stronger longitudinal dispersion than FluEgg due to substantial local turbulent eddies and velocity gradients. To summarize, FluEgg shows good performance in capturing dispersion due to vertical velocity profiles and cross-channel velocity gradients. SDrift shows excellent model capabilities of revealing various dispersion mechanisms in addition to the vertical and cross-channel velocity variations. They include the initial turbulent diffusion stage with growing dispersion coefficients and strong dispersion due to in-stream hydraulic structures and localized turbulence.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2022.110200","usgsCitation":"Li, G., Elliott, C.M., Call, B., Chapman, D., Jacobson, R.B., and Wang, B., 2023, Evaluations of Lagrangian egg drift models: From a laboratory flume to large channelized rivers: Ecological Modelling, v. 475, 110200, 11 p., https://doi.org/10.1016/j.ecolmodel.2022.110200.","productDescription":"110200, 11 p.","ipdsId":"IP-144141","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":412623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Lexington","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.87382488800966,\n 39.22446045728665\n ],\n [\n -93.87382488800966,\n 39.18910122319653\n ],\n [\n -93.76470370918322,\n 39.18910122319653\n ],\n [\n -93.76470370918322,\n 39.22446045728665\n ],\n [\n -93.87382488800966,\n 39.22446045728665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"475","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Geng","contributorId":298636,"corporation":false,"usgs":false,"family":"Li","given":"Geng","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":863099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Call, Bruce 0000-0001-9064-2231","orcid":"https://orcid.org/0000-0001-9064-2231","contributorId":217707,"corporation":false,"usgs":true,"family":"Call","given":"Bruce","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863102,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863103,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Bin","contributorId":298637,"corporation":false,"usgs":false,"family":"Wang","given":"Bin","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":863104,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250311,"text":"70250311 - 2023 - A practical guide to understanding and validating complex models using data simulations","interactions":[],"lastModifiedDate":"2023-12-01T13:10:09.553573","indexId":"70250311","displayToPublicDate":"2022-11-18T07:07:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A practical guide to understanding and validating complex models using data simulations","docAbstract":"
  1. Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4) and machine learning methods.
  2. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution, and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.
  3. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.
  4. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.14030","usgsCitation":"DiRenzo, G.V., Hanks, E., and Miller, D., 2023, A practical guide to understanding and validating complex models using data simulations: Methods in Ecology and Evolution, v. 14, no. 1, p. 203-217, https://doi.org/10.1111/2041-210X.14030.","productDescription":"15 p.","startPage":"203","endPage":"217","ipdsId":"IP-138387","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":445239,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14030","text":"Publisher Index Page"},{"id":435559,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99B0IJ7","text":"USGS data release","linkHelpText":"Simulations to understand and validate models"},{"id":423142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":889406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim","contributorId":332094,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":889407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A. W.","contributorId":332095,"corporation":false,"usgs":false,"family":"Miller","given":"David A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":889408,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241182,"text":"70241182 - 2023 - Spatial and temporal patterns in Arctic mosquito abundance","interactions":[],"lastModifiedDate":"2023-03-14T11:35:32.889095","indexId":"70241182","displayToPublicDate":"2022-11-18T06:33:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns in Arctic mosquito abundance","docAbstract":"
  1. Organisms that undergo a shift in ontogeny and habitat type often change their spatial distribution throughout their life cycle, but how this affects population dynamics remains poorly understood.
  2. We examined spatial and temporal patterns in Aedes nigripes abundance, a widespread univoltine Arctic mosquito species (Diptera: Culicidae), hypothesizing that the spatial distribution of adults would be closely tied to aquatic habitat.
  3. We tracked adult densities of A. nigripes near Kangerlussuaq, Greenland using emergence traps, CO2-baited traps, and sweep-nets.
  4. In back-to-back years of sampling (2017 and 2018) we found two-fold variation in overall abundance.
  5. Adults were spatially patchy when first emerging from aquatic habitats but within a week, mean capture rates for host-seeking adult females were similar across locations, even in places far from larval habitat.
  6. Daily variation in mosquito captures was primarily explained by weather, with virtually no mosquito activity when temperatures averaged less than 8°C or wind speeds exceeded 6 m/s. Gravid females (3% of resting adults) were spatially patchy on the landscape, but not always in the same places where most adults emerged.
  7. The spatial distribution of adults is quickly uncoupled from the spatial distribution of larvae because A. nigripes females may disperse far from their natal habitats in search of a blood-meal and high-quality oviposition habitat.
  8. 8. This research highlights the value of studying ecological processes that act at disparate life stages for understanding the population biology of organisms with complex life cycles.
","language":"English","publisher":"Royal Entomological Society","doi":"10.1111/een.13198","usgsCitation":"DeSiervo, M.H., Finger-Higgens, R.A., Ayres, M.P., Virginia, R.A., and Culler, L.E., 2023, Spatial and temporal patterns in Arctic mosquito abundance: Ecological Applications, v. 48, no. 1, p. 19-30, https://doi.org/10.1111/een.13198.","productDescription":"12 p.","startPage":"19","endPage":"30","ipdsId":"IP-138072","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445243,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/een.13198","text":"Publisher Index Page"},{"id":414081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -53.428585588096325,\n 68.18575031283581\n ],\n [\n -53.428585588096325,\n 64.90395580637608\n ],\n [\n -46.576026984634666,\n 64.90395580637608\n ],\n [\n -46.576026984634666,\n 68.18575031283581\n ],\n [\n -53.428585588096325,\n 68.18575031283581\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"DeSiervo, Melissa H","contributorId":303034,"corporation":false,"usgs":false,"family":"DeSiervo","given":"Melissa","email":"","middleInitial":"H","affiliations":[{"id":65619,"text":"Dept of Biological Sciences, Dartmouth College, Hanover, NH; Dept of Botany, University of Wyoming, Laramie, WY; Institute of Arctic Studies, The Dickey Center for International Understanding, Dartmouth College, Hanover,NH","active":true,"usgs":false}],"preferred":false,"id":866370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finger-Higgens, Rebecca A 0000-0002-7645-504X","orcid":"https://orcid.org/0000-0002-7645-504X","contributorId":290211,"corporation":false,"usgs":true,"family":"Finger-Higgens","given":"Rebecca","email":"","middleInitial":"A","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":866371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayres, Matthew P.","contributorId":219897,"corporation":false,"usgs":false,"family":"Ayres","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":35787,"text":"Department of Biological Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":866372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Virginia, Ross A","contributorId":303035,"corporation":false,"usgs":false,"family":"Virginia","given":"Ross","email":"","middleInitial":"A","affiliations":[{"id":65620,"text":"Dept of Environmental Sciences, Dartmouth College, Hanover, NH ; Institute of Arctic Studies, The Dickey Center for International Understanding, Dartmouth College, Hanover,NH","active":true,"usgs":false}],"preferred":false,"id":866373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Culler, Lauren E","contributorId":303036,"corporation":false,"usgs":false,"family":"Culler","given":"Lauren","email":"","middleInitial":"E","affiliations":[{"id":65620,"text":"Dept of Environmental Sciences, Dartmouth College, Hanover, NH ; Institute of Arctic Studies, The Dickey Center for International Understanding, Dartmouth College, Hanover,NH","active":true,"usgs":false}],"preferred":false,"id":866374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238803,"text":"70238803 - 2023 - A review of supervised learning methods for classifying animal behavioural states from environmental features","interactions":[],"lastModifiedDate":"2023-01-18T17:24:36.527931","indexId":"70238803","displayToPublicDate":"2022-11-16T07:46:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A review of supervised learning methods for classifying animal behavioural states from environmental features","docAbstract":"
  1. Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations.
  2. We review SL methods (weighted k-nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles.
  3. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise-averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package (xgboost) facilitate investigation of variable relationships and importance.
  4. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.14019","usgsCitation":"Bergen, S., Huso, M., Duerr, A.E., Braham, M.A., Schmuecker, S., Miller, T.A., and Katzner, T., 2023, A review of supervised learning methods for classifying animal behavioural states from environmental features: Methods in Ecology and Evolution, v. 14, no. 1, p. 189-202, https://doi.org/10.1111/2041-210X.14019.","productDescription":"14 p.","startPage":"189","endPage":"202","ipdsId":"IP-137834","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":445244,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14019","text":"Publisher Index Page"},{"id":410360,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bergen, Silas","contributorId":288432,"corporation":false,"usgs":false,"family":"Bergen","given":"Silas","email":"","affiliations":[{"id":61757,"text":"Winona State University","active":true,"usgs":false}],"preferred":false,"id":858757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":858758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duerr, Adam E.","contributorId":102324,"corporation":false,"usgs":true,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":858759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Braham, Missy A","contributorId":288433,"corporation":false,"usgs":false,"family":"Braham","given":"Missy","email":"","middleInitial":"A","affiliations":[{"id":61759,"text":"Conservation Science Global, Inc.","active":true,"usgs":false}],"preferred":false,"id":858760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmuecker, Sara","contributorId":213247,"corporation":false,"usgs":false,"family":"Schmuecker","given":"Sara","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":858761,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":858762,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858763,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263636,"text":"70263636 - 2023 - Rupture scenarios for the 3 June 1770 Haiti earthquake","interactions":[],"lastModifiedDate":"2025-02-18T15:35:58.705398","indexId":"70263636","displayToPublicDate":"2022-11-15T09:30:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rupture scenarios for the 3 June 1770 Haiti earthquake","docAbstract":"

