Invasive dreissenid mussels (Dreissena polymorpha and Dreissena rostriformis bugensis) have altered Great Lakes ecosystems through a multitude of effects on benthic habitats, food web structure, and nutrient cycling. This study explores whether spatially continuous geographic data of environmental factors can be utilized to predict Dreissena spp. spatial distributions on a lake-wide scale. Categorical variables were also assessed for significant relationships with Dreissena spp. biomass. Point observations from the 2017 Lake Huron benthic survey under the Cooperative Science and Monitoring Initiative (CSMI) were utilized for in situ measurements of dreissenid presence and biomass at 119 sites across Lake Huron. Basin, bathymetric zone, and tributary influence were found to have statistically significant relationships to dreissenid biomass. A boosted regression tree (BRT) model (ROC score 0.707) was developed to spatially predict dreissenid presence probability across Lake Huron from six environmental explanatory variables: April, May, and October chlorophyll, June dissolved organic carbon, January bottom temperature, and May bottom temperature. The importance of food availability and bottom temperature illuminated relationships between dreissenid mussels and periods of benthic-pelagic mixing in the spring and fall seasons. Future models could be improved through advancements in survey technology for improved geographic characterization of mussel habitat characteristics and environmental constraints.
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2024 - Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?","interactions":[],"lastModifiedDate":"2025-02-13T16:56:38.677011","indexId":"70263545","displayToPublicDate":"2024-07-16T10:52:06","publicationYear":"2024","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":"Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?","docAbstract":"The Rose Canyon fault is the southern extension of the larger Newport–Inglewood–Rose Canyon fault system, which represents a major structural boundary in the Inner Continental Borderland (ICB) offshore of southern California. Ten to fifteen percent of total plate boundary motion in southern California is thought to be accommodated by the faults of the ICB, but the exact distribution of slip is uncertain. With an onshore segment, the Rose Canyon fault offers an opportunity to measure the slip rate using traditional geodetic methods. In this study, we use Global Positioning System (GPS) surface velocities from a combined campaign and continuous GPS network to constrain elastic models of the Rose Canyon fault. We then compare the observed surface velocities with proposed conceptual models of regional fault connections that facilitate the transfer of slip into the Rose Canyon fault to assess how well the observations are explained by the models. The results of elastic half‐space models suggest that the Rose Canyon fault may be slipping toward the higher end of geologic estimates, with the preferred model indicating a slip rate of 2.4 ± 0.5 mm/yr. Although limited in terms of near‐fault benchmarks, we find an improved model fit using an asymmetrical elastic half‐space model and a higher slip rate, suggesting a potential rheological contrast across the Rose Canyon fault, similar to observations from the northern Newport–Inglewood fault segments. Observed GPS surface velocities, background seismicity, and gravity anomalies south of San Diego Bay point toward a more easterly trace for the Rose Canyon fault, suggesting a possible connection with the San Miguel–Vallecitos fault system. Such a connection could increase the potential rupture lengths of future earthquakes and have important consequences for regional seismic hazards.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230278","usgsCitation":"Singleton, D.M., Maloney, J., Agnew, D., and Rockwell, T., 2024, Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?: Bulletin of the Seismological Society of America, v. 114, no. 5, p. 2751-2766, https://doi.org/10.1785/0120230278.","productDescription":"16 p.","startPage":"2751","endPage":"2766","ipdsId":"IP-149537","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.03132304321895,\n 32.37326157121886\n ],\n [\n -114.89786575651428,\n 32.75462583665147\n ],\n [\n -114.93333927794629,\n 33.64921849858126\n ],\n [\n -118.29120454417611,\n 35.815309260323346\n ],\n [\n -121.93695511034596,\n 35.53619205111963\n ],\n [\n -121.70925414240213,\n 34.55709712444637\n ],\n [\n -118.03132304321895,\n 32.37326157121886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Singleton, Drake Moore 0000-0001-5346-0623","orcid":"https://orcid.org/0000-0001-5346-0623","contributorId":261207,"corporation":false,"usgs":true,"family":"Singleton","given":"Drake","email":"","middleInitial":"Moore","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":927318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Jillian","contributorId":304141,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":927319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agnew, Duncan 0000-0002-2360-7783","orcid":"https://orcid.org/0000-0002-2360-7783","contributorId":178605,"corporation":false,"usgs":false,"family":"Agnew","given":"Duncan","email":"","affiliations":[],"preferred":false,"id":927320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rockwell, Thomas","contributorId":175454,"corporation":false,"usgs":false,"family":"Rockwell","given":"Thomas","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":927321,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255882,"text":"fs20243027 - 2024 - The 3D Elevation Program—Supporting Mississippi's economy","interactions":[],"lastModifiedDate":"2024-07-15T16:52:08.385282","indexId":"fs20243027","displayToPublicDate":"2024-07-15T12:34:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3027","displayTitle":"The 3D Elevation Program—Supporting Mississippi’s Economy","title":"The 3D Elevation Program—Supporting Mississippi's economy","docAbstract":"Mississippi has a dispersed population of nearly three million residents in an area of approximately 48,400 square miles and has a favorable climate for agriculture, with abundant precipitation and minimal extreme temperatures. The topography consists mostly of low hills and lowland plains, with the highest elevation about 800 feet above sea level. An exception is the nearly flat Mississippi Alluvial Plain, or “Delta,” in the northwestern part of the State. Agriculture and forestry are Mississippi’s major industries. With 65 percent of its area forested, the State is one of the country’s top producers of lumber and wood-related products. In addition to agriculture and forest resources management, other important economic activities are infrastructure and construction management, flood risk management, and water supply and quality assessment. High-quality elevation data can help to support these activities. