{"pageNumber":"111","pageRowStart":"2750","pageSize":"25","recordCount":41032,"records":[{"id":70250443,"text":"70250443 - 2023 - Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska","interactions":[],"lastModifiedDate":"2023-12-09T14:53:33.90405","indexId":"70250443","displayToPublicDate":"2023-12-01T08:48:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska","docAbstract":"<p><span>This map of fractures, scarps, faults, and landslides was completed to identify areas in Glacier Bay National Park and Preserve that may present a landslide-generated tsunami hazard. To address the potential of landslide and tsunami hazards in the park, the National Park Service (NPS) and the US Geological Survey (USGS) partnered to conduct a multi-year hazard assessment of Glacier Bay National Park and Preserve. To produce the map described in this report, we used the newly acquired (2019-2020) light detection and ranging (LiDAR) 0.5 to 1.0 m digital elevation models (DEMs) that cover all the coastal areas of the park and extend up to the ridgetops in places with steep slopes. A bare earth DEM was used to identify and map areas of incipient landslides (i.e., fractures and scarps), fault scarps, and areas where landslides have clearly occurred in the past (i.e., areas where scars and deposits are clearly visible). This map provides a baseline data set that can be used to aid forecasts of where landslides are most likely to occur in the future.</span></p>","language":"English","publisher":"National Park Service","doi":"10.36967/2300706","collaboration":"National Park Service","usgsCitation":"Hults, C., Coe, J.A., and Avdievitch, N.N., 2023, Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska, iv, 14 p., https://doi.org/10.36967/2300706.","productDescription":"iv, 14 p.","ipdsId":"IP-147660","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":423385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park and Preserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -139.23040794383448,\n              59.9305550708161\n            ],\n            [\n              -139.23040794383448,\n              57.252640525398476\n            ],\n            [\n              -134.22064231883454,\n              57.252640525398476\n            ],\n            [\n              -134.22064231883454,\n              59.9305550708161\n            ],\n            [\n              -139.23040794383448,\n              59.9305550708161\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hults, Chad","contributorId":332290,"corporation":false,"usgs":false,"family":"Hults","given":"Chad","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":889926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avdievitch, Nikita N. 0000-0002-2507-2962","orcid":"https://orcid.org/0000-0002-2507-2962","contributorId":225492,"corporation":false,"usgs":true,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889928,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70257372,"text":"70257372 - 2023 - Simulation modeling to assess line transect distance sampling under a range of translocation scenarios","interactions":[],"lastModifiedDate":"2024-09-04T15:33:06.278948","indexId":"70257372","displayToPublicDate":"2023-12-01T08:35:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Simulation modeling to assess line transect distance sampling under a range of translocation scenarios","docAbstract":"<p><span>The accuracy of posttranslocation population monitoring methods is critical to assessing long-term success in translocation programs. Translocation can produce unique challenges to monitoring efforts; therefore, it is important to understand the flexibility and robustness of commonly used monitoring methods. In Florida, USA, thousands of gopher tortoises&nbsp;</span><i>Gopherus polyphemus</i><span>&nbsp;have been, and continue to be, translocated from development sites to permitted recipient sites. These recipient sites create a broad range of potential monitoring scenarios due to variability in soft-release strategies, habitat conditions, and population demographics. Line transect distance sampling is an effective method for monitoring natural tortoise populations, but it is currently untested for translocated populations. We therefore produced 3,024 individual-based, spatially explicit scenarios of translocated tortoise populations that differed in recipient site and tortoise population properties, based on real-world examples, literature review, and expert opinion. We virtually sampled simulated tortoise populations by using line transect distance sampling methods and built a Bayesian hierarchical model to estimate the population density for each simulation, which incorporated individual-level covariates (i.e., burrow width and burrow occupancy). Line transect distance sampling was largely appropriate for the conditions that typify gopher tortoise recipient sites, particularly when detection probability on the transect lines was greater than or equal to 0.85. Designing the layout of transects relative to the orientation of soft-release pens, to avoid possible sampling biases that lead to extreme outliers in estimates of tortoise densities, resulted in more accurate population estimates. We also suggest that use of individual-level covariates, applied using a Bayesian framework as demonstrated in our study, may improve the applicability of line transect distance sampling surveys in a variety of contexts and that simulation can be a powerful tool for assessing survey design in complex sampling situations.</span></p>","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-23-029","usgsCitation":"Jones, M.D., Smith, L., Gentry Richardson, K., DeSha, J., Castellón, T., Hipes, D., Kalfin, A., Halstead, N.T., and Hunter, E.A., 2023, Simulation modeling to assess line transect distance sampling under a range of translocation scenarios: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 385-399, https://doi.org/10.3996/JFWM-23-029.","productDescription":"15 p.","startPage":"385","endPage":"399","ipdsId":"IP-151978","costCenters":[{"id":199,"text":"Coop Res Unit 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 \"}}]}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Max D.","contributorId":342507,"corporation":false,"usgs":false,"family":"Jones","given":"Max","email":"","middleInitial":"D.","affiliations":[{"id":81880,"text":"Department of Fish and Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":910146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Lora L.","contributorId":342508,"corporation":false,"usgs":false,"family":"Smith","given":"Lora L.","affiliations":[{"id":81731,"text":"Jones Center at Ichauway","active":true,"usgs":false}],"preferred":false,"id":910147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gentry Richardson, Katherine","contributorId":342509,"corporation":false,"usgs":false,"family":"Gentry Richardson","given":"Katherine","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":910148,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeSha, J. Nicole","contributorId":342510,"corporation":false,"usgs":false,"family":"DeSha","given":"J. Nicole","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":910149,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Castellón, Traci","contributorId":342511,"corporation":false,"usgs":false,"family":"Castellón","given":"Traci","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":910150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hipes, Dan","contributorId":342512,"corporation":false,"usgs":false,"family":"Hipes","given":"Dan","email":"","affiliations":[{"id":81882,"text":"Florida Natural Areas Inventory","active":true,"usgs":false}],"preferred":false,"id":910151,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kalfin, Alex","contributorId":342513,"corporation":false,"usgs":false,"family":"Kalfin","given":"Alex","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":910152,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Halstead, Neal T.","contributorId":342514,"corporation":false,"usgs":false,"family":"Halstead","given":"Neal","email":"","middleInitial":"T.","affiliations":[{"id":81883,"text":"Wildlands Conservation","active":true,"usgs":false}],"preferred":false,"id":910153,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":911525,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70251485,"text":"70251485 - 2023 - Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations","interactions":[],"lastModifiedDate":"2024-02-13T13:12:40.92934","indexId":"70251485","displayToPublicDate":"2023-12-01T07:05:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations","docAbstract":"<div class=\"html-p\">Extreme water-level recurrence estimates for a complex estuary using a high-resolution 2D model and a new method for estimating remotely generated sea level anomalies (SLAs) at the model boundary have been developed. The hydrodynamic model accurately resolves the dominant physical processes contributing to extreme water levels across the Washington State waters of the Salish Sea, including the relative contribution of remote SLA and other non-tidal residual processes that drive extreme water levels above the predicted tide. The model’s predictions have errors of less than 15 cm (&lt;5% of 3–4 m tidal range) at eight tide gauge locations across the model domain. The influence of remote SLAs at the seaward boundary of the model was implemented using a multivariate regression of readily available and locally relevant wind, sea surface temperature, and pressure anomaly data, combined with El Niño Index data (R<sup>2</sup><span>&nbsp;</span>= 0.76). The hydrodynamic model simulations using the remote SLA predictor compared well with simulations using the widely used data-assimilative global ocean model HYCOM SLA data (root mean square difference of 5.5 cm). Extreme water-level recurrence estimates with and without remote SLA show that remote forcing accounts for 50–60% of the total water level anomaly observed along Salish Sea shorelines. The resulting model simulations across decadal timescales provide estimates of extreme water level recurrence across the Salish Sea, capturing climate variability important to long-term coastal hazard planning. This approach has widespread applications for other complex estuarine systems.</div>","language":"English","publisher":"MDPI","doi":"10.3390/w15234167","usgsCitation":"Grossman, E.E., Tehranirad, B., Nederhoff, K., Crosby, S., Stevens, A.W., VanArendonk, N.R., Nowacki, D.J., Erikson, L.H., and Barnard, P.L., 2023, Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations: Water, v. 15, no. 23, 4167, 24 p., https://doi.org/10.3390/w15234167.","productDescription":"4167, 24 p.","ipdsId":"IP-146065","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15234167","text":"Publisher Index Page"},{"id":425601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -130.71708563654397,\n              52.25092388193306\n            ],\n            [\n              -130.71708563654397,\n              44.0989255001023\n            ],\n            [\n              -118.67607001154398,\n              44.0989255001023\n            ],\n            [\n              -118.67607001154398,\n              52.25092388193306\n            ],\n            [\n              -130.71708563654397,\n              52.25092388193306\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"23","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":894698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tehranirad, Babak 0000-0002-1634-9165","orcid":"https://orcid.org/0000-0002-1634-9165","contributorId":299107,"corporation":false,"usgs":false,"family":"Tehranirad","given":"Babak","affiliations":[{"id":64774,"text":"contracted to USGS PCMSC","active":true,"usgs":false}],"preferred":false,"id":894699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nederhoff, Kees 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":334091,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Kees","affiliations":[{"id":39963,"text":"Deltares-USA","active":true,"usgs":false}],"preferred":true,"id":894700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crosby, Sean","contributorId":334092,"corporation":false,"usgs":false,"family":"Crosby","given":"Sean","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":894701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stevens, Andrew W. 0000-0003-2334-129X astevens@usgs.gov","orcid":"https://orcid.org/0000-0003-2334-129X","contributorId":139313,"corporation":false,"usgs":true,"family":"Stevens","given":"Andrew","email":"astevens@usgs.gov","middleInitial":"W.