The 2010 M 7.0 Haiti earthquake provided the impetus to reconsider historical earthquakes in Hispaniola (Bakun et al., 2012). That earthquake also shed new light on complex fault systems along Haiti’s southern peninsula (Douilly et al., 2013; Saint Fleur et al., 2015). Recently, the 2021 M 7.2 Nippes earthquake (Calais et al., 2022; Douilly et al., 2022), and a recent study reconsidering the 1860 sequence (Martin et al., 2022) further underscored the complexity of fault systems and large earthquake ruptures along the peninsula. Motivated by these studies and recent geological investigations (Prentice et al., 2010; Saint Fleur et al., 2020), we reconsider the 3 June 1770 Haiti earthquake to explore the conventional assumption that it was the last major (M ≥7.5) earthquake along the Enriquillo–Plantain Garden fault (EPGF). Accounts provide compelling evidence for substantial liquefaction in the Cul‐de‐Sac plain, one or more likely landslide‐driven tsunami in Gonaïves Bay, and extensive landsliding that created at least three documented landslide dams. We consider three end‐member rupture scenarios that are consistent with available constraints: two scenarios with M 7.7 and rupture lengths of 150–170 km, and one scenario with a ∼90 km rupture and M 7.5. Absent future work to identify and date paleoevents along the southern peninsula, none of these scenarios can be ruled out. Our preferred rupture model extends from the Miragoâne pull‐apart to near la Selle mountain, with a rupture length of 127 km, M 7.6, and a high stress drop. Rupture could have been on the EPGF or on an oblique thrust fault associated with overthrusting of the Massif de la Selle. The results do support the conclusion that the 1770 earthquake was the last major earthquake in southern Haiti, with a magnitude upward of M 7.5 and significantly more severe shaking in southern Haiti than during the 2010 earthquake.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220108","usgsCitation":"Hough, S.E., Martin, S.S., Symithe, S., and Briggs, R.W., 2023, Rupture scenarios for the 3 June 1770 Haiti earthquake: Bulletin of the Seismological Society of America, v. 113, no. 1, p. 157-185, https://doi.org/10.1785/0120220108.","productDescription":"29 p.","startPage":"157","endPage":"185","ipdsId":"IP-142025","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Haiti","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.67124675813444,\n 20.050612215978532\n ],\n [\n -74.00169473999779,\n 19.905986033577463\n ],\n [\n -74.57038450016687,\n 18.454888563255167\n ],\n [\n -74.53298185535479,\n 17.77196094061692\n ],\n [\n -71.6877773361758,\n 17.77196094061692\n ],\n [\n -71.67124675813444,\n 20.050612215978532\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Stacey S.","contributorId":187758,"corporation":false,"usgs":false,"family":"Martin","given":"Stacey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":927627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Symithe, Steeve","contributorId":350978,"corporation":false,"usgs":false,"family":"Symithe","given":"Steeve","affiliations":[{"id":83892,"text":"Faculté Des Sciences, Université d'Etat d'Haïti, Port au Prince, Haïti","active":true,"usgs":false}],"preferred":false,"id":927628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927629,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239408,"text":"70239408 - 2023 - Life-cycle model reveals sensitive life stages and evaluates recovery options for a dwindling Pacific salmon population","interactions":[],"lastModifiedDate":"2023-03-01T17:09:22.001565","indexId":"70239408","displayToPublicDate":"2022-11-15T06:51:44","publicationYear":"2023","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":"Life-cycle model reveals sensitive life stages and evaluates recovery options for a dwindling Pacific salmon population","docAbstract":"

Population models, using empirical survival rates estimates for different life stages, can help managers explore whether various management options could stabilize a declining population or restore it to former levels of abundance. Here we used two decades of data on five life stages of the Cedar River, USA Sockeye Salmon, Oncorhynchus nerka, population to create and parameterize a life-cycle model. This formerly large but unproductive population is now in steep decline, despite hatchery enhancement. We gathered population-specific data on survival during five stages: 1) egg-to-fry, 2) fry-to-presmolt, 3) presmolt-to-adult return from the ocean, 4) adult en route from the ocean to the spawning grounds, and 5) reproduction. We ground-truthed the model to ensure its fit to the data, and then we modified survival and other parameters during various stages to examine future scenarios. Our analyses revealed that low survival of juveniles in Lake Washington (stage 2: averaging only 3% over the last 20 years), survival of adults returning to fresh water to spawn (stage 4), and survival of adults on spawning grounds to reproduce (stage 5) are likely limiting factors. Combined increases in these stages and others (specifically, the proportion of fish taken into the hatchery to be spawned) might also recover the population. As in other integrated hatchery populations, managers must weigh options relating to balancing the fraction of natural- and hatchery-origin fish, and our results showed that increasing the fraction of fish taken into the hatchery alone will not recover the population. Our model brings together population-specific data to help managers weigh conservation strategies and understand which stages and habitats are most limiting and how much survival must increase to achieve recovery targets. By extension, our analyses also reveal the utility of such models in other cases where stage-specific data are available.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10859","usgsCitation":"Kendall, N.W., Unrein, J.R., Volk, C., Beauchamp, D., Fresh, K.L., and Quinn, T.P., 2023, Life-cycle model reveals sensitive life stages and evaluates recovery options for a dwindling Pacific salmon population: North American Journal of Fisheries Management, v. 43, no. 1, p. 203-230, https://doi.org/10.1002/nafm.10859.","productDescription":"28 p.","startPage":"203","endPage":"230","ipdsId":"IP-137770","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":467133,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/nafm.10859","text":"External Repository"},{"id":411778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kendall, Neala W.","contributorId":288624,"corporation":false,"usgs":false,"family":"Kendall","given":"Neala","email":"","middleInitial":"W.","affiliations":[{"id":61815,"text":"wafg","active":true,"usgs":false}],"preferred":false,"id":861483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Unrein, Julia R.","contributorId":172777,"corporation":false,"usgs":false,"family":"Unrein","given":"Julia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":861484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Volk, Carol","contributorId":300802,"corporation":false,"usgs":false,"family":"Volk","given":"Carol","affiliations":[{"id":35354,"text":"Seattle Public Utilities","active":true,"usgs":false}],"preferred":false,"id":861485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":861486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fresh, Kurt L.","contributorId":98597,"corporation":false,"usgs":false,"family":"Fresh","given":"Kurt","email":"","middleInitial":"L.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":861487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quinn, Thomas P.","contributorId":167272,"corporation":false,"usgs":false,"family":"Quinn","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24671,"text":"School of Aquatic and Fsiery Sciences, UW, Box 355020, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":861488,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239296,"text":"70239296 - 2023 - An interactive viewer to improve operational aftershock forecasts","interactions":[],"lastModifiedDate":"2023-01-06T14:43:02.358938","indexId":"70239296","displayToPublicDate":"2022-11-14T08:40:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"An interactive viewer to improve operational aftershock forecasts","docAbstract":"

