Consideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark-resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic-breeding shorebird of conservation concern, the red knot (Calidris canutus rufa), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs (Limulus polyphemus), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4439","usgsCitation":"Tucker, A.M., McGowan, C., Nuse, B., Lyons, J.E., Moore, C.T., Smith, D.R., Sweka, J., Anstead, K., DeRose-Wilson, A., and Clark, N., 2025, Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model: Ecosphere, v. 14, no. 2, e4439, 16 p., https://doi.org/10.1002/ecs2.4439.","productDescription":"e4439, 16 p.","ipdsId":"IP-139218","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466693,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4439","text":"Publisher Index Page"},{"id":466429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68003613362366,\n 39.75828902455723\n ],\n [\n -75.68003613362366,\n 38.72868626218502\n ],\n [\n -74.8091737193643,\n 38.72868626218502\n ],\n [\n -74.8091737193643,\n 39.75828902455723\n ],\n [\n -75.68003613362366,\n 39.75828902455723\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Anna Maureen 0000-0002-1473-2048 amtucker@usgs.gov","orcid":"https://orcid.org/0000-0002-1473-2048","contributorId":257906,"corporation":false,"usgs":true,"family":"Tucker","given":"Anna","email":"amtucker@usgs.gov","middleInitial":"Maureen","affiliations":[],"preferred":true,"id":923340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nuse, Bryan L.","contributorId":348305,"corporation":false,"usgs":false,"family":"Nuse","given":"Bryan L.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":923342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":923343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":923344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":923345,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sweka, John A.","contributorId":348306,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923346,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anstead, Kristen A.","contributorId":348307,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen A.","affiliations":[{"id":83332,"text":"Atlantic States Marine Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":923347,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeRose-Wilson, Audrey","contributorId":348308,"corporation":false,"usgs":false,"family":"DeRose-Wilson","given":"Audrey","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":923348,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Clark, Nigel A.","contributorId":348310,"corporation":false,"usgs":false,"family":"Clark","given":"Nigel A.","affiliations":[{"id":38864,"text":"British Trust for Ornithology","active":true,"usgs":false}],"preferred":false,"id":923349,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70273013,"text":"70273013 - 2025 - Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization","interactions":[],"lastModifiedDate":"2025-12-15T14:51:05.031613","indexId":"70273013","displayToPublicDate":"2021-12-25T08:48:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization","docAbstract":"Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat–abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8270","usgsCitation":"Nunes, L.A., Ribic, C., and Zuckerberg, B., 2025, Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization: Ecology and Evolution, v. 11, no. 22, p. 16006-16020, https://doi.org/10.1002/ece3.8270.","productDescription":"15 p.","startPage":"16006","endPage":"16020","ipdsId":"IP-126101","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497717,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8270","text":"Publisher Index Page"},{"id":497474,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Wisconsin Grasslands Conservation Area, Southwest Grasslands and Stream Conservation Area, Western Prairie Habitat Restoration Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.79195210637957,\n 44.827258718630105\n ],\n [\n -91.41144360585636,\n 43.85420962467394\n ],\n [\n -91.08303881583507,\n 42.83204929697988\n ],\n [\n -90.58076409826324,\n 42.48980507075734\n ],\n [\n -89.31258381269711,\n 42.531068987157965\n ],\n [\n -89.424469717728,\n 44.894480414879325\n ],\n [\n -92.79195210637957,\n 44.827258718630105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"22","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Nunes, Laura A.","contributorId":363905,"corporation":false,"usgs":false,"family":"Nunes","given":"Laura","middleInitial":"A.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zuckerberg, Benjamin","contributorId":363908,"corporation":false,"usgs":false,"family":"Zuckerberg","given":"Benjamin","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262519,"text":"70262519 - 2025 - What have we lost? Modeling dam impacts on American shad populations through their native range","interactions":[],"lastModifiedDate":"2025-01-23T18:02:41.609727","indexId":"70262519","displayToPublicDate":"2021-10-24T11:57:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"What have we lost? Modeling dam impacts on American shad populations through their native range","docAbstract":"American shad (Alosa sapidissima) are native to the east coast of North America from the St. Johns River, Florida, to the St. Lawrence River region in Canada. Since the 1800s, dams have reduced access to spawning habitat. To assess the impact of dams, we estimated the historically accessed spawning habitat in coastal rivers (485,618 river segments with 21,113 current dams) based on (i) width, (ii) distance from seawater, and (iii) slope (to exclude natural barriers to migration) combined with local knowledge. Estimated habitat available prior to dam construction (2,752 km2) was 41% greater than current fully accessible habitat (1,639 km2). River-specific population models were developed using habitat estimates and latitudinally appropriate life history parameters (e.g., size at age, maturity, iteroparity). Estimated coast-wide annual production potential was 69.1 million spawners compared with a dammed scenario (41.8 million spawners). Even with optimistic fish passage performance assumed for all dams (even if passage is completely absent), the dam-imposed deficit was alleviated by fewer than 3 million spawners. We estimate that in rivers modeled without dams, 98,000 metric tons of marine sourced biomass and nutrients were annually delivered, 60% of which was retained through carcasses, gametes and metabolic waste. Damming is estimated to have reduced this by more than one third. Based on our results, dams represent a significant and acute constraint to the population and, with other human impacts, reduce the fishery potential and ecological services attributed to the species.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.734213","usgsCitation":"Zydlewski, J.D., Stich, D.S., Roy, S., Bailey, M., Sheehan, T.F., and Sprankle, K., 2025, What have we lost? Modeling dam impacts on American shad populations through their native range: Frontiers in Marine Science, v. 8, 734213, 23 p., https://doi.org/10.3389/fmars.2021.734213.","productDescription":"734213, 23 p.","ipdsId":"IP-131060","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.734213","text":"Publisher Index Page"},{"id":481057,"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 -56.28219724835304,\n 51.669977422737105\n ],\n [\n -67.58323441275405,\n 50.88517823694593\n ],\n [\n -75.47655261205108,\n 44.788885115920294\n ],\n [\n -78.80188471400243,\n 40.10295269998778\n ],\n [\n -78.42083873696455,\n 36.01921260607415\n ],\n [\n -82.94641561076531,\n 32.75686616137071\n ],\n [\n -80.74112054616836,\n 27.66399349231928\n ],\n [\n -79.96534810037133,\n 29.923339649669487\n ],\n [\n -74.89083345921267,\n 35.07101636720273\n ],\n [\n -72.38821391976802,\n 39.93977209782662\n ],\n [\n -58.55637489176931,\n 46.202660856570446\n ],\n [\n -56.28219724835304,\n 51.669977422737105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":924430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Samuel G.","contributorId":276396,"corporation":false,"usgs":false,"family":"Roy","given":"Samuel G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":924427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bailey, Michael M.","contributorId":280279,"corporation":false,"usgs":false,"family":"Bailey","given":"Michael M.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sheehan, Timothy F","contributorId":215995,"corporation":false,"usgs":false,"family":"Sheehan","given":"Timothy","email":"","middleInitial":"F","affiliations":[{"id":39347,"text":"NOAA Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":924428,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sprankle, Kenneth","contributorId":349559,"corporation":false,"usgs":false,"family":"Sprankle","given":"Kenneth","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924429,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269697,"text":"70269697 - 2025 - Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","interactions":[],"lastModifiedDate":"2025-07-30T14:37:59.419554","indexId":"70269697","displayToPublicDate":"2021-02-01T09:32:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8912,"text":"International Journal of Applied Earth Observations and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","docAbstract":"The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ETa), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ETa, and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ETa yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ETa estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (rah) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2021.102298","usgsCitation":"Comini de Andrade, B., de Andrade Pinto, E., Ruhoff, A., and Senay, G.B., 2025, Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System: International Journal of Applied Earth Observations and Geoinformation, v. 98, 102298, 16 p., https://doi.org/10.1016/j.jag.2021.102298.","productDescription":"102298, 16 p.","ipdsId":"IP-125399","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2021.102298","text":"Publisher Index Page"},{"id":493182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Urucuia Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -43,\n -9.75\n ],\n [\n -47,\n -9.75\n ],\n [\n -47,\n -16\n ],\n [\n -43,\n -16\n ],\n [\n -43,\n -9.