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243027","usgsCitation":"Heleine, G.F., 2024, The 3D Elevation Program—Supporting Mississippi's economy: U.S. Geological Survey Fact Sheet 2024–3027, 2 p., https://doi.org/10.3133/fs20243027.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127131","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":430846,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3027/fs20243027.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3027 XML"},{"id":430845,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243027/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3027 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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Animal movement is a fundamental mechanism that shapes communities and ecosystems. Ungulates alter the ecosystems they inhabit and understanding their movements and distribution is critical for linking habitat with population dynamics. Predation risk has been shown to strongly influence ungulate movement patterns, such that ungulates may select habitat where predation risk is lower (refugia), adjust movement rates, temporal patterns, or selection of cover variables in areas with greater predation risk. We evaluated potential predation avoidance behavior in a population of plains bison inhabiting the north rim of Grand Canyon National Park (GRCA) and adjacent Kaibab National Forest (KNF). The KNF has year-round hunting managed by Arizona Game and Fish Department, whereas hunting is not allowed in GRCA. Human-maintained water sources on the KNF are particularly important resources for bison wherein they may be exposed to higher predation risk to access these resources. We used 2-h GPS locations for three years from 31 bison (n = 9 males; n = 22 females), and integrative step selection analysis to test four hypotheses about the potential for bison to reduce their risk from human predation by avoiding areas of high predation risk; moving faster in areas with high predation risk; entering high-risk areas at night when risk is reduced; and entering high-risk areas in habitats that provide cover (coniferous forest). The highest performing model indicated bison movement was 1.3 times faster per 2-h step interval than in areas with no hunting across all vegetation classes (coniferous forest, shrub, quaking aspen, grass-forb meadow) and across all topography classes (valley, slope, ridge). Bison moved more slowly in grass-forb meadows than all other vegetation types, and in valleys relative to slopes and ridges. Several radio-collared individuals had no GPS locations in KNF for the duration of the study. Bison avoided predation risk using two strategies: moving faster while in the KNF, and fully avoiding high-risk areas by remaining within GRCA. Management that manipulates or reduces timing of hunting seasons may reduce perceived predation risk and encourage bison to distribute into the KNF and across a broader range of available habitat.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4909","usgsCitation":"Salganek, S., Schoenecker, K., and Terwilliger, M., 2024, Using integrated step selection to determine effects of predation risk on bison habitat selection and movement: Ecosphere, v. 15, no. 7, e4909, 16 p., https://doi.org/10.1002/ecs2.4909.","productDescription":"e4909, 16 p.","ipdsId":"IP-148082","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492488,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4909","text":"Publisher Index Page"},{"id":492199,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","county":"Coconino County","otherGeospatial":"Kaibab Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.68098786953385,\n 36.999074018413296\n ],\n [\n -112.68098786953385,\n 35.85596339767277\n ],\n [\n -111.67893358079623,\n 35.85596339767277\n ],\n [\n -111.67893358079623,\n 36.999074018413296\n ],\n [\n -112.68098786953385,\n 36.999074018413296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Salganek, Skye","contributorId":357945,"corporation":false,"usgs":false,"family":"Salganek","given":"Skye","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":942896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":942897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terwilliger, Miranda L.N.","contributorId":357947,"corporation":false,"usgs":false,"family":"Terwilliger","given":"Miranda L.N.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":942898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256181,"text":"70256181 - 2024 - Modeling the potential habitat gained by planting sagebrush in burned landscapes","interactions":[],"lastModifiedDate":"2024-08-01T18:09:27.139004","indexId":"70256181","displayToPublicDate":"2024-07-15T07:03:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18016,"text":"Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the potential habitat gained by planting sagebrush in burned landscapes","docAbstract":"Many revegetation projects are intended to benefit wildlife species. Yet, there are few a priori evaluations that assess the potential efficiency of restoration actions in recovering wildlife habitats. We developed a spatial vegetation–habitat recovery model to gauge the degree to which field planting strategies could be expected to recover multi-factor habitat conditions for wildlife following wildfires. We simulated a wildfire footprint, multiple sagebrush (Artemisia spp.) planting scenarios, and tracked projected vegetation growth for 15 years post-fire. We used a vegetation transition framework to track and estimate the degree to which revegetation could accelerate habitat restoration for a Greater sage-grouse (Centrocercus) population within the Great Basin, western United States. We assessed the amount of habitat 15 years post-fire to estimate the degree to which revegetation could be expected to accelerate habitat restoration. Our results highlight a potential disconnect between the expansive areas required by wide-ranging wildlife such as sage-grouse and the relatively small areas that planting treatments have created. Habitat restorations and planting strategies that are intended to benefit sage-grouse may only speed up localized habitat restoration. This study provides an example of how linked revegetation–habitat modeling approaches can scope the expected return on restoration investment for habitat improvements and support the strategic use of limited restoration resources.