The U.S. Geological Survey (USGS) issues forecasts for aftershocks about 20 minutes after most earthquakes above M 5 in the United States and its territories, and updates these forecasts 75 times during the first year. Most of the forecasts are issued automatically, but some forecasts require manual intervention to maintain accuracy. It is important to identify the sequences whose forecasts will benefit from a modified approach so the USGS can provide accurate information to the public. The oaftools R package (Paris and Michael, 2022) includes functions that analyze and plot earthquake sequences and their forecasts to identify which sequences require such intervention. The package includes the Operational Aftershock Forecast (OAF) Viewer, which incorporates the functions into an interactive web environment that can be used to explore aftershock sequences. The OAF Viewer starts with a global map and table of mainshocks. After a mainshock has been selected, the map and a new table show its aftershocks and the OAF Viewer generates five analytical plots: (1) magnitude–time, which is used to look for patterns in the data; (2) cumulative number, to see how the productivity of the sequence compares to a Reasenberg and Jones (1989) aftershock model over time; (3) magnitude–frequency, to compare the ratio of large to small magnitudes and extrapolate to higher magnitudes with sparse data and lower magnitudes with incomplete data; (4) forecast success, to compare the forecasts with observations for a sequence; and (5) parameter–time, which examines the temporal evolution of the forecast model parameters. The user can interact with the functions provided by the oaftools package through the OAF Viewer or by incorporating the functions into their own analysis methods. The OAF Viewer will help seismologists understand complexities in the data, communicate with the public and emergency managers, and improve the OAF system by maintaining operational awareness.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220108","usgsCitation":"Paris, G.M., and Michael, A.J., 2023, An interactive viewer to improve operational aftershock forecasts: Seismological Research Letters, v. 94, no. 1, p. 473-484, https://doi.org/10.1785/0220220108.","productDescription":"12 p.","startPage":"473","endPage":"484","ipdsId":"IP-138814","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":435560,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PZTYEN","text":"USGS data release","linkHelpText":"OAF Tools - R package"},{"id":411488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Paris, Gabrielle Madison 0000-0001-5008-1441","orcid":"https://orcid.org/0000-0001-5008-1441","contributorId":300636,"corporation":false,"usgs":true,"family":"Paris","given":"Gabrielle","email":"","middleInitial":"Madison","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861019,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238623,"text":"70238623 - 2023 - Habitat associations of riverine fishes among rocky shoals","interactions":[],"lastModifiedDate":"2023-03-15T14:29:14.633881","indexId":"70238623","displayToPublicDate":"2022-11-14T07:09:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Habitat associations of riverine fishes among rocky shoals","docAbstract":"

Understanding species' associations with physical habitat conditions is a fundamental goal of ecology. For organisms that occupy lotic ecosystems, relationships to streamflow are of particular importance, but these associations are unstudied for most species. We tested the predictability of fish–microhabitat relationships in river shoals (shallow, rocky areas with relatively swift water flow) using a large data set from the Conasauga River in Georgia, USA. Our objective was to assess the consistency of species-specific relationships with flow-dependent variables (depth, velocity, Reynolds number and Froude number) while accounting for other microhabitat variables (e.g. vegetation). We used data from 8285 seine-sets collected during late summer or autumn at 26 sites over 12 years to relate occurrence and counts of 22 fish species to habitat variables using generalised linear multiple regression models. Results showed that microhabitat models explained a substantial amount of the variation in counts for some species, although other species were poorly predicted. We classified 16 species as velocity specialists and nine species as depth specialists, with six species specialised for depth and velocity and three species classified as depth and velocity generalists. The variability in habitat associations that we observed suggests that species will be unevenly affected by anthropogenic activities that alter flows.

","language":"English","publisher":"Wiley","doi":"10.1111/eff.12690","usgsCitation":"Baynes, A.Y., Freeman, M., McKay, S.K., and Wenger, S., 2023, Habitat associations of riverine fishes among rocky shoals: Ecology of Freshwater Fish, v. 32, no. 2, p. 336-347, https://doi.org/10.1111/eff.12690.","productDescription":"10 p.","startPage":"336","endPage":"347","ipdsId":"IP-144264","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445253,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12690","text":"Publisher Index Page"},{"id":409984,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Conasauga River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.05244590331915,\n 34.995585633321085\n ],\n [\n -85.05244590331915,\n 34.557353805927164\n ],\n [\n -84.68480239587811,\n 34.557353805927164\n ],\n [\n -84.68480239587811,\n 34.995585633321085\n ],\n [\n -85.05244590331915,\n 34.995585633321085\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Baynes, Anna Y.","contributorId":299585,"corporation":false,"usgs":false,"family":"Baynes","given":"Anna","email":"","middleInitial":"Y.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":858134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":858135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKay, S. Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":858136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":858137,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238547,"text":"70238547 - 2023 - High resolution spatiotemporal patterns of flow at the landscape scale in montane non-perennial streams","interactions":[],"lastModifiedDate":"2023-02-02T17:47:42.428852","indexId":"70238547","displayToPublicDate":"2022-11-14T06:43:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"High resolution spatiotemporal patterns of flow at the landscape scale in montane non-perennial streams","docAbstract":"

Intermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water-dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated.

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Best management practices (BMPs) have been predominantly used throughout the Chesapeake Bay watershed (CBW) to reduce nutrients and sediments entering streams, rivers, and the bay. These practices have been successful in reducing loads entering the estuary and have shown the potential to reduce other contaminants (pesticides, hormonally active compounds, pathogens) in localized studies and modeled load estimates. However, further understanding of relationships between BMPs and non-nutrient contaminant reductions at regional scales using sampled data would be beneficial. Total estrogenic activity was measured in surface water samples collected over a decade (2008–2018) in 211 undeveloped NHDPlus V2.1 watersheds within the CBW. Bayesian hierarchical modeling between total estrogenic activity and landscape predictors including landcover, runoff, BMP intensity, and a BMP*agriculture intensity interaction term indicates a 96% posterior probability that BMP intensity on agricultural land is reducing total estrogenic activity. Additionally, watersheds with high agriculture and low BMPs had a 49% posterior probability of exceeding an effects-based threshold in aquatic organisms of 1 ng/L but only a 1% posterior probability of exceeding this threshold in high-agriculture, high-BMP watersheds.

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0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":857808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devereux, Olivia H. 0000-0002-3911-3307","orcid":"https://orcid.org/0000-0002-3911-3307","contributorId":198108,"corporation":false,"usgs":false,"family":"Devereux","given":"Olivia H.","affiliations":[],"preferred":false,"id":857809,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256612,"text":"70256612 - 2023 - Attitudes of the Wildlife Society members toward uses of wildlife","interactions":[],"lastModifiedDate":"2024-08-26T16:32:00.194312","indexId":"70256612","displayToPublicDate":"2022-11-11T11:31:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Attitudes of the Wildlife Society members toward uses of wildlife","docAbstract":"

Large-scale sociological, geographic, and demographic changes affect the way people interact with and value wildlife. Beliefs and attitudes of stakeholders towards wildlife and uses of wildlife are also shifting along with these geographical and demographic changes. Changes in societal or professional attitudes toward uses of wildlife has potential to create alignment issues between wildlife professionals and society. To inform deliberations within The Wildlife Society (TWS) and the larger population of conservation professionals, we assessed and compared the change over time between 1998 and 2020 in value orientations, beliefs, and attitudes toward uses of wildlife and wildlife management practices of members of TWS as a proxy for practicing wildlife professionals. We administered a Qualtrics web-based survey (n = 3,247), January–March 2020. Respondents closely approximated TWS membership demographically, who identified as male (59.7%) and female (37.7%), and geographically within 50 U.S. states at the time of the survey. Results indicated wildlife conservation professionals express as broad of a spectrum of beliefs, albeit shifting, about consumptive uses of wildlife in hunting and trapping much as they did 2 decades ago. Change in attitudes and beliefs was modest but mutualistic or protectionist beliefs and attitudes increased, especially among younger professionals, toward the ethical acceptability of harvested animals involving fair chase and sportsmanship (72% in 1998; 93.2% in 2020), and in expressed acceptance of regulated hunting and trapping. Our work provides insights into potential focus areas of training and education, such as Conservation Leaders for Tomorrow and Trapping Matters.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1419","usgsCitation":"Menale, R., Riley, S.J., and Organ, J.F., 2023, Attitudes of the Wildlife Society members toward uses of wildlife, v. 47, no. 2, e1419, 17 p., https://doi.org/10.1002/wsb.1419.","productDescription":"e1419, 17 p.","ipdsId":"IP-138461","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445259,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1419","text":"Publisher Index Page"},{"id":433161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Menale, Rachel","contributorId":341365,"corporation":false,"usgs":false,"family":"Menale","given":"Rachel","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":908306,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riley, Shawn J.","contributorId":202177,"corporation":false,"usgs":false,"family":"Riley","given":"Shawn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908307,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908308,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256620,"text":"70256620 - 2023 - The effect of scent lures on detection is not equitable among sympatric species","interactions":[],"lastModifiedDate":"2024-08-27T14:51:35.039219","indexId":"70256620","displayToPublicDate":"2022-11-11T09:48:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"The effect of scent lures on detection is not equitable among sympatric species","docAbstract":"

Context: Camera trapping is an effective tool for cost-efficient monitoring of species over large temporal and spatial scales and it is becoming an increasingly popular method for investigating wildlife communities and trophic interactions. However, camera trapping targeting rare and elusive species can be hampered by low detection rates, which can decrease the accuracy and precision of results from common analytical approaches (e.g., occupancy modeling, capture-recapture). Consequently, researchers often employ attractants to increase detection without accounting for how attractants influence detection of species among trophic levels.

