The proliferation of genomic sequencing approaches has significantly impacted the field of phylogenetics. Target capture approaches provide a cost-effective, fast and easily applied strategy for phylogenetic inference of non-model organisms. However, several existing target capture processing pipelines are incapable of incorporating whole genome sequencing (WGS). Here, we develop a new pipeline for capture and de novo assembly of the targeted regions using whole genome re-sequencing reads. This new pipeline captured targeted loci accurately, and given its unbiased nature, can be used with any target capture probe set. Moreover, due to its low computational demand, this new pipeline may be ideal for users with limited resources and when high-coverage sequencing outputs are required. We demonstrate the utility of our approach by incorporating WGS data into the first comprehensive phylogenomic reconstruction of the freshwater mussel family Margaritiferidae. We also provide a catalogue of well-curated functional annotations of these previously uncharacterized freshwater mussel-specific target regions, representing a complementary tool for scrutinizing phylogenetic inferences while expanding future applications of the probe set.
Earthquake detection is the critical first step in earthquake early warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency, and sensor density are critical to providing real‐time earthquake alerts. Traditional EEW systems use fixed sensor networks or, more recently, networks of mobile phones equipped with microelectromechanical systems (MEMS) accelerometers. Internet of things edge devices, with built‐in tiny machine learning (tinyML) capable microcontrollers, and always‐on, internet‐connected, stationary MEMS accelerometers provide the opportunity to deploy ML‐based earthquake detection and warning using a single‐station approach at a global scale. Here, I test and evaluate tinyML deep learning algorithms for earthquake detection on a microcontroller. I show that the tinyML earthquake detection models can generalize to earthquakes outside the training set.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0220220322","usgsCitation":"Clements, T., 2023, Earthquake detection with tinyML: Seismological Research Letters, v. 94, no. 4, p. 2030-2039, https://doi.org/10.1785/0220220322.","productDescription":"10 p.","startPage":"2030","endPage":"2039","ipdsId":"IP-145890","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Clements, Timothy Hugh 0000-0001-6632-1796","orcid":"https://orcid.org/0000-0001-6632-1796","contributorId":350753,"corporation":false,"usgs":true,"family":"Clements","given":"Timothy Hugh","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926877,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70268406,"text":"70268406 - 2023 - Chapter 12 - Explainable AI for understanding ML-derived vegetation products","interactions":[],"lastModifiedDate":"2025-06-25T14:17:08.250239","indexId":"70268406","displayToPublicDate":"2023-04-23T09:15:24","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Chapter 12 - Explainable AI for understanding ML-derived vegetation products","docAbstract":"Current machine learning applications and algorithms have developed promise to produce autonomous systems that automatically perceive, learn, predict, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions, algorithmic paths, and actions to human users. The purpose of this chapter is to apply explainable artificial intelligence (XAI) to black-box models using an example of the U.S. Geological Survey's LANDFIRE Existing Vegetation Type (EVT). This chapter also demonstrates the tools developed to assist scientists/analysts in understanding and trusting prediction outcomes of vegetation type that streamline development of the LANDFIRE EVT product.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Artificial intelligence in earth science: Best practices and fundamental challenges","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier Inc","doi":"10.1016/B978-0-323-91737-7.00008-6","usgsCitation":"Ganji, G., and Chow Lin, W., 2023, Chapter 12 - Explainable AI for understanding ML-derived vegetation products, chap. of Artificial intelligence in earth science: Best practices and fundamental challenges, p. 317-335, https://doi.org/10.1016/B978-0-323-91737-7.00008-6.","productDescription":"19 p.","startPage":"317","endPage":"335","ipdsId":"IP-135266","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":491276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganji, Geetha Satya Mounika 0000-0003-2763-2617","orcid":"https://orcid.org/0000-0003-2763-2617","contributorId":357334,"corporation":false,"usgs":false,"family":"Ganji","given":"Geetha Satya Mounika","affiliations":[{"id":85411,"text":"KBR under contract to the USGS","active":true,"usgs":false}],"preferred":false,"id":941237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chow Lin, Wai Hang 0000-0003-0628-7711","orcid":"https://orcid.org/0000-0003-0628-7711","contributorId":357335,"corporation":false,"usgs":false,"family":"Chow Lin","given":"Wai Hang","affiliations":[{"id":85411,"text":"KBR under contract to the USGS","active":true,"usgs":false}],"preferred":false,"id":941238,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248270,"text":"70248270 - 2023 - Exploring the relevance of the multidimensionality of wildlife recreationists to conservation behaviors: A case study in Virginia","interactions":[],"lastModifiedDate":"2023-09-06T12:26:20.072182","indexId":"70248270","displayToPublicDate":"2023-04-23T07:22:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the relevance of the multidimensionality of wildlife recreationists to conservation behaviors: A case study in Virginia","docAbstract":"Wildlife recreationists' participation in conservation behaviors could provide key support to the conservation efforts of state fish and wildlife agencies. However, little is known about how identifying with multiple forms of wildlife recreation (i.e., hunters, anglers, birders, wildlife viewers) may influence participation in conservation behaviors, specifically for supporting state fish and wildlife agencies and their conservation goals. Using a mixed-mode survey of Virginia wildlife recreationists, we explored the hypothesized relationship between individuals' participation in conservation behaviors and their identification with multiple forms of consumptive and nonconsumptive wildlife recreation. We found wildlife recreation identity is multidimensional, with many individuals identifying with consumptive and nonconsumptive identities simultaneously. Further, consumptive-only recreationists (i.e., hunters and/or anglers) participated in conservation behaviors less often than nonconsumptive-only recreationists (i.e., birders and/or wildlife viewers) and recreationists with both consumptive and nonconsumptive identities were less likely to support a state fish and wildlife agency in the future. Our findings underscore the importance of all types of wildlife recreationists, especially those with intersecting identities, as state fish and wildlife agencies work to advance conservation. Hence, developing multi-faceted engagement strategies may enhance support for state fish and wildlife agencies among their growing wildlife recreation constituency.
Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simple R routine, and is less computationally-intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.