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"98","noUsgsAuthors":false,"publicationDate":"2021-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Comini de Andrade, Bruno César","contributorId":358913,"corporation":false,"usgs":false,"family":"Comini de Andrade","given":"Bruno César","affiliations":[{"id":85711,"text":"Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.","active":true,"usgs":false}],"preferred":false,"id":944465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Andrade Pinto, Eber José","contributorId":358914,"corporation":false,"usgs":false,"family":"de Andrade Pinto","given":"Eber José","affiliations":[{"id":85714,"text":"Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, and Serviço Geológico do Brasil-CPRM, Belo Horizonte, Brazil","active":true,"usgs":false}],"preferred":false,"id":944466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruhoff, Anderson","contributorId":269919,"corporation":false,"usgs":false,"family":"Ruhoff","given":"Anderson","email":"","affiliations":[{"id":56044,"text":"Universidade Federal do Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":944467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266186,"text":"70266186 - 2025 - Accuracy and precision of U–Pb zircon geochronology at high spatial resolution (7–20 μm spots) by laser ablation-ICP-single-collector-sector-field-mass spectrometry","interactions":[],"lastModifiedDate":"2025-04-29T15:08:25.529281","indexId":"70266186","displayToPublicDate":"2019-01-01T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2155,"text":"Journal of Analytical Atomic Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy and precision of U–Pb zircon geochronology at high spatial resolution (7–20 μm spots) by laser ablation-ICP-single-collector-sector-field-mass spectrometry","docAbstract":"Use of small spots (≤20µm) for laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) U-Pb zircon geochronology is of increasing interest in the Earth sciences because the temporal record of geologic processes is often preserved on a fine-scale within zircon grains. However the systematic biases and external sources of uncertainity of U-Pb ages is poorly defined when measured on small spots by LA-ICP-single-collector-sector-field (SF)-MS instrumentation. This study addresses the accuracy and precision for small spots and specifically the extent to which short ablation times limit Pb/U Down-Hole Fractionation (DHF), which largely controls the accuracy of the U-Pb ages. Six zircon reference materials (91500, FC-1, R33, Temora 2, Plešovice and Fish Canyon Tuff) were measured on spot sizes of 20, 15, 10 and 7 µm diameter. Laser fluence was increased from 3 to 6 J/cm2 with decreasing spot size to compensate partially for decreasing U and Pb signals. 91500 zircon was the calibration reference material. Raw count rate data were processed using Iolite version 3.63 software with the U-Pb Common Approach data reduction scheme and smoothed cubic spline DHF correction model. Samples were ablated for 30 seconds and results processed for the first 28, 15, 10 and 7 seconds of ablation (masking the initial 2 seconds) in order to assess the accuracy and precision of U-Pb ages as a function of ablation time. Measured 206Pb/238U ratios for the six zircon reference materials increase steadily with ablation time, reflecting DHF, but exhibit somewhat different patterns of increase for different zircons, producing the major source of uncertainty for the U-Pb ages. A secondary source of uncertainty is differences between the 206Pb/238U (normalized to their accepted values) for different zircons near the start of ablation, which may reflect matrix-dependent instrumental mass bias in the ICP. Nonetheless, processing data from only the first 10 to 15 seconds of ablation (50 to 75 laser pulses) restricts the extent of DHF and time-resolved Pb/U variations between different zircons to a sufficient degree to give concordant U-Pb ages on 20 to 7 µm spots that are accurate and precise to better than 1.4% using LA-ICP-single-collector-SF-MS instrumentation.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/c8ja00321a","collaboration":"Texas Tech University","usgsCitation":"Mukherjee, P., Souders, A., and Sylvester, P., 2025, Accuracy and precision of U–Pb zircon geochronology at high spatial resolution (7–20 μm spots) by laser ablation-ICP-single-collector-sector-field-mass spectrometry: Journal of Analytical Atomic Spectrometry, v. 34, no. 1, p. 180-192, https://doi.org/10.1039/c8ja00321a.","productDescription":"13 p.","startPage":"180","endPage":"192","ipdsId":"IP-102052","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":485136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mukherjee, P.","contributorId":206380,"corporation":false,"usgs":false,"family":"Mukherjee","given":"P.","email":"","affiliations":[{"id":13342,"text":"Mesa Community College","active":true,"usgs":false}],"preferred":false,"id":934835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Souders, Amanda 0000-0002-1367-8924","orcid":"https://orcid.org/0000-0002-1367-8924","contributorId":296423,"corporation":false,"usgs":true,"family":"Souders","given":"Amanda","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":934836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sylvester, Paul J.","contributorId":353961,"corporation":false,"usgs":false,"family":"Sylvester","given":"Paul J.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":934837,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260994,"text":"70260994 - 2024 - Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations","interactions":[],"lastModifiedDate":"2024-11-20T15:34:27.919637","indexId":"70260994","displayToPublicDate":"2025-01-01T08:25:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations","docAbstract":"The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2024.104641","usgsCitation":"Itzkin, M., Palmsten, M.L., Buckley, M.L., Birchler, J.J., and Torres-Garcia, L.M., 2024, Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations: Coastal Engineering, v. 195, 104641, 13 p., https://doi.org/10.1016/j.coastaleng.2024.104641.","productDescription":"104641, 13 p.","ipdsId":"IP-156552","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2024.104641","text":"Publisher Index Page"},{"id":464342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Duck","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.76606524647661,\n 36.204406383193415\n ],\n [\n -75.76606524647661,\n 36.151059222902816\n ],\n [\n -75.73552468770632,\n 36.151059222902816\n ],\n [\n -75.73552468770632,\n 36.204406383193415\n ],\n [\n -75.76606524647661,\n 36.204406383193415\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"195","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Itzkin, Michael 0000-0003-0693-0607","orcid":"https://orcid.org/0000-0003-0693-0607","contributorId":291846,"corporation":false,"usgs":true,"family":"Itzkin","given":"Michael","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buckley, Mark L. 0000-0002-1909-4831","orcid":"https://orcid.org/0000-0002-1909-4831","contributorId":203481,"corporation":false,"usgs":true,"family":"Buckley","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torres-Garcia, Legna M. 0000-0002-6786-5944 ltorresgarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-5944","contributorId":196150,"corporation":false,"usgs":true,"family":"Torres-Garcia","given":"Legna","email":"ltorresgarcia@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":918822,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261333,"text":"70261333 - 2024 - Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis","interactions":[],"lastModifiedDate":"2024-12-06T15:15:40.076596","indexId":"70261333","displayToPublicDate":"2025-01-01T08:10:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis","docAbstract":"The increasing threat of post-wildfire hazards creates an imperative for improved post-wildfire flooding and debris flow prediction capabilities. Because rainfall is a primary driver of predictive hydrology and debris flow initiation and inundation models, recent efforts have emphasized the need for interdisciplinary collaboration between meteorology and post-wildfire hazard science that develops more accurate rainfall estimates with longer lead times. In this work, we identified critical knowledge gaps for developing rainfall estimates and filled those gaps by reviewing recent literature and synthesizing pre-existing datasets. Gap areas were organized into the following general topics: a) rainfall intensity-duration-frequency relations, b) time-varying rainfall, c) spatially varying rainfall, and d) rainfall regimes.