","language":"English","publisher":"MDPI","doi":"10.3390/conservation4030024","usgsCitation":"Heinrichs, J., O’Donnell, M.S., Orning, E.K., Pyke, D.A., Ricca, M.A., Coates, P.S., and Aldridge, C.L., 2024, Modeling the potential habitat gained by planting sagebrush in burned landscapes: Conservation, v. 4, no. 3, p. 364-377, https://doi.org/10.3390/conservation4030024.","productDescription":"14 p.","startPage":"364","endPage":"377","ipdsId":"IP-110620","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":439279,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/conservation4030024","text":"Publisher Index Page"},{"id":434928,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4WHHV","text":"USGS data release","linkHelpText":"veg_sim: Modeling Greater sage-grouse habitat suitability 15-years post simulated fire event and sagebrush transplanting (2015-2030)"},{"id":431438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.21272893162755,\n 42.005085754708546\n ],\n [\n -117.21272893162755,\n 40.091378407222805\n ],\n [\n -113.98274846287774,\n 40.091378407222805\n ],\n [\n -113.98274846287774,\n 42.005085754708546\n ],\n [\n -117.21272893162755,\n 42.005085754708546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":907004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":907005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orning, Elizabeth Kari 0000-0002-1376-729X","orcid":"https://orcid.org/0000-0002-1376-729X","contributorId":315548,"corporation":false,"usgs":true,"family":"Orning","given":"Elizabeth","email":"","middleInitial":"Kari","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":907006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":907007,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":907059,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":907060,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":907008,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256026,"text":"70256026 - 2024 - Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall","interactions":[],"lastModifiedDate":"2024-07-16T11:45:30.466723","indexId":"70256026","displayToPublicDate":"2024-07-15T06:38:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall","docAbstract":"Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I15, the model generally under-forecasted the inundation area. The observed peak I15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I15 than observed. Using these I15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.
Volcanic unrest and eruptions are associated with surface deformation and landscape change that can be detected, characterized, and tracked via remote sensing measurements. Subsurface processes, including magma accumulation, withdrawal, and transport, can cause displacements at the surface that are best tracked at subaerial volcanoes with interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) measurements, although non-volcanic activity, like hydrothermal and tectonic sources, can complicate interpretations. Surface change is often associated with the emplacement of volcanic deposits, which modify the landscape and can experience post-emplacement deformation or morphological changes over time. Measurement of surface topography at volcanoes via remote means is a particularly important capability, given the control that topography exerts on many volcanic hazards and the potential for topographic change measurements to provide information about eruption rates. A much broader set of tools is available to investigate surface change at volcanoes, including not only InSAR and GNSS, but also synthetic aperture radar amplitude data, visible imagery, and lidar, acquired from airborne, ground-based, and satellite platforms. These data can also be used to identify instability of volcanic flanks and even have potential for use in detecting airborne ash plumes. Although hidden from traditional airborne and space-based remote sensing, deformation and surface change associated with submarine volcanism can be investigated with pressure sensors and bathymetric measurements—the below-water remote sensing analogs of GNSS and InSAR, respectively.