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":894702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"VanArendonk, Nathan R.","contributorId":334097,"corporation":false,"usgs":false,"family":"VanArendonk","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":894703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nowacki, Daniel J. 0000-0002-7015-3710 dnowacki@usgs.gov","orcid":"https://orcid.org/0000-0002-7015-3710","contributorId":174586,"corporation":false,"usgs":true,"family":"Nowacki","given":"Daniel","email":"dnowacki@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":894704,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":894705,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":894706,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70250655,"text":"70250655 - 2023 - A simple approach to modeling light attenuation in the Sacramento-San Joaquin Delta using commonly available data","interactions":[],"lastModifiedDate":"2023-12-22T12:57:30.745882","indexId":"70250655","displayToPublicDate":"2023-12-01T06:52:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10389,"text":"San Francisco Estuary and Watershed Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A simple approach to modeling light attenuation in the Sacramento-San Joaquin Delta using commonly available data","docAbstract":"<div id=\"main\"><div data-reactroot=\"\"><div class=\"body\"><div><div class=\"c-columns--sticky-sidebar\"><div class=\"c-tabs\"><div class=\"c-tabs__content\"><div class=\"c-tabcontent\"><div class=\"c-clientmarkup\"><p>The diffuse attenuation coefficient of photosynthetically active radiation (KdPAR) is commonly used to predict light attenuation in aquatic productivity models, but obtaining measurements of PAR to compute KdPAR is difficult. In situ calculations of KdPAR require multiple measurements of PAR through the water column, and these measurements are infeasible for real-time recording. Instead, predictive models using surface-water measurements may be used. Traditional KdPAR models are based on open-ocean habitats and rely on chlorophyll—as a proxy measurement for phytoplankton abundance—as the main predictive parameter. However, elevated suspended sediments and dissolved organic materials may also affect KdPAR values of inland water bodies and estuaries. In this study, we leverage KdPAR calculations derived from in situ light measurements collected along with surface-water-quality parameters across the Sacramento-San Joaquin River Delta in California, USA (the Delta). Sampling occurred between January of 2013 and May of 2014. We also explored regional and seasonal effects, but these did not clearly affect the model. Ultimately, the best-performing model included surface-level turbidity only (R2 = 0.91). The simplicity of the model facilitates use of KdPAR estimates for a variety of purposes throughout the Delta, including euphotic depth calculations, and as inputs to primary-productivity and habitat-suitability models. We demonstrate the model’s usability with two open-sources data sets (one spatially dense, and one temporally dense), and estimate KdPAR, euphotic depth, and primary productivity within the Delta. We provide calculations for each estimation, allowing users to easily adopt these models and apply them to their own data or with open-sourced data, which are abundant.</p></div></div></div></div></div></div></div></div></div>","language":"English","publisher":"University of California","doi":"10.15447/sfews.2023v21iss4art5","usgsCitation":"Richardson, E.T., Bouma-Gregson, K., O’Donnell, K., and Bergamaschi, B.A., 2023, A simple approach to modeling light attenuation in the Sacramento-San Joaquin Delta using commonly available data: San Francisco Estuary and Watershed Sciences, v. 21, no. 4, 5, 15 p., https://doi.org/10.15447/sfews.2023v21iss4art5.","productDescription":"5, 15 p.","ipdsId":"IP-149629","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2023v21iss4art5","text":"Publisher Index Page"},{"id":423859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.26086948103463,\n              38.478699734733226\n            ],\n            [\n              -122.26086948103463,\n              37.77218409560264\n            ],\n            [\n              -121.28033969587833,\n              37.77218409560264\n            ],\n            [\n              -121.28033969587833,\n              38.478699734733226\n            ],\n            [\n              -122.26086948103463,\n              38.478699734733226\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"21","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Katy 0000-0003-2323-8970 kodonnell@usgs.gov","orcid":"https://orcid.org/0000-0003-2323-8970","contributorId":5640,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Katy","email":"kodonnell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890902,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70250597,"text":"70250597 - 2023 - Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling","interactions":[],"lastModifiedDate":"2023-12-19T12:40:36.280538","indexId":"70250597","displayToPublicDate":"2023-12-01T06:38:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3824,"text":"Letters in Biomathematics","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling","docAbstract":"Mathematical biology is a wide field of study with many venues that undergraduate students can access through research. However, the topics of study for these students can be overwhelming, and many topics of study yield either only trivial results or abstract outcomes that are nonintuitive and diffcult to understand. We have used natural resource modeling, and more specifically, a partnership between academic researchers and federal scientists, as a bridge between undergraduate research and mathematical biology. Our collaboration is an interdisciplinary team that combines biology, mathematics, and statistics professors with government research scientists. As a team, we have mentored students through opportunities such as a Research Experiences for Undergraduates and other projects. In this article, we provide an overview of how we develop questions for undergraduates and outline two case studies, both of which resulted in peer reviewed journal articles. Last, we describe how we also transfer the results from these undergraduate projects to resource managers so the results may be applied to real world problems.","language":"English","publisher":"Intercollegiate Biomathematics Alliance","usgsCitation":"Erickson, R.A., Baumann, D., Bennie, B., Bungula, W., Cupp, A.R., Diffendorfer, J., Eager, E., Haro, R.J., Jankowski, K.J., Larson, D.M., Sandland, G., Van Appledorn, M., and Peirce, J.P., 2023, Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling: Letters in Biomathematics, v. 10, no. 1, p. 185-191.","productDescription":"7 p.","startPage":"185","endPage":"191","ipdsId":"IP-147034","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":423742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":423737,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/635/367"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":890502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baumann, Douglas","contributorId":328549,"corporation":false,"usgs":false,"family":"Baumann","given":"Douglas","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":890503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennie, Barbara","contributorId":328550,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":890504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bungula, Wako","contributorId":315367,"corporation":false,"usgs":false,"family":"Bungula","given":"Wako","email":"","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":890505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890506,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":890507,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eager, Eric A.","contributorId":140447,"corporation":false,"usgs":false,"family":"Eager","given":"Eric A.","affiliations":[{"id":13504,"text":"Department of Mathematics, University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":890508,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":890509,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890510,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Larson, Danelle M. 0000-0001-6349-6267","orcid":"https://orcid.org/0000-0001-6349-6267","contributorId":228838,"corporation":false,"usgs":true,"family":"Larson","given":"Danelle","email":"","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890511,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sandland, Greg J.","contributorId":190137,"corporation":false,"usgs":false,"family":"Sandland","given":"Greg J.","affiliations":[],"preferred":false,"id":890512,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890513,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"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":890514,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70257366,"text":"70257366 - 2023 - A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes","interactions":[],"lastModifiedDate":"2024-08-23T16:23:27.893177","indexId":"70257366","displayToPublicDate":"2023-12-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes","docAbstract":"<p><span>A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad-scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll&nbsp;</span><i>a</i><span>&nbsp;(Chl&nbsp;</span><i>a</i><span>), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl&nbsp;</span><i>a</i><span>) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/lno.12457","usgsCitation":"Lapierre, J., Webster, K.E., Hanks, E., Wagner, T., Soranno, P.A., McCullough, I., Reinl, K.L., Domka, M., and Lotting, N.R., 2023, A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes: Limnology and Oceanography, v. 69, no. 12, p. 2759-2773, https://doi.org/10.1002/lno.12457.","productDescription":"15 p.","startPage":"2759","endPage":"2773","ipdsId":"IP-145952","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, Mail Stop 511<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Minnesota</li><li>Water Supply and Quality</li><li>Forest Resources Management</li><li>Infrastructure and Construction Management</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-11-30","noUsgsAuthors":false,"publicationDate":"2023-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bergeson, Mitch 0000-0002-4675-8082 mbergeson@usgs.gov","orcid":"https://orcid.org/0000-0002-4675-8082","contributorId":331686,"corporation":false,"usgs":true,"family":"Bergeson","given":"Mitch","email":"mbergeson@usgs.gov","affiliations":[],"preferred":true,"id":888470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nail, David 0000-0003-0793-2305 dnail@usgs.gov","orcid":"https://orcid.org/0000-0003-0793-2305","contributorId":331534,"corporation":false,"usgs":true,"family":"Nail","given":"David","email":"dnail@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":888471,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