Keys to Dickcissel (Spiza americana) management include providing dense, moderate-to-tall vegetation, particularly with a well-developed forb component, and moderately deep litter. Dickcissels have been reported to use grassland habitats with 4–166 centimeters (cm) average vegetation height, 6–85 cm visual obstruction reading, 11–68 percent grass cover, 1–86 percent forb cover, less than or equal to (≤) 10 percent shrub cover, less than (<) 27 percent bare ground cover, <30 percent litter cover, and ≤ 6 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842OO","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Zimmerman, A.L., and Euliss, B.R., 2023, The effects of management practices on grassland birds—Dickcissel (Spiza americana), chap. OO of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 47 p., https://doi.org/10.3133/pp1842OO.","productDescription":"v, 47 p.","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097130","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":416091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/oo/coverthb.jpg"},{"id":416092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/oo/pp1842oo.pdf","text":"Report","size":"2.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–OO"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
The ability of reefs to protect coastlines from storm-driven flooding hinges on their capacity to keep pace with sea-level rise. Here, we show how and whether coral restoration could achieve the often-cited goal of reversing the impacts of coral-reef degradation to preserve this essential function. We combined coral-growth measurements and carbonate-budget assessments of reef-accretion potential at Buck Island Reef, U.S. Virgin Islands, with hydrodynamic modeling to quantify future coastal flooding under various coral-restoration, sea-level rise, and storm scenarios. Our results provide guidance on how restoration of Acropora palmata, if successful, could mitigate the most extreme impacts of coastal flooding by reversing projected trajectories of reef erosion and allowing reefs to keep pace with the ~0.5 m of sea-level rise expected by 2100 with moderate carbon-emissions reductions. This highlights the potential long-term benefits of pursuing coral-reef restoration alongside climate-change mitigation to support the persistence of essential coral-reef ecosystem services.
Emissions of methane (CH4) and nitrous oxide (N2O) from soils to the atmosphere can offset the benefits of carbon sequestration for climate change mitigation. While past study has suggested that both CH4 and N2O emissions from tidal freshwater forested wetlands (TFFW) are generally low, the impacts of coastal droughts and drought-induced saltwater intrusion on CH4 and N2O emissions remain unclear. In this study, a process-driven biogeochemistry model, Tidal Freshwater Wetland DeNitrification-DeComposition (TFW-DNDC) was applied to examine the responses of CH4 and N2O emissions to episodic drought-induced saltwater intrusion in TFFW along the Waccamaw River and Savannah River, USA. These sites encompass landscape gradients of both surface and porewater salinity as influenced by Atlantic Ocean tides superimposed on periodic droughts. Surprisingly, CH4 and N2O emission responsiveness to coastal droughts and drought-induced saltwater intrusion varied greatly between river systems and among local geomorphologic settings. This reflected the complexity of wetland CH4 and N2O emissions and suggests that simple linkages to salinity may not always be relevant, as non-linear relationships dominated our simulations. Along the Savannah River, N2O emissions in the moderate-oligohaline tidal forest site tended to increase dramatically under the drought condition, while CH4 emission decreased. For the Waccamaw River, emissions of both CH4 and N2O in the moderate-oligohaline tidal forest site tended to decrease under the drought condition, but the capacity of the moderate-oligohaline tidal forest to serve as a carbon sink was substantially reduced due to significant declines in net primary productivity and soil organic carbon sequestration rates as salinity killed the dominant freshwater vegetation. These changes in fluxes of CH4 and N2O reflect crucial synergistic effects of soil salinity and water level on C and N dynamics in TFFW due to drought-induced seawater intrusion.
South Manitou Island, part of Sleeping Bear Dunes National Lakeshore in northern Lake Michigan, is a post-glacial lacustrine landscape with substantial geomorphic changes including landslides, shoreline and bluff retreat, and sand dune movement. These changes involve interrelated processes, and are influenced to different extents by lake level, climate change, and land use patterns, among other factors. The utility of DEM of Difference (DoD) and other terrain analyses were investigated as a means of understanding interrelated geomorphologic changes and processes across multiple decades and at multiple scales. A 1m DEM was developed from 1955 historical aerial imagery using Structure from Motion Multi-View Stereo (SfM-MVS) and compared to a 2016 lidar-based DEM to quantify change. Landslides, shoreline erosion, bluff retreat, and sand dune movement were investigated throughout South Manitou Island. While the DoD indicates net loss or gain, interpretation of change must take into consideration the SfM-MVS source of the historical DEM. In the case of landslides, where additional understanding may be gleaned through review of the timing of lake high- and lowstands together with DoD values. Landscape-scale findings quantified cumulative feedbacks between interrelated processes. These findings could be upscaled to assess changes across the entire park, informing future change investigations and land management decisions.
