The number of large, high-severity wildfires has been increasing across the western United States over the last several decades. It is not fully understood how changes in the frequency of large, severe wildfires may impact the resilience of conifer forests, due to alterations in regeneration success or failure. Our research investigates 30 years of conifer recovery patterns within 34 high-severity wildfire complexes (1988–1991) of the Northern Rocky Mountains. We evaluate the capability of snow-cover Landsat to characterize conifer tree recolonization of high-severity burn patches. Snow-cover images isolate conifer-specific vegetation signals by diminishing spectral contributions from soil and deciduous vegetation. The presence of conifer regeneration was successfully classified by snow-cover Landsat at >10% canopy cover at 98% accuracy and modeled at 3-year intervals post-fire. Spectral detectability of regenerating conifer vegetation began 11–19 years post-fire, varying across forest types. Thirty years post-fire, 65% of the total high-severity burn area had been recolonized by conifer trees, with differences observed between forest types: 72% of lodgepole pine, 77% of Douglas-fir, and 44% of fir-spruce severely burned areas containing conifer regeneration. Projected recovery timelines to pre-fire conifer vegetation also differed between lodgepole pine (29.5 years), Douglas-fir (36.9 years), and fir-spruce forests (48.7 years), as estimated from snow-cover NDVI trends. Although we generally documented patterns of conifer resilience, we also identified reduced likelihoods of recovery within high-severity burn patches exhibiting greater area-to-perimeter ratios, aridity, south-facing aspects, slopes, and elevation. Snow-cover Landsat imagery was shown to improve the characterization of post-fire forest recovery and may be applied to support forest restoration decision-making following high-severity wildfire.
Predation can play an important role in structuring ecological communities, and predator–prey dynamics can be altered following the introduction of new species. An unauthorized introduction of redside shiner (Richardsonius balteatus) into reservoirs in the Upper Skagit River, Washington, USA created concern that a consequent shift in predator–prey dynamics in the reservoirs could reduce recruitment and production of native salmonids in the basin. We estimated predation mortality in Ross Lake on nonnative redside shiner and juvenile native salmonids to evaluate the potential role of predation in regulating these populations and limiting survival of native species of concern. We used bioenergetics modelling and stable isotope analysis combined with directed field measurements of growth, seasonal diet and thermal experience of piscivorous salmonids to quantify their consumption demand on prey fishes to evaluate the relative magnitude of predation mortality on invasive redside shiners and native salmonids. While redside shiner are the dominant prey fish species in Ross Lake, the modest biomass of native salmonids consumed could translate into substantial mortality, the magnitude of which depended on the timing and size at which prey fishes were eaten. This information provides important context for how nonnative species may indirectly impact native species through shared predation (apparent competition) and can inform conservation decisions surrounding nonnative species control, sustainability of native salmonids and introductions of anadromous fishes.
Understanding the response of coastal barriers to future changes in rates of sea level rise, sediment availability, and storm intensity/frequency is essential for coastal planning, including socioeconomic and ecological management. Identifying drivers of past changes in barrier morphology, as well as barrier sensitivity to these forces, is necessary to accomplish this. Using remote sensing, field, and laboratory analyses, we reconstruct the mesoscale (decades–centuries) evolution of central Fire Island, a portion of a 50 km barrier island fronting Long Island, New York, USA. We find that the configuration of the modern beach and foredune at Fire Island is radically different from the system's relict morphostratigraphy. Central Fire Island is comprised of at least three formerly inlet-divided rotational barriers with distinct subaerial beach and dune–ridge systems that were active prior to the mid-19th century. Varying morphologic states reflected in the relict barriers (e.g., progradational and transgressive) contrast with the modern barrier, which is dominated by a tall and nearly continuous foredune and is relatively static, except for erosion and drowning of its fringing marsh. We suggest that this state shift indicates a transition from a regime dominated by inlet-mediated gradients in alongshore sediment availability to one where human impacts exerted greater influence on island evolution from the late 19th century onward. The retention of some geomorphic capital in Fire Island's relict subaerial features combined with its static nature renders the barrier increasingly susceptible to narrowing and passive submergence. This may lead to an abrupt geomorphic state shift in the future, a veiled vulnerability that may also exist in other stabilized barriers.
The Rocky Mountain Population (RMP) of greater sandhill cranes uses a key stopover area, the San Luis Valley (SLV) in Colorado. Parameters of migration phenology can differ between autumn and spring and are affected by weather and environmental factors. We hypothesized that sandhill cranes in the SLV would have a longer stopover duration in autumn than in spring, and that wind assistance, crosswinds, temperature change, barometric air pressure, and surface water area would influence persistence probability. We used data from sandhill cranes fitted with transmitters that spanned autumn and spring, 2015-2022. We used an open robust design mark-recapture model to estimate stopover duration, arrival probability, and persistence probability. We examined the effects of weather and surface water on the persistence probability for 106 sandhill cranes in the SLV. Stopover duration was longer in autumn than in spring and had higher variability across years. Arrival probability to the SLV peaked on 13 October in autumn and 21 February in spring. Persistence probability declined around mid-December in autumn and mid-March in spring. We found that several weather covariates influenced persistence in both seasons. In autumn, sandhill cranes departed the SLV with higher tailwinds, lower crosswinds, and higher surface water availability. In spring, sandhill cranes departed the SLV with lower crosswinds and higher barometric air pressure at the surface and higher wind speeds at altitudes of about 3,000 m. The effect of wind speed was stronger later in the spring. Given the lower variability of arrival and persistence probability and shorter stopover duration in spring compared to autumn, we suspect that RMP sandhill cranes are using a time-minimization strategy during spring. However, given the use of supportive winds and weather conditions ideal for soaring, RMP sandhill cranes appear to be using strategies that save energy in both seasons. Our study identifies the optimal timing of water management and surveys for RMP sandhill cranes and confirms that weather influences their persistence. Understanding differences in migration patterns between seasons and the factors that influence persistence at stopover sites will also be important for anticipating phenological impacts from climate change and land use alterations.