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Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, Mail Stop 511<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Montana</li><li>Agriculture and Precision Farming</li><li>Flood Risk Management</li><li>Geologic Resource Assessment and Hazard Mitigation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-11-30","noUsgsAuthors":false,"publicationDate":"2023-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Tom 0000-0002-5682-8988","orcid":"https://orcid.org/0000-0002-5682-8988","contributorId":304658,"corporation":false,"usgs":true,"family":"Carlson","given":"Tom","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":888663,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70250366,"text":"70250366 - 2023 - Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui","interactions":[],"lastModifiedDate":"2023-12-05T13:13:51.187375","indexId":"70250366","displayToPublicDate":"2023-11-30T07:05:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui","docAbstract":"<p class=\"abstract_block\">Haleakalā National Park and montane areas on east Maui, Hawaiian Archipelago, support critical nesting habitat for endangered ‘ua‘u Hawaiian petrel<span>&nbsp;</span><i>Pterodroma sandwichensis</i>. Habitat loss, non-native predators, and damage by feral ungulates are limiting factors for ground-nesting petrels at Haleakalā and throughout Hawai‘i. Because nesting habitats differ among the Hawaiian Islands, habitat distribution modeling for Hawaiian petrel has been island specific. Based on 2453 known nest site locations, we provide the first landscape-scale predictive model describing relative abundance and habitat available for nesting petrels throughout upper Haleakalā (1830 to 3055 m). We evaluated (principal components analyses and Pearson’s correlation) 13 spatial landscape and climate predictor variables associated with nest sites and the background landscape followed by random forest modeling to predict nest site density. Six variables (elevation, slope, topographic position index at 2 scales, heat load index, presence-absence ash/cinder, and presence-absence vegetation) indicated nest sites occurred non-randomly throughout the central part of the summit and crater; greatest concentrations were predicted along the crater rim and a ridgeline extending southwest from the summit. Moderately high predicted density occurred in the northeastern and northern crater. Lower elevations to the north, west, and south flanks of Haleakalā had relatively fewer predicted nest sites. Although we focused on higher elevations on Haleakalā, there is no reason to suspect that conservation efforts would not be successful at lower elevations, provided nesting petrels were protected from invasive predators, grazing ungulates, and significant land alteration.</p>","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01280","usgsCitation":"Adams, J., Felis, J., Klinger, R.C., Kelsey, E.C., Tamayose, J., Kaholoa’a, R., Bailey, C.N., Penniman, J.F., Learned, J., Ganter, C., Medeiros, J., and Chen, H., 2023, Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui: Endangered Species Research, v. 52, p. 231-246, https://doi.org/10.3354/esr01280.","productDescription":"16 p.","startPage":"231","endPage":"246","ipdsId":"IP-148421","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01280","text":"Publisher Index Page"},{"id":423239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Haleakalā, Maui","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.31986912490262,\n              20.79241841577982\n            ],\n            [\n              -156.31986912490262,\n              20.617670800708467\n            ],\n            [\n              -156.03010472060575,\n              20.617670800708467\n            ],\n            [\n              -156.03010472060575,\n              20.79241841577982\n            ],\n            [\n              -156.31986912490262,\n              20.79241841577982\n            ]\n         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Joy","contributorId":332150,"corporation":false,"usgs":false,"family":"Tamayose","given":"Joy","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":889574,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaholoa’a, Raina","contributorId":332151,"corporation":false,"usgs":false,"family":"Kaholoa’a","given":"Raina","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":889575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bailey, Cathleen Natividad","contributorId":220473,"corporation":false,"usgs":false,"family":"Bailey","given":"Cathleen","email":"","middleInitial":"Natividad","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":889576,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Penniman, Jay F.","contributorId":332154,"corporation":false,"usgs":false,"family":"Penniman","given":"Jay","email":"","middleInitial":"F.","affiliations":[{"id":79395,"text":"Maui Nui Seabird Recovery Project","active":true,"usgs":false}],"preferred":false,"id":889577,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Learned, Jennifer","contributorId":332155,"corporation":false,"usgs":false,"family":"Learned","given":"Jennifer","email":"","affiliations":[{"id":79395,"text":"Maui Nui Seabird Recovery Project","active":true,"usgs":false}],"preferred":false,"id":889578,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ganter, Ciara","contributorId":332156,"corporation":false,"usgs":false,"family":"Ganter","given":"Ciara","email":"","affiliations":[{"id":79397,"text":"Hawai'i State of Dep. of Land and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":889579,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Medeiros, John","contributorId":167591,"corporation":false,"usgs":false,"family":"Medeiros","given":"John","email":"","affiliations":[{"id":24766,"text":"4. State of Hawaii, Division of Forestry and Wildlife-Maui, 54 South High Street # 101, Wailuku, HI 96793.","active":true,"usgs":false}],"preferred":false,"id":889580,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chen, Huisheng","contributorId":332157,"corporation":false,"usgs":false,"family":"Chen","given":"Huisheng","email":"","affiliations":[{"id":79398,"text":"NPS; University of Hawai'i","active":true,"usgs":false}],"preferred":false,"id":889581,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70250805,"text":"70250805 - 2023 - Practical guide to measuring wetland carbon pools and fluxes","interactions":[],"lastModifiedDate":"2024-01-08T17:36:42.174027","indexId":"70250805","displayToPublicDate":"2023-11-28T07:39:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Practical guide to measuring wetland carbon pools and fluxes","docAbstract":"<p><span>Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first&nbsp;</span><i>define</i><span>&nbsp;each of the major C pools and fluxes and provide&nbsp;</span><i>rationale</i><span>&nbsp;for their importance to wetland C dynamics. For each approach, we clarify&nbsp;</span><i>what</i><span>&nbsp;component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as&nbsp;</span><i>where</i><span>&nbsp;and&nbsp;</span><i>when</i><span>&nbsp;an approach is typically used,&nbsp;</span><i>who</i><span>&nbsp;can conduct the measurements (expertise, training requirements), and&nbsp;</span><i>how</i><span>&nbsp;approaches are conducted, including considerations on equipment complexity and costs. Finally, we review&nbsp;</span><i>key covariates</i><span>&nbsp;and&nbsp;</span><i>ancillary measurements</i><span>&nbsp;that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s13157-023-01722-2","usgsCitation":"Bansal, S., Creed, I., Tangen, B., Bridgham, S.D., Desai, A.R., Krauss, K., Neubauer, S.C., Noe, G.E., Rosenberry, D.O., Trettin, C.C., Wickland, K., Allen, S.T., Arias-Ortiz, A., Armitage, A.R., Baldocchi, D., Banerjee, K., Bastviken, D., Berg, P., Bogard, M.J., Chow, A.T., Conner, W.H., Craft, C., Creamer, C., Delsontro, T., Duberstein, J., Eagle, M.J., Fennessey, M.S., Finkelstein, S.A., Goeckede, M., Grunwald, S., Halibisky, M., Herbert, E.R., Jahangir, M., Johnson, O., Jones, M.C., Kelleway, J., Knox, S., Kroeger, K.D., Kuehn, K., Lobb, D., Loder, A., Ma, S., Maher, D., McNicol, G., Meier, J., Middleton, B.A., Mills, C.T., Mistry, P., Mitra, A., Mobilian, C., Nahlik, A.M., Newman, S., O’Connell, J., Oikawa, P., Post van der Burg, M., Schutte, C.A., Song, C., Stagg, C.L., Turner, J., Vargas, R., Waldrop, M., Wallin, M., Wang, Z.A., Ward, E., Willard, D., Yarwood, S.A., and Zhu, X., 2023, Practical guide to measuring wetland carbon pools and fluxes: Wetlands, v. 43, 105, 169 p., https://doi.org/10.1007/s13157-023-01722-2.","productDescription":"105, 169 p.","ipdsId":"IP-138829","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":441538,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-023-01722-2","text":"Publisher Index Page"},{"id":424194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","noUsgsAuthors":false,"publicationDate":"2023-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creed, Irena F.","contributorId":81209,"corporation":false,"usgs":false,"family":"Creed","given":"Irena F.","affiliations":[{"id":27655,"text":"Department of Biology, University of Western Ontario, London, ON Canada","active":true,"usgs":false}],"preferred":false,"id":891563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891564,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bridgham, Scott D.","contributorId":177413,"corporation":false,"usgs":false,"family":"Bridgham","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":891565,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Desai, Ankur R. 0000-0002-5226-6041","orcid":"https://orcid.org/0000-0002-5226-6041","contributorId":20622,"corporation":false,"usgs":false,"family":"Desai","given":"Ankur","email":"","middleInitial":"R.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":891566,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":223022,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":891567,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neubauer, Scott C","contributorId":169723,"corporation":false,"usgs":false,"family":"Neubauer","given":"Scott","email":"","middleInitial":"C","affiliations":[{"id":25575,"text":"Dept. of Biology, Virginia Commonwealth University, Richmond, VA","active":true,"usgs":false}],"preferred":false,"id":891568,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - 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,{"id":70250206,"text":"70250206 - 2023 - At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution","interactions":[],"lastModifiedDate":"2023-11-28T12:55:24.267682","indexId":"70250206","displayToPublicDate":"2023-11-28T06:52:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\">Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a longstanding challenge and a particular impediment to automated analysis. This paper demonstrates that sinuosity can vary with the magnitude of units at which it is measured (measurement scales). The paper derives a scale-specific sinuosity (S3) metric intended to measure stream bend geometry across a range of measurement scales. The metric is warranted for analysis and modeling at measurement scales equal or similar to the spatial process and landscape conditions under investigation. Derived from the Richardson (1961) plot, the S3 metric quantifies a frequency signature of<span>&nbsp;</span>planform<span>&nbsp;bend sizes spanning a range&nbsp;of measurement units, enabling visual and quantitative analysis of bend geometry in linear stream features. Derivation of the metric makes evident a systematic relationship between stream bend size, sinuosity, and measurement scale, formalizing a relationship between sinuosity and fractal dimension. The paper shows the utility of the S3 metric in examining bend patterns for synthetic and real-world linear stream data.</span></p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2023.108734","usgsCitation":"Stanislawski, L., Kronenfeld, B.J., Buttenfield, B.P., and Shavers, E.J., 2023, At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution: Geomorphology, v. 436, 108734, 11 p., https://doi.org/10.1016/j.geomorph.2023.108734.","productDescription":"108734, 11 p.","ipdsId":"IP-147902","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":441543,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2023.108734","text":"Publisher Index Page"},{"id":499546,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13VXT4Z","text":"USGS data release","linkHelpText":"Scale_Specific_Sinuosity"},{"id":423008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"436","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":888907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":888908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888909,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70250191,"text":"70250191 - 2023 - Understanding the drivers of volcano deformation through geodetic model verification and validation","interactions":[],"lastModifiedDate":"2023-11-28T12:51:48.239575","indexId":"70250191","displayToPublicDate":"2023-11-28T06:44:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the drivers of volcano deformation through geodetic model verification and validation","docAbstract":"<p><span>Volcano geodesy often involves the use of models to explain observed surface deformation. A variety of forward models are used, from analytical point sources to numerical simulations that consider complex magma system geometries, topography, and material properties. Various inversion methods can then be used to relate observed volcano data to models. Ideally, forward models should be verified through intercomparison, to check for implementation errors and quantify the error induced by any approximations used. Additionally, forward models and inversion methods should be validated through tests with synthetic and/or real data, to determine their ability to match data and estimate parameter values within uncertainty. However, to date, there have not been comprehensive verification and validation efforts in volcano geodesy. Here, we report on the first phase of the Drivers of Volcano Deformation (DVD) exercises, which were designed to build community involvement through web-based exercises involving calculations of static elastic displacement around pressurized magma reservoirs. The forward model exercises begin with a spherical reservoir in a homogeneous half space, then introduce topography, heterogeneous elastic properties, and spheroidal geometries. The inversion exercises provide synthetic noisy surface displacement data for a spherical reservoir in a homogeneous half space and assess consistency in estimates of reservoir location and volume/pressure change. There is variability in the results from both forward modeling and inversions, which highlights the strengths and limitations of different forward models, as well as the importance of inversion method choice and uncertainty quantification. This first phase of the DVD exercises serves as a community resource and will facilitate further efforts to develop standards of reproducibility.