","language":"English","publisher":"MDPI","doi":"10.3390/ijgi12040173","usgsCitation":"DeWitt, J.D., and Ashland, F., 2023, Investigating geomorphic change using a structure from motion elevation model created from historical aerial imagery: A case study in northern Lake Michigan, USA: ISPRS International Journal of Geo-information, v. 12, no. 4, 173, 26 p., https://doi.org/10.3390/ijgi12040173.","productDescription":"173, 26 p.","ipdsId":"IP-135951","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijgi12040173","text":"Publisher Index Page"},{"id":420775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Lake Michigan, Sleeping Bear Dunes National Lakeshore, South Manitou Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.11501011248272,\n 44.99859848151604\n ],\n [\n -86.11151860548817,\n 45.0008204742272\n ],\n [\n -86.10593219429762,\n 45.001314238683136\n ],\n [\n -86.10348813940179,\n 45.00316581749374\n ],\n [\n -86.10086950915604,\n 45.005017336463794\n ],\n [\n -86.09598139936371,\n 45.00538763307685\n ],\n [\n -86.09283904306896,\n 45.00699222407434\n ],\n [\n -86.09091871422216,\n 45.009954427829854\n ],\n [\n -86.09301361841888,\n 45.01242281393826\n ],\n [\n -86.09685427611251,\n 45.013780380951914\n ],\n [\n -86.09842545426024,\n 45.015508146971996\n ],\n [\n -86.09999663240727,\n 45.0192103271973\n ],\n [\n -86.09999663240727,\n 45.02291226806037\n ],\n [\n -86.09929833100836,\n 45.02673735548902\n ],\n [\n -86.09580682401447,\n 45.02969853796566\n ],\n [\n -86.09161701562104,\n 45.03117907175729\n ],\n [\n -86.0863797551296,\n 45.031425823665614\n ],\n [\n -86.07922216579134,\n 45.030438809648956\n ],\n [\n -86.07485778204867,\n 45.02920501819111\n ],\n [\n -86.07974589184035,\n 45.03574380985694\n ],\n [\n -86.08428485093289,\n 45.03993811304554\n ],\n [\n -86.08882381002545,\n 45.04215850202999\n ],\n [\n -86.0940610705169,\n 45.04203514934687\n ],\n [\n -86.10209153660396,\n 45.04351536398906\n ],\n [\n -86.10628134499738,\n 45.04474884693579\n ],\n [\n -86.11186775618796,\n 45.04622899136464\n ],\n [\n -86.11727959202933,\n 45.04931250258707\n ],\n [\n -86.12531005811573,\n 45.047339074553776\n ],\n [\n -86.13246764745402,\n 45.044872193767304\n ],\n [\n -86.13857778469426,\n 45.04326866420851\n ],\n [\n -86.142243867038,\n 45.04067825228134\n ],\n [\n -86.14398962053563,\n 45.03623727324839\n ],\n [\n -86.14800485357848,\n 45.027230896540175\n ],\n [\n -86.151496360573,\n 45.02315905560701\n ],\n [\n -86.15586074431567,\n 45.01711245445824\n ],\n [\n -86.15708277176357,\n 45.01291647839395\n ],\n [\n -86.15673362106448,\n 45.00600478877976\n ],\n [\n -86.15725734711346,\n 45.00452360392279\n ],\n [\n -86.15586074431567,\n 45.001314238683136\n ],\n [\n -86.15376584011963,\n 45.00180799888372\n ],\n [\n -86.14765570287939,\n 45.000079819564775\n ],\n [\n -86.14119641494,\n 44.997610901540185\n ],\n [\n -86.13822863399447,\n 44.99711710516928\n ],\n [\n -86.13351509955272,\n 44.99600604777606\n ],\n [\n -86.131420195356,\n 44.99699365541156\n ],\n [\n -86.12775411301226,\n 44.99724055466106\n ],\n [\n -86.12548463346563,\n 44.99859848151604\n ],\n [\n -86.11919992087614,\n 44.997610901540185\n ],\n [\n -86.11501011248272,\n 44.99859848151604\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":882939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashland, Francis 0000-0001-9948-0195 fashland@usgs.gov","orcid":"https://orcid.org/0000-0001-9948-0195","contributorId":198587,"corporation":false,"usgs":true,"family":"Ashland","given":"Francis","email":"fashland@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":882940,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70242863,"text":"pp1842O - 2023 - The effects of management practices on grassland birds—Golden Eagle (Aquila chrysaetos)","interactions":[{"subject":{"id":70242863,"text":"pp1842O - 2023 - The effects of management practices on grassland birds—Golden Eagle (Aquila chrysaetos)","indexId":"pp1842O","publicationYear":"2023","noYear":false,"chapter":"O","displayTitle":"The Effects of Management Practices on Grassland Birds—Golden Eagle (Aquila chrysaetos)","title":"The effects of management practices on grassland birds—Golden Eagle (Aquila chrysaetos)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2023-12-20T21:15:22.8997","indexId":"pp1842O","displayToPublicDate":"2023-04-20T14:09:32","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"O","displayTitle":"The Effects of Management Practices on Grassland Birds—Golden Eagle (Aquila chrysaetos)","title":"The effects of management practices on grassland birds—Golden Eagle (Aquila chrysaetos)","docAbstract":"Keys to Golden Eagle (Aquila chrysaetos) management in western North America’s grasslands, particularly those of the Great Plains region, include maintaining open, mostly undeveloped landscapes that sustain at least modest population levels of suitable prey (most typically rabbits [Leporidae] and prairie dogs or ground squirrels [Sciuridae]); safeguarding nesting territories (that is, breeding areas), especially nest structures within territories, from human disturbances; mitigating major sources of anthropogenic mortality, particularly electrocution on powerlines, shooting, collisions with structures and vehicles, and poisoning by lead and rodenticides; and averting climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842O","usgsCitation":"Murphy, R.K., DeLong, J.P., Igl, L.D., and Shaffer, J.A., 2023, The effects of management practices on grassland birds—Golden Eagle (Aquila chrysaetos), chap. O of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 65 p., https://doi.org/10.3133/pp1842O.","productDescription":"v, 65 p.","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-093912","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":416072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/o/coverthb.jpg"},{"id":416073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/o/pp1842o.pdf","text":"Report","size":"2.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–O"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
The U.S. Geological Survey, in cooperation with Gwinnett County Department of Water Resources, established the Long-Term Trend Monitoring program in 1996 to monitor and analyze the hydrologic and water-quality conditions in Gwinnett County, Georgia. Gwinnett County is a suburban to urban area northeast of the city of Atlanta in north-central Georgia. The monitoring program currently consists of 15 watersheds ranging in size from 1.3 to about 161 square miles. This report synthesizes watershed characteristics and hydrologic and water-quality monitoring data collected for water years (WYs) 2002–20.
The 15 study watersheds were characterized for land-surface elevations, average land-surface slopes, septic densities, sanitary sewer densities, and detention pond areas. Temporal patterns in watershed characteristics were determined for land cover (2001–19), percent imperviousness (2000–20), population density (2000–20), and building density (1950–2022). In 2001, most of the watersheds had at least 45 percent of their land cover composed of developed land cover groups, and by 2019, at least 59 percent of each watershed was developed. Land cover changes occurred most rapidly between 2004 and 2008 at most watersheds. Percent imperviousness in the study watersheds varied substantially and ranged from 14.75 to 55.13 percent in 2019.
Precipitation and runoff were quantified at all study watersheds for WYs 2002–20, and the hydrologic cycle was evaluated both annually and seasonally. Several 1-year or longer droughts occurred during this period. Study area precipitation averaged 51.5 inches per year and runoff averaged 22.5 inches per year. Variations in annual runoff were largely determined by annual precipitation but were also dependent upon watershed storage. Runoff varied seasonally because of high evapotranspiration rates in the summer and changes in base flow associated with seasonal changes in watershed storage. Fifty-one percent of runoff in the study area occurred as base flow. Watersheds with higher imperviousness had higher stormflows because of increased surface runoff and lower base flows because of reduced infiltration that recharges watershed storage.