Mountain lakes are sensitive indicators of anthropogenically driven global change, with lake sediment records documenting increased primary production during the twentieth century. Atmospheric nutrient deposition and warming have been attributed to changes in other Western mountain lakes, however, the intensity of these drivers varies. We analyzed a sediment core representing a 270-year record from Santa Fe Lake, New Mexico, to constrain the southern margin of Rocky Mountain lakes and quantify patterns of change in lake biogeochemistry, production, and diatoms since 1750. Lake sediments were dated using 210Pb and analyzed for carbon (C), nitrogen (N), stable isotopes (δ13C, δ15N), diatoms, and phototrophic pigments. The abundance of cyanobacteria, purple sulfur-reducing bacteria, and diatom pigments were elevated during the stable conditions of the Little Ice Age; these phototrophic groups declined in the late 1800s and reached a minimum by 1950. From 1950 to 2020, sediments recorded an increased abundance of cryptophyte, diatom, and chlorophyte groups. The C and N (percentage dry mass) increased after 1950, whereas δ15N and δ13C values declined. Changes since the mid-twentieth century are contemporaneous with warming trends in the Southwest and modest deposition of atmospheric N. Our findings highlight the geographic variability of mountain lake responses to changing environmental conditions.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230430.2024.2303810","usgsCitation":"Shampain, A., Baron, J., Leavitt, P.R., and Spaulding, S., 2024, Climatic variability as a principal driver of primary production in the southernmost subalpine Rocky Mountain lake: Arctic, Antarctic, and Alpine Research, v. 56, no. 1, 2303810, 18 p., https://doi.org/10.1080/15230430.2024.2303810.","productDescription":"2303810, 18 p.","ipdsId":"IP-153651","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":440125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2024.2303810","text":"Publisher Index Page"},{"id":427400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Santa Fe Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.74147810102569,\n 35.85975006476575\n ],\n [\n -106.27,\n 35.85975006476575\n ],\n [\n -106.27,\n 35.66069461088024\n ],\n [\n -105.74147810102569,\n 35.66069461088024\n ],\n [\n -105.74147810102569,\n 35.85975006476575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Shampain, Anna 0000-0001-5447-7638","orcid":"https://orcid.org/0000-0001-5447-7638","contributorId":331317,"corporation":false,"usgs":false,"family":"Shampain","given":"Anna","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":898146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leavitt, Peter R.","contributorId":335357,"corporation":false,"usgs":false,"family":"Leavitt","given":"Peter","email":"","middleInitial":"R.","affiliations":[{"id":27547,"text":"University of Regina","active":true,"usgs":false}],"preferred":false,"id":898148,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898149,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252676,"text":"70252676 - 2024 - Modeled flooding by tsunamis and a storm versus observed extent of coral erratics on Anegada, British Virgin Islands— Further evidence for a great Caribbean earthquake six centuries ago","interactions":[],"lastModifiedDate":"2024-04-02T14:36:15.942014","indexId":"70252676","displayToPublicDate":"2024-03-14T09:29:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Modeled flooding by tsunamis and a storm versus observed extent of coral erratics on Anegada, British Virgin Islands— Further evidence for a great Caribbean earthquake six centuries ago","docAbstract":"Models of near-field tsunamis and an extreme hurricane provide further evidence for a great precolonial earthquake along the Puerto Rico Trench. The models are benchmarked to brain-coral boulders and cobbles on Anegada, 125 km south of the trench. The models are screened by their success in flooding the mapped sites of these erratics, which were emplaced some six centuries ago. Among 25 tsunami scenarios, 19 have megathrust sources and the rest posit normal faulting on the outer rise. The modeled storm, the most extreme of 15 hurricanes of category 5, produces tsunami-like bores from surf beat. In the tsunami scenarios, simulated flow depth is 1 m or more at all the clast sites, and 2 m or more at nearly all, given either a megathrust rupture 255 km long with 7.5 m of dip slip and M8.45, or an outer-rise rupture 130 km long with 11.4 m of dip slip and M8.17. By contrast, many coral clasts lie beyond the reach of simulated flooding from the extreme hurricane. The tsunami screening may underestimate earthquake size by neglecting trees and shrubs that likely impeded both the simulated flows and the observed clasts; and it may overestimate earthquake size by leaving coastal sand barriers intact. The screening results broadly agree with those from previously published tsunami simulations. In either successful scenario, the average recurrence interval spans thousands of years, and flooding on the nearest Caribbean shores begins within a half-hour.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JB028387","usgsCitation":"Wei, Y., ten Brink, U.S., and Atwater, B.F., 2024, Modeled flooding by tsunamis and a storm versus observed extent of coral erratics on Anegada, British Virgin Islands— Further evidence for a great Caribbean earthquake six centuries ago: JGR Solid Earth, v. 129, no. 3, e2023JB028387, 26 p., https://doi.org/10.1029/2023JB028387.","productDescription":"e2023JB028387, 26 p.","ipdsId":"IP-160030","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440127,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jb028387","text":"Publisher Index Page"},{"id":427312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"British Virgin Islands","otherGeospatial":"Anegada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -64.26970963209003,\n 18.687395079258394\n ],\n [\n -64.27098250709774,\n 18.70548066405327\n ],\n [\n -64.31107821074197,\n 18.74887709066421\n ],\n [\n -64.34353638253225,\n 18.7524941354379\n ],\n [\n -64.41895422673412,\n 18.747371507694695\n ],\n [\n -64.41927244548606,\n 18.73561900967063\n ],\n [\n -64.39986110161998,\n 18.719646355609257\n ],\n [\n -64.35181007008187,\n 18.715426911972713\n ],\n [\n -64.3206243759603,\n 18.713016020229745\n ],\n [\n -64.30407748159904,\n 18.70156264195039\n ],\n [\n -64.280847288407,\n 18.685284968543982\n ],\n [\n -64.26970963209003,\n 18.687395079258394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wei, Yong","contributorId":242870,"corporation":false,"usgs":false,"family":"Wei","given":"Yong","affiliations":[{"id":48562,"text":"JISAO, University of Washington, WA 98105 USA","active":true,"usgs":false}],"preferred":false,"id":897881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"ten Brink, Uri S. 0000-0001-6858-3001","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":201741,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri","email":"","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":897882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwater, Brian F. 0000-0003-1155-2815","orcid":"https://orcid.org/0000-0003-1155-2815","contributorId":335255,"corporation":false,"usgs":false,"family":"Atwater","given":"Brian","email":"","middleInitial":"F.