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-023-01687-4","usgsCitation":"Crozier, J.A., Karlstrom, L., Montgomery-Brown, E.K., Angarita, M., Cayol, V., Bato, M.G., Wang, T., Grapenthin, R., Shreve, T., Anderson, K.R., Astort, A., Bodart, O., Cannavò, F., Currenti, G., Dabaghi, F., Erickson, B.A., Garg, D., Head, M., Iozzia, A., Kim, Y., Le Mével, H., Lizama, C.N., Rucker, C., Silverii, F., and Zhan, Y., 2023, Understanding the drivers of volcano deformation through geodetic model verification and validation: Bulletin of Volcanology, v. 85, 74, 23 p., https://doi.org/10.1007/s00445-023-01687-4.","productDescription":"74, 23 p.","ipdsId":"IP-156025","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467072,"rank":0,"type":{"id":41,"text":"Open Access External 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,{"id":70250167,"text":"70250167 - 2023 - Arctic-boreal lakes of interior Alaska dominated by contemporary carbon","interactions":[],"lastModifiedDate":"2023-11-24T12:35:19.407915","indexId":"70250167","displayToPublicDate":"2023-11-24T06:19:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Arctic-boreal lakes of interior Alaska dominated by contemporary carbon","docAbstract":"<p><span>Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.</span></p>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ad0993","usgsCitation":"Garcia-Tigreros, F., Elder, C.D., Kurek, M.R., Miller, B.L., Xu, X., Wickland, K., Czimczik, C.I., Dornblaser, M.M., Striegl, R.G., Kyzivat, E.D., Smith, L., Spencer, R., Miller, C.E., and Butman, D., 2023, Arctic-boreal lakes of interior Alaska dominated by contemporary carbon: Environmental Research Letters, v. 18, no. 12, 124024, 11 p., https://doi.org/10.1088/1748-9326/ad0993.","productDescription":"124024, 11 p.","ipdsId":"IP-148398","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":441558,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0993","text":"Publisher Index Page"},{"id":422881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Boot Lake, Canvasback Lake,  Greenpepper Lake, Shack Lake, Thumb Lake, Twelvemile Lake, Twin Lake, West Crazy Lake, YF18 Lake, Yukon Flats National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -152.32495243819622,\n              65.09586209258472\n            ],\n            [\n              -149.03799309568313,\n              64.59463623905711\n            ],\n            [\n              -143.9761424480567,\n              65.63219990838482\n            ],\n            [\n              -141.00690728046249,\n              66.43516910407672\n            ],\n            [\n              -140.9727162057442,\n              68.73213031511489\n            ],\n            [\n              -148.19579980608407,\n              67.27696483824582\n            ],\n            [\n              -153.60488278174276,\n              67.04326208119708\n            ],\n            [\n              -152.32495243819622,\n              65.09586209258472\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Tigreros, Fenix 0000-0001-8694-9046","orcid":"https://orcid.org/0000-0001-8694-9046","contributorId":194744,"corporation":false,"usgs":false,"family":"Garcia-Tigreros","given":"Fenix","email":"","affiliations":[],"preferred":false,"id":888627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elder, Clayton D.","contributorId":201542,"corporation":false,"usgs":false,"family":"Elder","given":"Clayton","email":"","middleInitial":"D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":888628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurek, Martin R.","contributorId":300567,"corporation":false,"usgs":false,"family":"Kurek","given":"Martin","email":"","middleInitial":"R.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":888629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Benjamin L.","contributorId":331727,"corporation":false,"usgs":false,"family":"Miller","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":888630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":888631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - 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,{"id":70250773,"text":"70250773 - 2023 - Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths","interactions":[],"lastModifiedDate":"2024-01-04T13:09:31.702227","indexId":"70250773","displayToPublicDate":"2023-11-23T07:06:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>Groundwater discharge to streams is a nonpoint source of nitrogen (N) that confounds N mitigation efforts and represents a significant portion of the annual N loading to watersheds. However, we lack an understanding of where and how much groundwater N enters streams and watersheds. Nitrogen concentrations at the end of groundwater flowpaths are the culmination of biogeochemical and physical processes from the contributing land area where groundwater recharges, within the aquifer system, and in the near-stream riparian area where groundwater discharges to streams. Our research objectives were to quantify the spatial distribution of N concentrations at groundwater discharges throughout a mixed land-use watershed and to evaluate how relationships among contributing and riparian land cover, modeled aquifer characteristics, and groundwater discharge biogeochemistry explain the spatial variation in groundwater discharge N concentrations. We accomplished this by integrating high-resolution thermal infrared surveys to locate groundwater discharge, biogeochemical sampling of groundwater, and a particle tracking model that links groundwater discharge locations to their contributing area land cover. Groundwater N loading from groundwater discharges within the watershed varied substantially between and within streambank groundwater discharge features. Groundwater nitrate concentrations were spatially heterogeneous ranging from below 0.03–11.45 mg-N/L, varying up to 20-fold within meters. When combined with the particle tracking model results and land cover metrics, we found that groundwater discharge nitrate concentrations were best predicted by a linear mixed-effect model that explained over 60% of the variation in nitrate concentrations, including aquifer chemistry (dissolved oxygen, Cl<sup>−</sup>, SO<sub>4</sub><sup>2−</sup>), riparian area forested land cover, and modeled physical aquifer characteristics (discharge, Euclidean distance). Our work highlights the significant spatial variability in groundwater discharge nitrate concentrations within mixed land-use watersheds and the need to understand groundwater N processing across the many spatiotemporal scales within groundwater cycling.</p></div>","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ad0c86","usgsCitation":"Moore, E.M., Barclay, J.R., Haynes, A.B., Jackson, K.E., Bisson, A.M., Briggs, M., and Helton, A.M., 2023, Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths: Environmental Research Letters, v. 18, 124039, 12 p., https://doi.org/10.1088/1748-9326/ad0c86.","productDescription":"124039, 12 p.","ipdsId":"IP-150250","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0c86","text":"Publisher Index Page"},{"id":424111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Farmington River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -73.18499513799182,\n              42.06150696284237\n            ],\n            [\n              -73.18499513799182,\n              41.43446844111949\n            ],\n            [\n              -72.30059572392948,\n              41.43446844111949\n            ],\n            [\n              -72.30059572392948,\n              42.06150696284237\n            ],\n            [\n              -73.18499513799182,\n              42.06150696284237\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Eric M. 0000-0002-2479-2677","orcid":"https://orcid.org/0000-0002-2479-2677","contributorId":332922,"corporation":false,"usgs":false,"family":"Moore","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":79689,"text":"University of Connecticut, Department Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":891365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haynes, Adam B.","contributorId":332923,"corporation":false,"usgs":false,"family":"Haynes","given":"Adam","email":"","middleInitial":"B.","affiliations":[{"id":79690,"text":"University of California Santa Cruz, Earth & Planetary Sciences Department","active":true,"usgs":false}],"preferred":false,"id":891367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Kevin E. 0000-0002-2156-5073","orcid":"https://orcid.org/0000-0002-2156-5073","contributorId":332924,"corporation":false,"usgs":false,"family":"Jackson","given":"Kevin","email":"","middleInitial":"E.","affiliations":[{"id":79691,"text":"University of Maryland, Center for Envirnomental Science","active":true,"usgs":false}],"preferred":false,"id":891368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bisson, Alaina M.","contributorId":332925,"corporation":false,"usgs":false,"family":"Bisson","given":"Alaina","email":"","middleInitial":"M.","affiliations":[{"id":79692,"text":"Harvard University Arboretum","active":true,"usgs":false}],"preferred":false,"id":891369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":891370,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Helton, Ashley M. 0000-0001-6928-2104","orcid":"https://orcid.org/0000-0001-6928-2104","contributorId":298703,"corporation":false,"usgs":false,"family":"Helton","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":891371,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250273,"text":"70250273 - 2023 - A community convention for ecological forecasting: Output files and metadata version 1.0","interactions":[],"lastModifiedDate":"2023-11-30T13:09:22.449203","indexId":"70250273","displayToPublicDate":"2023-11-23T07:04:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A community convention for ecological forecasting: Output files and metadata version 1.0","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4686","usgsCitation":"Dietze, M.C., Thomas, R.Q., Peters, J., Boettiger, C., Koren, G., Shiklomanov, A.N., and Ashander, J., 2023, A community convention for ecological forecasting: Output files and metadata version 1.0: Ecosphere, v. 14, no. 11, e4686, 23 p., https://doi.org/10.1002/ecs2.4686.","productDescription":"e4686, 23 p.","ipdsId":"IP-146045","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441562,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4686","text":"Publisher Index Page"},{"id":423087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietze, Michael C.","contributorId":266096,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":54896,"text":"Department of Earth & Environment, Boston University, Boston, MA 02215, USA","active":true,"usgs":false}],"preferred":false,"id":889240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":889241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peters, Jody","contributorId":216790,"corporation":false,"usgs":false,"family":"Peters","given":"Jody","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":889242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boettiger, Carl 