Aims: We aimed to evaluate the influences of a commonly used non-species-specific olfactory lure (i.e. sardines) and sampling design on detection of four species (i.e. bobcat [Lynx rufus], coyote [Canis latrans], raccoon [Procyon lotor], and eastern cottontail [Sylvilagus floridanus]) that represented a range of foraging guilds in an agricultural landscape.

Methods: We set 180 camera stations, each for ∼28 days, during the summer of 2019. We set cameras with one of three lure treatments: (1) olfactory lure, (2) no olfactory lure, or (3) olfactory lure only during the latter half of the survey. We evaluated the influence of the lure at three temporal scales of detection (i.e. daily probability of detection, independent sequences per daily detection, and triggers per independent sequence).

Key results: The lure tended to positively influence detection of coyotes and raccoons but negatively influenced detection of bobcats and eastern cottontails. The influence of the lure varied among temporal scales of detection.

Conclusions: Scent lures can differentially influence detection of species within or among tropic levels, and the influence of a scent lure may vary among temporal scales.

Implications: Our results demonstrate the importance of evaluating the influence of an attractant for each focal species when using camera data to conduct multi-species or community analyses, accounting for variation in sampling strategies across cameras, and identifying the appropriate species-specific temporal resolution for assessing variation in detection data. Furthermore, we highlight that care should be taken when using camera data as an index of relative abundance (e.g. as is commonly done with prey species) when there is variation in the use of lures across cameras.

","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WR22094","usgsCitation":"Dart, M.M., Perkins, L., Jenks, J., Hatfield, G., and Lonsinger, R.C., 2023, The effect of scent lures on detection is not equitable among sympatric species: Wildlife Research, v. 50, no. 3, p. 190-200, https://doi.org/10.1071/WR22094.","productDescription":"11 p.","startPage":"190","endPage":"200","ipdsId":"IP-135401","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dart, Marlin M.","contributorId":340675,"corporation":false,"usgs":false,"family":"Dart","given":"Marlin","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Lora B.","contributorId":224968,"corporation":false,"usgs":false,"family":"Perkins","given":"Lora B.","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":908347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenks, Jonathan A.","contributorId":264322,"corporation":false,"usgs":false,"family":"Jenks","given":"Jonathan A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatfield, Gary","contributorId":341397,"corporation":false,"usgs":false,"family":"Hatfield","given":"Gary","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238549,"text":"70238549 - 2023 - Geochemistry of the Cretaceous Mowry Shale in the Wind River Basin, Wyoming","interactions":[],"lastModifiedDate":"2023-07-11T15:28:59.13242","indexId":"70238549","displayToPublicDate":"2022-11-11T07:22:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of the Cretaceous Mowry Shale in the Wind River Basin, Wyoming","docAbstract":"

The siliceous nature of the Mowry Shale distinguishes it from many of the well-studied organic-rich mudstones of the Cretaceous Western Interior Seaway. Available models of organic enrichment in mudstones rarely incorporate detailed biomarker, bulk organic, inorganic, and mineralogy data. Here, we used these data to evaluate how variations in organic matter source, productivity, dilution, and preservation modulated organic matter accumulation during the deposition of the Mowry Shale, while also demonstrating the benefits of this integrated approach. An organic stable carbon isotope vertical profile for the Mowry Shale is presented to test whether the Mowry Shale was deposited during oceanic anoxic event 1d (OAE 1d), thereby contributing to organic enrichment in the Mowry Shale.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36382.1","usgsCitation":"French, K.L., Birdwell, J.E., and Lillis, P.G., 2023, Geochemistry of the Cretaceous Mowry Shale in the Wind River Basin, Wyoming: GSA Bulletin, v. 135, no. 7-8, p. 1899-1922, https://doi.org/10.1130/B36382.1.","productDescription":"24 p.","startPage":"1899","endPage":"1922","ipdsId":"IP-131176","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":445264,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b36382.1","text":"Publisher Index Page"},{"id":435562,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FKDVK2","text":"USGS data release","linkHelpText":"Data release for Geochemistry of the Cretaceous Mowry Shale in the Wind River Basin, Wyoming"},{"id":409791,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.00,\n 43.00\n ],\n [\n -107.00,\n 42.20\n ],\n [\n -106.2,\n 42.20\n ],\n [\n -106.2,\n 43.00\n ],\n [\n -107.00,\n 43.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"135","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2022-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":false,"id":857833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":857834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lillis, Paul G. 0000-0002-7508-1699 plillis@usgs.gov","orcid":"https://orcid.org/0000-0002-7508-1699","contributorId":1817,"corporation":false,"usgs":true,"family":"Lillis","given":"Paul","email":"plillis@usgs.gov","middleInitial":"G.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":857835,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238554,"text":"70238554 - 2023 - Ecologically relevant moisture and temperature metrics for assessing dryland ecosystem dynamics","interactions":[],"lastModifiedDate":"2023-04-11T16:54:56.776638","indexId":"70238554","displayToPublicDate":"2022-11-11T06:51:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Ecologically relevant moisture and temperature metrics for assessing dryland ecosystem dynamics","docAbstract":"

In drylands, water-limited regions that cover ~40% of the global land surface, ecosystems are primarily controlled by access to soil moisture and exposure to simultaneously hot and dry conditions. Quantifying ecologically relevant environmental metrics is difficult in drylands because the response of vegetation to moisture and temperature conditions is not easily explained solely by climate-based metrics. To address this knowledge gap, we developed and examined 27 climate and ecological drought metrics across dryland areas of the western U.S. Included in the 27 metrics is a suite of 19 largely new “ecological drought metrics” that are designed to quantify multiple aspects of environmental limitation in drylands, including overall growing conditions, seasonal fluctuations, seasonal moisture timing, exposure to extreme drought, and recruitment potential for perennial plants. To quantify these metrics, we simulated water balance pools and fluxes of daily soil moisture at multiple depths with historical weather from 1970-2010 using the SOILWAT2 ecosystem water balance model. We assessed the relationships among these metrics and their spatial and temporal patterns. We found that the inclusion of ecological drought metrics substantially increased the dimensionality of the climate metrics dataset; the number of independent variables needed to explain 90% of the variance in the dataset increased with the addition of ecological drought metrics. Spatial patterns in overall growing conditions represented well-known differences among ecoregions, for example high temperatures and low precipitation in the southwest and cool temperatures and greater precipitation in the northeast. Seasonal fluctuation in soil water availability (SWA) was greatest in the southwest (Mojave Desert) while fluctuation in climatic water deficit (CWD) was greatest in the northwest (northern Great Basin and Columbia Plateau). Seasonal timing of moisture also differed among metrics; the timing of wet degree days (WDD), SWA and CWD were only weakly related to seasonal timing of precipitation. Plant recruitment metrics varied strongly across western drylands. In the Great Plains, recruitment events occurred more frequently and lasted longer than in the intermountain regions, where recruitment events were comparatively rare and short. These ecological drought metrics provide new insight into patterns of soil moisture and temperature that shape the structure and function of dryland ecosystems. The metrics will be useful for assessing the potential impact of climate change on dryland ecosystems and developing adaptive resource management strategies to sustain dryland ecosystem services in a changing world.