Recent key research advances include the increasing availability of gridded quantitative rainfall estimates, the expanded use of distributed hydrologic and erosion models that incorporate spatial and temporal variability in rainfall, and the linking of concepts and modeling from the atmospheric and climate sciences with post-wildfire hazard science. We prototype a rainfall regime regionalization schema that captures self-similar properties of rainfall intensity (k, the maximum rainfall intensity) and temporal scaling (n, the decay rate). Our k-n relations schema could serve as a framework for organizing, interpreting, and predicting post-wildfire hydrologic and erosional responses. Finally, we summarize salient gaps for implementing spatiotemporally varying rainfall as the driver of post-wildfire hydrologic models designed to improve the prediction of flooding and debris flow hazards to the built environment for emergency managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104990","usgsCitation":"Collar, N.M., Moody, J.A., and Ebel, B., 2024, Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis: Earth-Science Reviews, v. 260, 104990, 32 p., https://doi.org/10.1016/j.earscirev.2024.104990.","productDescription":"104990, 32 p.","ipdsId":"IP-164156","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":464885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"260","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collar, Natalie M. 0000-0003-4711-0090","orcid":"https://orcid.org/0000-0003-4711-0090","contributorId":306155,"corporation":false,"usgs":false,"family":"Collar","given":"Natalie","email":"","middleInitial":"M.","affiliations":[{"id":66376,"text":"Colorado School of Mines, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":920406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":920407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":920408,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261548,"text":"70261548 - 2024 - Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","interactions":[],"lastModifiedDate":"2024-12-13T15:13:03.033614","indexId":"70261548","displayToPublicDate":"2025-01-01T07:58:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","docAbstract":"Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.122520","usgsCitation":"Suarez-Castro, A.F., Robertson, D., Lehner, B., de Souza, M.L., Kittridge, M., Saad, D., Linke, S., McDowell, R.W., Ranjbar, M.H., Ausseil, O., and Hamilton, D.P., 2024, Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales: Water Research, v. 268, no. 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Zealand\"}}]}","volume":"268","issue":"Part A","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Suarez-Castro, Andres Felipe 0000-0002-6621-3821","orcid":"https://orcid.org/0000-0002-6621-3821","contributorId":347155,"corporation":false,"usgs":false,"family":"Suarez-Castro","given":"Andres","email":"","middleInitial":"Felipe","affiliations":[{"id":83086,"text":"Griffith University, Brisbane, Australia","active":true,"usgs":false}],"preferred":false,"id":920984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lehner, Bernhard","contributorId":347156,"corporation":false,"usgs":false,"family":"Lehner","given":"Bernhard","affiliations":[{"id":36802,"text":"McGill University, Canada","active":true,"usgs":false}],"preferred":false,"id":920986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Souza, Marcelo L.","contributorId":347157,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L.","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":920987,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kittridge, Michael","contributorId":347158,"corporation":false,"usgs":false,"family":"Kittridge","given":"Michael","email":"","affiliations":[{"id":83088,"text":"Headwaters Hydrology, NZ","active":true,"usgs":false}],"preferred":false,"id":920988,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920989,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Linke, Simon 0000-0002-1797-3947","orcid":"https://orcid.org/0000-0002-1797-3947","contributorId":347159,"corporation":false,"usgs":false,"family":"Linke","given":"Simon","email":"","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":920990,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDowell, Rich W.","contributorId":347160,"corporation":false,"usgs":false,"family":"McDowell","given":"Rich","email":"","middleInitial":"W.","affiliations":[{"id":83090,"text":"Lincoln University, NZ","active":true,"usgs":false}],"preferred":false,"id":920991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ranjbar, Mohammad H.","contributorId":347161,"corporation":false,"usgs":false,"family":"Ranjbar","given":"Mohammad","email":"","middleInitial":"H.","affiliations":[{"id":80337,"text":"Griffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ausseil, Olivier","contributorId":347162,"corporation":false,"usgs":false,"family":"Ausseil","given":"Olivier","email":"","affiliations":[{"id":83091,"text":"Girffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920993,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hamilton, David P. 0000-0002-9341-8777","orcid":"https://orcid.org/0000-0002-9341-8777","contributorId":347163,"corporation":false,"usgs":false,"family":"Hamilton","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":83086,"text":"Griffith University, Brisbane, Australia","active":true,"usgs":false}],"preferred":false,"id":920994,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70261890,"text":"sir20245126 - 2024 - Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","interactions":[],"lastModifiedDate":"2025-07-10T15:28:29.176134","indexId":"sir20245126","displayToPublicDate":"2024-12-31T15:00:00","publicationYear":"2024","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":"2024-5126","displayTitle":"Simulating Present and Future Groundwater/Surface-Water Interactions and Stream Temperatures in Beaver Creek, Kenai Peninsula, Alaska","title":"Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","docAbstract":"In many places, coldwater ecosystems are facing increasing pressure from anthropogenic warming. This study examined stream temperatures and the water balance in the Beaver Creek watershed on the Kenai Peninsula in south-central Alaska—an area that is experiencing rapid warming. Low-gradient streams near the Kenai coast provide important spawning and rearing habitat for salmon but may be especially vulnerable to rising temperatures, because of long residence times, inflows from abundant riparian wetlands, and reliance on groundwater discharge that may also warm, or decrease in volume with rising evapotranspiration. In recent decades, observed maximum 7-day temperatures have consistently exceeded statistical (regression-based) projections. Here we simulate total streamflows and temperatures with a physics-based model that links the Soil Water Balance, MODFLOW 6 and SNTEMP simulation codes on a 7-day timestep. The model is based on existing data and groundwater levels, instream flows, and stream temperatures collected during 2019–23. Future climate scenarios were developed for 2023–50 from downscaled climate projections.
Results indicate that groundwater discharge is about 64 percent of the total streamflow during the months of May through September. Total streamflow and groundwater discharge are expected to remain similar to current conditions through 2050. Stream temperatures are expected to rise; by midcentury, near the Beaver Creek mouth the model predicts 34 to 63 additional days per year with average weekly temperatures above 13 degrees Celsius, 14 to 81 additional days with average weekly temperatures above 15 degrees Celsius, and routine exceedances of 20 degrees Celsius during the warmest periods. Projected stream temperatures vary spatially. Areas of high groundwater inflows in the lower main stem and some tributaries may be most resilient to warming air temperatures during dry conditions. During storm events, groundwater-dominated tributaries may have the coolest stream temperatures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245126","usgsCitation":"Leaf, A.T., Haserodt, M.J., Meyer, B.E., Westenbroek, S.M., and Koch, J.C., 2024, Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5126, 111 p., https://doi.org/10.3133/sir20245126.","productDescription":"Report: ix, 111 p.; 2 Data Releases; Dataset","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167012","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":465606,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.XML"},{"id":465583,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14UAWGB","text":"USGS data release","linkHelpText":"Surface water and groundwater hydrology and temperature, Beaver Creek, Kenai Peninsula, Alaska, 2022–2023"},{"id":465585,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465584,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"http://doi.org/10.5066/P9K30VAP","text":"USGS data release","linkHelpText":"Soil water balance, groundwater flow, and stream temperature models for Beaver Creek, Alaska, 2019 to 2050"},{"id":492014,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118271.htm","linkFileType":{"id":5,"text":"html"}},{"id":465607,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5126/images/"},{"id":465605,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245126/full"},{"id":465582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.pdf","text":"Report","size":"34.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5126"},{"id":465581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5126/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaver Creek, Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.3330540056542,\n 60.957367731319806\n ],\n [\n -152.38912986860709,\n 59.25155522317334\n ],\n [\n -148.50867874659667,\n 59.25563148250791\n ],\n [\n -148.50867874659667,\n 60.95766646209441\n ],\n [\n -152.3330540056542,\n 60.957367731319806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Wolf harvest season setting is complicated and controversial. State law requires Montana Fish, Wildlife and Parks (MFWP) to both reduce the wolf population and avoid federal relisting under the Endangered Species Act (Montana Fish, Wildlife and Parks, 2002). Disparate stakeholder groups each have different objectives for wolf management. For instance, big game advocates want to see improved big game populations and hunting opportunities in northwest Montana, while wolf advocates want to see regulations that minimize wolf mortality. Decision making about season setting tries to balance these objectives. Wolf hunting and trapping season decisions are made by the Montana Fish and Wildlife Commission and are informed by annual wolf abundance estimates from an integrated patch occupancy model (iPOM, Sells et al., 2022c) as well as the predictions of wolf abundance into the future under potential constant harvest levels. Parametric uncertainty (uncertainty surrounding the value of a parameter) from the iPOM estimates is propagated through to future projections, providing the Commission with plausible and worst-case outcomes of different levels of public harvest over the short term, i.e., five years into the future, on the wolf population in Montana (Parks et al., 2024).