0000-0002-1642-628X","orcid":"https://orcid.org/0000-0002-1642-628X","contributorId":332018,"corporation":false,"usgs":false,"family":"Boettiger","given":"Carl","email":"","affiliations":[{"id":79359,"text":"Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":889243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koren, Gerband","contributorId":332019,"corporation":false,"usgs":false,"family":"Koren","given":"Gerband","email":"","affiliations":[{"id":79360,"text":"Copernicus Institute of Sustainable Development, Utrecht University, Netherlands","active":true,"usgs":false}],"preferred":false,"id":889244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shiklomanov, Alexy N.","contributorId":332020,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexy","email":"","middleInitial":"N.","affiliations":[{"id":79361,"text":"NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":889245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ashander, Jaime 0000-0002-1841-4768","orcid":"https://orcid.org/0000-0002-1841-4768","contributorId":294949,"corporation":false,"usgs":true,"family":"Ashander","given":"Jaime","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":889246,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250186,"text":"70250186 - 2023 - Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","interactions":[],"lastModifiedDate":"2023-11-28T12:52:16.525451","indexId":"70250186","displayToPublicDate":"2023-11-23T06:48:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","docAbstract":"<div class=\"JournalAbstract\"><p>Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2023.1241622","usgsCitation":"Cho, J., Karwan, D., Skalak, K., Pizzuto, J., and Huffman, M., 2023, Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time: Frontiers in Water, v. 5, 1241622, 24 p., https://doi.org/10.3389/frwa.2023.1241622.","productDescription":"1241622, 24 p.","ipdsId":"IP-153828","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2023.1241622","text":"Publisher Index Page"},{"id":423007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":888728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karwan, Diana","contributorId":331761,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":888729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":888730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pizzuto, James","contributorId":331762,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":888731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huffman, Max","contributorId":331763,"corporation":false,"usgs":false,"family":"Huffman","given":"Max","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":888732,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250853,"text":"70250853 - 2023 - Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure","interactions":[],"lastModifiedDate":"2024-01-10T15:20:56.832051","indexId":"70250853","displayToPublicDate":"2023-11-22T09:12:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"title":"Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure","docAbstract":"<p><span>Pollinator diversity and abundance are declining globally. Cropland agriculture and the corresponding use of agricultural pesticides may contribute to these declines, while increased pollinator habitat (flowering plants) can help mitigate them. Here we tested whether the relative effect of wildflower plantings on pollinator diversity and counts were modified by proportion of nearby agricultural land cover and pesticide exposure in 24 conserved grasslands in Iowa, USA. Compared with general grassland conservation practices, wildflower plantings led to only a 5% increase in pollinator diversity and no change in counts regardless of the proportion of cropland agriculture within a 1 km radius. Pollinator diversity increased earlier in the growing season and with per cent flower cover. Unexpectedly, neither insecticide nor total pesticide concentrations on above-ground passive samplers were related to pollinator diversity. However, pollinator community composition was most strongly related to date of sampling, total pesticide concentration, and forb or flower cover. Our results indicate very little difference in pollinator diversity between grassland conservation practices with and without wildflower plantings. Given the relatively high economic costs of wildflower plantings, our research provides initial evidence that investment in general grassland conservation may efficiently conserve pollinator diversity in temperate regions of intensive cropland agriculture.</span></p>","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.231093","usgsCitation":"Kraus, J.M., Smalling, K., Vandever, M.W., Givens, C.E., Smith, C., Kolpin, D., and Hladik, M.L., 2023, Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure: Royal Society Open Science, v. 10, no. 11, https://doi.org/10.1098/rsos.231093.","productDescription":"231093, 12 p.","startPage":"231093","ipdsId":"IP-157224","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":441567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.231093","text":"Publisher Index Page"},{"id":435116,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q0NAF8","text":"USGS data release","linkHelpText":"Plant and insect pollinator diversity data from Conservation Reserve Program fields across an agricultural gradient in eastern Iowa"},{"id":424274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -94.06613018879129,\n              43.47459165083325\n            ],\n            [\n              -94.06613018879129,\n              40.39777384484779\n            ],\n            [\n              -90.07425648496523,\n              40.39777384484779\n            ],\n            [\n              -90.07425648496523,\n              43.47459165083325\n            ],\n            [\n              -94.06613018879129,\n              43.47459165083325\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"10","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":891794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandever, Mark W. 0000-0003-0247-2629 vandeverm@usgs.gov","orcid":"https://orcid.org/0000-0003-0247-2629","contributorId":197674,"corporation":false,"usgs":true,"family":"Vandever","given":"Mark","email":"vandeverm@usgs.gov","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":247691,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Cassandra 0000-0003-1088-1772 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-1088-1772","contributorId":193491,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891798,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891799,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891800,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250629,"text":"70250629 - 2023 - Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir","interactions":[],"lastModifiedDate":"2023-12-21T13:08:53.986552","indexId":"70250629","displayToPublicDate":"2023-11-22T07:06:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.</p></div></div>","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2023.1739","usgsCitation":"Ribeiro, R., Matthiopoulos, J., Lindgre, F., Tello, C., Zariquiey, C.M., Valderrama, W., Rocke, T.E., and Streicker, D.G., 2023, Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir: Proceedings of the Royal Society B: Biological Sciences, v. 209, no. 2011, 20231739, 11 p., https://doi.org/10.1098/rspb.2023.1739.","productDescription":"20231739, 11 p.","ipdsId":"IP-152468","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":441568,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Edinburgh, Edinburgh, UK","active":true,"usgs":false}],"preferred":false,"id":890630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tello, Carlos","contributorId":244267,"corporation":false,"usgs":false,"family":"Tello","given":"Carlos","email":"","affiliations":[{"id":48877,"text":"ILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":890631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zariquiey, Carlos M.","contributorId":332605,"corporation":false,"usgs":false,"family":"Zariquiey","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":79511,"text":"ILLARIY (Asociación para el Desarrollo y Conservación de los Recursos Naturales), Lima, Perú","active":true,"usgs":false}],"preferred":false,"id":890632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Valderrama, William","contributorId":244269,"corporation":false,"usgs":false,"family":"Valderrama","given":"William","email":"","affiliations":[{"id":48878,"text":"eILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":890633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":890634,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Streicker, Daniel G. 0000-0001-7475-2705","orcid":"https://orcid.org/0000-0001-7475-2705","contributorId":152378,"corporation":false,"usgs":false,"family":"Streicker","given":"Daniel","email":"","middleInitial":"G.","affiliations":[{"id":12473,"text":"University of Glasgow","active":true,"usgs":false}],"preferred":false,"id":890635,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70251598,"text":"70251598 - 2023 - Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform","interactions":[],"lastModifiedDate":"2024-02-20T12:13:11.257977","indexId":"70251598","displayToPublicDate":"2023-11-22T06:10:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Studies involving acoustic telemetry typically use stationary acoustic receivers arranged in an array or grid. Unmanned surface vehicle (USV)-based mobile receivers offer advantages over the latter approach: the USV can be programmed to autonomously carry a receiver to and from target locations, more readily adapting to a survey’s spatial scope and scale. This work examines the acoustic detection performance of a low-cost USV developed as a flexible sensing platform. The USV was fitted with an acoustic receiver and operated over multiple waypoints set at increasing distances from the transmitter in two modes: drifting and station-keeping. While drifting, the USV was allowed to drift from the waypoint; while station-keeping, the USV used its thruster to hold position. Detection performance of the USV was similar to that of stationary receivers while drifting, but significantly worse while station-keeping. Noise from the USV thruster was hypothesized as a potential cause of poor detection performance during station-keeping. Detection performance varied with the depth of the tethered receiver such that detection range was greater during the deepest (4.6&nbsp;m) trials than during shallower (1.1 and 2.9&nbsp;m) trials. These results provide insight and guidance on how a USV can be best used for acoustic telemetry, namely, navigating to a planned waypoint, drifting and lowering the receiver to a desired depth for listening, and then navigating to the next waypoint.