Turbidity, water temperature, and specific conductance were continuously measured at each study site. These constituents varied seasonally, diurnally, and with streamflow. A minimum of two base-flow and six stormflow samples were collected per year at each watershed and were analyzed for 21 water-quality constituents (water temperature, laboratory specific conductance, pH, and turbidity, biochemical and chemical oxygen demand, suspended sediments, nutrients, base cations, trace metals, and total dissolved solids). Concentrations of most particulate constituents were approximately one-half or more orders of magnitude higher in stormflow samples than in base-flow samples. Total copper and zinc stormflow concentrations exceeded the national recommended aquatic life criteria for acute conditions to varying degrees.
Annual loads and yields were estimated for 12 constituents (which include suspended sediments, nutrients, base cations, trace metals, and total dissolved solids) using a surrogate regression model approach and the Beale load estimator. Loads were typically higher for years with higher runoff. The proportional range of annual loads for total suspended solids, suspended-sediment concentrations, total phosphorus, and total lead, however, were 3.2 to 4.8 times larger than for annual runoff. Higher-than-expected annual sediment loads occurred in the years that also had some of the highest peak flows during the period, indicating that large storms are responsible for much of the sediment transport. Large development projects in proximity to streams also were related to years with high sediment loads. Yields from the Crooked Creek and North Fork Peachtree Creek watersheds were typically among the highest for 8 of the 12 constituents. These watersheds had the two highest amounts of developed medium plus high intensity land cover and the two highest percentages of imperviousness. Moderate to strong correlations were identified between seven of the constituent yields and the percentage of developed medium and high intensity land cover groups. Temporal trends in concentrations and loads were identified for 140 of the 300 possible watershed-time period-constituent combinations. There were substantially more negative than positive temporal trends identified during WYs 2003–10, whereas the number of negative and positive temporal trends were similar during WYs 2010–20. Measures of sediment transport had the most negative temporal trends. A few watersheds had consistent trends across several constituents; however, these trends did not appear to be associated with temporal changes in development or imperviousness.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions and trends for the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality and determine changes in water-quality conditions. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
https://www.usgs.gov/centers/lsawsc
Researchers routinely study streamflow data to understand the effects of natural climate variability and anthropogenic climate change, and to develop methods for estimating streamflow at ungaged locations. These studies require streamflow data that are not modified or largely altered by other anthropogenic activities, such as reservoirs or diversions. This report discusses a method for identifying basins with reservoir regulation using a decadal impact metric that characterizes the degree of regulation of a given river reach. The method is applied to U.S. Geological Survey streamgage basins from eight States in the Central United States. Using this metric, 140 streamgages with known regulation effects (annual peak streamflow values qualified with a code 6) were evaluated for their impact metric values in decades with annual peak streamflow values qualified with code 6. Based on the distribution of median impact metric values at these regulated basins, a threshold value of 0.1 was identified as the value that when exceeded was the most characteristic of the regulated streamgage basins in the study area. Streamgage basins from nine States with peak streamflow values that were not qualified with code 6 were evaluated for impact metric values equal to or greater than the established threshold. About 13 percent of streamgages (136 of 1,017) had an impact metric equal to or greater than the identified regulated threshold at some point in their periods of record. The method discussed in this report, which has limitations owing to characteristics of the data underlying the dam impact metric, provides a regionally consistent approach to identifying regulated U.S. Geological Survey streamgage basins.
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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The groundwater and surface-water supply of the Central Platte Natural Resources District supports a large agricultural economy from the High Plains aquifer and Platte River, respectively. This study provided the Central Platte Natural Resources District with an advanced numerical modeling tool to assist with the update of their Groundwater Management Plan.
An integrated hydrologic model, called the Central Platte Integrated Hydrologic Model, was constructed using the MODFLOW-One-Water Hydrologic Model code with the Newton solver. This code integrates climate, landscape, surface water, and groundwater-flow processes in a fully coupled approach. Model framework included 163 rows; 327 columns; 2,640 feet cell sides; and 3 vertical layers. A predevelopment model simulated steady-state hydrologic conditions prior to April 30, 1895, and a development period model discretized into 610 stress periods simulated transient hydrologic conditions from May 1, 1895, to December 31, 2016, using 170 biannual stress periods from 1895 to 1980, and monthly stress periods from May 1, 1980, to December 31, 2016.
Calibration of the Central Platte Integrated Hydrologic Model involved two phases: a manual adjustment of parameters, followed by the automated calibration completed using BeoPEST that was facilitated by the employment of the singular value decomposition-assist features of PEST that specified 50 super parameters assembled from the 435 adjustable parameters and Tikhonov regularization. The average absolute groundwater-level residuals for model layers one, two, and three were 6.1, 12.4, and 7.4 feet, respectively. Calibrated horizontal hydraulic conductivity was about 70, 32, and 35 feet per day for layers 1, 2, and 3, respectively. The largest development period inflow to groundwater was recharge from deep percolation past the root zone, averaging 1,122,257 acre-feet per year (2.7 inches per year), and the largest outflow was to irrigation wells, averaging 693,171 acre-feet per year (10.2 inches per year for the Central Platte Natural Resources District). Other substantial groundwater outflows included evapotranspiration and base flow. For the total development period, there was a net change in storage of −122,393 acre-feet per year (−0.3 inch per year).