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":897883,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255250,"text":"70255250 - 2024 - Incorporating life history diversity in an integrated population model to inform viability analysis","interactions":[],"lastModifiedDate":"2024-06-13T14:29:03.449351","indexId":"70255250","displayToPublicDate":"2024-03-14T09:22:46","publicationYear":"2024","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":"Incorporating life history diversity in an integrated population model to inform viability analysis","docAbstract":"Life history diversity can significantly affect population dynamics and effects of management actions. For instance, variation in individual responses to environmental variability can reduce extirpation risk to populations, as the portfolio effect dampens temporal variability in abundance. Moreover, differences in habitat use may cause individuals to respond differently to habitat management and climate variability. To explore the role of life history diversity in population trajectories, population models need to incorporate within-population variation. Integrated population modeling (IPM) is a population modeling approach that offers several advantages for sharing information and propagating uncertainty across datasets. In this study, we developed an IPM for an endangered population of Chinook salmon (Oncorhynchus tshawytscha) in the Wenatchee River, Washington, USA, that accounts for diversity in juvenile life histories, spawning location, and return age. Our analysis revealed that diversity in the age of juvenile emigration from natal streams had a portfolio effect, resulting in a 20% reduction in year-to-year variability in adult abundance in population projections. Our population viability analysis suggests that management interventions may be necessary to meet recovery goals, and our model should be useful for simulating the outcomes of proposed actions.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0118","usgsCitation":"Sorel, M.H., Jorgensen, J.C., Zabel, R.W., Scheuerell, M.D., Murdoch, A.R., Kamphaus, C.M., and Converse, S.J., 2024, Incorporating life history diversity in an integrated population model to inform viability analysis: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 5, p. 535-548, https://doi.org/10.1139/cjfas-2023-0118.","productDescription":"14 p.","startPage":"535","endPage":"548","ipdsId":"IP-153564","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":440129,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1139/cjfas-2023-0118","text":"External Repository"},{"id":430133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.65380079789186,\n 48.385800374959246\n ],\n [\n -122.85878973700216,\n 48.35952757767319\n ],\n [\n -122.85878973700216,\n 45.39007143816454\n ],\n [\n -118.65380079789189,\n 45.42475769972472\n ],\n [\n -118.65380079789186,\n 48.385800374959246\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"81","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":903859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgensen, Jeffrey C.","contributorId":339208,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":903860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zabel, Richard W.","contributorId":272049,"corporation":false,"usgs":false,"family":"Zabel","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murdoch, Andrew R.","contributorId":339213,"corporation":false,"usgs":false,"family":"Murdoch","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":903863,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kamphaus, Cory M.","contributorId":339215,"corporation":false,"usgs":false,"family":"Kamphaus","given":"Cory","email":"","middleInitial":"M.","affiliations":[{"id":39287,"text":"Yakama Nation Fisheries","active":true,"usgs":false}],"preferred":false,"id":903864,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903865,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70258782,"text":"70258782 - 2024 - Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm","interactions":[],"lastModifiedDate":"2024-10-03T16:05:53.476234","indexId":"70258782","displayToPublicDate":"2024-03-14T08:41:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm","docAbstract":"Simulation models are valuable tools for estimating ecosystem response to environmental conditions and are particularly relevant for investigating climate change impacts. However, because of high computational requirements, models are often applied over a coarse grid of points or for representative locations. Spatial interpolation of model output can be necessary to guide decision-making, yet interpolation is not straightforward because the interpolated values must maintain the covariance structure among variables. We present methods for two key steps for utilizing limited simulations to generate detailed maps of multivariate and time series output. First, we present a method to select an optimal set of simulation sites that maximize the area represented for a given number of sites. Then, we introduce a multivariate matching approach to interpolate simulation results to detailed maps for the represented area. This approach links simulation output to environmentally analogous matched sites according to user-defined criteria. We demonstrate the methods with case studies using output from (1) an individual-based plant simulation model to illustrate site selection, and (2) an ecosystem water balance simulation model to illustrate interpolation. For the site selection case study, we identified 200 simulation sites that represented 96% of a large study area (1.12 × 106 km2) at a ~1-km resolution. For the interpolation case study, we generated ~1-km resolution maps across 4.38 × 106 km2 of drylands in North America from a 10 × 10 km grid of simulated sites. Estimates of interpolation errors using cross validation were low (less than 10% of the range of each variable). Our point selection and interpolation methods, which are available as an easy-to-use R package, provide a means of cost-effectively generating detailed maps of expensive, complex simulation output (e.g., multivariate and time series) at scales relevant for local conservation planning. Our methods are flexible and allow the user to identify relevant matching criteria to balance interpolation uncertainty with areal coverage to enhance inference and decision-making at management-relevant scales across large areas.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4811","usgsCitation":"Renne, R.R., Schlaepfer, D.R., Palmquist, K.A., Lauenroth, W.K., and Bradford, J., 2024, Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm: Ecosphere, v. 15, no. 3, e4811, 18 p., https://doi.org/10.1002/ecs2.4811.","productDescription":"e4811, 18 p.","ipdsId":"IP-133218","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467024,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/ecs2.4811","text":"Publisher Index Page"},{"id":462276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Renne, Rachel R.","contributorId":213935,"corporation":false,"usgs":false,"family":"Renne","given":"Rachel","email":"","middleInitial":"R.","affiliations":[{"id":38934,"text":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA","active":true,"usgs":false}],"preferred":false,"id":914056,"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":914057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":914058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":914059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":914060,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252498,"text":"70252498 - 2024 - Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation","interactions":[],"lastModifiedDate":"2024-04-10T16:05:11.77161","indexId":"70252498","displayToPublicDate":"2024-03-14T06:51:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16692,"text":"Journal of the American Water Resources Assocation","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation","docAbstract":"Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water-quality effects of MPs are uncertain. We interpreted water-quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water-quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36-year time-series model suggests that changes in manure affected flow-normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water-quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long-term monitoring-based studies.
StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.
The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.
StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1514","usgsCitation":"Ries, K.G., III, Steeves, P.A., and McCarthy, P., 2024, StreamStats—A quarter century of delivering web-based geospatial and hydrologic information to the public, and lessons learned: U.S. Geological Survey Circular 1514, 40 p., https://doi.org/10.3133/cir1514.","productDescription":"viii, 40 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-102663","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":499073,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116170.htm","linkFileType":{"id":5,"text":"html"}},{"id":426017,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.XML","description":"CIR 1514 XML"},{"id":426018,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1514/images/"},{"id":426016,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1514/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1514 HTML"},{"id":426015,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.pdf","text":"Report","size":"7.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1514 PDF"},{"id":426014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1514/coverthb.jpg"}],"contact":"National Coordinator, StreamStats
U.S. Geological Survey
1728 Lampman Drive, Suite D
Billings, MT 59102
Alaska’s Arctic coast has some of the highest coastal erosion rates in the world, primarily driven by permafrost thaw and increasing wave energy. In the Arctic, a warming climate is driving sea ice cover to decrease in space and time. A lack of long-term observational wave data along Alaska’s coast challenges the ability of engineers, scientists, and planners to study and address threats and effects from wave-driven erosion and flooding. To overcome the lack of available observational wave data in the nearshore in this study by the U.S. Geological Survey, waves were downscaled with the Simulating WAves Nearshore numerical wave model (SWAN) for the hindcast period of 1979 to 2019 from the United States-Canada border to the Bering Sea utilizing nine model domains. For each domain, the model was forced at the open boundary with 2,500 representative “sea states,” which are likely combinations of significant wave heights, mean wave periods, mean wave directions, and wind speeds and directions. The sea states were obtained from the European Centre for Medium-Range Weather Forecasts “ERA5” dataset for reanalysis of winds and waves using a multivariant maximum-dissimilarity algorithm. The SWAN runs created a downscaled wave database at each grid point, which was used to reconstruct the 40-year time series in the nearshore along the 5- and 10-meter isobaths at locations approximately 400 m apart and corresponding to transects spaced approximately 50 m alongshore, as developed for USGS shoreline-change assessments. Reconstructed time series were compared to observations to validate the numerical model and the downscaled wave database method and showed overall good agreements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231094","programNote":"Prepared in cooperation with Deltares USA and the University of California, Santa Cruz","usgsCitation":"Engelstad, A.C., Erikson, L.H., Reguero, B.G., Gibbs, A.E., and Nederhoff, K., 2024, Database and time series of nearshore waves along the Alaskan coast from the United States-Canada border to the Bering Sea: U.S. Geological Survey Open-File Report 2023–1094, 23 p., https://doi.org/10.3133/ofr20231094.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-132323","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499201,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116169.htm","linkFileType":{"id":5,"text":"html"}},{"id":426586,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231094/full"},{"id":426585,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1094/images"},{"id":426584,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1094/ofr20231094.xml"},{"id":426583,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1094/covrthb.jpg"},{"id":426582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1094/ofr20231094.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426581,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P931CSO9","text":"USGS Data Release","description":"Engelstad, A.C., Erikson, L.H., Reguero, B.G., Gibbs, A.E., Nederhoff, K.M., 2024, Nearshore wave time-series along the coast of Alaska computed with a numerical wave model: U.S. Geological Survey data release, https://doi.org/10.5066/P931CSO9.","linkHelpText":"Nearshore wave time-series along the coast of Alaska computed with a numerical wave model"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.34514806758992,\n 65.52631288766165\n ],\n [\n -140.04436681759015,\n 65.52631288766165\n ],\n [\n -140.04436681759015,\n 71.42344314984271\n ],\n [\n -168.34514806758992,\n 71.42344314984271\n ],\n [\n -168.34514806758992,\n 65.52631288766165\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Martian gullies resemble water-carved gullies on Earth, yet their present-day activity cannot be explained by water-driven processes. The sublimation of CO2 has been proposed as an alternative driver for sediment transport, but how this mechanism works remains unknown. Here we combine laboratory experiments of CO2-driven granular flows under Martian atmospheric pressure with 1D climate simulation modelling to unravel how, where, and when CO2 can drive present-day gully activity. Our work shows that sublimation of CO2 ice, under Martian atmospheric conditions can fluidize sediment and creates morphologies similar to those observed on Mars. Furthermore, the modelled climatic and topographic boundary conditions for this process, align with present-day gully activity. These results have implications for the influence of water versus CO2-driven processes in gully formation and for the interpretation of gully landforms on other planets, as their existence is no longer definitive proof for flowing liquids.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-024-01298-7","usgsCitation":"Roelofs, L., Conway, S.J., de Haas, T., Dundas, C., Lewis, S.R., McElwaine, J., Pasquon, K., Raack, J., Sylvest, M., and Patel, M., 2024, How, when and where current mass flows in Martian gullies are driven by CO2 sublimation: Communications Earth and Environment, v. 5, 125, 9 p., https://doi.org/10.1038/s43247-024-01298-7.","productDescription":"125, 9 p.","ipdsId":"IP-143281","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":440137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-024-01298-7","text":"Publisher Index Page"},{"id":433000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Roelofs, Lonneke","contributorId":343523,"corporation":false,"usgs":false,"family":"Roelofs","given":"Lonneke","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":911294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Susan J.","contributorId":203697,"corporation":false,"usgs":false,"family":"Conway","given":"Susan","email":"","middleInitial":"J.","affiliations":[{"id":36693,"text":"University of Nantes","active":true,"usgs":false}],"preferred":false,"id":911295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Haas, Tjalling","contributorId":336830,"corporation":false,"usgs":false,"family":"de Haas","given":"Tjalling","affiliations":[],"preferred":false,"id":911296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":911297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Stephen R.","contributorId":64081,"corporation":false,"usgs":true,"family":"Lewis","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":911298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McElwaine, Jim","contributorId":201623,"corporation":false,"usgs":false,"family":"McElwaine","given":"Jim","affiliations":[{"id":25252,"text":"Durham