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1186/s40317-023-00350-1","usgsCitation":"Gaskell, E.M., Funnell, T.R., Holbrook, C., Hondorp, D.W., and Tan, X., 2023, Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform: Animal Biotelemetry, v. 11, 41, 13 p., https://doi.org/10.1186/s40317-023-00350-1.","productDescription":"41, 13 p.","ipdsId":"IP-155456","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":441574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-023-00350-1","text":"Publisher Index Page"},{"id":425784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Hammond Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -84.18848857240516,\n              45.624082075270366\n            ],\n            [\n              -84.18848857240516,\n              45.56498039215245\n            ],\n            [\n              -84.12224845490009,\n              45.56498039215245\n            ],\n            [\n              -84.12224845490009,\n              45.624082075270366\n            ],\n            [\n              -84.18848857240516,\n              45.624082075270366\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaskell, Eric M.","contributorId":334194,"corporation":false,"usgs":false,"family":"Gaskell","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":894996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funnell, Tyler Reid 0000-0002-9074-3531","orcid":"https://orcid.org/0000-0002-9074-3531","contributorId":334195,"corporation":false,"usgs":true,"family":"Funnell","given":"Tyler","email":"","middleInitial":"Reid","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":895000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250365,"text":"70250365 - 2023 - Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary","interactions":[],"lastModifiedDate":"2023-12-05T12:53:18.910274","indexId":"70250365","displayToPublicDate":"2023-11-21T06:47:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary","docAbstract":"<div class=\"JournalAbstract\"><p>Understanding the drivers of variability in plant diversity from local to landscape spatial scales is a challenge in ecological systems. Environmental gradients exist at several spatial scales and can be nested hierarchically, influencing patterns of plant diversity in complex ways. As plant community dynamics influence ecosystem function, understanding the drivers of plant community variability across space is paramount for predicting potential shifts in ecosystem function from global change. Determining the scales at which stress gradients influence vegetation composition is crucial to inform management and restoration of tidal marshes for specific functions. Here, we analyzed vegetation community composition in 51 tidal marshes from the San Francisco Bay Estuary, California, USA. We used model-based compositional analysis and rank abundance curves to quantify environmental (elevation/tidal frame position, distance to channel, and channel salinity) and species trait (species form, wetland indicator status, and native status) influences on plant community variability at the marsh site and estuary scales. While environmental impacts on plant diversity varied by species and their relationships to each other, overall impacts increased in strength from marsh to estuary scales. Relative species abundance was important in structuring these tidal marsh communities even with the limited species pools dominated by a few species. Rank abundance curves revealed different community structures by region with higher species evenness at plots higher in the tidal frame and adjacent to freshwater channels. By identifying interactions (species–species, species–environment, and environment–trait) at multiple scales (local, landscape), we begin to understand how variability measurements could be interpreted for conservation and land management decisions.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2023.1215964","usgsCitation":"Rankin, L.L., Jones, S., Janousek, C.N., Buffington, K., Takekawa, J., and Thorne, K., 2023, Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary: Frontiers in Ecology and Evolution, v. 11, 1215964, 16 p., https://doi.org/10.3389/fevo.2023.1215964.","productDescription":"1215964, 16 p.","ipdsId":"IP-156855","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441577,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1215964","text":"Publisher Index Page"},{"id":435118,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F802H","text":"USGS data release","linkHelpText":"Marsh Vegetation Surveys Across the San Francisco Bay Estuary, 2008-2018"},{"id":423234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.94698987792064,\n              38.31950691888645\n            ],\n            [\n              -122.94698987792064,\n              37.20784358966503\n            ],\n            [\n              -121.36495862792067,\n              37.20784358966503\n            ],\n            [\n              -121.36495862792067,\n              38.31950691888645\n            ],\n            [\n              -122.94698987792064,\n              38.31950691888645\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rankin, Lyndsay L. 0000-0003-4968-1946","orcid":"https://orcid.org/0000-0003-4968-1946","contributorId":332147,"corporation":false,"usgs":true,"family":"Rankin","given":"Lyndsay","email":"","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Scott F. 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":204137,"corporation":false,"usgs":false,"family":"Jones","given":"Scott F.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":889565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":889566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takekawa, John 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203688,"corporation":false,"usgs":false,"family":"Takekawa","given":"John","affiliations":[{"id":36688,"text":"Suisun Resource Conservation District","active":true,"usgs":false}],"preferred":false,"id":889568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250205,"text":"70250205 - 2023 - Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty","interactions":[],"lastModifiedDate":"2023-11-28T13:21:31.650315","indexId":"70250205","displayToPublicDate":"2023-11-20T07:08:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5213,"text":"Epidemics","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41467-023-42680-x","usgsCitation":"Howerton, E., Contamin, L., Mullany, L.C., Qin, M., Reich, N.G., Bents, S., Borchering, R.K., Jung, S., Loo, S.L., Smith, C.P., Levander, J., Kerr, J., Espino, J., van Panhuis, W., Hochheiser, H., Galanti, M., Yamana, T.K., Pei, S., Shaman, J.L., Rainwater-Lovett, K., Kinsey, M., Tallaksen, K., Wilson, S., Shin, L., Lemaitre, J.C., Kaminsky, J., Dent Hulse, J., Lee, E.C., McKee, C., Hill, A., Karlen, D., Chinazzi, M., Davis, J.T., Mu, K., Xiong, X., Pastore Piontti, A., Vespignani, A., Rosenstrom, E.T., Ivy, J.S., Mayorga, M.E., Swann, J.L., Espana, G., Cavany, S., Moore, S., Perkins, A., Hladish, T.J., Pillai, A.N., Ben Toh, K., Longini, I., Chen, S., Paul, R., Janies, D., Thill, J., Bouchnita, A., Bi, K., Lachmann, M., Fox, S., Ancel Meyers, L., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Hurt, B., Chen, J., Mortveit, H., Wilson, A., Marathe, M., Hoops, S., Bhattacharya, P., Machi, D., Gunnels, B.L., Healy, J.M., Slayton, R.B., Johansson, M.A., Biggerstaff, M., Truelove, S., Runge, M.C., Shea, K., Viboud, C., and Lessler, J., 2023, Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty: Epidemics, v. 14, 7260, 15 p., https://doi.org/10.1038/s41467-023-42680-x.","productDescription":"7260, 15 p.","ipdsId":"IP-154417","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441580,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-023-42680-x","text":"Publisher Index Page"},{"id":423011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2023-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Howerton, Emily 0000-0002-0639-3728","orcid":"https://orcid.org/0000-0002-0639-3728","contributorId":258035,"corporation":false,"usgs":false,"family":"Howerton","given":"Emily","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":888822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Contamin, Lucie","contributorId":258068,"corporation":false,"usgs":false,"family":"Contamin","given":"Lucie","email":"","affiliations":[],"preferred":false,"id":888823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mullany, Luke C","contributorId":301869,"corporation":false,"usgs":false,"family":"Mullany","given":"Luke","email":"","middleInitial":"C","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":888824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qin, Michelle","contributorId":296526,"corporation":false,"usgs":false,"family":"Qin","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":888825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reich, Nicholas G.","contributorId":258146,"corporation":false,"usgs":false,"family":"Reich","given":"Nicholas","email":"","middleInitial":"G.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":888826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bents, Samantha","contributorId":331818,"corporation":false,"usgs":false,"family":"Bents","given":"Samantha","email":"","affiliations":[{"id":52216,"text":"National Institutes of Health Fogarty International Center","active":true,"usgs":false}],"preferred":false,"id":888827,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Borchering, Rebecca K. 0000-0003-4309-2913","orcid":"https://orcid.org/0000-0003-4309-2913","contributorId":258031,"corporation":false,"usgs":false,"family":"Borchering","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":888828,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jung, Sung-mok","contributorId":331819,"corporation":false,"usgs":false,"family":"Jung","given":"Sung-mok","email":"","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":888829,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loo, Sara L","contributorId":331821,"corporation":false,"usgs":false,"family":"Loo","given":"Sara","email":"","middleInitial":"L","affiliations":[{"id":79288,"text":"Johns Hopkins University Infectious Disease Dynamics","active":true,"usgs":false}],"preferred":false,"id":888830,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Claire P.","contributorId":258036,"corporation":false,"usgs":false,"family":"Smith","given":"Claire","email":"","middleInitial":"P.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":888831,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Levander, 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,{"id":70251057,"text":"70251057 - 2023 - Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system","interactions":[],"lastModifiedDate":"2024-01-19T13:26:30.651329","indexId":"70251057","displayToPublicDate":"2023-11-19T07:24:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system","docAbstract":"<p>In coastal aquifers, dynamic stresses such as climate forcings, groundwater withdrawals, and ocean tidal fluctuations cause nonlinear responses to groundwater levels. Such responses to the stresses impact groundwater resources and related flooding and infrastructure risks at multiple scales. We used time-series models such as transfer-function models and wavelet analysis to quantify the relative contribution of these stresses to groundwater-level fluctuation in wells from the unconfined and confined aquifers in an Atlantic coastal aquifer. Climate forcings, such as precipitation and temperature, explained most of the groundwater-level variation for wells in the unconfined aquifer, whereas groundwater withdrawals were the dominant driver of groundwater levels for wells in the confined aquifer. The impact of groundwater withdrawals also was detected in several wells in the unconfined aquifer. Although the influence of ocean tides on groundwater levels commonly is observed in coastal aquifers, we found that daily groundwater withdrawals can obscure the semi-diurnal coherence signal of the two series. The magnitude of groundwater-level fluctuation that could be explained solely by tides was minor compared to that explained by climate or withdrawal stresses. Transfer-function modeling showed seasonal withdrawals from wells in confined aquifers had a significant, yet heterogeneous influence on groundwater levels in coastal aquifers, which highlights climate and withdrawals as key compounding stresses in coastal hydrology. This study demonstrates the value of time-series approaches to advance characterization of groundwater systems in areas with limited hydrogeologic parameter information.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2023.130426","usgsCitation":"Yang, G., and McCoy, K., 2023, Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system: Journal of Hydrology, v. 627, no. Part B, 130426, 12 p., https://doi.org/10.1016/j.jhydrol.2023.130426.","productDescription":"130426, 12 p.","ipdsId":"IP-150305","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":441582,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2023.130426","text":"Publisher Index Page"},{"id":424621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Virginia Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.26319664375336,\n              37.04695559476376\n            ],\n            [\n              -76.26319664375336,\n              36.59981472352801\n            ],\n            [\n              -75.83944187250752,\n              36.59981472352801\n            ],\n            [\n              -75.83944187250752,\n              37.04695559476376\n            ],\n            [\n              -76.26319664375336,\n              37.04695559476376\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"627","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Guoxiang 0000-0001-5587-3683","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":267279,"corporation":false,"usgs":false,"family":"Yang","given":"Guoxiang","affiliations":[{"id":55459,"text":"NSA Contractor to USGS VA and WV WSC","active":true,"usgs":false}],"preferred":false,"id":892914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Kurt J. 0000-0002-9756-8238","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":216196,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt J.