Des Moines Water Works (DMWW) is a regional municipal water utility that provides residential and commercial water resources to about 600,000 customers in Des Moines, Iowa, and surrounding municipalities in central Iowa. DMWW has identified a need for increased water supply and is exploring the potential for expanding groundwater production capabilities in the Des Moines River alluvial aquifer, where it operates two radial collector wells (RCWs). The U.S. Geological Survey, in cooperation with DMWW, completed a study of the Des Moines River alluvial aquifer and interactions of the RCWs with the aquifer; no previously published model has included the existing well locations, which is the focus of this model. A conceptual and numerical groundwater flow model have been developed to characterize the Des Moines River alluvial aquifer under existing conditions, to simulate water levels observed in the RCWs, and to provide publicly accessible hydrologic data and research that advance understanding of the regional hydrologic system and can potentially be used in the future to evaluate groundwater production scenarios. Model performance was assessed by comparing observed and simulated groundwater levels that included water level elevations, water level changes, water level inequality observations, surface water streamflow, and change in surface water volume from upstream to downstream. Water table elevation in the aquifer layers is on average slightly overestimated with average absolute value error less than 1.5 meters at both RCWs and less than 2.5 meters for all observation wells in the alluvial aquifer layers. The model also accurately simulated water tables greater than the RCW design minimum (a water level threshold at which RCW pumping is reduced) in all timesteps for which water level observation data existed. Water table elevation error was higher in other model layers that were not the focus of the study, and the model did not accurately match streamflow targets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245059","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"Bristow, E.L., and Davis, K.W., 2024, Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa: U.S. Geological Survey Scientific Investigations Report 2024–5059, 47 p., https://doi.org/10.3133/sir20245059.","productDescription":"Report: ix, 47 p.; 3 Data Releases; 1 Dataset","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154246","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":430905,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5059"},{"id":430904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5059/coverthb.jpg"},{"id":430906,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.XML"},{"id":430907,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5059/images/"},{"id":430908,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245059/full"},{"id":430909,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":430910,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZDDVY","text":"USGS data release","linkHelpText":"MODFLOW 6 groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":430911,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":430912,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499480,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117123.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Iowa","city":"Des Moines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.75578446475713,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.70743368403336\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Emerging infectious diseases with zoonotic potential often have complex socioecological dynamics and limited ecological data, requiring integration of epidemiological modeling with surveillance. Although our understanding of SARS-CoV-2 has advanced considerably since its detection in late 2019, the factors influencing its introduction and transmission in wildlife hosts, particularly white-tailed deer (Odocoileus virginianus), remain poorly understood. We use a Susceptible-Infected-Recovered-Susceptible epidemiological model to investigate the spillover risk and transmission dynamics of SARS-CoV-2 in wild and captive white-tailed deer populations across various simulated scenarios. We found that captive scenarios pose a higher risk of SARS-CoV-2 introduction from humans into deer herds and subsequent transmission among deer, compared to wild herds. However, even in wild herds, the transmission risk is often substantial enough to sustain infections. Furthermore, we demonstrate that the strength of introduction from humans influences outbreak characteristics only to a certain extent. Transmission among deer was frequently sufficient for widespread outbreaks in deer populations, regardless of the initial level of introduction. We also explore the potential for fence line interactions between captive and wild deer to elevate outbreak metrics in wild herds that have the lowest risk of introduction and sustained transmission. Our results indicate that SARS-CoV-2 could be introduced and maintained in deer herds across a range of circumstances based on testing a range of introduction and transmission risks in various captive and wild scenarios. Our approach and findings will aid One Health strategies that mitigate persistent SARS-CoV-2 outbreaks in white-tailed deer populations and potential spillback to humans.
International Ocean Discovery Program (IODP) Expedition 374 sailed to the Ross Sea in 2018 to reconstruct paleoenvironments, track the history of key water masses, and assess model simulations that show warm-water incursions from the Southern Ocean led to the loss of marine-based Antarctic ice sheets during past interglacials. IODP Site U1523 (water depth 828 m) is located at the continental shelf break, northeast of Pennell Bank on the southeastern flank of Iselin Bank, where it lies beneath the Antarctic Slope Current (ASC). This site is sensitive to warm-water incursions from the Ross Sea Gyre and modified Circumpolar Deep Water (mCDW) today and during times of past warming climate. Multiple incursions of subpolar or temperate planktic foraminifera taxa occurred at Site U1523 after 3.8 Ma and prior to ∼ 1.82 Ma. Many of these warm-water taxa incursions likely represent interglacials of the latest Early Pliocene and Early Pleistocene, including Marine Isotope Stage (MIS) Gi7 to Gi3 (∼ 3.72–3.65 Ma), and Early Pleistocene MIS 91 or 90 (∼ 2.34–2.32 Ma) and MIS 77–67 (∼ 2.03–1.83 Ma) and suggest warmer-than-present conditions and less ice cover in the Ross Sea. However, a moderately resolved age model based on four key events prohibits us from precisely correlating with Marine Isotope Stages established by the LR04 Stack; therefore, these correlations are best estimates. Diatom-rich intervals during the latest Pliocene at Site U1523 include evidence of anomalously warm conditions based on the presence of subtropical and temperate planktic foraminiferal species in what likely correlates with interglacial MIS G17 (∼ 2.95 Ma), and a second interval that likely correlates with MIS KM3 (∼ 3.16 Ma) of the mid-Piacenzian Warm Period. Collectively, these multiple incursions of warmer-water planktic foraminifera provide evidence for polar amplification during super-interglacials of the Pliocene and Early Pleistocene. Higher abundances of planktic and benthic foraminifera during the Mid- to Late Pleistocene associated with interglacials of the MIS 37–31 interval (∼ 1.23–1.07 Ma), MIS 25 (∼ 0.95 Ma), MIS 15 (∼ 0.60 Ma), and MIS 6–5e transition (∼ 0.133–0.126 Ma) also indicate a reduced ice shelf and relatively warm conditions, including multiple warmer interglacials during the Mid-Pleistocene Transition (MPT). A decrease in sedimentation rate after ∼ 1.78 Ma is followed by a major change in benthic foraminiferal biofacies marked by a decrease in Globocassidulina subglobosa and a decrease in mud (< 63 µm) after ∼ 1.5 Ma. Subsequent dominance of Trifarina earlandi biofacies beginning during MIS 15 (∼ 600 ka) indicate progressive strengthening of the Antarctic Slope Current along the shelf edge of the Ross Sea during the mid to Late Pleistocene. A sharp increase in foraminiferal fragmentation after the MPT (∼ 900 ka) and variable abundances of T. earlandi indicate higher productivity, a stronger but variable ASC during interglacials, and/or corrosive waters, suggesting changes in water masses entering (mCDW) and exiting (High Salinity Shelf Water or Dense Shelf Water) the Ross Sea since the MPT.