University","active":true,"usgs":false}],"preferred":false,"id":911299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pasquon, Kelly","contributorId":343526,"corporation":false,"usgs":false,"family":"Pasquon","given":"Kelly","email":"","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":911300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raack, Jan","contributorId":343527,"corporation":false,"usgs":false,"family":"Raack","given":"Jan","email":"","affiliations":[{"id":82107,"text":"Westfalische Wilhelms-Universitat, Innomago GmbH","active":true,"usgs":false}],"preferred":false,"id":911301,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sylvest, Matt","contributorId":343528,"corporation":false,"usgs":false,"family":"Sylvest","given":"Matt","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Patel, Manish","contributorId":343529,"corporation":false,"usgs":false,"family":"Patel","given":"Manish","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911303,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259800,"text":"70259800 - 2024 - Arsenic and other geogenic contaminants in global groundwater","interactions":[],"lastModifiedDate":"2024-10-25T15:56:51.495002","indexId":"70259800","displayToPublicDate":"2024-03-12T10:50:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic and other geogenic contaminants in global groundwater","docAbstract":"Geogenic groundwater contaminants (GGCs) affect drinking-water availability and safety, with up to 60% of groundwater sources in some regions contaminated by more than recommended concentrations. As a result, an estimated 300–500 million people are at risk of severe health impacts and premature mortality. In this Review, we discuss the sources, occurrences and cycling of arsenic, fluoride, selenium and uranium, which are GGCs with widespread distribution and/or high toxicity. The global distribution of GGCs is controlled by basin geology and tectonics, with GGC enrichment in both orogenic systems and cratonic basement rocks. This regional distribution is broadly influenced by climate, geomorphology and hydrogeochemical evolution along groundwater flow paths. GGC distribution is locally heterogeneous and affected by in situ lithology, groundwater flow and water–rock interactions. Local biogeochemical cycling also determines GGC concentrations, as arsenic, selenium and uranium mobilizations are strongly redox-dependent. Increasing groundwater extraction and land-use changes are likely to modify GGC distribution and extent, potentially exacerbating human exposure to GGCs, but the net impact of these activities is unknown. Integration of science, policy, community involvement programmes and technological interventions is needed to manage GGC-enriched groundwater and ensure equitable access to clean water.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00519-z","usgsCitation":"Mukherjee, A., Coomar, P., Sarkar, S., Johannesson, K., Fryar, A., Schreiber, M., Ahmed, K.M., Alam, M.A., Bhattacharya, P., Bundschuh, J., Burgess, W., Chakraborty, M., Coyte, R., Farooqi, A., Guo, H., Ijumulana, J., Jeelani, G., Mondal, D., Nordstrom, D.K., Podgorski, J., Polya, D., Scanlon, B.R., Shamsudduha, M., Tapia, J., and Vengosh, A., 2024, Arsenic and other geogenic contaminants in global groundwater: Nature Reviews Earth & Environment, v. 5, p. 312-328, https://doi.org/10.1038/s43017-024-00519-z.","productDescription":"17 p.","startPage":"312","endPage":"328","ipdsId":"IP-162090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467025,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://discovery.ucl.ac.uk/id/eprint/10191625/","text":"External Repository"},{"id":463197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mukherjee, Abhijit","contributorId":213833,"corporation":false,"usgs":false,"family":"Mukherjee","given":"Abhijit","email":"","affiliations":[],"preferred":false,"id":916735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coomar, Poulomee","contributorId":345478,"corporation":false,"usgs":false,"family":"Coomar","given":"Poulomee","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sarkar, Soumyajit","contributorId":345479,"corporation":false,"usgs":false,"family":"Sarkar","given":"Soumyajit","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johannesson, Karen H.","contributorId":150171,"corporation":false,"usgs":false,"family":"Johannesson","given":"Karen H.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":916749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fryar, Alan","contributorId":345484,"corporation":false,"usgs":false,"family":"Fryar","given":"Alan","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":916745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schreiber, Madeline","contributorId":248255,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","affiliations":[{"id":49841,"text":"Virginia Tech, Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":916755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ahmed, Kazi M.","contributorId":345480,"corporation":false,"usgs":false,"family":"Ahmed","given":"Kazi","email":"","middleInitial":"M.","affiliations":[{"id":65425,"text":"University of Dhaka","active":true,"usgs":false}],"preferred":false,"id":916738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Alam, Mohd. A.","contributorId":345481,"corporation":false,"usgs":false,"family":"Alam","given":"Mohd.","email":"","middleInitial":"A.","affiliations":[{"id":82597,"text":"University de Santiago de Chile","active":true,"usgs":false}],"preferred":false,"id":916739,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bhattacharya, Prosun","contributorId":184213,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Prosun","email":"","affiliations":[],"preferred":false,"id":916740,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bundschuh, Jochen","contributorId":184215,"corporation":false,"usgs":false,"family":"Bundschuh","given":"Jochen","email":"","affiliations":[],"preferred":false,"id":916741,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Burgess, William","contributorId":345482,"corporation":false,"usgs":false,"family":"Burgess","given":"William","email":"","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":916742,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chakraborty, Madhumita","contributorId":345510,"corporation":false,"usgs":false,"family":"Chakraborty","given":"Madhumita","email":"","affiliations":[],"preferred":false,"id":916817,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Coyte, Rachel","contributorId":340050,"corporation":false,"usgs":false,"family":"Coyte","given":"Rachel","email":"","affiliations":[{"id":81437,"text":"New Mexico Institute of Mining and Technology, Earth and Environmental Science Department, Socorro, NM","active":true,"usgs":false}],"preferred":false,"id":916743,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Farooqi, Abida","contributorId":345483,"corporation":false,"usgs":false,"family":"Farooqi","given":"Abida","email":"","affiliations":[{"id":82598,"text":"Quaid-i-Azam University, Islamabad","active":true,"usgs":false}],"preferred":false,"id":916744,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Guo, Huaming","contributorId":138510,"corporation":false,"usgs":false,"family":"Guo","given":"Huaming","email":"","affiliations":[{"id":12433,"text":"China University of Geosciences","active":true,"usgs":false}],"preferred":false,"id":916746,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ijumulana, Julian","contributorId":345485,"corporation":false,"usgs":false,"family":"Ijumulana","given":"Julian","email":"","affiliations":[{"id":82599,"text":"KTH Royal