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70241016,"text":"ofr20231015 - 2023 - Evaluating management alternatives for Wyoming elk feedgrounds in consideration of chronic wasting disease","interactions":[],"lastModifiedDate":"2026-02-11T20:44:17.631411","indexId":"ofr20231015","displayToPublicDate":"2023-11-17T17:35:00","publicationYear":"2023","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":"2023-1015","displayTitle":"Evaluating Management Alternatives for Wyoming Elk Feedgrounds in Consideration of Chronic Wasting Disease","title":"Evaluating management alternatives for Wyoming elk feedgrounds in consideration of chronic wasting disease","docAbstract":"<h1>Executive Summary</h1><p>The authors used decision and modeling analyses to evaluate management alternatives for a decision on whether to permit <i>Cervus canadensis</i> (elk) feeding on two sites on Bridger-Teton National Forest, Dell Creek and Forest Park. Supplemental feeding of elk could increase the transmission of chronic wasting disease (CWD) locally and disease spread regionally, potentially impacting elk populations over time with wider implications for <i>Odocoileus hemionus</i> (mule deer) and <i>Odocoileus virginianus</i> (white-tailed deer) populations and hunting, tourism, and regional revenue. Supplemental feeding is thought to improve overwinter elk survival and reduce the commingling of elk with cattle during months when brucellosis transmission risk is highest. We worked with the U.S. Department of Agriculture Forest Service to identify their fundamental objectives and associated performance metrics related to this feedground decision. We then developed disease and habitat selection models to quantify the effect of four management alternatives on select performance metrics. The four alternatives were to continue to permit feeding, phaseout permits to feed in three years, permit feeding on an emergency basis, or stop permitting feeding. In this report, we present methods and summarized results on disease and habitat selection models and summaries of other performance metrics analyzed by BIO-WEST, Inc. and Cirrus Ecological Solutions as part of an Environmental Impact Statement.</p><p>Data from Wyoming Game and Fish Department (WGFD) supported the assumption that supplemental elk feeding allows for larger elk populations in a region. We documented that herd units (HU) without feedgrounds had 23 percent lower densities of elk per area of winter range when compared against HUs with feedgrounds, after accounting for differences in sightability of elk during counts on and off feedgrounds. Thus, throughout our analyses, we assumed feedground closures would reduce elk carrying capacity resulting in an average decline of previously fed elk population segments by 23 percent (5th and 95th percentiles = [11 percent, 35 percent]) by year 20. Most of that decline occurred within the first few years after a feedground ceases to operate. We used a panel of CWD experts to help estimate CWD trans-mission in fed and unfed elk population segments. In aggregate, the expert panel estimated that median values of direct and indirect transmission of CWD are expected to be 1.9 and 4 times higher, respectively, in fed elk populations compared to unfed elk. We used these disease transmission estimates in combination with local elk demographic rates and carrying capacity estimates to project disease and population dynamics.</p><p>In year 20, we predicted CWD prevalence would increase to 42 percent (5th and 95th percentiles = [29 percent, 55 percent]), and 13 percent (5th and 95th percentiles = [4 percent, 26 percent]) on average for fed and unfed elk population segments, respectively, given a starting prevalence of 1.6 percent. The prevalence estimates for the unfed elk population segments are in the range of previous observations of CWD in elk in the western United States. The average CWD prevalence from 2016 to 2018 in the unfed elk population of Wind Cave National Park in South Dakota was 18 percent overall but up to 30 percent in some regions (Sargeant and others, 2021). Meanwhile, CWD prevalence in the Iron Mountain and Laramie Peak elk herds in Wyoming from 2016 to 2018 was 14 percent and 7 percent, respectively, despite being present since at least 2002 (Wyoming Game and Fish Department, 2020b).</p><p>From 2016 to 2020, elk that were fed at Dell Creek and Forest Park constituted on average 12–20 percent of the total elk on their respective HUs. As a result, the differences between management alternatives are modest when considering the closure of only one feedground on a HU. The no feeding alternative for Forest Park resulted in a CWD prevalence of 17 percent (SD = 7 percent) in the Afton HU compared to 20 percent (SD = 7 percent) with continued feeding by year 20. In the Upper Green River HU, no feeding on Dell Creek resulted in a CWD prevalence of 27 percent (SD = 6 percent) compared to 30 percent (SD = 5 percent) with continued feeding. In terms of disease-associated mortality, we predicted the closure of Forest Park and Dell Creek feedgrounds would reduce the total number of CWD mortalities by 9 percent in the Upper Green River HU and 26 percent in the Afton HU during the 20-year timespan.</p><p>Our spatial analyses predicted that management alternative effects vary by HU as a function of private property and other wildlife winter ranges proximity relative to feedground location. The predicted number of elk abortions on private land, as a proxy for brucellosis risk to cattle, may increase by 8–21 percent in the absence of feeding at Dell Creek and Forest Park.</p><p>Eight feedgrounds are located on Bridger-Teton National Forest, all of which have permits that have expired or will expire prior to 2028. In addition, WGFD could change their management of feedgrounds given new information; therefore, we also assessed the cumulative effects of continued feeding, phaseout, and no feeding management alternatives across five HUs south of Jackson, Wyoming (Afton HU, Fall Creek HU, Piney HU, Pinedale HU, and Upper Green River HU). These five HUs ranged from about 41 to 85 percent of the elk herd using feedgrounds, which corresponded to a CWD prevalence at year 20 of 23–34 percent if all feedgrounds in those five HUs remained open relative to 12 to 14 percent if all feedgrounds were closed. We predicted feedground closures may result in immediate reductions in population size relative to alternatives that continue feeding (for example, continued feeding and emergency feeding alternatives); however, over longer periods of time, CWD-associated mortality leads to larger population reductions. The no feeding alternative resulted in higher elk population sizes compared to the continued feeding alternative after about 10 years of implementation. Delayed action under a phaseout alternative resulted in increasing the CWD prevalence to 20 percent relative to 12 to 14 percent, on average, without feeding on HUs with a large population of fed elk such as the Upper Green River HU.</p><p>Summarizing our cumulative results across all five of the analyzed HUs, we predicted continued feeding will lead to fewer elk by year 20 (mean = 8,300, standard deviation [SD] = 740) compared to no feeding at U.S. Department of Agri-culture Forest Service sites (10,700, SD = 890). The closure of all feedgrounds was projected to result in the largest elk populations at year 20 (12,500, SD = 980). No feeding at all sites also resulted in the largest cumulative harvest of 57,700 (SD = 2,600) compared to 51,100 (SD = 3,800) for continued feeding at all current feedground sites on the five HUs. Continued feeding also resulted in the lowest brucellosis costs to producers ($194,600, SD = $11,500) compared to no feeding on all feedgrounds ($243,000, SD = $13,700). Assuming moderate reductions in hunter interest because of increasing CWD prevalence in elk, we predicted that no feeding resulted in regional revenues generated by hunting activities of $190 million (SD = $10 million) compared to $173 million (SD = $10 million) for continued feeding over the 20-year timeframe.</p><p>Recent CWD detections in mule deer and elk in Grand Teton National Park has elevated the importance of the cur-rent decision on whether, and how, to permit elk feeding on Dell Creek and Forest Park and the management of the other feedgrounds. Aggressive male harvest has slowed, but not stopped, the increasing prevalence of CWD in mule deer (Conner and others, 2021). It is unclear whether harvest management can be an effective tool to slow the spread of CWD in elk. There are also no effective treatments or vaccines for CWD, and it is unlikely that any will be developed that can be easily deployed in the near future. Thus, reducing artificial aggregations is one of the few management approaches suggested by the Western Association of Fish and Wildlife Agencies (Almberg and others, 2017).</p><p>Future surveillance and monitoring can be designed to resolve uncertainties that can improve future decision-making. If feedgrounds close, research could quantify elk population reductions in the absence of feeding, the redistribution of fed elk to other places, or the consequences of elk movement on private property. If feedgrounds remain open, research could assess how rapidly CWD spreads in artificial aggregations of elk; however, surveillance programs would need to be designed with sufficient power to detect initial changes of CWD prevalence. Delaying action on feedground management was projected to be costly. Results of the phaseout alternative relative to the no feeding alternative suggested a 3-year delay was enough for substantial long-term changes in CWD prevalence. The long-term persistence of infectious CWD prions in the environment suggests that feedground management decisions may have long-lasting consequences.</p><p>Our results indicated tradeoffs in the ability of a management agency to achieve all their objectives, and all management alternatives resulted in significant reductions in elk population size. This report contains the foundational elements for formal decision analysis methods, which can be implemented to help decision makers transparently evaluate the consequences of decision alternatives and identify the set of actions that best achieve agency and stakeholder priorities.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231015","collaboration":"Prepared in cooperation with U.S. Department of Agriculture, National Park Service, U.S. Fish and Wildlife Service, and Wyoming Game and Fish Department","usgsCitation":"Cook, J.D., Cross, P.C., Tomaszewski, E.M., Cole, E.K., Campbell Grant, E.H., Wilder, J.M., and Runge, M.C., 2023, Evaluating management alternatives for Wyoming Elk feedgrounds in consideration of chronic wasting disease (ver. 2.0, November 2023): U.S. Geological Survey Open-File Report 2023–1015, 50 p., https://doi.org/10.3133/ofr20231015.","productDescription":"Report: ix, 50 p.; Software Release","onlineOnly":"Y","ipdsId":"IP-145385","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":499766,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114473.htm","linkFileType":{"id":5,"text":"html"}},{"id":422707,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1015/versionHist.txt","size":"4.0kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1015 history file"},{"id":422706,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1015/ofr20231015.pdf","text":"Report","size":"7.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1015"},{"id":419233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1015/coverthb2.jpg"},{"id":422704,"rank":2,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9R7XWO1","text":"USGS software release—","linkHelpText":"Simulating chronic wasting disease on Wyoming elk feedgrounds (version 2.0)."