The calibrated Central Platte Integrated Hydrologic Model was used to simulate eight different potential future climate and irrigation pumping conditions from January 1, 2017, to December 31, 2049. Simulated future groundwater levels within the Central Platte Natural Resources District varied significantly between scenarios and locally, from 13.8 feet below to 7.6 feet above baseline 1982 groundwater levels. Most areas exhibited groundwater-level declines for the drought scenarios and rises for the alternate irrigation scenarios. Changes in scenario groundwater levels correlated with the relations between farm net recharge and irrigation pumping. Linear “first order second moment” techniques indicated that the uncertainty in projected groundwater altitudes was reduced by 15.33 feet through model calibration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235024","collaboration":"Prepared in cooperation with the Central Platte Natural Resources District and the Nebraska Natural Resources Commission","usgsCitation":"Traylor, J.P., Guira, M., and Peterson, S.M., 2023, An integrated hydrologic model to support the Central Platte Natural Resources District Groundwater Management Plan, central Nebraska: U.S. Geological Survey Scientific Investigations Report 2023–5024, 143 p., https://doi.org/10.3133/sir20235024.","productDescription":"Report: xii, 143 p.; 2 Tables; Data Release; Dataset; 3 Figures: 11.00 x 8.50 inches","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123254","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":416070,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":500878,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114681.htm","linkFileType":{"id":5,"text":"html"}},{"id":416058,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_tables1.1_to_4.24.zip","text":"Appendix tables","size":"36 kB","linkFileType":{"id":7,"text":"csv"}},{"id":416057,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G3Q5XK","text":"USGS data release","linkHelpText":"MODFLOW-One-Water model used to support the Central Platte Natural Resources District Groundwater Management Plan, central Nebraska"},{"id":416056,"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":416068,"rank":11,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig11.pdf","text":"Figure 11 (layered)","size":"1.60 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416055,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5024/downloads","text":"Appendix tables","linkFileType":{"id":3,"text":"xlsx"}},{"id":416054,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5024/images"},{"id":416067,"rank":10,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig07b.pdf","text":"Figure 7B (layered)","size":"3.37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416053,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5024"},{"id":416066,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig04b.pdf","text":"Figure 4B (layered)","size":"847 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":416051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5024/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.333,\n 41.51085969164163\n ],\n [\n -100.35,\n 41.51085969164163\n ],\n [\n -100.35,\n 40.11583169634787\n ],\n [\n -97.333,\n 40.11583169634787\n ],\n [\n -97.333,\n 41.51085969164163\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Field relations as well as geochemical and petrologic studies of metaigneous rocks assigned to the Pennsylvanian–Permian Petersburg batholith identify at least two distinct rock types: foliated metagranitoid gneiss and massive to porphyritic granite. Foliated metagranitoid gneiss of mostly granodioritic composition is geochemically distinct from associated massive and porphyritic granitic rocks. These gneissic rocks yield radiometric ages from ca. 425 Ma to ca. 403 Ma and document that many of the rocks assigned to the late Paleozoic Petersburg batholith are 100 m.y. older than the youngest portions of the composite batholith and are part of an earlier infrastructural terrane. Two samples of massive equigranular granite southwest of Petersburg, Virginia, yield ages of ca. 321 Ma and ca. 317 Ma, which are 15–20 m.y. older than ca. 300 Ma ages for porphyritic granite, massive granite, and monzodiorite near Richmond, Virginia. Geologic mapping shows that the Early Pennsylvanian granite southwest of Petersburg is separated from Late Pennsylvanian to early Permian granite near Richmond by a map-scale septum of Silurian–Devonian foliated metagranitoid gneiss, referred to herein as the informal Pocoshock Creek gneiss. Laser ablation–inductively coupled plasma–mass spectrometry data from one sample of a quartz-muscovite felsic schist xenolith show a peak age mode of ca. 529 Ma that we interpret to be the maximum depositional age. Inherited zircons from foliated metagranitoid gneiss and massive equigranular granite range from ca. 631 Ma to ca. 376 Ma, but many are Cambrian. Neoproterozoic–Cambrian quartz-muscovite felsic schist and amphibolite, Silurian–Devonian Pocoshock Creek gneiss, and Pennsylvanian–Permian granite comprise a fault-bounded terrane referred to herein as the Dinwiddie terrane. Ages of inherited cores in zircon from igneous rocks and limited detrital zircon geochronology suggest the terrane is of peri-Gondwanan affinity. U/Pb ages of healed fractures in zircon grains from foliated metagranitoid gneiss indicate low-grade deformation of the gneiss at ca. 378–376 Ma, while ca. 320–280 Ma rims on many grains record intrusion of late Paleozoic granite. The temperature-time-deformation history of the Dinwiddie terrane is distinct from the adjacent Goochland and Roanoke Rapids terranes. Orogen-scale dextral transpression likely translated the Dinwiddie terrane southward during the Alleghanian orogeny, at which time they were intruded by Pennsylvanian to Permian granite.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02546.1","usgsCitation":"Carter, M.W., McAleer, R.J., Holm-Denoma, C., Occhi, M.E., Owens, B.E., and Vazquez, J.A., 2023, Redefinition of the Petersburg batholith and implications for crustal inheritance in the Dinwiddie terrane, Virginia, USA: Geosphere, v. 19, no. 3, p. 900-932, https://doi.org/10.1130/GES02546.1.","productDescription":"33 p.","startPage":"900","endPage":"932","ipdsId":"IP-133680","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443798,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1130/ges02546.1","text":"Publisher Index Page"},{"id":435366,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92IZPID","text":"USGS data release","linkHelpText":"Whole Rock Geochemistry and Uranium Lead Isotopic Data from the Dinwiddie Terrane, Virginia, USA"},{"id":420240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Dinwiddie terrane","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.49748525226563,\n 37.798681296457076\n ],\n [\n -78.21468653839521,\n 37.79869080070846\n ],\n [\n -78.24158158662492,\n 36.599064764101655\n ],\n [\n -77.49748525226563,\n 36.599064764101655\n ],\n [\n -77.49748525226563,\n 37.798681296457076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":881212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":881213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":881214,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Occhi, Marcie E.","contributorId":328758,"corporation":false,"usgs":false,"family":"Occhi","given":"Marcie","email":"","middleInitial":"E.","affiliations":[{"id":78483,"text":"Virginia Energy - Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":881215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, Brent E.","contributorId":178190,"corporation":false,"usgs":false,"family":"Owens","given":"Brent","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":881216,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":881217,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242949,"text":"70242949 - 2023 - Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models","interactions":[],"lastModifiedDate":"2023-06-09T15:19:19.221059","indexId":"70242949","displayToPublicDate":"2023-04-20T06:46:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models","docAbstract":"Understanding the temporal and spatial scales at which wildlife move is vital for conservation and management. This is especially important for semi-aquatic species that make frequent inter-wetland movements to fulfil life-history requirements.
We aimed to investigate the drivers of movement and space-use of the imperilled spotted turtle (Clemmys guttata), a seasonal wetland specialist, in three large, isolated wetland complexes in Virginia, USA.