An alternative approach to inform wolf management and harvest decisions is through adaptive management. Adaptative management is appropriate for decisions that are made iteratively and when monitoring data are collected to learn about the outcomes from decisions, where monitoring data help to reduce critical uncertainties regarding ecosystem function or management outcomes (Walters, 1986; Williams, 2011). Management strategy evaluation (MSE) is one way to develop an adaptive management framework. MSE was developed by fisheries managers and scientists to more accurately and fully incorporate various forms of uncertainty, consider long-term time horizons, and add more transparency in a fisheries context (Punt et al., 2016). It has been used routinely and has become a standard approach for complicated and contentious marine fisheries management situations, yet it has been underutilized in wildlife management (but see Bunnefeld et al., 2013, 2011).
MSE is a forward simulation approach for testing prospective management options or strategies over a wide range of possible states (Punt et al., 2016). A MSE framework captures the ‘truth’ or what is happening in the system (termed the ‘the operating model’) and the information available to the decision makers (termed ‘the estimation model’ or ‘management strategy’). More precisely, there are four main processes modeled. First, models are constructed based on current understanding and data to represent ‘truth’. Second, the collection of monitoring data is simulated from the ‘truth’ model. Third, the simulated monitoring data are fit to an estimation model and the next time step’s population metrics are predicted from the estimated parameters. Fourth, based on the estimation model results and the predictions, the decision-making process is simulated following a management strategy, whereby a decision is made and the implementation of this decision feeds back into the ‘truth’ model (Figure 1). This process continues through time. Additionally, each simulation through time is repeated to capture the full range of stochasticity and uncertainty.
Patterns of phytochemistry localisation in plant tissues are diverse within and across leaves. These spatial heterogeneities are important to the fitness of herbivores, but their effects on herbivore foraging and dietary experience remain elusive. We manipulated the spatial variance and clusteredness of a plant toxin in a synthetic diet landscape on which individual caterpillars fed. We monitored caterpillars with cameras across most of their larval development. Caterpillars that fed on diets with a lower spatial variance and more clustered arrangement of toxins had overall worse performance, mostly because those caterpillars ate less, moved more, ingested more toxin, or failed to physiologically acclimate. Using empirically parameterised individual-based models, we found that differences in movement away from, not towards, less toxic food drove a body size-dependent effect of clusteredness. Hence, the spatial pattern of phytochemicals itself, beyond mean concentration, can have important consequences for herbivores through complex interactions with herbivore foraging.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.70044","usgsCitation":"Pan, V.S., Ghosh, E., Ode, P.J., Wetzel, W.C., Gilbert, K.J., and Pearse, I.S., 2024, Large differences in herbivore performance emerge from simple herbivore behaviors and fine-scale spatial heterogeneity in phytochemistry: Ecology Letters, v. 28, no. 1, e70044, 12 p., https://doi.org/10.1111/ele.70044.","productDescription":"e70044, 12 p.","ipdsId":"IP-171430","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":497402,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70044","text":"Publisher Index Page"},{"id":497197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Pan, Vincent S. 0000-0001-9892-7805","orcid":"https://orcid.org/0000-0001-9892-7805","contributorId":332717,"corporation":false,"usgs":false,"family":"Pan","given":"Vincent","middleInitial":"S.","affiliations":[{"id":79600,"text":"Department of Integrative Biology, Michigan State University, 288 Farm Lane, East Lansing, Michigan, USA 48824","active":true,"usgs":false}],"preferred":false,"id":951695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ghosh, Enakshi","contributorId":363457,"corporation":false,"usgs":false,"family":"Ghosh","given":"Enakshi","affiliations":[{"id":80402,"text":"Colorado State U","active":true,"usgs":false}],"preferred":false,"id":951696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ode, Paul J.","contributorId":197314,"corporation":false,"usgs":false,"family":"Ode","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":951697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wetzel, William C.","contributorId":229437,"corporation":false,"usgs":false,"family":"Wetzel","given":"William","email":"","middleInitial":"C.","affiliations":[{"id":41642,"text":"Michigan State U","active":true,"usgs":false}],"preferred":false,"id":951698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilbert, Kadeem J.","contributorId":342370,"corporation":false,"usgs":false,"family":"Gilbert","given":"Kadeem","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":951699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":951700,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266181,"text":"70266181 - 2024 - Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California","interactions":[],"lastModifiedDate":"2025-04-29T14:07:48.72537","indexId":"70266181","displayToPublicDate":"2024-12-31T09:03:52","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California","docAbstract":"Mammoth Lakes, California hosts a productive hydrothermal system within the seismically active south moat of Long Valley Caldera. Surficial evidence of the shallow hydrothermal system includes discrete zones of tree-kill dispersed between Shady Rest Park and the Casa Diablo Geothermal Power Plant (40 MW), as well as east of the power plant. The tree-kill areas are associated with elevated diffuse CO₂ emissions, heated ground, hydrothermal alteration, diffuse soil H₂S emissions, and gas vents. Previous mapping delineates prominent north and northwest trending structures within the south moat along the southwestern edge of the resurgent dome that may accommodate gas and fluid flow at the Shady Rest Park and Basalt Canyon Tree Kill Areas (SRTKA and BCTKA, respectively). Both tree-kill areas are also located along contacts between resurgent rhyolite, mafic lavas, and surficial deposits which may provide additional pathways for gas and fluid migration in the shallow subsurface.\nCharacterizing structure and lithology using geophysical anomalies is critical to determining primary structural controls on the hydrothermal system and the extent of subsurface alteration at these sites. We conducted ground and airborne-based potential field geophysical surveys to map gravity and magnetic anomalies. These anomalies are then used to model subsurface geology, structure, and hydrothermal alteration. Here we present our preliminary geophysical mapping and modelling results at both tree-kill locations. Gravity and magnetic data suggest complex structural intersections are coincident with heated ground and gas emissions at the SRTKA and BCTKA. Hydrothermal systems are often observed or interpreted to exploit fault intersections which can serve as highly permeable pathways for hydrothermal fluid and gas discharge, enabling economic geothermal energy production. Geophysical mapping and modelling are an effective means of investigating such structural complexity at Mammoth Lakes due to the presence of unidentified and concealed structures.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to Save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Rising Conference (GRC)","usgsCitation":"Anderson, J.E., Glen, J.M., Bouligand, C., Rea-Downing, G.H., and Earney, T.E., 2024, Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California, in Using the Earth to Save the Earth, v. 48, p. 1613-1639.","productDescription":"17 p.","startPage":"1613","endPage":"1639","ipdsId":"IP-169794","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":485127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":485121,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035013"}],"country":"United States","state":"California","otherGeospatial":"Mammoth Lakes","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Jacob Elliott 0000-0002-0709-2548","orcid":"https://orcid.org/0000-0002-0709-2548","contributorId":329989,"corporation":false,"usgs":true,"family":"Anderson","given":"Jacob","email":"","middleInitial":"Elliott","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouligand, Claire 0000-0002-2923-1780","orcid":"https://orcid.org/0000-0002-2923-1780","contributorId":345142,"corporation":false,"usgs":false,"family":"Bouligand","given":"Claire","email":"","affiliations":[{"id":82499,"text":"Univ. Grenoble Alpes, Univ. Savoie Mont Blanc","active":true,"usgs":false}],"preferred":false,"id":934810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rea-Downing, Grant Harold 0000-0002-8567-683X","orcid":"https://orcid.org/0000-0002-8567-683X","contributorId":333087,"corporation":false,"usgs":true,"family":"Rea-Downing","given":"Grant","email":"","middleInitial":"Harold","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934811,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934812,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271332,"text":"70271332 - 2024 - Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","interactions":[],"lastModifiedDate":"2025-09-05T15:31:48.53114","indexId":"70271332","displayToPublicDate":"2024-12-31T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","docAbstract":"The spread of non-native freshwater mussels in North America is a growing threat that has already resulted in substantial ecological and economic damage to infested areas. A primary vector by which invasive mussels spread is watercraft that are transported over land from an infested waterbody to an uninfested waterbody. Management efforts such as watercraft inspection and detection programs that attempt to intercept infected watercraft can help limit this type of spread, but optimizing the effectiveness of these programs under limited resources is complicated. Studies have looked at developing watercraft inspection and decontamination program strategies that focus on policy-specific objectives such as maximizing the number of infested watercraft interceptions. However, there is limited work that has considered the heterogeneous impact of protection efforts across different regions and waterbodies. Knowledge about this heterogeneity can highlight regions that would benefit the most from protection as well as regions that would require less effort to protect, e.g., areas with naturally unsuitable water quality for dreissenids. To this end, we construct a composite relative risk index (CRR) for watersheds within the Missouri River Basin, a region in the United States on the front line of dreissenid spread. The CRR uses a model that mirrors an expected value model but uses relative indexing as a proxy for the model components. The CRR incorporates a wide array of data sets to account for the direct and indirect damages from a potential infestation along with the risk of an infestation occurring. Our results suggest that the relative priority of a specific watershed—measured through CRR—can depend on whether we consider the entire Missouri River Basin or just the watersheds in the same state. This also indicates substantial state-level heterogeneity in the CRR. Another contribution is that the CRR index includes user-specified weights for certain parameters so that a user can adjust the relative importance of various factors to match their specific context. An accompanying web tool allows users to view the CRR results and adjust multiple parameters to see the resulting impacts on the CCR for watersheds in the Missouri River Basin.