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/jm-43-211-2024","usgsCitation":"Seidenstein, J.L., Leckie, R., McKay, R., De Santis, L., Harwood, D., and IODP Expedition 374 Scientists, 2024, Pliocene–Pleistocene warm-water incursions and water mass changes on the Ross Sea continental shelf (Antarctica) based on foraminifera from IODP Expedition 374: Journal of Micropalaeontology, v. 43, no. 2, p. 211-238, https://doi.org/10.5194/jm-43-211-2024.","productDescription":"28 p.","startPage":"211","endPage":"238","ipdsId":"IP-154696","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":488299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/jm-43-211-2024","text":"Publisher Index Page"},{"id":483338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Ross Ice Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n -70\n ],\n [\n -179.9,\n -78\n ],\n [\n -150,\n -78\n ],\n [\n -150,\n -70\n ],\n [\n -179.9,\n -70\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 170,\n -70\n ],\n [\n 170,\n -78\n ],\n [\n 179.9,\n -78\n ],\n [\n 179.9,\n -70\n ],\n [\n 170,\n -70\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Seidenstein, Julia Lynn 0000-0002-0585-1977","orcid":"https://orcid.org/0000-0002-0585-1977","contributorId":290625,"corporation":false,"usgs":true,"family":"Seidenstein","given":"Julia","email":"","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":930738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leckie, R. 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Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e., ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. 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","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biae050","usgsCitation":"Moss, W.E., Crausbay, S., Rangwala, I., Wason, J., Trauernicht, C., Stevens-Rumann, C.S., Sala, A., Rottler, C.M., Pederson, G.T., Miller, B.W., Magness, D., Littell, J., Frelich, L., Frazier, A.G., Davis, K., Coop, J., Cartwright, J.M., and Booth, R.K., 2024, Drought as an emergent driver of ecological transformation in the twenty-first century: BioScience, v. 74, no. 8, p. 524-538, https://doi.org/10.1093/biosci/biae050.","productDescription":"15 p.","startPage":"524","endPage":"538","ipdsId":"IP-153234","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":490028,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biae050","text":"Publisher Index Page"},{"id":430975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moss, Wynne Emily 0000-0002-2813-1710","orcid":"https://orcid.org/0000-0002-2813-1710","contributorId":338331,"corporation":false,"usgs":true,"family":"Moss","given":"Wynne","email":"","middleInitial":"Emily","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crausbay, Shelley","contributorId":217758,"corporation":false,"usgs":false,"family":"Crausbay","given":"Shelley","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":906094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rangwala, Imtiaz","contributorId":259891,"corporation":false,"usgs":false,"family":"Rangwala","given":"Imtiaz","affiliations":[{"id":52460,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":906095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wason, Jay","contributorId":300108,"corporation":false,"usgs":false,"family":"Wason","given":"Jay","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":906096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":906097,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stevens-Rumann, Camille S.","contributorId":274486,"corporation":false,"usgs":false,"family":"Stevens-Rumann","given":"Camille","email":"","middleInitial":"S.","affiliations":[{"id":56622,"text":"Forest Restoration Institute, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":906098,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sala, Anna","contributorId":147094,"corporation":false,"usgs":false,"family":"Sala","given":"Anna","email":"","affiliations":[{"id":5103,"text":"The University of Montana, Division of Biological Sciences, Missoula, Montana 59812","active":true,"usgs":false}],"preferred":false,"id":906099,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rottler, Caitlin M.","contributorId":138853,"corporation":false,"usgs":false,"family":"Rottler","given":"Caitlin","email":"","middleInitial":"M.","affiliations":[{"id":12546,"text":"Univ of Wyoming, Department of Botany, 1000 E. 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However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036260","usgsCitation":"Elliott, S.M., Hornberger, M.I., Rosenberry, D.O., Frus, R., and Webb, R.M., 2024, A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps: Water Resources Research, v. 60, no. 7, e2023WR036260, 20 p.; Data Release, https://doi.org/10.1029/2023WR036260.","productDescription":"e2023WR036260, 20 p.; Data Release","ipdsId":"IP-156361","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439286,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036260","text":"Publisher Index Page"},{"id":434931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97JZOVY","text":"USGS data release","linkHelpText":"Annotated bibliography of 64 papers reviewed and summarized in a conceptual framework to assess post-wildfire water quality"},{"id":432026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, Michelle I. 0000-0002-7787-3446 mhornber@usgs.gov","orcid":"https://orcid.org/0000-0002-7787-3446","contributorId":1037,"corporation":false,"usgs":true,"family":"Hornberger","given":"Michelle","email":"mhornber@usgs.gov","middleInitial":"I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":907312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":907313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":340187,"corporation":false,"usgs":false,"family":"Frus","given":"Rebecca J.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":907314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Richard M. 0000-0001-9531-2207 rmwebb@usgs.gov","orcid":"https://orcid.org/0000-0001-9531-2207","contributorId":1570,"corporation":false,"usgs":true,"family":"Webb","given":"Richard","email":"rmwebb@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907315,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256047,"text":"70256047 - 2024 - Seasonality of retreat rate of a wave-exposed marsh edge","interactions":[],"lastModifiedDate":"2024-07-16T11:53:48.411487","indexId":"70256047","displayToPublicDate":"2024-07-10T06:53:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Seasonality of retreat rate of a wave-exposed marsh edge","docAbstract":"Wave-driven erosion of marsh boundaries is a major cause of marsh loss, but little research has captured the effect of seasonal differences on marsh-edge retreat rates to illuminate temporal patterns of when the majority of this erosion is occurring. 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Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.