","language":"English","publisher":"Wiley","doi":"10.1002/eco.2509","usgsCitation":"Chenoweth, D.A., Schlaepfer, D.R., Chambers, J., Brown, J.L., Urza, A., Hanberry, B., Board, D., Crist, M., and Bradford, J., 2023, Ecologically relevant moisture and temperature metrics for assessing dryland ecosystem dynamics: Ecohydrology, v. 16, no. 3, e2509, https://doi.org/10.1002/eco.2509.","productDescription":"e2509","ipdsId":"IP-144651","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445266,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2509","text":"Publisher Index Page"},{"id":409787,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Chenoweth, D. A.","contributorId":299480,"corporation":false,"usgs":false,"family":"Chenoweth","given":"D.","email":"","middleInitial":"A.","affiliations":[{"id":64858,"text":"SBSC?","active":true,"usgs":false}],"preferred":false,"id":857873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":857874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chambers, J. C.","contributorId":299481,"corporation":false,"usgs":false,"family":"Chambers","given":"J. C.","affiliations":[{"id":64861,"text":"USDA Forest Service, Rocky Mountain Research Station, Reno, Nevada","active":true,"usgs":false}],"preferred":false,"id":857875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, J. L.","contributorId":299482,"corporation":false,"usgs":false,"family":"Brown","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":64861,"text":"USDA Forest Service, Rocky Mountain Research Station, Reno, Nevada","active":true,"usgs":false}],"preferred":false,"id":857876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Urza, A. K.","contributorId":299483,"corporation":false,"usgs":false,"family":"Urza","given":"A. K.","affiliations":[{"id":64861,"text":"USDA Forest Service, Rocky Mountain Research Station, Reno, Nevada","active":true,"usgs":false}],"preferred":false,"id":857877,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hanberry, Brice","contributorId":219278,"corporation":false,"usgs":false,"family":"Hanberry","given":"Brice","affiliations":[{"id":39985,"text":"USDA Forest Service, Rapid City, SD","active":true,"usgs":false}],"preferred":false,"id":857878,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Board, D.","contributorId":299484,"corporation":false,"usgs":false,"family":"Board","given":"D.","email":"","affiliations":[{"id":64861,"text":"USDA Forest Service, Rocky Mountain Research Station, Reno, Nevada","active":true,"usgs":false}],"preferred":false,"id":857879,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crist, M.","contributorId":299485,"corporation":false,"usgs":false,"family":"Crist","given":"M.","affiliations":[{"id":64862,"text":"USDOI Bureau of Land Management, National Interagency Fire Center, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":857880,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":857881,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238452,"text":"70238452 - 2023 - Deep learning for pockmark detection: Implications for quantitative seafloor characterization","interactions":[],"lastModifiedDate":"2022-12-01T16:22:19.640379","indexId":"70238452","displayToPublicDate":"2022-11-11T06:38:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Deep learning for pockmark detection: Implications for quantitative seafloor characterization","docAbstract":"

Occurring globally, pockmarks are seafloor depressions associated with seabed fluid escape. Pockmark ubiquity and morphologic heterogeneity result in an irregular seafloor that can be difficult to quantitatively describe. To address this challenge, we test the hypothesis that deep-learning based object detection and segmentation can be used to develop data-driven models for pockmark identification and characterization. This study describes the development, testing, and deployment of eight separate deep learning-based pockmark detection models using publicly available, gridded bathymetric data from the Belfast Bay, Maine, USA, Blue Hill Bay, Maine, USA, and Passamaquoddy Bay, New Brunswick, Canada estuarine pockmark fields. The models tested include three types of convolutional neural network architectures, as well as a generative adversarial network. We find that the data-driven models consistently resolve pockmarks from the background seafloor, allowing for quick and accurate delineation of pockmarks in a variety of seabed habitats. With these delineations we examine and compare the morphology of the muddy estuarine pockmark fields. We then compare these morphometric results to pockmark fields in two distinct settings, the sandy German Bight and the Aquitaine continental slope. We find that in all the pockmark fields a power law relationship, generally, exists between pockmark area and pockmark depth, though this relationship deteriorates with the smallest pockmarks, suggesting that there may be a minimum size needed for geomorphic stability. These results show that the training data and trained models developed here can be applied for quick detection and characterization of pockmarks where other high-resolution bathymetry is available, demonstrating the value of data-driven detection models for characterizing morphologically complex seafloors. Last, the morphologic characteristics of pockmarks identified in this study will aid future studies in relating pockmark size to environmental characteristics like seabed sediment texture and regional gradient.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2022.108524","usgsCitation":"Lundine, M., Brothers, L.L., and Trembanis, A., 2023, Deep learning for pockmark detection: Implications for quantitative seafloor characterization: Geomorphology, v. 421, 108524, 20 p., https://doi.org/10.1016/j.geomorph.2022.108524.","productDescription":"108524, 20 p.","ipdsId":"IP-140860","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":445268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2022.108524","text":"Publisher Index Page"},{"id":409583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"421","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lundine, Mark","contributorId":299298,"corporation":false,"usgs":false,"family":"Lundine","given":"Mark","affiliations":[{"id":64810,"text":"School of Marine Science and Policy, University of Delaware, Lewes, DE, 19958","active":true,"usgs":false}],"preferred":false,"id":857520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":857521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trembanis, Arthur","contributorId":299299,"corporation":false,"usgs":false,"family":"Trembanis","given":"Arthur","email":"","affiliations":[{"id":64812,"text":"School of Marine Science and Policy, University of Delaware, Newark, DE","active":true,"usgs":false}],"preferred":false,"id":857522,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238508,"text":"70238508 - 2023 - Dispersal limitations increase vulnerability under climate change for reptiles and amphibians in the southwestern United States","interactions":[],"lastModifiedDate":"2022-12-15T15:53:47.985829","indexId":"70238508","displayToPublicDate":"2022-11-09T07:17:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Dispersal limitations increase vulnerability under climate change for reptiles and amphibians in the southwestern United States","docAbstract":"

Species conservation plans frequently rely on information that spans political and administrative boundaries, especially when predictions are needed of future habitat under climate change; however, most species conservation plans and their requisite predictions of future habitat are often limited in geographical scope. Moreover, dispersal constraints for species of concern are not often incorporated into distribution models, which can result in overly optimistic predictions of future habitat. We used a standard modeling approach across a suite of 23 taxa of amphibians and reptiles in the North American deserts (560,024 km2 across 13 ecoregions) to assess impacts of climate change on habitat and combined landscape population dispersal simulations with species distribution modeling to reduce the risk of predicting future habitat in areas that are not available to species given their dispersal abilities. We used 3 general circulation models and 2 representative concentration pathways (RCPs) to represent multiple scenarios of future habitat potential and assess which study species may be most vulnerable to changes forecasted under each climate scenario. Amphibians were the most vulnerable taxa, but the most vulnerable species tended to be those with the lowest dispersal ability rather than those with the most specialized niches. Under the most optimistic climate scenario considered (RCP 2.6; a stringent scenario requiring declining emissions from 2020 to near zero emissions by 2100), 76% of the study area may experience a loss of >20% of the species examined, while up to 87% of the species currently present may be lost in some areas under the most pessimistic climate scenario (RCP 8.5; a scenario wherein greenhouse gases continue to increase through 2100 based on trajectories from the mid-century). Most areas with high losses were concentrated in the Arizona and New Mexico Plateau ecoregion, the Edwards Plateau in Texas, and the Southwestern Tablelands in New Mexico and Texas, USA. Under the most pessimistic climate scenario, all species are predicted to lose some existing habitat, with an average of 34% loss of extant habitat across all species. Even under the most optimistic scenario, we detected an average loss of 24% of extant habitat across all species, suggesting that changing climates may influence the ranges of reptiles and amphibians in the Southwest.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22317","usgsCitation":"Inman, R.D., Esque, T., and Nussear, K.E., 2023, Dispersal limitations increase vulnerability under climate change for reptiles and amphibians in the southwestern United States: Journal of Wildlife Management, v. 87, no. 1, e22317, 24 p., https://doi.org/10.1002/jwmg.22317.","productDescription":"e22317, 24 p.","ipdsId":"IP-132931","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":409674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, New Mexico, Texas, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.8246490580444,\n 32.99421031359047\n ],\n [\n -111.07855530804426,\n 31.4327067145355\n ],\n [\n -107.73871155804437,\n 31.4327067145355\n ],\n [\n -106.9476959330445,\n 31.507669666229916\n ],\n [\n -105.2777740580443,\n 30.225196629424758\n ],\n [\n -103.43207093304414,\n 29.00267655402955\n ],\n [\n -102.11371155804439,\n 29.61579003237854\n ],\n [\n -101.05902405804407,\n 29.844754425911688\n ],\n [\n -101.05902405804407,\n 30.7553503055194\n ],\n [\n -102.28949280804434,\n 32.105204045845085\n ],\n [\n -103.95941468304405,\n 33.21508124910966\n ],\n [\n -106.06878968304416,\n 34.16573932380892\n ],\n [\n -109.7601959330445,\n 34.81769394466865\n ],\n [\n -112.30902405804453,\n 35.536082625489954\n ],\n [\n -113.71527405804427,\n 37.234101477980246\n ],\n [\n -116.17621155804434,\n 38.483104366137184\n ],\n [\n -117.4945709330446,\n 39.37194265043874\n ],\n [\n -119.07660218304432,\n 39.16781523555832\n ],\n [\n -119.6918365580442,\n 37.513484906858324\n ],\n [\n -119.34027405804426,\n 36.03516403665938\n ],\n [\n -118.98871155804434,\n 34.81769394466865\n ],\n [\n -116.9672271830442,\n 34.020169800955856\n ],\n [\n -115.8246490580444,\n 32.99421031359047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857669,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261713,"text":"70261713 - 2023 - Biophysical warming patterns of an open-top chamber and its short-term influence on a Phragmites wetland ecosystem in China","interactions":[],"lastModifiedDate":"2024-12-19T15:34:06.227243","indexId":"70261713","displayToPublicDate":"2022-11-08T09:25:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19858,"text":"China Geology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Biophysical warming patterns of an open-top chamber and its short-term influence on a Phragmites wetland ecosystem in China","title":"Biophysical warming patterns of an open-top chamber and its short-term influence on a Phragmites wetland ecosystem in China","docAbstract":"