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2024.112526","usgsCitation":"Raymond, J., Bair, L., Counihan, T., Daniel, W., Duntugan, S., Neilson, M., and Springborn, M.R., 2024, Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin: Ecological Indicators, v. 170, 112526, 14 p., https://doi.org/10.1016/j.ecolind.2024.112526.","productDescription":"112526, 14 p.","ipdsId":"IP-160154","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495378,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2024.112526","text":"Publisher Index Page"},{"id":495287,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13FBRRU","text":"USGS data release","linkHelpText":"webDR: Spatial exploration of invasion risk of dreissenid mussels in the Missouri River Basin at the HUC10 watershed scale"},{"id":495286,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14DZRCH","text":"USGS data release","linkHelpText":"Composite relative risk indices for dreissenid mussel introductions in the Missouri River Basin: scripts and output"},{"id":495201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Missouri River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.70584383361135,\n 49.63943542878238\n ],\n [\n -114.45126027881234,\n 47.4909847362492\n ],\n 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0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":948067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":214505,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":948068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duntugan, Sofie","contributorId":360981,"corporation":false,"usgs":false,"family":"Duntugan","given":"Sofie","affiliations":[{"id":86140,"text":"formerly: US Geological Survey, Western Fisheries Research Center, Seattle, WA 98115","active":true,"usgs":false}],"preferred":false,"id":948069,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neilson, Matthew 0000-0002-5139-5677","orcid":"https://orcid.org/0000-0002-5139-5677","contributorId":214507,"corporation":false,"usgs":true,"family":"Neilson","given":"Matthew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":948070,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Springborn, Michael R.","contributorId":207552,"corporation":false,"usgs":false,"family":"Springborn","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":37562,"text":"University of California Davis, 1 Shields Avenue Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":948071,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261830,"text":"ofr20241080 - 2024 - Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania","interactions":[],"lastModifiedDate":"2025-08-15T16:08:29.355622","indexId":"ofr20241080","displayToPublicDate":"2024-12-30T12:40:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1080","displayTitle":"Hydrogeologic Investigations and a Preliminary Conceptual Model of the Groundwater System at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania","title":"Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania","docAbstract":"The U.S. Geological Survey (USGS) conducted hydrogeologic investigations, reviewed existing data, and developed a preliminary conceptual model of the groundwater system as part of technical support of the U.S. Environmental Protection Agency (EPA) at the North Penn Area 1 Superfund Site (hereafter, the NP1 Site) located within the Borough of Souderton in Montgomery County, Pennsylvania. Field work and monitoring took place during 2012–18. The area is underlain by sedimentary formations that form a fractured-rock aquifer used for drinking water and industrial supply. The EPA placed the Site on the National Priorities List in 1989, identifying tetrachloroethylene (PCE) and trichloroethylene (TCE) as contaminants of concern.
During 2012–18, the USGS conducted field activities that included drilling an 82-foot (ft)-deep monitoring well (MG 2220) in 2016, reconstructing a 208-ft-deep former industrial production well (MG 668 [Granite Knitting Mill]), and collecting borehole geophysical and video logs and water levels from those and five additional wells, which ranged in depth from about 50 to 200 ft below land surface. Continuous water levels were collected during 2014–17, and a synoptic set of water levels were measured in April 2018 in the seven wells.
The borehole geophysical logs (caliper, acoustic televiewer, natural gamma, single-point resistance, vertical flow, and fluid temperature and resistivity) and borehole video logs in the seven wells were evaluated to assess potential for lithologic correlation and to identify and describe water-bearing features, which included both low- and high-angle fractures and other openings oriented along dipping bedding planes, joints, or possible faults. Borehole geophysical logs collected by USGS in 1992 in a 300-ft-deep former production well near the Site were also evaluated. Few to no distinctive features were identified on geophysical logs (natural gamma and single-point resistance) that could be used for correlation, thus limiting this approach to determining local geologic structure. Extensive fracturing in the upper 62 ft of monitoring well MG 2220 indicates that the well was likely drilled through a zone of faulting, and other evidence of faulting is present in the area near the Site. Assessment of continuous water levels showed hydraulic connections among some wells as indicated by rising or falling water levels in response to changes in pumping rates at nearby wells. A map of water levels measured in April 2018 indicates potential for groundwater flow generally toward the stream to the south and southwest of the Site, but the limited water-level data are insufficient to describe vertical groundwater gradients or lateral gradients in any detail.
Review of 1999–2022 volatile organic compound (VOC) monitoring data collected by the Pennsylvania Department of Environmental Protection for five monitoring wells indicates that the highest groundwater concentrations of PCE and TCE were found in samples from extraction well MG 2201 (S-1) downgradient from, and nearest to, the previously identified Site contaminant source area, and these concentrations fluctuated through time. PCE concentrations were higher than TCE concentrations in samples from all five monitoring wells and were much higher than TCE concentrations in samples from extraction well MG 2201 (S-1). Temporally variable recharge is a possible factor affecting observed fluctuations in PCE concentrations in groundwater samples from well extraction MG 2201 (S-1), as indicated by a general inverse relation between PCE concentrations and water levels in a nearby long-term observation well. The PCE concentration of 1,830 micrograms per liter (μg/L) in a May 2018 water sample from monitoring well MG 2220 was more than four times the PCE concentration of 444 μg/L in a December 2017 sample from the nearby extraction well MG 2201 (S-1), which is open to fewer fractures. Low concentrations of VOCs were measured in surface water at two stream sites downgradient from wells with the highest groundwater VOC concentrations at the Site, indicating that discharge of contaminated groundwater to the stream is likely.
Development of a conceptual model of the groundwater system was constrained by limited data. In areas with no pumping, groundwater-flow directions generally are thought to be controlled by topography and geologic structure (bedding orientation) and likely to the south and southwest of the Site, with local flow directions affected by orientations of fractures, joints, and local faults. Additional investigations that could help improve the conceptual model of the groundwater system and help delineate the extent of groundwater contamination and its transport are discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241080","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Risser, D.W., Goode, D.J., and Bird, P.H., 2024, Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania: U.S. Geological Survey Open-File Report 2024–1080, 78 p., https://doi.org/10.3133/ofr20241080.","productDescription":"xi, 78 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151018","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":494216,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118273.htm","linkFileType":{"id":5,"text":"html"}},{"id":465486,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1080 XML"},{"id":465485,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241080/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1080 HTML"},{"id":465479,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1080/images/"},{"id":465476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.pdf","text":"Report","size":"18.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1080 PDF"},{"id":465475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1080/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","city":"Souderton","otherGeospatial":"North Penn Area 1 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.33380565402877,\n 40.30337215850042\n ],\n [\n -75.33067431094733,\n 40.30297414782885\n ],\n [\n -75.32310689850118,\n 40.30864557850933\n ],\n [\n -75.32121504538941,\n 40.31133187946756\n ],\n [\n -75.32415067952832,\n 40.31496319053656\n ],\n [\n -75.33002194780529,\n 40.3133714069823\n ],\n [\n -75.33432754454195,\n 40.307053646040714\n ],\n [\n -75.33380565402877,\n 40.30337215850042\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, Pennsylvania 17070
New geologic mapping (Wells and others, 2020b) and geophysical mapping (Blakely and others, 2000; McPhee and others, 2014; Wells and others, 2020a) document kilometers of Cenozoic right-lateral offset along the Gales Creek Fault Zone, a major, northwest-striking fault zone forming the boundary between the Tualatin Valley and the Coast Range. The Bureau of Reclamation’s (Reclamation) Scoggins Dam (fig. 1), in the Coast Range foothills west of Forest Grove, Oregon, lies within the Gales Creek Fault Zone as mapped by Wells and others (2020a, 2020b; fig. 2).