We used VHF radio-transmitters to radio-locate adult and juvenile turtles, and estimated movement and space-use during their active and aestivation seasons (March–August). We then used generalised linear mixed models to examine how movement and space-use varied, based on intrinsic turtle characteristics and extrinsic wetland and climatic factors.
We show that, on average, individual spotted turtles used five wetlands per year (range 3–13), and that their inter-wetland movement and movement distance varied seasonally in accordance with wetland availability and breeding phenology. Spotted turtle movement and space-use was influenced by the arrangement and size of the wetland complexes, with turtles moving further and occupying larger home-ranges as size and distance between wetlands increased. Inter-wetland movement was not influenced by intrinsic turtle effects but larger adult turtles moved further, used more wetlands, and had larger home-ranges than smaller turtles.
Turtle responses to variation in season and wetland configuration highlight the need for complex and dynamic landscapes required to sustain this species.
This study has important conservation implications showing that spotted turtles rely on a large number of diverse wetlands, as well as upland habitat, to fulfil their resource needs – and that these habitat associations vary seasonally. Results from our study can aid the understanding of spatial and temporal variation in patch characteristics (e.g. quality and extent) and inter-patch movement by organisms, which is critical for the conservation and management of semi-aquatic species and other organisms that occupy patchy habitat complexes.
Species distribution models have become increasingly important tools for species conservation. This modeling approach can help guide conservation practitioners and inform decisions. Distribution models are particularly relevant for rare species, whose habitat associations are often not well understood. Using species occurrence data, and a variety of predictor variables, we developed three individual distribution models and a weighted ensemble model for the Puerto Rican harlequin butterfly (Atlantea tulita). The ensemble model had the greatest accuracy (AUC = 0.92). Further, the ensemble model indicated 7.1% of the main island of Puerto Rico encompassed suitable habitat for the harlequin butterfly. However, only 0.5% was classified as including the greatest suitability. Using an ensemble modeling approach to delineate areas of the island with suitable environmental conditions may improve habitat conservation efforts for the species.
","language":"English","publisher":"University of Puerto Rico at Mayaguez","doi":"10.18475/cjos.v53i1.a3","usgsCitation":"Ramirez-Reyes, C., Vilella, F., Evans, K.O., Street, G., Pacheco, C., Monzon, O., and Morales Perez, A.L., 2023, Geographic distribution of the Puerto Rican Harlequin Butterfly (Atlantea tulita): An ensemble modeling approach: Caribbean Journal of Science, v. 53, no. 1, p. 37-44, https://doi.org/10.18475/cjos.v53i1.a3.","productDescription":"8 p.","startPage":"37","endPage":"44","ipdsId":"IP-149541","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Puerto 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O.","contributorId":340725,"corporation":false,"usgs":false,"family":"Evans","given":"Kristine","email":"","middleInitial":"O.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Street, Garrett","contributorId":340727,"corporation":false,"usgs":false,"family":"Street","given":"Garrett","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pacheco, Carlos","contributorId":340728,"corporation":false,"usgs":false,"family":"Pacheco","given":"Carlos","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Monzon, Omar","contributorId":340731,"corporation":false,"usgs":false,"family":"Monzon","given":"Omar","email":"","affiliations":[{"id":81651,"text":"Para la Naturaleza","active":true,"usgs":false}],"preferred":false,"id":907490,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morales Perez, Alcides L.","contributorId":340733,"corporation":false,"usgs":false,"family":"Morales Perez","given":"Alcides","email":"","middleInitial":"L.","affiliations":[{"id":81651,"text":"Para la Naturaleza","active":true,"usgs":false}],"preferred":false,"id":907491,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242796,"text":"sir20235037 - 2023 - Documentation of linear regression models for computing water-quality constituent concentrations using continuous real-time water-quality data for the North Fork Ninnescah River and Cheney Reservoir, Kansas, 2014–21","interactions":[],"lastModifiedDate":"2026-03-06T21:20:18.065954","indexId":"sir20235037","displayToPublicDate":"2023-04-18T10:53:24","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5037","displayTitle":"Documentation of Linear Regression Models for Computing Water-Quality Constituent Concentrations using Continuous Real-Time Water-Quality Data for the North Fork Ninnescah River and Cheney Reservoir, Kansas, 2014–21","title":"Documentation of linear regression models for computing water-quality constituent concentrations using continuous real-time water-quality data for the North Fork Ninnescah River and Cheney Reservoir, Kansas, 2014–21","docAbstract":"Cheney Reservoir, in south-central Kansas, was constructed to provide a reliable municipal water supply for the city of Wichita, Kansas, and to provide downstream flood control, wildlife habitat, and recreation. Cheney Reservoir will continue to be important for municipal water supply use as needs increase with ongoing population growth and urban development. Advanced notification of changing water-quality conditions near water-treatment facility intakes and in source waters allows water-treatment facilities and resource planning officials to proactively monitor changing conditions. The U.S. Geological Survey (USGS), in cooperation with the City of Wichita, collected water-quality data at the North Fork Ninnescah River above Cheney Reservoir (USGS station 07144780) and Cheney Reservoir near Cheney, Kans. (USGS station 07144790), monitoring sites to update and develop regression models relating continuous water-quality constituents, streamflow, reservoir storage, and seasonal components to discretely sampled water-quality constituent concentrations of interest. Linear regression analysis was used to update and develop models for alkalinity, major ions, nutrients (nitrogen and phosphorus species), total and dissolved organic carbon, total suspended solids, suspended sediment, fecal indicator bacteria, and atrazine at the North Fork Ninnescah River site and total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, and suspended sediment at the Cheney Reservoir site. New and updated models for both sites are applicable to the period of YSI EXO water-quality monitor and sensor deployment (November 14, 2015, through September 30, 2021, at the North Fork Ninnescah River site; October 1, 2014, through September 30, 2021, at the Cheney Reservoir site). Models and resulting water-quality information included in this report can be used in real time, potentially as guidance for water-treatment processes, and can be used to characterize changes in water-quality conditions over time in Cheney Reservoir and its contributing drainage basin provided that the deployed equipment, sensors, and location do not change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235037","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Kramer, A.R., and Puls, K.A., 2023, Documentation of linear regression models for computing water-quality constituent concentrations using continuous real-time water-quality data for the North Fork Ninnescah River and Cheney Reservoir, Kansas, 2014–21: U.S. Geological Survey Scientific Investigations Report 2023–5037, 20 p., https://doi.org/10.3133/sir20235037.","productDescription":"Report: vii, 20 p.; 18 Appendixes; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-145994","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":500907,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114680.htm","linkFileType":{"id":5,"text":"html"}},{"id":415924,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235037/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":415917,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5037/downloads","text":"Appendixes 1–18"},{"id":415916,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5037/images"},{"id":415918,"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":415915,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5037/sir20235037.