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Donald 0000-0001-5913-2372 domartin@usgs.gov","orcid":"https://orcid.org/0000-0001-5913-2372","contributorId":4450,"corporation":false,"usgs":true,"family":"Martin","given":"Donald","email":"domartin@usgs.gov","affiliations":[],"preferred":true,"id":912937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herbert, Deidre Mary 0000-0001-8707-3218","orcid":"https://orcid.org/0000-0001-8707-3218","contributorId":302591,"corporation":false,"usgs":true,"family":"Herbert","given":"Deidre","email":"","middleInitial":"Mary","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buchwald, Cheryl A. 0000-0001-8968-5023 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0000-0001-6548-8164","orcid":"https://orcid.org/0000-0001-6548-8164","contributorId":204240,"corporation":false,"usgs":true,"family":"Paulinski","given":"Scott R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kristen Valseth","contributorId":344134,"corporation":false,"usgs":false,"family":"Kristen Valseth","affiliations":[],"preferred":false,"id":912925,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70257524,"text":"70257524 - 2024 - Monitoring questing winter tick abundance on traditional moose hunting lands","interactions":[],"lastModifiedDate":"2024-08-16T12:04:39.448077","indexId":"70257524","displayToPublicDate":"2024-07-09T06:54:23","publicationYear":"2024","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":"Monitoring questing winter tick abundance on traditional moose hunting lands","docAbstract":"An important symbolic and subsistence animal for many Native American Tribes, the moose (Alces alces; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (Dermacentor albipictus) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot-based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western-central Massachusetts, USA. We analyzed the data using occupancy and N-mixture models. Detection probability peaked during mid-October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub-optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.
This cooperative study between the City of Durham Public Works Department, Stormwater Division and U.S. Geological Survey evaluated whether alternate monitoring strategies that incorporated samples collected across an increased range of streamflows would improve nutrient load estimates for Ellerbe and Sandy Creeks, two small, highly urbanized streams in the City of Durham, North Carolina. Water-quality and streamflow data collected between January 2009 and December 2020 were used to develop instream nutrient-load models using the U.S. Geological Survey R-LOADEST program. This study compared model results from two sampling scenarios: routine monthly (fixed frequency) sampling combined with targeted high-streamflow sampling (scenario A), and fixed frequency sampling only (scenario B).
Calibration diagnostic results were used to select the final, or most optimal, models. Most final models included seasonality terms to compensate for intra-annual variability in the data. Storm-runoff samples provided better definition at higher streamflows and improved the overall concentration versus flow relations for all constituents, except nitrate + nitrite. Uncertainties in the nutrient load estimates were lower and less variable for the scenario A tests compared to the scenario B tests.
Five time steps representing 12-, 9-, 7-, 6-, and 5-year subsets of the overall dataset were used to examine the effect of prediction period length on the computed loads and uncertainties. In focusing on the scenario A results, nutrient loads tended to be higher for the shorter time steps. These shorter time steps also produced higher errors, or uncertainty, in the load estimates compared to longer time steps. Evaluations of annual nutrient loads during 2016–20 indicated that the most consistent load estimates and tightest confidence intervals were obtained for longer 12- and 9-year time steps. Estimated loads were more variable and uncertain when based on the shorter 6- and 5-year time steps. The degree of uncertainty (standard error of prediction) in the nutrient load estimation results was influenced by sampling approach, calibration time step, and hydrologic characteristics during the model period of interest.
Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106133","usgsCitation":"Alzraiee, A.H., and Niswonger, R.G., 2024, A probabilistic approach to training machine learning models using noisy data: Environmental Modelling & Software, v. 179, 106133, 15 p., https://doi.org/10.1016/j.envsoft.2024.106133.","productDescription":"106133, 15 p.","ipdsId":"IP-151600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106133","text":"Publisher Index Page"},{"id":433694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":912927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259730,"text":"70259730 - 2024 - Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization","interactions":[],"lastModifiedDate":"2024-10-22T11:54:57.460967","indexId":"70259730","displayToPublicDate":"2024-07-08T06:53:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2351,"text":"Journal of Industrial Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization","docAbstract":"The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.