Active faults of the Gales Creek Fault Zone defined by paleoseismic trenching (Redwine and others, 2017, 2019b, Horst and others, 2018, 2019, 2021, and Wells and others, 2020a) are presently mapped as projecting through the existing dam. The Pacific Northwest Region of Reclamation requested assistance with geologic studies around Scoggins Dam to provide better understanding of fault locations and their activity, which are needed to design a modification of the dam (Maguire, 2019a, b). The scope of this project includes detailed geology of the existing Scoggins Dam site, Henry Hagg Lake, the reservoir behind the dam, and Scoggins Valley downstream of the existing dam, particularly around a potential new dam site, where Scoggins Creek cuts through a narrow gap formed by a resistant felsic tuff bed that crosses the valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3528","collaboration":"Bureau of Reclamation, Clean Water Services","usgsCitation":"Wells, R.E., Bennett, S.E.K., Redwine, J.R., Staisch, L.M., Holm-Denoma, C.S., and Mahan, S.A., 2024, Geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon: U.S. Geological Survey Scientific Investigations Map 3528, 4 sheets, scales 1:2,000 and, 1:12,000, 37 p. pamphlet, https://doi.org/10.3133/sim3528.","productDescription":"Pamphlet: x, 37 p.; 4 Sheets: 43.91 x 34.01 inches or smaller; 3 Data Releases","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-121928","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":494215,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118270.htm","linkFileType":{"id":5,"text":"html"}},{"id":465359,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ZSKV3","text":"USGS Data Release","description":"Pianowski, L.S., Holm-Denoma, C.S., Staisch, L.M., and Wells, R.E., 2023, U-Pb zircon data for Cenozoic clastic and volcaniclastic units deformed along the Gales Creek Fault Zone, northwestern Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P91ZSKV3.","linkHelpText":"U-Pb zircon data for Cenozoic clastic and volcaniclastic units deformed along the Gales Creek Fault Zone, northwestern Oregon"},{"id":465358,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PVK0Q3","text":"USGS Data Release","description":"Mahan, S.A., Krolczyk, E.T., and Redwine, J., 2022, Data Release for Luminescence; Geologic map of Scoggins Dam, Henry Hagg Lake, and the Scoggins Valley area, Washington County, Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P9PVK0Q3.","linkHelpText":"Data Release for Luminescence; Geologic map of Scoggins Dam, Henry Hagg Lake, and the Scoggins Valley area, Washington County, Oregon"},{"id":465357,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M5300X","text":"USGS Data Release","description":"Wells, R.E., Bennett, S.E.K., Redwine, J.R., Staisch, L.M., Holm-Denoma, C.S., and Mahan, S.A.,2024, Digital data for the geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P9M5300X.","linkHelpText":"Digital data for the geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon"},{"id":465356,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_pamphlet.pdf","text":"Pamphlet","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465355,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet4.pdf","text":"Sheet 4","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Cross Sections of the Option 3 Dam Site"},{"id":465354,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet3.pdf","text":"Sheet 3","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of the Option 3 Dam Site"},{"id":465353,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet2.pdf","text":"Sheet 2","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of Scoggins Dam"},{"id":465352,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet1.pdf","text":"Sheet 1","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon"},{"id":465351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3528/covrthb.jpg"}],"country":"United States","state":"Oregon","county":"Washington County","otherGeospatial":"Henry Hagg Lake, Scoggins Dam, Scoggins Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.30887944247891,\n 45.55096796789627\n ],\n [\n -123.30887944247891,\n 45.44121330051283\n ],\n [\n -123.1570053249566,\n 45.44121330051283\n ],\n [\n -123.1570053249566,\n 45.55096796789627\n ],\n [\n -123.30887944247891,\n 45.55096796789627\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine (Pinus albicaulis) trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP). Here, we used this subfossil wood record to develop tree-ring-based temperature estimates for the upper-elevation climate conditions that resulted in ancient forest establishment and growth and the subsequent regional ice-patch growth and downslope shift of treeline. Results suggest that mid-Holocene forest establishment and growth occurred under warm-season (May-Oct) mean temperatures of 6.2 °C (±0.2 °C), until a multicentury cooling anomaly suppressed temperatures below 5.8 °C, resulting in stand mortality by c. 5,440 y BP. Transient climate model simulations indicate that regional cooling was driven by changes in summer insolation and Northern Hemisphere volcanism. The initial cooling event was followed centuries later (c. 5,100 y BP) by sustained Icelandic volcanic eruptions that forced a centennial-scale 1.0 °C summer cooling anomaly and led to rapid ice-patch growth and preservation of the trees. With recent warming (c. 2000–2020 CE), warm-season temperatures now equal and will soon exceed those of the mid-Holocene period of high treeline. It is likely that perennial ice cover will again disappear from the region, and treeline may expand upslope so long as plant-available moisture and disturbance are not limiting.
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High-quality statewide elevation data are useful in managing this very diverse landscape. For example, the short coastline, including the Great Bay estuary and the Hampton-Seabrook marshes, is of disproportionately high value to New Hampshire’s tourist economy. The vulnerability of the coast to the effects of sea-level rise underscores the need for accurate, high-quality nearshore topographic elevation data and offshore bathymetric data to effectively manage the coast’s valuable resources, which include important fisheries, habitat, and infrastructure. Another important use for accurate elevation data in New Hampshire is in the evaluation of flood hazards and their potential environmental and infrastructure effects. This evaluation includes mapping of inundation and sediment transport, and assessing the associated costs of flooding. Addressing this challenge requires detailed knowledge of both surface topography and inland bathymetry. Other important activities having a substantial economic element and needing accurate elevation data include geologic resource assessment and hazard mitigation, urban and regional planning, infrastructure and construction management, and cultural resources preservation and management. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional model of the Earth’s surface and aboveground features.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Prevention and early detection of invasive species are championed as the most cost-effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula, is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes (Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine-assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult-to-detect invasive species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70113","usgsCitation":"Fuller, A.K., Augustine, B., Clifton, E., Hajek, A., Blumenthal, A., Beese, J., Hurt, A., and Brown-Lima, C., 2024, Effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly: Ecological Applications, v. 15, no. 12, e70113, 22 p., https://doi.org/10.1002/ecs2.70113.","productDescription":"e70113, 22 p.","ipdsId":"IP-161109","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466698,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70113","text":"Publisher Index Page"},{"id":466010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, 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Conference","active":true,"usgs":false}],"preferred":false,"id":922720,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurt, Aimee","contributorId":347881,"corporation":false,"usgs":false,"family":"Hurt","given":"Aimee","affiliations":[{"id":40629,"text":"Working Dogs for Conservation","active":true,"usgs":false}],"preferred":false,"id":922721,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown-Lima, Carrie J.","contributorId":347882,"corporation":false,"usgs":false,"family":"Brown-Lima","given":"Carrie J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922722,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261925,"text":"70261925 - 2024 - Nitrogen deposition weakens soil carbon control of nitrogen dynamics across the contiguous United States","interactions":[],"lastModifiedDate":"2025-01-03T15:17:09.57762","indexId":"70261925","displayToPublicDate":"2024-12-26T09:08:36","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen deposition weakens soil carbon control of nitrogen dynamics across the contiguous United States","docAbstract":"Anthropogenic nitrogen (N) deposition is unequally distributed across space and time, with inputs to terrestrial ecosystems impacted by industry regulations and variations in human activity. Soil carbon (C) content normally controls the fraction of mineralized N that is nitrified (ƒnitrified), affecting N bioavailability for plants and microbes. However, it is unknown whether N deposition has modified the relationships among soil C, net N mineralization, and net nitrification. To test whether N deposition alters the relationship between soil C and net N transformations, we collected soils from coniferous and deciduous forests, grasslands, and residential yards in 14 regions across the contiguous United States that vary in N deposition rates. We quantified rates of net nitrification and N mineralization, soil chemistry (soil C, N, and pH), and microbial biomass and function (as beta-glucosidase (BG) and N-acetylglucosaminidase (NAG) activity) across these regions. Following expectations, soil C was a driver of ƒnitrified across regions, whereby increasing soil C resulted in a decline in net nitrification and ƒnitrified. The ƒnitrified value increased with lower microbial enzymatic investment in N acquisition (increasing BG:NAG ratio) and lower active microbial biomass, providing some evidence that heterotrophic microbial N demand controls the ammonium pool for nitrifiers. However, higher total N deposition increased ƒnitrified, including for high soil C sites predicted to have low ƒnitrified, which decreased the role of soil C as a predictor of ƒnitrified. Notably, the drop in contemporary atmospheric N deposition rates during the 2020 COVID-19 pandemic did not weaken the effect of N deposition on relationships between soil C and ƒnitrified. Our results suggest that N deposition can disrupt the relationship between soil C and net N transformations, with this change potentially explained by weaker microbial competition for N. Therefore, past N inputs and soil C should be used together to predict N dynamics across terrestrial ecosystems.