Institute of Technology, Stockholm","active":true,"usgs":false}],"preferred":false,"id":916747,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Jeelani, Gh","contributorId":345486,"corporation":false,"usgs":false,"family":"Jeelani","given":"Gh","email":"","affiliations":[{"id":82600,"text":"University of Kashmir","active":true,"usgs":false}],"preferred":false,"id":916748,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mondal, Debapriya","contributorId":345487,"corporation":false,"usgs":false,"family":"Mondal","given":"Debapriya","email":"","affiliations":[{"id":82601,"text":"London School of Hygiene and Tropical Medicine","active":true,"usgs":false}],"preferred":false,"id":916750,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":916751,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Podgorski, Joel 0000-0003-2522-1021","orcid":"https://orcid.org/0000-0003-2522-1021","contributorId":336777,"corporation":false,"usgs":false,"family":"Podgorski","given":"Joel","email":"","affiliations":[{"id":80861,"text":"Department of Water Resources and Drinking Water, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":916752,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Polya, David","contributorId":197748,"corporation":false,"usgs":false,"family":"Polya","given":"David","email":"","affiliations":[],"preferred":false,"id":916753,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Scanlon, Bridget R. 0000-0002-1234-4199","orcid":"https://orcid.org/0000-0002-1234-4199","contributorId":328586,"corporation":false,"usgs":false,"family":"Scanlon","given":"Bridget","email":"","middleInitial":"R.","affiliations":[{"id":78414,"text":"Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, J.J. Pickle Research Campus, Bldg. 130, 10100 Burnet Rd., Austin, TX 78758-4445","active":true,"usgs":false}],"preferred":false,"id":916754,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Shamsudduha, Mohd.","contributorId":345488,"corporation":false,"usgs":false,"family":"Shamsudduha","given":"Mohd.","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":916756,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Tapia, Joseline","contributorId":345489,"corporation":false,"usgs":false,"family":"Tapia","given":"Joseline","email":"","affiliations":[{"id":82602,"text":"Universidad Católica Del Norte, Antofagasta, Chile","active":true,"usgs":false}],"preferred":false,"id":916757,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Vengosh, Avner","contributorId":208460,"corporation":false,"usgs":false,"family":"Vengosh","given":"Avner","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":916758,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70252861,"text":"70252861 - 2024 - Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","interactions":[],"lastModifiedDate":"2024-09-23T15:25:30.720271","indexId":"70252861","displayToPublicDate":"2024-03-12T07:06:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","docAbstract":"Less than a year after the 2018 Kīlauea caldera collapse and eruption, water appeared in newly deepened Halemaʻumaʻu crater. The lake—unprecedented in the written record—grew to a depth of ∼50 m before lava from the December 2020 eruption boiled it away. Surface water heightened concerns of potential phreatic or phreatomagmatic explosions but also offered a new means of possibly identifying eruption precursors. The U.S. Geological Survey Hawaiian Volcano Observatory (HVO) monitored the lake via direct visual observation, webcams, thermal imaging, colorimetry, and laser rangefinders. HVO also employed uncrewed aircraft systems to sample the water and measure near-lake gas composition. The lake's δD and δ18O indicate a groundwater source with substantial evaporation. The initial sample had a salinity (total dissolved solids concentration) of 71,000 mg/L and was rich in sulfate (∼53,000 mg/L), iron (∼500 mg/L), and magnesium (∼10,000 mg/L). Subsequent samples were slightly more dilute. The water's pH (∼4), δ34S (+4.3‰), and surface temperatures (up to 85°C) suggest, rather than significant scrubbing of magmatic volatiles, leaching of basalt and reactions with sulfate minerals resulted in high concentrations of sulfate and other solutes. Thermodynamic modeling and precipitate mineralogy indicate that water composition was controlled by iron oxidation and sulfate dissolution. Although the lake exhibited no detectable precursors before the next eruption, and phreatic or phreatomagmatic explosions did not materialize, our multi-parameter approach to monitoring yielded an enhanced understanding of the hydrologic, geologic, and magmatic conditions that led to the formation of the unique and short-lived lake.
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of “social” and “solitary” inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species’ context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.
","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.2312252121","usgsCitation":"Twining, J., Sutherland, C., Zalewski, A., Cove, M., Birks, J., Wearn, O.R., Haysom, J., Wereszczuk, A., Manzo, E., Bartolommei, P., Mortelliti, A., Evans, B., Gerber, B., McGreevy, T., Ganoe, L.S., Masseloux, J., Mayer, A.E., Wierzbowska, I., Loch, J., Akins, J., Drummey, D., McShea, W., Manke, S., Pardo, L., Boyce, A., Li, S., Binti Ragai, R., Sukmasuang, R., Villafane Trujillo, A.J., Lopez-Gonzalez, C., Lara-Diaz, N.E., Cosby, O., Waggershauser, C.N., Bamber, J., Stewart, F., Fisher, J., Fuller, A.K., Perkins, K., and Powell, R.A., 2024, Using global remote camera data of a solitary species complex to evaluate the drivers of group formation: PNAS, v. 121, no. 12, e2312252121, 8 p., https://doi.org/10.1073/pnas.2312252121.","productDescription":"e2312252121, 8 p.","ipdsId":"IP-151912","costCenters":[{"id":199,"text":"Coop Res Unit 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,{"id":70257405,"text":"70257405 - 2024 - Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","interactions":[],"lastModifiedDate":"2024-08-30T15:53:54.80827","indexId":"70257405","displayToPublicDate":"2024-03-11T08:39:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","docAbstract":"Prior research documented relationships between brown-headed cowbird (Molothrus ater) brood parasitism and edge effects, proximity of perches, and nest exposure. Those relationships have not been evaluated in agroecosystems containing extremes of fragmentation and vegetation diversity.
We compared three existing hypotheses on how cowbirds locate host nests with two new hypotheses regarding habitat amount and vegetation diversity to determine how the configuration and location of agricultural conservation practices affect grassland bird nest parasitism rates and predicted rates for eight common conservation practices.
We assessed cowbird parasitism of grassland bird nests on corn and soybean farms in Iowa, USA, and measured perch proximity, nest exposure, edge effects, habitat amount, and vegetation diversity for each nest. We fit a global generalized linear mixed-effects model and compared importance of model parameters using odds ratios. We predicted parasitism likelihood for every subset model and averaged predictions to explore individual effects.
The variables that most influenced parasitism rates included main effects for nest initiation day-of-season (OR = 0.71, CI95 = 0.60–0.84) and the landscape variables of distance to nearest crop edge (0.63, 0.51–0.76) and proportion of grass land cover within 660 m (0.75, 0.57–1.00). We found little support that perch proximity, nest exposure, or native vegetation diversity affected parasitism. We also assessed parasitism likelihood by conservation practice and found no significant differences.