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":415914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5037/sir20235037.pdf","text":"Report","size":"1.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5037"},{"id":415913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5037/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir, North Fork Ninnescah River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.75,\n 37.5\n ],\n [\n -97.75,\n 38.1\n ],\n [\n -99.1,\n 38.1\n ],\n [\n -99.1,\n 37.5\n ],\n [\n -97.75,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Catostomidae (catostomids) are suckers of the order Cypriniformes, and the majority of species are native to North America; however, species in this group are understudied and rarely managed. The popularity in bowfishing and gigging for suckers in the United States has increased concerns related to overfishing. Little information exists about the relative gear effectiveness for sampling catostomids. We sought to evaluate the relative effectiveness of boat electrofishing for sampling Black Redhorse Moxostoma duquesnei, Golden Redhorse M. erythrurum, Northern Hogsucker Hypentelium nigricans, White Sucker Catostomus commersonii, and Spotted Sucker Minytrema melanops populations in Lake Eucha, Oklahoma. We used an information theoretic approach to determine the abiotic variables related to sucker catch per effort (C/f). Our analysis indicated that sucker C/f was highest during the night and decreased with increasing water temperature. Sucker size structure was significantly different between daytime and nighttime samples; however, effect size estimates for size structure comparisons indicated that size distributions exhibited moderate overlap. Distributional comparisons indicated that daytime and nighttime samples were similar for fish greater than 180 mm in total length. Effect size estimates also indicated little association between the proportion of each species captured and time of day or water temperature. Night electrofishing in reservoirs at water temperatures from 16 to 25°C yielded the most precise C/f estimates, with the highest numbers of suckers collected at water temperatures from 6 to 15°C. Further study of the relationship between abiotic variables and catostomid catchability using various gears will be beneficial to agencies interested in these populations.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-22-052","usgsCitation":"Zentner, D.L., Brewer, S.K., and Shoup, D.E., 2023, Environment affects sucker catch rate, size structure, species composition, and precision in boat electrofishing samples: Journal of Fish and Wildlife Management, v. 14, no. 1, p. 135-152, https://doi.org/10.3996/JFWM-22-052.","productDescription":"18 p.","startPage":"135","endPage":"152","ipdsId":"IP-140012","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":443813,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-22-052","text":"Publisher Index Page"},{"id":433060,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Lake Eucha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.93975036194603,\n 36.38140481612922\n ],\n [\n -94.93850942940529,\n 36.36874724463541\n ],\n [\n -94.92320459473427,\n 36.34776259578841\n ],\n [\n -94.84802476496563,\n 36.32752008971409\n ],\n [\n -94.80180002781772,\n 36.34526380928274\n ],\n [\n -94.79642265347414,\n 36.35775675172556\n ],\n [\n -94.84750770974023,\n 36.361753979640646\n ],\n [\n -94.88763119522902,\n 36.37441109676014\n ],\n [\n -94.93975036194603,\n 36.38140481612922\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Zentner, Douglas L.","contributorId":341038,"corporation":false,"usgs":false,"family":"Zentner","given":"Douglas","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":907841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, D. E.","contributorId":341039,"corporation":false,"usgs":false,"family":"Shoup","given":"D.","email":"","middleInitial":"E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907842,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242844,"text":"70242844 - 2023 - The invasive Asian benthic foraminifera Trochammina hadai Uchio, 1962: Identification of a new local in Normandy (France) and a discussion on its putative introduction pathways","interactions":[],"lastModifiedDate":"2023-04-20T11:44:23.128119","indexId":"70242844","displayToPublicDate":"2023-04-18T06:41:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":868,"text":"Aquatic Invasions","active":true,"publicationSubtype":{"id":10}},"title":"The invasive Asian benthic foraminifera Trochammina hadai Uchio, 1962: Identification of a new local in Normandy (France) and a discussion on its putative introduction pathways","docAbstract":"The invasive benthic foraminifera Trochammina hadai has been found for the first time in Europe along the coast of Normandy. Its native range of distribution is in Asia (Japan and Korea), and it has also been introduced along the coasts of western North America, Brazil and Australia. Morphological and molecular assessments confirm that specimens found in Le Havre and Caen-Ouistreham harbors belong to the Asiatic type. Like in Asia, T. hadai was found in transitional waters with muddy sediments. It exhibited high relative abundances (up to about 40%) confirming that T. hadai is a highly competitive species. In the present study, it was nearly absent from natural transitional waters and very abundant in heavily modified habitats like harbors, suggesting that ballast waters may likely be the vector of introduction. It was not recorded farther north along the coast of the Hauts-de-France. It is further hypothesized that the finding of a few specimens outside the harbor may facilitate the expansion of T. hadai in the English Channel by means of propagules dispersion.
Silver carp (Hypophthalmichthys molitrix) are planktivorous fish that were originally introduced to the United States for use in fish production ponds and have since escaped these enclosures and are invading the Mississippi River Basin. The silver carp invasion of the Illinois River has a myriad of negative effects on native ecosystems. In this paper, we introduce key dependencies that are likely important in the population dynamics of silver carp: length-dependent egg production and density-dependent growth. Using movement data between two adjacent pools of the Illinois River, we conduct numerical simulations to explore the theoretical effect of harvesting and the use of movement barriers. Results of our model provide insights on how the number of silver carp may respond to movement barriers placed between adjacent harvesting sites.