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The Selkirk, Yaak, and Cabinet grizzly bear (Ursus arctos) populations are among the smallest in North America and are near the southernmost extent of the species’ range. These populations received little to no effective migration for generations but have recently experienced increased gene flow through natural migration and a population augmentation program. A long-term dataset of grizzly bear microsatellite genotypes from 1973 to 2021 presented a unique opportunity to examine genetic trends in these populations over time. We used this dataset of 464 bears to evaluate if gene flow affected observed heterozygosity (HO), expected heterozygosity (HE), allelic richness (AR), and average pairwise relatedness (r) in each of these populations. We also estimated effective population size (Ne) using the temporal and linkage disequilibrium (LD) methods. Post gene flow, AR increased in the Selkirk and Cabinet populations and r decreased in all three populations. We did not observe any significant changes in HE or HO, but HE values in our populations were significantly higher than those estimated using a model without gene flow. Our Ne estimates were consistent between the temporal and LD methods and ranged from 15.2 to 15.8, 15.4–17.5, and 5.6–8.9 for the Selkirk, Yaak, and Cabinet populations, respectively. Overall, our findings indicate that gene flow is increasing or maintaining genetic diversity in these populations. However, Ne remains low and additional connectivity or augmentation may be needed, particularly in the Cabinet population.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-024-01666-y","usgsCitation":"Turnock, M., Teisberg, J., Kasworm, W., Falcy, M.R., Proctor, M., and Waits, L., 2024, Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations: Conservation Genetics, v. 26, p. 279-291, https://doi.org/10.1007/s10592-024-01666-y.","productDescription":"13 p.","startPage":"279","endPage":"291","ipdsId":"IP-166778","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01666-y","text":"Publisher Index Page"},{"id":486760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Idaho, Montana, Washington","otherGeospatial":"British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.3821304045668,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 50.132013169608314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Turnock, Megan F.","contributorId":356036,"corporation":false,"usgs":false,"family":"Turnock","given":"Megan F.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teisberg, Justin E.","contributorId":356039,"corporation":false,"usgs":false,"family":"Teisberg","given":"Justin E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasworm, Wayne F.","contributorId":356042,"corporation":false,"usgs":false,"family":"Kasworm","given":"Wayne F.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falcy, Matthew Richard 0000-0002-3332-2239","orcid":"https://orcid.org/0000-0002-3332-2239","contributorId":288500,"corporation":false,"usgs":true,"family":"Falcy","given":"Matthew","email":"","middleInitial":"Richard","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938586,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Proctor, Michael F.","contributorId":356045,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael F.","affiliations":[{"id":84901,"text":"Birchdale Ecological, Ltd.","active":true,"usgs":false}],"preferred":false,"id":938587,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":356046,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938588,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263381,"text":"70263381 - 2024 - Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform","interactions":[],"lastModifiedDate":"2025-02-10T16:17:14.052147","indexId":"70263381","displayToPublicDate":"2024-12-24T13:39:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform","docAbstract":"Salt marsh systems require a net import of inorganic sediment to maintain their structure in response to sea‐level rise. Marshes are affected by physical processes including tides, waves, sediment transport, and the influence of vegetation, and these processes interact in complex ways leading to sediment accretion or erosion. We implement a 3‐D hydrodynamic sediment transport model in an idealized marsh‐bay complex with a gently sloping edge, and use it as a laboratory to explore the processes leading to bed elevation change through the bay‐marsh continuum. We use the novel equadratures method for efficient sensitivity analysis to test the roles of wave, vegetation, and sediment parameters on wave dissipation, bed shear stress, sediment fluxes, and deposition and erosion across a transect spanning bay shallows to the marsh. Within the explored bounds of parameter uncertainty, significant wave height (Hsig), settling velocity (ws), and critical shear stress (τcrit) most strongly affect accretion on the marsh platform. Deposition is affected more by parameter‐parameter interactions, that is, both τcrit and ws or both Hsig and ws, than by a single parameter varying alone. The sediment that accretes on the marsh platform originates beyond the marsh edge, indicating that the dynamics of the adjacent mudflat are important for predicting the fate of the marsh. Applying efficient sensitivity analysis techniques can empower process‐based models to test more parameters, larger ranges, and longer timeframes, enabling future predictions of marsh response to sea‐level rise based on physical processes
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JF007945","usgsCitation":"Allen, R., Ganju, N., Kalra, T., Aretxabaleta, A., and Lacy, J.R., 2024, Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform: JGR Earth Surface, v. 129, no. 10, e2024JF007945, 21 p., https://doi.org/10.1029/2024JF007945.","productDescription":"e2024JF007945, 21 p.","ipdsId":"IP-159248","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"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}],"links":[{"id":487466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jf007945","text":"Publisher Index Page"},{"id":481871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"China Camp march, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 38.016667\n ],\n [\n -122.5,\n 38\n ],\n [\n -122.466667,\n 38\n ],\n [\n -122.466667,\n 38.016667\n ],\n [\n -122.5,\n 38.016667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Rachel 0000-0002-0287-6466","orcid":"https://orcid.org/0000-0002-0287-6466","contributorId":216002,"corporation":false,"usgs":true,"family":"Allen","given":"Rachel","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalra, Tarandeep 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":304428,"corporation":false,"usgs":false,"family":"Kalra","given":"Tarandeep","email":"tkalra@usgs.gov","affiliations":[{"id":66067,"text":"Jupiter Intelligence, San Mateo, California","active":true,"usgs":false}],"preferred":false,"id":926718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lacy, Jessica R. 0000-0002-2797-6172","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":201703,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261835,"text":"70261835 - 2024 - Self-guided decision support groundwater modelling with Python","interactions":[],"lastModifiedDate":"2024-12-30T15:49:23.123549","indexId":"70261835","displayToPublicDate":"2024-12-24T09:16:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19861,"text":"Journal of Open Source Education","active":true,"publicationSubtype":{"id":10}},"title":"Self-guided decision support groundwater modelling with Python","docAbstract":"The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.","language":"English","publisher":"Open Journals","doi":"10.21105/jose.00240","usgsCitation":"Hugman, R., White, J., Fienen, M., Hemmings, B., and Markovich, K., 2024, Self-guided decision support groundwater modelling with Python: Journal of Open Source Education, v. 7, no. 82, 240, 6 p., https://doi.org/10.21105/jose.00240.","productDescription":"240, 6 p.","ipdsId":"IP-166010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/jose.00240","text":"Publisher Index Page"},{"id":465530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hugman, Rui 0000-0003-0891-3886","orcid":"https://orcid.org/0000-0003-0891-3886","contributorId":299138,"corporation":false,"usgs":false,"family":"Hugman","given":"Rui","affiliations":[{"id":64778,"text":"Univeristy of Flinders","active":true,"usgs":false}],"preferred":false,"id":921991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hemmings, Brioch","contributorId":260167,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Markovich, Katie","contributorId":347560,"corporation":false,"usgs":false,"family":"Markovich","given":"Katie","affiliations":[{"id":83190,"text":"INTERA Geosciences","active":true,"usgs":false}],"preferred":false,"id":921995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262817,"text":"70262817 - 2024 - A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization","interactions":[],"lastModifiedDate":"2025-01-23T15:25:55.35842","indexId":"70262817","displayToPublicDate":"2024-12-24T08:18:56","publicationYear":"2024","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":"A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization","docAbstract":"Background
The number of telemetry studies focused on lake whitefish (Coregonus clupeaformis) in the Laurentian Great Lakes has steadily increased over the last decade, but field tests of immobilization methods used for tag implantation, which have the potential to affect survival and behavior of fish after release, are lacking. We compared post-tagging survival and behavior of lake whitefish that were immobilized for tag implantation using electroimmobilization via a transcutaneous electrical nerve stimulation (TENS) unit or by chemical immobilization via exposure to 10% eugenol.