Our results provide evidence to support the edge effect and habitat amount hypotheses, but not the nest exposure, vegetation diversity, or perch proximity hypotheses.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10980-024-01816-0","usgsCitation":"Stephenson, M., Yuza, K.L., Schulte, L., and Klaver, R.W., 2024, Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes: Landscape Ecology, v. 39, 69, 16 p., https://doi.org/10.1007/s10980-024-01816-0.","productDescription":"69, 16 p.","ipdsId":"IP-144994","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-01816-0","text":"Publisher Index Page"},{"id":433377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephenson, Matthew D.","contributorId":342652,"corporation":false,"usgs":false,"family":"Stephenson","given":"Matthew D.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910259,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuza, Kyla L.","contributorId":342654,"corporation":false,"usgs":false,"family":"Yuza","given":"Kyla","email":"","middleInitial":"L.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910260,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulte, Lisa A.","contributorId":342657,"corporation":false,"usgs":false,"family":"Schulte","given":"Lisa A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":910262,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251898,"text":"ofr20241013 - 2024 - Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","interactions":[],"lastModifiedDate":"2024-12-04T14:29:22.266119","indexId":"ofr20241013","displayToPublicDate":"2024-03-11T08:24:27","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1013","displayTitle":"Growth, Survival, and Cohort Formation of Juvenile Lost River (Deltistes luxatus) and Shortnose Suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 Monitoring Report","title":"Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","docAbstract":"The work reported in this publication provides updated data and interpretation for sampling years 2015 and 2022 of the juvenile monitoring project. The study objectives, background, study area, species description, and methods remained the same or similar throughout the years, while the executive summary, results, and discussion were updated each year. Therefore much of this paper was originally presented in previous reports (Bart and others 2020a, b; Bart and others, 2021; Burdick and others, 2016; Burdick and others, 2018; Martin and others, 2022) and is repeated here for the reader’s convenience.
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only because of adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth; (2) better understand when juvenile sucker mortality is greatest; and (3) identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey (USGS) monitoring program, begun in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker production, juvenile sucker apparent survival, growth, species composition, and health.
Upper Klamath Lake indices of year-class strength suggest that the 2022 age-0 cohort is the lowest since standardized monitoring began. The 2021 cohort, like most cohorts, had moderately low catch rates their first year of life, with a steep drop off during the second year. Although the 2020 cohort persisted through the September 2022 sampling, this cohort was sparsely represented after the first year with no representatives from this cohort captured from July 2021 through July 2022. Despite apparently low fall through spring apparent survival, the relatively large 2019 cohort persisted in our 2020–21 samples, but has not been detected since June 2021. Klamath largescale (Catostomus snyderi) and shortnose suckers were only differentiated from each other starting in 2020. Shortnose suckers dominated the age-1 catch in 2020 and 2022, whereas age-1 Klamath largescale suckers were slightly more prevalent in 2021. Although there were occasionally age-2 and older suckers captured, none of these fish were Lost River suckers. Except for 2015, 2017, and 2021, there were more age-0 Lost River suckers than presumed shortnose suckers in Upper Klamath Lake. However, in all years sampled, there were more age-1 presumed shortnose suckers than Lost River suckers.
Age distribution of suckers captured in Clear Lake indicates greater juvenile survival than in Upper Klamath Lake. Most juvenile suckers captured throughout the years were from the 2016 and 2017 cohorts; however, by 2022 most of these fish were no longer susceptible to standard trap nets and were not as prevalent in 2022 juvenile catches, and these suckers presumedly recruited to the adult population. As the 2016 and 2017 cohorts catches declined, so did the catch in overall numbers of suckers. Excluding age-0 catches, the 2016 cohort catches peaked at age-3 and the 2017 catches peaked at age-2. In 2022, the majority of the catch was composed of age-3 to age-5 suckers. The majority of suckers captured in Clear Lake during this multiyear project were classified as the combination of Klamath largescale suckers and shortnose suckers from the Lost River Basin, from the 2016 and 2017 cohorts. The few suckers identified as Lost River or definitive shortnose suckers were from the 2016 and 2017 cohorts. A lack of age-0 suckers captured in Clear Lake during years with low spawning tributary inflow or lake levels suggested that low water prevented spawning and year class formation. However, recent data indicate that some cohorts with Klamath largescale and shortnose sucker genetics that were not captured as age-0 suckers were detected in later years at age-1 or age-2. This finding indicates that juvenile suckers in Clear Lake may spend one or more years in the tributaries and that these cohorts may primarily be represented by Klamath largescale suckers.
The first 7 years of this monitoring program indicated different patterns in recruitment and survival of juvenile suckers between Upper Klamath and Clear Lakes. Since the monitoring program began in 2015, age-0 sucker catch rates, interpreted as indices of year-class strength, were greatest in Upper Klamath Lake in 2016 and 2019. In those years, Lost River suckers made up the majority of age-0 sucker catches. However, in 2017 and 2020, the age-1 sucker catches from these cohorts were mainly composed of shortnose suckers or suckers with genetic markers of both Klamath largescale and shortnose suckers, indicating a low first year survival for Lost River suckers even when age-0 catches were high. Age-0 suckers do not fully recruit to our sampling gear in Upper Klamath Lake until August, experience high mortality by September, and are almost undetectable in subsequent years. In Clear Lake, suckers are often not captured until age-1 or age-2 and juvenile annual survival appears much greater; however, there does appear to be a drop-off in catch rates as the suckers age and become less susceptible to the fishing gear.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241013","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Martin, B.A., Caldwell, J.M., Krause, J.R., and Harris, A.C., 2024, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report: U.S. Geological Survey Open-File Report 2024–1013, 39 p., https://doi.org/10.3133/ofr20241013.","productDescription":"Report: vi, 39 p.; 1 Data Release","onlineOnly":"Y","ipdsId":"IP-159115","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":426337,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.XML"},{"id":426335,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KYGEG","text":"USGS data release","description":"USGS data release","linkHelpText":"Upper Klamath Lake and Clear Lake sampling for suckers from 2015 through 2022. Reston, Virginia: U.S. Geological Survey, Klamath Falls Field Station, Klamath Falls, Oregon"},{"id":426334,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241013/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1013"},{"id":426333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.pdf","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1013"},{"id":426332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.jpg"},{"id":426336,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1013/images"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01266997658502,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.93494622821743\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78179025160858,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.649405814487636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Harvest