Passive-warming, open-top chambers (OTCs) are widely applied for studying the effects of future climate warming on coastal wetlands. In this study, a set of six OTCs were established at a Phragmites wetland located in the Yellow River Delta of Dongying City, China. With data collected through online transmission and in-situ sensors, the attributes and patterns of realized OTCs warming are demonstrated. The authors also quantified the preliminary influence of experimental chamber warming on plant traits. OTCs produced an elevated average air temperature of 0.8°C (relative to controls) during the growing season (June to October) of 2018, and soil temperatures actually decreased by 0.54°C at a depth of 5 cm and 0.46°C at a depth of 30 cm in the OTCs. Variations in diel patterns of warming depend greatly on the heat sources of incoming radiation in the daytime versus soil heat flux at night. Warming effects were often larger during instantaneous analyses and influenced OTCs air temperatures from −2.5°C to 8.3°C dependent on various meteorological conditions at any given time, ranging from cooling influences from vertical heat exchange and vegetation to radiation-associated warming. Night-time temperature depressions in the OTCs were due to the low turbulence inside OTCs and changes in surface soil-atmosphere heat transfer. Plant shoot density, basal diameter, and biomass of Phragmites decreased by 23.2%, 6.3%, and 34.0%, respectively, under experimental warming versus controls, and plant height increased by 4.3%, reflecting less carbon allocation to stem structures as plants in the OTCs experienced simultaneous wind buffering. While these passive-warming OTCs created the desired warming effects both to the atmosphere and soils, pest damages on the plant leaves and lodging within the OTCs were extensive and serious, creating the need to consider control options for these chambers and the replicated OTCs studies underway in other Chinese Phragmites marshes (Panjin and Yancheng).

","language":"English","publisher":"China Geological Survey (CGS) and the Chinese Academy of Geological Sciences (CAGS)","doi":"10.31035/cg2022064","usgsCitation":"Yu, X., Ye, S., Pei, L., Xie, L., Krauss, K., Chapman, S.K., and Brix, H., 2023, Biophysical warming patterns of an open-top chamber and its short-term influence on a Phragmites wetland ecosystem in China: China Geology, v. 6, no. 4, p. 594-610, https://doi.org/10.31035/cg2022064.","productDescription":"17 p.","startPage":"594","endPage":"610","ipdsId":"IP-172931","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467134,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31035/cg2022064","text":"Publisher Index Page"},{"id":465333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","state":"Shandong Province","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 119.016667,\n 37.758333\n ],\n [\n 119.016667,\n 37.783333\n ],\n [\n 118.983333,\n 37.783333\n ],\n [\n 118.983333,\n 37.758333\n ],\n [\n 119.016667,\n 37.758333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yu, Xue-yang","contributorId":347381,"corporation":false,"usgs":false,"family":"Yu","given":"Xue-yang","email":"","affiliations":[{"id":83154,"text":"Key Laboratory of Coastal Wetland Biogeosciences, Qingdao Institute of Marine Geology, China Geological Survey","active":true,"usgs":false}],"preferred":false,"id":921567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ye, Si-yuan","contributorId":347382,"corporation":false,"usgs":false,"family":"Ye","given":"Si-yuan","email":"","affiliations":[{"id":83154,"text":"Key Laboratory of Coastal Wetland Biogeosciences, Qingdao Institute of Marine Geology, China Geological Survey","active":true,"usgs":false}],"preferred":false,"id":921568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pei, Li-xin","contributorId":347383,"corporation":false,"usgs":false,"family":"Pei","given":"Li-xin","email":"","affiliations":[{"id":83154,"text":"Key Laboratory of Coastal Wetland Biogeosciences, Qingdao Institute of Marine Geology, China Geological Survey","active":true,"usgs":false}],"preferred":false,"id":921569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xie, Liu-juan","contributorId":347384,"corporation":false,"usgs":false,"family":"Xie","given":"Liu-juan","email":"","affiliations":[{"id":83154,"text":"Key Laboratory of Coastal Wetland Biogeosciences, Qingdao Institute of Marine Geology, China Geological Survey","active":true,"usgs":false}],"preferred":false,"id":921570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":921571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Samantha K.","contributorId":303864,"corporation":false,"usgs":false,"family":"Chapman","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":921572,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brix, Hans","contributorId":146735,"corporation":false,"usgs":false,"family":"Brix","given":"Hans","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":921573,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239403,"text":"70239403 - 2023 - Drought related changes in water quality surpass effects of experimental flows on trout growth downstream of Lake Powell reservoir","interactions":[],"lastModifiedDate":"2023-03-01T17:10:22.533001","indexId":"70239403","displayToPublicDate":"2022-11-08T07:20:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Drought related changes in water quality surpass effects of experimental flows on trout growth downstream of Lake Powell reservoir","docAbstract":"
Flows released from reservoirs are often modified to mitigate the negative ecosystem effects of dams. We estimated the effects of two experimental flows, fall-timed floods and elimination of sub-daily variation in flows on weekends, on growth rates of rainbow trout (Oncorhynchus mykiss) in the Colorado River downstream from Glen Canyon Dam. Experimental flow effects were compared to effects of water temperature, phosphorous concentration, solar insolation, and competition, by fitting mixed effect von Bertalanffy models to ~ 10,000 observations of growth from mark-recapture between 2012 and 2021. There was strong support for models predicting faster growth during intervals with higher solar insolation, and lower water temperature and competition for prey. Effects of phosphorus and experimental flows were small and uncertain. Drought-related increases in dam release temperatures during summer and fall were predicted to result in severe weight loss for larger trout and could eventually threaten the viability of the population and the fishery it supports. The effects of water temperature and competition on fish growth substantially exceeded the effects of controlled floods and steadier flows.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2022-0142","usgsCitation":"Korman, J., Deemer, B., Yackulic, C., Kennedy, T., and Giardina, M.A., 2023, Drought related changes in water quality surpass effects of experimental flows on trout growth downstream of Lake Powell reservoir: Canadian Journal of Fisheries and Aquatic Sciences, v. 80, no. 3, p. 424-438, https://doi.org/10.1139/cjfas-2022-0142.","productDescription":"15 p.","startPage":"424","endPage":"438","ipdsId":"IP-141100","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435563,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XU3SQP","text":"USGS data release","linkHelpText":"Rainbow trout growth data and growth covariate data from Glen Canyon, Colorado River, Arizona, 2012-2021"},{"id":411782,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Lake Powell reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.7996180959762,\n 37.635491823116155\n ],\n [\n -111.7996180959762,\n 36.615463907400354\n ],\n [\n -110.6849791564707,\n 36.615463907400354\n ],\n [\n -110.6849791564707,\n 37.635491823116155\n ],\n [\n -111.7996180959762,\n 37.635491823116155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":861468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861470,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giardina, Mariah Aurelia 0000-0001-6753-0450","orcid":"https://orcid.org/0000-0001-6753-0450","contributorId":300798,"corporation":false,"usgs":true,"family":"Giardina","given":"Mariah","email":"","middleInitial":"Aurelia","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861472,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240155,"text":"70240155 - 2023 - Integrated assessment of chemical and biological recovery after diversion and treatment of acid mine drainage in a Rocky Mountain stream","interactions":[],"lastModifiedDate":"2023-01-31T13:09:43.44887","indexId":"70240155","displayToPublicDate":"2022-11-08T07:05:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Integrated assessment of chemical and biological recovery after diversion and treatment of acid mine drainage in a Rocky Mountain stream","docAbstract":"

Responses of stream ecosystems to gradual reductions in metal loading following remediation or restoration activities have been well documented in the literature. However, much less is known about how these systems respond to the immediate or more rapid elimination of metal inputs. Construction of a water treatment plant on the North Fork of Clear Creek (NFCC; CO, USA), a US Environmental Protection Agency Superfund site, captured, diverted, and treated the two major point-source inputs of acid mine drainage (AMD) and provided an opportunity to investigate immediate improvements in water quality. We conducted a 9-year study that included intensive within- and among-year monitoring of receiving-stream chemistry and benthic communities before and after construction of the treatment plant. Results showed a 64%–86% decrease in metal concentrations within months at the most contaminated sites. Benthic communities responded with increased abundance and diversity, but downstream stations remained impaired relative to reference conditions, with significantly lower taxonomic richness represented by a few dominant taxa (i.e., Baetis sp., Hydropsyche sp., Simulium sp., Orthocladiinae). Elevated metal concentrations from apparent residual sources, and relatively high conductivity from contributing major ions not removed during the treatment process, are likely limiting downstream recovery. Our study demonstrates that direct AMD treatment can rapidly improve water quality and benefit aquatic life, but effectiveness is limited, in part, to the extent that inputs of metals are captured and treated. Consideration should also be given to the effects of elevated major ion concentrations from the treated effluent not removed during the lime treatment process. Continued chemical and biological monitoring will be needed to quantify the NFCC recovery trajectory and to inform future remediation strategies. 