}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.03672229293583,\n              43.73180346838649\n            ],\n            [\n              -111.03672229293583,\n              42.40523773968059\n            ],\n            [\n              -109.27478197144448,\n              42.40523773968059\n            ],\n            [\n              -109.27478197144448,\n              43.73180346838649\n            ],\n            [\n              -111.03672229293583,\n              43.73180346838649\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Version 1.0: March 2023: Version 2.0: November 2023","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/norock/\" data-mce-href=\"https://www.usgs.gov/centers/norock/\">Northern Rocky Mountain Science Center</a><br>U.S. Geological Survey<br>2327 University Way, Suite 2 <br>Bozeman, MT 59715</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Executive Summary</li><li>Introduction</li><li>Decision Framing</li><li>Chronic Wasting Disease, Population Size, and Harvest Projections</li><li>Spatio-Temporal Analysis of Elk Distributions</li><li>Consequences</li><li>Conclusions and Future Directions</li><li>References Cited</li><li>Appendix 1. Additional Chronic Wasting Disease Analysis Details</li></ul>","publishedDate":"2023-03-09","revisedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D. 0000-0001-7000-8727","orcid":"https://orcid.org/0000-0001-7000-8727","contributorId":291411,"corporation":false,"usgs":true,"family":"Cook","given":"Jonathan","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":865728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":865729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tomaszewski, Emily M. 0000-0002-3766-8990","orcid":"https://orcid.org/0000-0002-3766-8990","contributorId":302889,"corporation":false,"usgs":true,"family":"Tomaszewski","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":865730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Eric K.","contributorId":302890,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":65572,"text":"U.S. Fish and Wildlife Service, National Elk Refuge","active":true,"usgs":false}],"preferred":false,"id":865731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilder, James M.","contributorId":302891,"corporation":false,"usgs":false,"family":"Wilder","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":865733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865734,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250099,"text":"sir20235066 - 2023 - Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","interactions":[],"lastModifiedDate":"2026-03-09T16:53:50.063749","indexId":"sir20235066","displayToPublicDate":"2023-11-17T13:55:00","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":"2023-5066","displayTitle":"Updates to the Regional Groundwater-Flow Model of the New Jersey Coastal Plain, 1980–2013","title":"Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","docAbstract":"<p>A 21-layer three-dimensional transient groundwater-flow model of the New Jersey Coastal Plain was developed and calibrated by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection to simulate groundwater-flow conditions during 1980–2013, incorporating average annual groundwater withdrawals and average annual groundwater recharge. This model is the third version of the New Jersey Coastal Plain regional groundwater-flow model that was initially developed as part of the USGS Regional Aquifer System Analysis (RASA) program. The model simulates groundwater flow in 11 aquifers and 10 intervening confining units of the New Jersey Coastal Plain to provide a regional overview of groundwater conditions. Averaged groundwater withdrawal data for 1980 to 2013 were used in the model. The 11 aquifers in New Jersey are, from shallowest to deepest, the Holly Beach water-bearing zone and the confined Cohansey aquifer in Cape May County; the Rio Grande water-bearing zone; the Atlantic City 800-foot sand; the Piney Point, Vincentown, and Wenonah-Mount Laurel aquifers; the Englishtown aquifer system; and the upper, middle, and lower aquifers of the Potomac-Raritan-Magothy (PRM) aquifer system.</p><p>The model was developed with the MODFLOW–2005 numerical code and the UCODE parameter estimation technique and calibrated using water-level and base-flow observations. A total of 3,453 water-level observations from 392 wells in New Jersey and 48 wells in Delaware from 1983 to 2013 were used in model calibration, which includes historical water-level trends for 29 wells in New Jersey during 1980–2013 presented in time-series hydrographs. In addition, derived observations also were included by calculating the vertical gradient at 33 pairs of nested observation wells in New Jersey, for a total of 210 observations. Changes in water levels over time were calculated for 134 wells in New Jersey and four wells in Delaware where water levels had varied substantially (approximately 10 ft) over the 30-year span of synoptic water-level measurements, for a total of 767 observations. A total of 1,485 base-flow observations in 47 surface-water basins in New Jersey from 1980 to 2013 were used in model calibration.</p><p>Updates to the groundwater-flow model include the conversion to a fully three-dimensional model from the previous quasi-three-dimensional model. The new model will allow for potential future uses such as particle tracking or simulation of variable-density groundwater flow that could not be accomplished with earlier versions of the model. Spatially and temporally variable recharge estimated by using a soil-water balance model resulted in a spatially and temporally finer discretization. The Rio Grande water-bearing zone was added to the model as an aquifer layer to refine estimates of simulated flow in Atlantic and Cape May Counties, New Jersey. Hydrogeologic parameters were updated to include the confining units in New Jersey and corresponding hydrogeologic units in Delaware and eastern Maryland.</p><p>The simulated water levels for the New Jersey Coastal Plain aquifers were compared to water-level measurements made during 1980–2013. The average residual for 4,243 water-level observations for New Jersey (simulated water levels minus measured water levels) is 1.5 feet. The simulated water-level contours for the confined aquifers for 2013 were compared to potentiometric surfaces produced from water levels measured during 2013. Simulated water levels generally matched the 2013 potentiometric surfaces of the confined aquifers in the areas of large withdrawals. Hydrographs of wells in the confined Coastal Plain aquifers of New Jersey show that simulated water levels generally match the magnitude and seasonal variation of the observed water levels. Hydrographs of base flow for the 47 streamgaging stations in New Jersey indicate that most of the simulated and estimated data match reasonably well.</p><p>Groundwater withdrawals are an important resource for water supply, agricultural, industrial, and commercial needs in the New Jersey Coastal Plain. Groundwater withdrawals from the New Jersey Coastal Plain aquifers have resulted in persistent, regionally extensive cones of depression in the Englishtown aquifer system and Wenonah-Mount Laurel aquifer in Ocean and Monmouth Counties; Wenonah-Mount Laurel and upper, middle, and lower PRM aquifers in Camden County; and Atlantic City 800-foot sand in Atlantic County. Because hydrologic stresses and water-management needs change with time, periodic updates to the groundwater-flow model are required to provide current information about hydrologic conditions in the New Jersey Coastal Plain and to maintain its usefulness as a tool to manage water resources and develop water-resource strategies. The current updates will support the continued application of this model as a tool for evaluating the regional effects of changes in groundwater withdrawals and of current and potential future water-management strategies on groundwater levels in the New Jersey Coastal Plain.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235066","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Gordon, A.D., and Carleton, G.B., 2023, Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2023–5066, 116 p., https://doi.org/10.3133/sir20235066","productDescription":"Report: xii, 116 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500947,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115639.htm","linkFileType":{"id":5,"text":"html"}},{"id":422695,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5066/images/"},{"id":422693,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5066"},{"id":422696,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6RXFC","text":"USGS data release","linkHelpText":"MODFLOW-2005 model used to simulate the regional groundwater flow system in the updated New Jersey Coastal Plain model, 1980-2013"},{"id":422694,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.XML"},{"id":422692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5066"},{"id":422691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5066/coverthb.jpg"}],"country":"United States","otherGeospatial":"New Jersey Coastal Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -74.49018324613056,\n              41.03712838002892\n            ],\n            [\n              -75.25922621488034,\n              41.417217443631785\n            ],\n            [\n              -77.41254652738019,\n              39.17183412365296\n            ],\n            [\n              -75.22626723050551,\n              37.8132834585617\n            ],\n            [\n              -72.98505629300531,\n              40.4043207917766\n            ],\n            [\n              -74.49018324613056,\n              41.03712838002892\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\">New Jersey Water Science Center</a><br>3450 Princeton Pike, Suite 110<br>Lawrenceville, New Jersey 08648</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"https://pubs.er.usgs.gov/contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Simulation of Groundwater Flow</li><li>Summary</li><li>References Cited</li><li>Appendix 1: Soil-Water Balance Methodology</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gordon, Alison D. 0000-0002-9502-8633","orcid":"https://orcid.org/0000-0002-9502-8633","contributorId":221457,"corporation":false,"usgs":true,"family":"Gordon","given":"Alison","email":"","middleInitial":"D.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carleton, Glen B. 0000-0002-7666-4407","orcid":"https://orcid.org/0000-0002-7666-4407","contributorId":306147,"corporation":false,"usgs":false,"family":"Carleton","given":"Glen","email":"","middleInitial":"B.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":888331,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70250011,"text":"fs20233042 - 2023 - The 3D Elevation Program—Supporting Missouri’s economy","interactions":[],"lastModifiedDate":"2024-01-25T17:25:00.353292","indexId":"fs20233042","displayToPublicDate":"2023-11-17T13:50:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3042","displayTitle":"The 3D Elevation Program—Supporting Missouri’s Economy","title":"The 3D Elevation Program—Supporting Missouri’s economy","docAbstract":"<h1>Introduction</h1><p>Because of its geography, Missouri is frequently subject to natural disasters. Ice storms, severe thunderstorms, tornadoes, and flooding are all common occurrences. Since 1990, Missouri has received 40 Federal major disaster declarations. Floods and droughts severely affect the State’s agriculture, which is a leading industry. Another potential major hazard is the New Madrid seismic zone (NMSZ), located in southeastern Missouri. Because Missouri is a major producer of lead, manufacturing and mining are very important to the State’s economy, as are restoring and reclaiming lands damaged by historical mining activities. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233042","usgsCitation":"Nail, D.S., 2023, The 3D Elevation Program—Supporting Missouri’s economy: U.S. Geological Survey Fact Sheet 2023–3042, 2 p., https://doi.org/10.3133/fs20233042","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127176","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":422536,"rank":5,"type":{"id":31,"text":"Publication 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, Mail Stop 511<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Missouri</li><li>Agriculture and Precision Farming</li><li>Flood Risk Management</li><li>Geologic Resource Assessment and Hazard Mitigation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nail, David 0000-0003-0793-2305 dnail@usgs.gov","orcid":"https://orcid.org/0000-0003-0793-2305","contributorId":331534,"corporation":false,"usgs":true,"family":"Nail","given":"David","email":"dnail@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":887991,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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