in walleye Sander vitreus fisheries is size-selective and could influence phenotypic traits of spawners; however, contributions of individual spawners to recruitment are unknown. We used parentage analyses using single nucleotide polymorphisms to test whether parental traits were related to the probability of offspring survival in Escanaba Lake, Wisconsin. From 2017 to 2020, 1339 adults and 1138 juveniles were genotyped and 66% of the offspring were assigned to at least one parent. Logistic regression indicated the probability of reproductive success (survival of age-0 to first fall) was positively (but weakly) related to total length and growth rate in females, but not age. No traits analyzed were related to reproductive success for males. Our analysis identified the model with the predictors' growth rate and year for females and the models with year and age and year for males as the most likely models to explain variation in reproductive success. Our findings indicate that interannual variation (i.e., environmental conditions) likely plays a key role in determining the probability of reproductive success in this population and provide limited support that female age, length, and growth rate influence recruitment.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13665","usgsCitation":"Davis, R.P., Simmons, L.M., Shaw, S., Sass, G., Sard, N., Isermann, D.A., Larson, W.A., and Homola, J.J., 2024, Demographic patterns of walleye (Sander vitreus) reproductive success in a Wisconsin population: Evolutionary Applications, v. 17, no. 3, e13665, 16 p., https://doi.org/10.1111/eva.13665.","productDescription":"e13665, 16 p.","ipdsId":"IP-156437","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440160,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/eva.13665","text":"External Repository"},{"id":433563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Vilas County","otherGeospatial":"Escanaba Lake","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.9879,46.0971],[-88.9329,46.0746],[-88.9332,45.9822],[-89.0478,45.9822],[-89.0477,45.8953],[-89.1091,45.8973],[-89.1752,45.8993],[-89.1754,45.859],[-89.3008,45.8606],[-89.3007,45.9014],[-89.3628,45.8987],[-89.4256,45.8987],[-89.5498,45.8988],[-89.6741,45.8987],[-89.7571,45.8985],[-89.797,45.898],[-89.8199,45.8984],[-89.9212,45.8981],[-89.9846,45.8974],[-90.0428,45.8972],[-90.0442,45.9823],[-90.0134,45.9824],[-89.9853,45.9821],[-89.9289,45.9818],[-89.9282,46.0693],[-89.9288,46.1558],[-89.9287,46.2428],[-89.929,46.3],[-89.7599,46.268],[-89.7368,46.2636],[-89.5829,46.2347],[-89.5331,46.2252],[-89.5133,46.2215],[-89.4272,46.2048],[-89.3759,46.1949],[-89.2666,46.1737],[-89.2302,46.1662],[-89.0854,46.1365],[-88.9879,46.0971]]]},\"properties\":{\"name\":\"Vilas\",\"state\":\"WI\"}}]}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Robert P.","contributorId":342846,"corporation":false,"usgs":false,"family":"Davis","given":"Robert","email":"","middleInitial":"P.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Levi M.","contributorId":342849,"corporation":false,"usgs":false,"family":"Simmons","given":"Levi","email":"","middleInitial":"M.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaw, Stephanie L.","contributorId":342852,"corporation":false,"usgs":false,"family":"Shaw","given":"Stephanie L.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sass, Greg G.","contributorId":342855,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sard, Nicholas M.","contributorId":342858,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas M.","affiliations":[{"id":81942,"text":"State University of New York-Oswego","active":true,"usgs":false}],"preferred":false,"id":910446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larson, Wesley A.","contributorId":342859,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley","email":"","middleInitial":"A.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Homola, Jared Joseph 0000-0003-3821-7224","orcid":"https://orcid.org/0000-0003-3821-7224","contributorId":303741,"corporation":false,"usgs":true,"family":"Homola","given":"Jared","email":"","middleInitial":"Joseph","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910449,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254289,"text":"70254289 - 2024 - Modern coral range expansion off southeast Florida falls short of Late Holocene baseline","interactions":[],"lastModifiedDate":"2024-05-17T12:12:12.214655","indexId":"70254289","displayToPublicDate":"2024-03-09T07:08:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13795,"text":"Nature Communications Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Modern coral range expansion off southeast Florida falls short of Late Holocene baseline","docAbstract":"As thermal stress and disease outbreaks decimate coral reefs throughout the tropics, there is growing evidence that higher latitude marine environments may provide crucial refuges for many at-risk, temperature-sensitive coral species. However, our understanding of how coral populations expand into new areas and sustain themselves over time is constrained by the limited scope of modern observations. Here, we provide geological insights into coral range expansions by reconstructing the composition of a Late Holocene-aged subfossil coral death assemblage on the southeast Florida reef tract and comparing it to modern reefs throughout the region. Our findings show that the Late Holocene coral assemblages were dominated by now critically endangered Acropora species between ~3500 and 1800 years before present, mirroring classic zonation patterns characteristic of healthy pre-1970s Caribbean reefs. In contrast, the modern reefs off southeast Florida are becoming increasingly dominated by stress-tolerant species like Porites astreoides and Siderastrea siderea despite modest expansions of Acropora cervicornis over the past several decades. Our results suggest that ongoing anthropogenic stressors, not present during the Late Holocene, are likely limiting the ability of modern higher latitude reefs in Florida to function as long-term climate refugia.
Remote sensing observations of Searles Lake following the 2019 moment magnitude 7.1 Ridgecrest, California, earthquake reveal an area where surface ejecta is arranged in a repeating hexagonal pattern that is collocated with a solution-mining operation. By analyzing geologic and geotechnical data, here we show that the hexagonal surface ejecta is likely not a result of liquefaction. Instead, we propose dissolution cavity collapse (DCC) as an alternative driving mechanism. We support this theory with pre-event Interferometric Synthetic Aperture Radar data, which reveals differential subsidence patterns and the creation of subsurface void space. We also find that DCC is likely triggered at a lower shaking threshold than classical liquefaction. This and other unknown mechanisms can masquerade as liquefaction, introducing bias into liquefaction prediction models that rely on liquefaction inventories. This paper also highlights the opportunities and drawbacks of using remote sensing data to disentangle the complex factors that influence earthquake-triggered ground failure.
The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one‐hour windows for over 15 yr of vertical‐component data from the nine‐station Caribbean network (CU) and look at background noise within the 0.05–300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75–3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4–8 s range more typical of globally observed secondary microseisms. A simple surface‐wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast (<∼50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75–3 s. This may be because power from nearshore wave action at 0.75–3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of ∼25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.