","language":"English","publisher":"Wiley","doi":"10.1002/etc.5515","usgsCitation":"Kotalik, C.J., Meyer, J.S., Cadmus, P., Ranville, J.F., and Clements, W.H., 2023, Integrated assessment of chemical and biological recovery after diversion and treatment of acid mine drainage in a Rocky Mountain stream: Environmental Toxicology and Chemistry, v. 42, no. 2, p. 512-524, https://doi.org/10.1002/etc.5515.","productDescription":"13 p.","startPage":"512","endPage":"524","ipdsId":"IP-142082","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":445271,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5515","text":"Publisher Index Page"},{"id":435564,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RQVHRO","text":"USGS data release","linkHelpText":"Stream water chemistry and benthic macroinvertebrate data from the North Fork Clear Creek and Clear Creek, Colorado, USA, from 2011-2019, before and after acid mine drainage treatment"},{"id":412494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.66319931456442,\n 39.91001938862544\n ],\n [\n -105.66319931456442,\n 39.61244722626233\n ],\n [\n -105.21020565688403,\n 39.61244722626233\n ],\n [\n -105.21020565688403,\n 39.91001938862544\n ],\n [\n -105.66319931456442,\n 39.91001938862544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kotalik, Christopher James 0000-0001-6739-6036","orcid":"https://orcid.org/0000-0001-6739-6036","contributorId":301847,"corporation":false,"usgs":true,"family":"Kotalik","given":"Christopher","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":862794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Joseph S.","contributorId":173130,"corporation":false,"usgs":false,"family":"Meyer","given":"Joseph","email":"","middleInitial":"S.","affiliations":[{"id":27156,"text":"Colorado School of Mines/ARCADIS Inc.","active":true,"usgs":false}],"preferred":false,"id":862795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cadmus, Pete","contributorId":173609,"corporation":false,"usgs":false,"family":"Cadmus","given":"Pete","email":"","affiliations":[{"id":27254,"text":"Colorado Parks and Wildlife; Colorado State University","active":true,"usgs":false}],"preferred":false,"id":862796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ranville, James F.","contributorId":141192,"corporation":false,"usgs":false,"family":"Ranville","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":13709,"text":"Colorrado School of Mines, Golden","active":true,"usgs":false}],"preferred":false,"id":862797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clements, William H.","contributorId":178714,"corporation":false,"usgs":false,"family":"Clements","given":"William","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":862798,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245594,"text":"70245594 - 2023 - Spatial patterns and seasonal timing of increasing riverine specific conductance from 1998 to 2018 suggest legacy contamination in the Delaware River Basin","interactions":[],"lastModifiedDate":"2023-06-26T11:44:09.312936","indexId":"70245594","displayToPublicDate":"2022-11-08T06:39:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns and seasonal timing of increasing riverine specific conductance from 1998 to 2018 suggest legacy contamination in the Delaware River Basin","docAbstract":"

Increasing salinization of freshwater threatens water supplies that support a range of human and ecological uses. The latest assessments of Delaware River Basin (DRB) surface-water-quality changes indicate widespread salinization has occurred in recent decades, which may lead to meaningful degradation in water quality. To better understand how and when salinity transport occurs and implications for DRB streams, this study: 1) explores the variability of specific conductance (SC) trends spatially and seasonally from 1998 to 2018, and 2) investigates how trends relate to streamflow, land disturbance, and impervious surface area to better understand regional salinization drivers. We find widespread increases in SC across the DRB, with several sites in the lower basin exceeding thresholds for aquatic life and experiencing increasing frequencies of exceedance over time. In general, the greatest basin wide increases in SC occurred during low flow conditions, indicating that a legacy component resulting from subsurface retention and transport processes has driven observed changes in riverine SC. For a subset of sites in the lower basin, where impervious area and cumulative land disturbance are higher, the greatest SC increases occurred during high flow conditions in winter months. Given the patterns of SC and watershed changes across the basin, as well as strong relationships between SC trends and sodium and chloride trends, deicing salt appears to be a likely driver of observed SC change. Even if deicing salt application plateaus or declines in coming years, the continued release and transport of the legacy subsurface component may still contribute to elevated DRB riverine SC.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159691","usgsCitation":"Rumsey, C., Hammond, J., Murphy, J.C., Shoda, M.E., and Soroka, A.M., 2023, Spatial patterns and seasonal timing of increasing riverine specific conductance from 1998 to 2018 suggest legacy contamination in the Delaware River Basin: Science of the Total Environment, v. 858, no. Part 1, 159691, 13 p., https://doi.org/10.1016/j.scitotenv.2022.159691.","productDescription":"159691, 13 p.","ipdsId":"IP-140126","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":445274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.159691","text":"Publisher Index Page"},{"id":418452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.67378246807358,\n 42.61393642387205\n ],\n [\n -76.67378246807358,\n 38.063286063929894\n ],\n [\n -74.69709151035819,\n 38.063286063929894\n ],\n [\n -74.69709151035819,\n 42.61393642387205\n ],\n [\n -76.67378246807358,\n 42.61393642387205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"858","issue":"Part 1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shoda, Megan E. 0000-0002-5343-9717 meshoda@usgs.gov","orcid":"https://orcid.org/0000-0002-5343-9717","contributorId":4352,"corporation":false,"usgs":true,"family":"Shoda","given":"Megan","email":"meshoda@usgs.gov","middleInitial":"E.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":876190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876191,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238899,"text":"70238899 - 2023 - First principles calibration of 40Ar abundances in 40Ar/39Ar mineral neutron fluence monitors: Methodology and preliminary results","interactions":[],"lastModifiedDate":"2023-02-14T14:50:56.284984","indexId":"70238899","displayToPublicDate":"2022-11-07T08:06:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1822,"text":"Geostandards and Geoanalytical Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"First principles calibration of 40Ar abundances in 40Ar/39Ar mineral neutron fluence monitors: Methodology and preliminary results","title":"First principles calibration of 40Ar abundances in 40Ar/39Ar mineral neutron fluence monitors: Methodology and preliminary results","docAbstract":"

The accuracy and traceability of geochronometers are of vital importance to questions asked by many Earth scientists. The widely applied 40Ar/39Ar geochronometer relies on the co-irradiation of samples with neutron fluence monitors (reference materials) of known ages; the ages and uncertainties of these monitors are critical to our ability to apply this chronometer. Previously, first principles, astronomical and optimisation calibrations have been made. The first principles method for determining the age of monitor minerals is the K-Ar method, which involves measurement of their 40K and 40Ar* abundances. The AQuA (Absolute Quantities of Argon) pipette system, which emits calibrated quantities of 40Ar* via the ideal gas law, was used to calibrate the sensitivity of the system across a range of source pressures and estimate 40Ar* abundances in neutron fluence monitors. These 40Ar abundances were combined with existing 40K abundance data for these monitors. Ages for HD-B1 and MD2 (GA1550) biotite fluence monitors were calculated and combined with intercalibration data for HD-B1 and Fish Canyon sanidine (FCs) to determine ages for FCs. Current results do not have the targeted accuracy when compared with previous calibrations; however, we show how the extensive methodology development presented here can be used towards making reliable future measurements.