","language":"English","publisher":"Wiley","doi":"10.1111/jiec.13517","usgsCitation":"Ryter, J.W., Bhuwalka, K., O’Rourke, M., Montanelli, L., Cohen-Tanugi, D., Roth, R., and Olivetti, E., 2024, Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization: Journal of Industrial Ecology, v. 28, no. 4, p. 709-726, https://doi.org/10.1111/jiec.13517.","productDescription":"18 p.","startPage":"709","endPage":"726","ipdsId":"IP-157688","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":466986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jiec.13517","text":"Publisher Index Page"},{"id":463085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryter, John W. 0000-0002-0343-7553","orcid":"https://orcid.org/0000-0002-0343-7553","contributorId":345416,"corporation":false,"usgs":true,"family":"Ryter","given":"John","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":916487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bhuwalka, Karan 0000-0002-1963-6717","orcid":"https://orcid.org/0000-0002-1963-6717","contributorId":345417,"corporation":false,"usgs":false,"family":"Bhuwalka","given":"Karan","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rourke, Michelena","contributorId":345418,"corporation":false,"usgs":false,"family":"O’Rourke","given":"Michelena","email":"","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":916489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montanelli, Luca 0000-0002-7784-7627","orcid":"https://orcid.org/0000-0002-7784-7627","contributorId":345419,"corporation":false,"usgs":false,"family":"Montanelli","given":"Luca","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cohen-Tanugi, David 0000-0003-2488-4819","orcid":"https://orcid.org/0000-0003-2488-4819","contributorId":345420,"corporation":false,"usgs":false,"family":"Cohen-Tanugi","given":"David","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, Richard","contributorId":257597,"corporation":false,"usgs":false,"family":"Roth","given":"Richard","affiliations":[{"id":52064,"text":"Materials Systems Lab, MIT","active":true,"usgs":false}],"preferred":false,"id":916492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olivetti, Elsa 0009-0005-4190-8319","orcid":"https://orcid.org/0009-0005-4190-8319","contributorId":345421,"corporation":false,"usgs":false,"family":"Olivetti","given":"Elsa","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916493,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261225,"text":"70261225 - 2024 - Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","interactions":[],"lastModifiedDate":"2024-12-02T16:04:23.669174","indexId":"70261225","displayToPublicDate":"2024-07-08T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","docAbstract":"Northern peatlands are a globally significant source of methane (CH4), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH4 production in these systems will be key to predicting annual emissions changes, with stable carbon isotopes (δ13C-CH4) being a powerful tool for characterizing these drivers. Given that δ13C signatures of CH4 are used in top-down atmospheric inversion models to partition sources, our ability to model CH4 production pathways and associated δ13C-CH4 signatures in peatland types impacted by a changing climate is critical. We sought to characterize the role of environmental conditions, including both hydrologic and vegetation patterns associated with permafrost thaw, on δ13C-CH4 signatures from a diverse set of high-latitude peatlands. We measured porewater and emitted CH4 stable isotopes, pH, and vegetation composition from five boreal-Arctic peatlands. Porewater δ13C-CH4 was strongly associated with peatland type, with δ13C enriched values obtained from more minerotrophic fens (-61.2 ± 9.1‰) compared to permafrost-free bogs (-74.1 ± 9.4‰) and raised permafrost bogs (-81.6 ± 11.5‰). Variation in porewater δ13C-CH4 was best explained by sedge cover, CH4 concentration, and the interactive effect of peatland type and pH (r2 = 0.50, p < 0.001). Emitted δ13C-CH4 varied greatly but was positively correlated with porewater δ13C-CH4, suggesting that porewater data can be used to predict changing emissions signatures from these systems. We calculated a weighted mean mixed atmospheric CH4 signature for northern peatlands of -65.3 ± 7‰ and show that this signature is more sensitive to landscape drying (4 to 10 % depletion in δ13C) than wetting (1.5 to 5% enrichment in δ13C) under permafrost thaw scenarios. Our results suggest northern peatland δ13C-CH4 signatures are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007837","usgsCitation":"Kuhn, M.A., Varner, R.K., McCalley, C.K., Perryman, C.R., Aurela, M., Burke, S.A., Chanton, J., Crill, P., DelGreco, J., Deng, J., Heffernan, L., Herrick, C., Hodgkins, S.B., Jones, C.P., Juutinen, S., Kane, E., Lamit, L.J., Larmola, T., Lilleskov, E., Olefeldt, D., Palace, M.W., Rich, V.I., Schulze, C., Shorter, J.H., Sullivan, F., Sonnentag, O., Turetsky, M., and Waldrop, M., 2024, Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007837, 17 p., https://doi.org/10.1029/2023JG007837.","productDescription":"e2023JG007837, 17 p.","ipdsId":"IP-162425","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466987,"rank":2,"type":{"id":40,"text":"Open 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McKenzie A.","contributorId":333403,"corporation":false,"usgs":false,"family":"Kuhn","given":"McKenzie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":919944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varner, Ruth K.","contributorId":346749,"corporation":false,"usgs":false,"family":"Varner","given":"Ruth","email":"","middleInitial":"K.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":919945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCalley, Carmody K.","contributorId":346731,"corporation":false,"usgs":false,"family":"McCalley","given":"Carmody","email":"","middleInitial":"K.","affiliations":[{"id":32390,"text":"Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":919946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perryman, Clarice R.","contributorId":346809,"corporation":false,"usgs":false,"family":"Perryman","given":"Clarice","email":"","middleInitial":"R.