","language":"English","publisher":"Illinois State University","doi":"10.30707/SPORA9.1.1681910439.35821","usgsCitation":"Coles, C., Balas, E., Peirce, J.P., Sandland, G., and Erickson, R.A., 2023, Using integral projection models to explore management strategies for silver carp (Hypophthalmichthys molitrix): SPORA, v. 9, no. 1, p. 37-48, https://doi.org/10.30707/SPORA9.1.1681910439.35821.","productDescription":"12 p.","startPage":"37","endPage":"48","ipdsId":"IP-142939","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":443822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.30707/spora9.1.1681910439.35821","text":"Publisher Index Page"},{"id":421886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coles, Cameron","contributorId":330805,"corporation":false,"usgs":false,"family":"Coles","given":"Cameron","email":"","affiliations":[{"id":79022,"text":"Central College (Iowa)","active":true,"usgs":false}],"preferred":false,"id":885968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Balas, Elizabeth","contributorId":330806,"corporation":false,"usgs":false,"family":"Balas","given":"Elizabeth","email":"","affiliations":[{"id":79023,"text":"Susquehanna University","active":true,"usgs":false}],"preferred":false,"id":885969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peirce, James P 0000-0002-7147-3695","orcid":"https://orcid.org/0000-0002-7147-3695","contributorId":316559,"corporation":false,"usgs":false,"family":"Peirce","given":"James","email":"","middleInitial":"P","affiliations":[{"id":47908,"text":"University of Wisconsin - La Crosse","active":true,"usgs":false}],"preferred":false,"id":885970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sandland, Greg J.","contributorId":190137,"corporation":false,"usgs":false,"family":"Sandland","given":"Greg J.","affiliations":[],"preferred":false,"id":885971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":885972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243164,"text":"70243164 - 2023 - Insectivorous bat foraging tracks the availability of aquatic flies (Diptera)","interactions":[],"lastModifiedDate":"2023-06-09T15:23:55.963016","indexId":"70243164","displayToPublicDate":"2023-04-17T07:02:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Insectivorous bat foraging tracks the availability of aquatic flies (Diptera)","docAbstract":"Rivers and their adjacent riparian zones are model ecosystems for observing cross-ecosystem energy transfers. Aquatic insects emerging from streams, for example, are resource subsidies that support riparian consumers such as birds, spiders, lizards, and bats. We collaborated with recreational river runners in Grand Canyon, Arizona, USA, to record acoustic bat activity and sample riparian insects using light traps at dusk from April through October 2017–2020. River runners collected these data on 1,428 events over 611 sampling nights at 410 sites throughout a 470-km segment of the Colorado River. We documented 71 insect taxa in light traps and recorded 19 bat species with acoustic detectors. We hypothesized that bat activity along this highly regulated river segment would be influenced primarily by variation in prey availability, as compared to other habitat descriptors. We predicted that bat activity would be positively related to aquatic insect catch rates and unrelated to terrestrial insect abundance. We fit Bayesian regression models to test these hypotheses and to quantify the relationship between bat activity and a suite of environmental variables: time of year, time of day, distance from perennial tributaries, distance from rapids, channel width, geomorphic reach, tall vegetation cover, air temperature, and lunar phase. Bat activity was positively related to the abundance of aquatic flies (Diptera), which outcompeted all other prey categories and structural habitat descriptors in our models. Within our dusk sampling period, activity of small myotis (California myotis [Myotis californicus] and Yuma myotis [M. yumanensis]) was high late in the evening and canyon bats (Parastrellus hesperus), conversely, were more active early in the evening. Activity of canyon bats varied seasonally, with peak activity in August. Our results support the hypothesis that aquatic prey, specifically aquatic flies, are key predictors of bat activity along a large, regulated river corridor and demonstrate the power of community science as a tool for ecosystem monitoring.
Monthly temperature and precipitation data for 923 United States Geological Survey 8-digit hydrologic units are used as inputs to a monthly water balance model to compute monthly actual evapotranspiration, soil moisture storage, and runoff across the western United States (U.S.) for the period 1900 through 2020. Time series of these water balance variables are examined to characterize and explain the dry conditions across the western U.S. since the year 2000. Results indicate that although precipitation deficits account for most of the changes in actual evapotranspiration and runoff, increases in temperature primarily explain decreases in soil moisture storage. Specifically, temperature has been particularly impactful on the magnitude of negative departures of soil moisture storage during the spring (April through June) and summer (July through September) seasons. These effects on soil moisture may be particularly detrimental to agriculture in regions already stressed by drought such as the western U.S.
Communities residing on barrier islands depend upon the ability of barriers to withstand forcings such as waves, sea-level rise, and storms, particularly under stresses from climate change. Using a barrier island evolution model, we compare barrier response to linear versus accelerating sea-level rise. Results suggest that barriers are more likely to drown under accelerating rather than linear sea-level rise. The dominant style of barrier drowning also shifts from width drowning to height drowning. When our model of barrier evolution is coupled with a myopic economic decision-making model for beach nourishment and managed retreat, the general coastal management behavior is unchanged. However, the timing and position at which interventions are made differ. Therefore, decisions based on the assumption of constant sea-level rise rates rather than increasing rates may result in actions that are detrimental to communities and potentially reduce the barrier’s ability to maintain its subaerial landform.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0008","usgsCitation":"Palermo, R.E., Ashton, A.D., Jin, D., Hoagland, P., and Lorenzo-Trueba, J., 2023, The evolution of natural and developed barriers under accelerating sea levels, in The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 77-89, https://doi.org/10.1142/9789811275135_0008.","productDescription":"13 p.","startPage":"77","endPage":"89","ipdsId":"IP-147679","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Palermo, Rose Elizabeth 0000-0002-7438-361X","orcid":"https://orcid.org/0000-0002-7438-361X","contributorId":300046,"corporation":false,"usgs":true,"family":"Palermo","given":"Rose","email":"","middleInitial":"Elizabeth","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":859335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashton, Andrew D.","contributorId":300047,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":16633,"text":"WHOI","active":true,"usgs":false}],"preferred":false,"id":859336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jin, Di","contributorId":300048,"corporation":false,"usgs":false,"family":"Jin","given":"Di","email":"","affiliations":[{"id":16633,"text":"WHOI","active":true,"usgs":false}],"preferred":false,"id":859337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoagland, Porter","contributorId":300049,"corporation":false,"usgs":false,"family":"Hoagland","given":"Porter","email":"","affiliations":[{"id":16633,"text":"WHOI","active":true,"usgs":false}],"preferred":false,"id":859338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorenzo-Trueba, Jorge","contributorId":297143,"corporation":false,"usgs":false,"family":"Lorenzo-Trueba","given":"Jorge","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":859339,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}