","language":"English","publisher":"Wiley","doi":"10.1111/ggr.12464","usgsCitation":"Morgan, L.E., Davidheiser-Kroll, B., Kuiper, K.F., Mark, D.F., McLean, N.M., and Wijbrans, J., 2023, First principles calibration of 40Ar abundances in 40Ar/39Ar mineral neutron fluence monitors: Methodology and preliminary results: Geostandards and Geoanalytical Research, v. 47, no. 1, p. 91-104, https://doi.org/10.1111/ggr.12464.","productDescription":"14 p.","startPage":"91","endPage":"104","ipdsId":"IP-134452","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":445276,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ggr.12464","text":"Publisher Index Page"},{"id":410541,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":859090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidheiser-Kroll, Brett","contributorId":176175,"corporation":false,"usgs":false,"family":"Davidheiser-Kroll","given":"Brett","email":"","affiliations":[],"preferred":false,"id":859091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuiper, Klaudia F. 0000-0001-6345-5019","orcid":"https://orcid.org/0000-0001-6345-5019","contributorId":229575,"corporation":false,"usgs":false,"family":"Kuiper","given":"Klaudia","email":"","middleInitial":"F.","affiliations":[{"id":41675,"text":"Faculty of Earth and Life Sciences, VU University Amsterdam","active":true,"usgs":false}],"preferred":false,"id":859092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mark, Darren F. 0000-0002-0707-9773","orcid":"https://orcid.org/0000-0002-0707-9773","contributorId":229571,"corporation":false,"usgs":false,"family":"Mark","given":"Darren","email":"","middleInitial":"F.","affiliations":[{"id":41672,"text":"Isotope Geoscience Unit, Scottish Universities Environmental Research Centre (SUERC)","active":true,"usgs":false}],"preferred":false,"id":859093,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Noah M. 0000-0003-0388-1862","orcid":"https://orcid.org/0000-0003-0388-1862","contributorId":299948,"corporation":false,"usgs":false,"family":"McLean","given":"Noah","email":"","middleInitial":"M.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":859094,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wijbrans, Jan 0000-0002-8091-1239","orcid":"https://orcid.org/0000-0002-8091-1239","contributorId":229585,"corporation":false,"usgs":false,"family":"Wijbrans","given":"Jan","email":"","affiliations":[{"id":41681,"text":"Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1085, 1081HV, Amsterdam, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":859095,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238717,"text":"70238717 - 2023 - Dynamic estimates of geologic CO2 storage resources in the Illinois Basin constrained by reinjectivity of brine extracted for pressure management","interactions":[],"lastModifiedDate":"2023-02-14T14:43:24.80581","indexId":"70238717","displayToPublicDate":"2022-11-07T07:10:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5800,"text":"Greenhouse Gases: Science and Technology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Dynamic estimates of geologic CO2 storage resources in the Illinois Basin constrained by reinjectivity of brine extracted for pressure management","title":"Dynamic estimates of geologic CO2 storage resources in the Illinois Basin constrained by reinjectivity of brine extracted for pressure management","docAbstract":"

Geologic carbon storage (GCS) is likely to be an important part of global efforts to decarbonize the energy industry. Widespread deployment of GCS relies on strategies to maximize CO2 injection rates while minimizing reservoir pressurization that could induce seismicity and/or fluid leakage into groundwater resources. Brine extraction from CO2 storage formations with subsurface reinjection elsewhere could mitigate pressure buildup associated with GCS. Therefore, evaluation of CO2 storage resources should consider the injectivity of produced brine in geologic layers above or below the CO2 storage formation. For this study, a methodology was developed to estimate brine injectivity from formation depth and thickness using flow modeling and optimization techniques. The methodology was demonstrated in the Illinois Basin, where GCS in the Mt. Simon Sandstone is ongoing. Based on pressure constraints and considering only regions of the shallower units where salinity and sealing conditions were met, maximum brine injection rates were estimated within the Mt. Simon and three overlying hydrostratigraphic layers. Results indicate that a large area exists where CO2 injectivity could be optimized by brine extraction and reinjection. 

","language":"English","publisher":"Wiley","doi":"10.1002/ghg.2189","usgsCitation":"Plampin, M.R., Anderson, S.T., Cahan, S., and Finsterle, S., 2023, Dynamic estimates of geologic CO2 storage resources in the Illinois Basin constrained by reinjectivity of brine extracted for pressure management: Greenhouse Gases: Science and Technology, v. 13, no. 1, p. 31-47, https://doi.org/10.1002/ghg.2189.","productDescription":"17 p.","startPage":"31","endPage":"47","ipdsId":"IP-133192","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488623,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ghg.2189","text":"Publisher Index Page"},{"id":410104,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana","otherGeospatial":"Illinois Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.41305951012697,\n 42.44134708537527\n ],\n [\n -90.41305951012697,\n 37.15746823595089\n ],\n [\n -84.82597977109455,\n 37.15746823595089\n ],\n [\n -84.82597977109455,\n 42.44134708537527\n ],\n [\n -90.41305951012697,\n 42.44134708537527\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":858349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":858350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahan, Steven M. 0000-0002-4776-3668","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":205929,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":858351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finsterle, Stefan","contributorId":299677,"corporation":false,"usgs":false,"family":"Finsterle","given":"Stefan","email":"","affiliations":[{"id":64929,"text":"Finsterle GeoConsulting, Inc.","active":true,"usgs":false}],"preferred":false,"id":858352,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239392,"text":"70239392 - 2023 - Growth portfolios buffer climate-linked environmental change in marine systems","interactions":[],"lastModifiedDate":"2023-03-15T14:34:59.845546","indexId":"70239392","displayToPublicDate":"2022-11-07T07:01:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Growth portfolios buffer climate-linked environmental change in marine systems","docAbstract":"

Large-scale, climate-induced synchrony in the productivity of fish populations is becoming more pronounced in the world's oceans. As synchrony increases, a population's “portfolio” of responses can be diminished, in turn reducing its resilience to strong perturbation. Here we argue that the costs and benefits of trait synchronization, such as the expression of growth rate, are context dependent. Contrary to prevailing views, synchrony among individuals could actually be beneficial for populations if growth synchrony increases during favorable conditions, and then declines under poor conditions when a broader portfolio of responses could be useful. Importantly, growth synchrony among individuals within populations has seldom been measured, despite well-documented evidence of synchrony across populations. Here, we used century-scale time series of annual otolith growth to test for changes in growth synchronization among individuals within multiple populations of a marine keystone species (Atlantic cod, Gadus morhua). On the basis of 74,662 annual growth increments recorded in 13,749 otoliths, we detected a rising conformity in long-term growth rates within five northeast Atlantic cod populations in response to both favorable growth conditions and a large-scale, multidecadal mode of climate variability similar to the East Atlantic Pattern. The within-population synchrony was distinct from the across-population synchrony commonly reported for large-scale environmental drivers. Climate-linked, among-individual growth synchrony was also identified in other Northeast Atlantic pelagic, deep-sea and bivalve species. We hypothesize that growth synchrony in good years and growth asynchrony in poorer years reflects adaptive trait optimization and bet hedging, respectively, that could confer an unexpected, but pervasive and stabilizing, impact on marine population productivity in response to large-scale environmental change.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3918","usgsCitation":"Campana, S., Smolinski, S., Black, B., Morrongiello, J., Alexandroff, S., Andersson, C., Bogstad, B., Butler, P., Denechaud, C., Frank, D.C., Geffen, A., Aanestad, J., Gronkjaer, P., Hjorleifsson, E., Jonsdottir, I.G., Meekan, M., Mette, M.J., Tanner, S.E., van der Sleen, P., and von Leesen, G., 2023, Growth portfolios buffer climate-linked environmental change in marine systems: Ecology, v. 104, no. 3, e3918, 16 p., https://doi.org/10.1002/ecy.3918.","productDescription":"e3918, 16 p.","ipdsId":"IP-128844","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":445283,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3918","text":"Publisher Index 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Stock assessment modeling provides a means to estimate the population dynamics of invasive fishes and may do so despite data limitations. Blue catfish (Ictalurus furcatus) were introduced to the Chesapeake Bay watershed to support recreational fisheries but also consume species of conservation need and economic importance. To assess management tradeoffs, managers need to understand the current status of the population and anticipate future population abundance and trends. A Bayesian size-based stock assessment model was used to estimate blue catfish abundance, fishing mortality, and size structure over time (2001–2016) in the tidal James River. The model estimated population size increases until around 2006, with declines in total abundance after 2011 and large blue catfish (≥80 cm total length) after 2001. These first estimates of blue catfish population dynamics in the Chesapeake Bay region provide inputs for projection models to evaluate prospective management actions and identify monitoring needs.

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Mass capture of flightless geese during the summer is a common trapping technique to obtain large numbers of individuals for research and marking, but few studies have assessed the impacts of this method on the survival of after-hatch-year geese. We evaluated the effects of holding time and captured flock size on the survival of >26,000 subadult (second yr) and adult (≥third yr) greater white-fronted geese (Anser albifrons frontalis) banded in Alaska, USA, 1999–2017. We constructed models with and without capture effects to analyze our band-recovery data and used Akaike's Information Criterion to rank our model set. Models that included both capture-related variables ranked highest. Longer individual holding times negatively affected survival during the first year after banding, and effects were greatest during the earliest years of our study when holding times were generally longer and protocols to minimize negative capture effects were less refined. There was a positive relationship between survival and captured flock size. We suggest practitioners reduce holding times of geese during mass captures to the extent practicable and continually evaluate and refine their methods to minimize negative capture effects.

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