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aurela, Mika","contributorId":346810,"corporation":false,"usgs":false,"family":"Aurela","given":"Mika","affiliations":[{"id":82974,"text":"Finnish Meteorological Institute, Climate System Research, 00560 Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":919948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burke, Sophia A.","contributorId":346811,"corporation":false,"usgs":false,"family":"Burke","given":"Sophia","email":"","middleInitial":"A.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 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Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919953,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Heffernan, Liam","contributorId":346816,"corporation":false,"usgs":false,"family":"Heffernan","given":"Liam","email":"","affiliations":[{"id":82978,"text":"Evolutionary Biology Centre, Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 752 36, Uppsala, Sweden.","active":true,"usgs":false}],"preferred":false,"id":919954,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Herrick, Christina","contributorId":346817,"corporation":false,"usgs":false,"family":"Herrick","given":"Christina","email":"","affiliations":[{"id":82976,"text":"Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH 03824 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Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":919963,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Palace, Michael W.","contributorId":346826,"corporation":false,"usgs":false,"family":"Palace","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919964,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Rich, Virginia I.","contributorId":346827,"corporation":false,"usgs":false,"family":"Rich","given":"Virginia","email":"","middleInitial":"I.","affiliations":[{"id":18950,"text":"Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA","active":true,"usgs":false}],"preferred":false,"id":919965,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Schulze, Christopher","contributorId":346828,"corporation":false,"usgs":false,"family":"Schulze","given":"Christopher","email":"","affiliations":[{"id":82984,"text":"Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":919966,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Shorter, Joanne H.","contributorId":346829,"corporation":false,"usgs":false,"family":"Shorter","given":"Joanne","email":"","middleInitial":"H.","affiliations":[{"id":82985,"text":"Aerodyne Research, Inc., Billerica, MA 02421","active":true,"usgs":false}],"preferred":false,"id":919967,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sullivan, Franklin","contributorId":346830,"corporation":false,"usgs":false,"family":"Sullivan","given":"Franklin","email":"","affiliations":[{"id":82986,"text":"Département de géographie, Université de Montréal, Montréal, Québec, Canada","active":true,"usgs":false}],"preferred":false,"id":919968,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sonnentag, Oliver","contributorId":346831,"corporation":false,"usgs":false,"family":"Sonnentag","given":"Oliver","affiliations":[{"id":82987,"text":"Department of Ecology and Evolutionary Biology. University of Colorado Boulder. Boulder CO 80309","active":true,"usgs":false}],"preferred":false,"id":919969,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Turetsky, Merritt R.","contributorId":346832,"corporation":false,"usgs":false,"family":"Turetsky","given":"Merritt R.","affiliations":[],"preferred":false,"id":919970,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":919971,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"id":70255868,"text":"70255868 - 2024 - Climate change vulnerability of Arctic char across Scandinavia","interactions":[],"lastModifiedDate":"2024-07-09T11:52:23.159911","indexId":"70255868","displayToPublicDate":"2024-07-07T06:50:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change vulnerability of Arctic char across Scandinavia","docAbstract":"Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range-restricted species at the highest elevations and latitudes of the globe. Consequently, range-restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid-1990s) to evaluate the climate vulnerability of Arctic char (Salvelinus alpinus), the world's most cold-adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike (Esox lucius). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold-water specialist, indicating the need for pro-active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.
The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration.
The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios (18O
Parturition timing has long been a topic of interest in ungulate research. However, few studies have examined parturition timing at fine scale (e.g., <1 day). Predator activity and environmental conditions can vary considerably with diel timing, which may result in selective pressure for parturition to occur during diel times that maximize the likelihood of neonate survival. We monitored parturition events and early-life survival of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) in Utah, USA to better understand diel timing of parturition in temperate ungulates. Diel timing of parturition was moderately synchronous among conspecifics and influenced by environmental variables on the date of parturition. For elk, parturition events were most common during the morning crepuscular period and generally occurred later (i.e., closer to 12:00) when a relatively large proportion of the moon was illuminated. For mule deer, parturition events were most common during the diurnal period and generally occurred later (i.e., closer to 15:00) on cold, wet dates. Diel timing of parturition did not influence neonate survival, but larger datasets may be required to verify the apparent lack of influence. Although additional work could evaluate alternative variables that might affect parturition timing, our data provide an improved and finer scale understanding of reproductive ecology and phenology in ungulates.