Results
Acoustic tags were implanted into 126 adult lake whitefish (N = 126; N = 67 TENS treatment group, N = 59 eugenol treatment group) collected from the Fox River, Wisconsin, during the spawning period in November 2021. We found no significant differences between treatments in the number of days that lake whitefish spent in the Fox River following tagging (TENS mean = 13.4 days, eugenol mean = 14.7), and also found that the proportions of fish within each treatment group that returned to the Fox River during fall 2022 (51% from TENS treatment group, 49% from eugenol treatment group) did not differ from the proportions for all fish that were confirmed to be alive at that time. The best Cormack–Jolly–Seber model indicated no differences in survival between the two treatment groups (monthly survival = 0.980, 95% CI 0.970–0.987). Fish immobilized using TENS underwent almost immediate induction and recovery from surgeries, while fish immobilized using eugenol had induction times that ranged 167–487 s (mean = 347 s) and recovery times that ranged 51–2358 s (mean = 1242 s).
Conclusions
Short- and long-term behavior (time to exit of Fox River, return to Fox River in the next spawning season) and monthly survival estimates of lake whitefish did not differ between the immobilization treatments. Either method may be suitable for immobilization during tag implantation, but the additional time needed for induction and recovery of fish when using eugenol may be a limiting factor in some field-based tagging situations.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-024-00393-y","usgsCitation":"Izzo, L., Dembkowski, D., Binder, T., Hansen, S., Vandergoot, C., and Isermann, D.A., 2024, A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization: Animal Biotelemetry, v. 12, 39, 10 p., https://doi.org/10.1186/s40317-024-00393-y.","productDescription":"39, 10 p.","ipdsId":"IP-169427","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-024-00393-y","text":"Publisher Index Page"},{"id":480989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River, Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.05808785412178,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.69153434457186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":349826,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":349827,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Thomas R.","contributorId":349828,"corporation":false,"usgs":false,"family":"Binder","given":"Thomas R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924891,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Scott P.","contributorId":349829,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924892,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergoot, Christopher S.","contributorId":349830,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924893,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924894,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261520,"text":"sim3514 - 2024 - Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","interactions":[],"lastModifiedDate":"2025-08-15T16:11:32.54982","indexId":"sim3514","displayToPublicDate":"2024-12-23T10:32:03","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3514","displayTitle":"Geologic Map and Structure Sections Along the Southern Part of the Bartlett Springs Fault Zone and Adjacent Areas from Cache Creek to Lake Berryessa, Northern Coast Ranges, California","title":"Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","docAbstract":"Located in the Coast Ranges of northern California, the Bartlett Springs Fault Zone is the easternmost fault in the San Andreas Fault system in northern California. The fault is a right-lateral, strike-slip structure considered capable of producing an earthquake of moment magnitude 7. The purpose of this mapping is to better characterize the geology and earthquake hazards associated with the southern part of the Bartlett Springs Fault Zone and to help identify any evidence of active uplift on the faults bounding the Coast Ranges. Although the area immediately surrounding the Bartlett Springs Fault Zone is sparsely populated, its southern segment presents a potential seismic hazard to northern California communities as far away as the San Francisco Bay region and Sacramento. There are also nearby water resources, mineral resources, and public lands used for public recreation.
The Coast Ranges of northern California are a series of northwest-southeast-oriented mountain ranges and valleys located north of the San Francisco Bay region, between the Pacific Ocean to the west and the Sacramento Valley to the east. The region has rugged terrain, high mountain peaks that reach more than 2,400 meters above sea level, isolated and narrow valley bottoms on which most human settlements are located, and large drainage systems that tend to follow the northwest-southeast-oriented topographic grain. The physiographic character of the region is shaped by its bedrock geology, deformational history, and active faulting.
The basement rocks of the northern Coast Ranges consist of the Franciscan Complex and the Great Valley complex, the latter of which consists of two informal units, the Coast Range ophiolite and the Great Valley sequence. The Franciscan Complex and the Great Valley complex are in structural contact along the Coast Range Fault, a regional-scale structure and fundamental crustal boundary.
The Franciscan Complex and the Great Valley complex are superposed by active, northwest-southeast-striking strike-slip faults that are associated with seismicity swarms. These active strike-slip faults can produce moderate to large earthquakes that have moment magnitudes of 7–8. In places, these active structures bound large ranges and valleys, suggesting that much of the modern topographic expression is the result of active deformation processes.
This report contains new 1:24,000-scale geologic mapping along the southern part of the Bartlett Springs Fault Zone between Clear Lake and Lake Berryessa. The map area spans 738 square kilometers in northern Napa County, southern Lake County, and parts of Yolo and Colusa Counties. The south and east borders of the map are 90 kilometers north of San Francisco and 70 kilometers west of Sacramento, respectively. The map area is within the Knoxville mining district, which has a history of mercury and gold mining dating back to the mid-19th century. The two main towns in the region, Lower Lake and Clearlake, California, are west-northwest of the map area. Approximately 71,000 people live in the cities and rural communities located within a 40-kilometer radius of the center of the map area.
The bedrock geology, cross sections, and structural data presented herein are critical for evaluating the long-term evolution of the Bartlett Springs Fault Zone. This work will supplement studies on local seismic hazards, liquefaction potential, landslide hazards, earthquake geology, natural resources, groundwater resources, engineering geology, and tectonic history by providing the background information for site-specific investigations on these subjects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3514","usgsCitation":"Melosh, B.L., Bodtker, J.W., and Valin, Z.C., 2024, Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey Scientific Investigations Map 3514, 2 sheets, scale 1:24,000, 20 p. pamphlet, https://doi.org/10.3133/sim3514.","productDescription":"Pamphlet: vi, 20 p.; 2 Sheets: 46.15 x 78.86 inches and 58.26 x 41.78 inches; Data Release","numberOfPages":"20","additionalOnlineFiles":"Y","ipdsId":"IP-128914","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494218,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118060.htm","linkFileType":{"id":5,"text":"html"}},{"id":465095,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YJRCZD","text":"USGS Data Release","description":"Melosh, B.L., Bodtker, J.W., Valin, Z.C., and Sullivan, K., 2024, Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey data release, https://doi.org/10.5066/P1YJRCZD.","linkHelpText":"Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California"},{"id":465094,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3514/covrthb.jpg"},{"id":465093,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet2.pdf","text":"Sheet 2","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465092,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet1.pdf","text":"Sheet 1","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465091,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_pamphlet.pdf","text":"Pamphlet","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Northern Coast Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5457,\n 39.0012\n ],\n [\n -122.5457,\n 38.6099\n ],\n [\n -122.2368,\n 38.6099\n ],\n [\n -122.2368,\n 39.0012\n ],\n [\n -122.5457,\n 39.0012\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
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Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (Chelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70136","usgsCitation":"Cullen, J., Domit, C.A., Lamont, M., Marshall, C., Santos, A.J., Sasso, C.R., Al Ansi, M., Hart, K., and Fuentes, M.M., 2024, A comparative framework to develop transferable species distribution models for animal telemetry data: Ecosphere, v. 15, no. 12, e70136, 20 p., https://doi.org/10.1002/ecs2.70136.","productDescription":"e70136, 20 p.","ipdsId":"IP-155304","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70136","text":"Publisher Index Page"},{"id":486722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, Qatar, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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