{"pageNumber":"105","pageRowStart":"2600","pageSize":"25","recordCount":40783,"records":[{"id":70253914,"text":"70253914 - 2023 - Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities","interactions":[],"lastModifiedDate":"2024-05-03T15:46:15.033278","indexId":"70253914","displayToPublicDate":"2023-10-20T10:39:40","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities","docAbstract":"

We present the validation methodology and results of Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS). The DSWx-HLS product is the first of the DSWx suite, comprised of products each which map water from Earth Observation optical and SAR satellites. We detail the generation of high-resolution (3 m) validation datasets from a globally-stratified sample of dry, moderate, and wet sites. We provide the precise accounting of the classification metrics used to verify the Observational Products for End-users from Remote Sensing Analysis (OPERA) project requirements. We also report broader classification metrics across the validation datasets considered. OPERA performs validation in the public domain to ensure that the validation activities are transparent and reproducible. The resulting validation datasets and provisional OPERA products are publicly available; the software used for validation is also open-source.

","conferenceTitle":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 16-21, 2023","conferenceLocation":"Pasadena, CA","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS52108.2023.10283397","usgsCitation":"Arena, N., Bato, G., Bekaert, D., Bonnema, M., Chan, S., Chapman, B., Jones, J., Handwerger, A., Lewandowski, A., Marshak, C., Sangha, S., and Venkataramani, K., 2023, Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, July 16-21, 2023, p. 2723-2726, https://doi.org/10.1109/IGARSS52108.2023.10283397.","productDescription":"4 p.","startPage":"2723","endPage":"2726","ipdsId":"IP-153954","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arena, Nicholas","contributorId":336167,"corporation":false,"usgs":false,"family":"Arena","given":"Nicholas","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bato, Grace","contributorId":336168,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekaert, David","contributorId":336169,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew","contributorId":336170,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chan, Steven","contributorId":336171,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Bruce","contributorId":336172,"corporation":false,"usgs":false,"family":"Chapman","given":"Bruce","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":900093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handwerger, Alexander L.","contributorId":336174,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander L.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900094,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lewandowski, Alex","contributorId":336176,"corporation":false,"usgs":false,"family":"Lewandowski","given":"Alex","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900095,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marshak, Charlie","contributorId":336178,"corporation":false,"usgs":false,"family":"Marshak","given":"Charlie","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900096,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sangha, Simran","contributorId":336183,"corporation":false,"usgs":false,"family":"Sangha","given":"Simran","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900097,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Venkataramani, Karthik","contributorId":336185,"corporation":false,"usgs":false,"family":"Venkataramani","given":"Karthik","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900098,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70256488,"text":"70256488 - 2023 - Change-point models for identifying behavioral transitions in wild animals","interactions":[],"lastModifiedDate":"2024-08-07T15:40:00.947336","indexId":"70256488","displayToPublicDate":"2023-10-20T10:36:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Change-point models for identifying behavioral transitions in wild animals","docAbstract":"

Animal behavior can be difficult, time-consuming, and costly to observe in the field directly. Innovative modeling methods, such as hidden Markov models (HMMs), allow researchers to infer unobserved animal behaviors from movement data, and implementations often assume that transitions between states occur multiple times. However, some behavioral shifts of interest, such as parturition, migration initiation, and juvenile dispersal, may only occur once during an observation period, and HMMs may not be the best approach to identify these changes. We present two change-point models for identifying single transitions in movement behavior: a location-based change-point model and a movement metric-based change-point model. We first conducted a simulation study to determine the ability of these models to detect a behavioral transition given different amounts of data and the degree of behavioral shifts. We then applied our models to two ungulate species in central Pennsylvania that were fitted with global positioning system collars and vaginal implant transmitters to test hypotheses related to parturition behavior. We fit these models in a Bayesian framework and directly compared the ability of each model to describe the parturition behavior across species. Our simulation study demonstrated that successful change point estimation using either model was possible given at least 12 h of post-change observations and 15 min fix interval. However, our models received mixed support among deer and elk in Pennsylvania due to behavioral variation between species and among individuals. Our results demonstrate that when the behavior follows the dynamics proposed by the two models, researchers can identify the timing of a behavioral change. Although we refer to detecting parturition events, our results can be applied to any behavior that results in a single change in time.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-023-00430-0","usgsCitation":"Gundermann, K., Diefenbach, D.R., Walter, W., Corondi, A., Banfield, J., Wallingford, B., Stainbrook, D., Rosenberry, C., and Buderman, F., 2023, Change-point models for identifying behavioral transitions in wild animals: Movement Ecology, v. 11, 65, 15 p., https://doi.org/10.1186/s40462-023-00430-0.","productDescription":"65, 15 p.","ipdsId":"IP-149981","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441819,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-023-00430-0","text":"Publisher Index Page"},{"id":432342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gundermann, K.P.","contributorId":340853,"corporation":false,"usgs":false,"family":"Gundermann","given":"K.P.","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":907608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corondi, A","contributorId":340854,"corporation":false,"usgs":false,"family":"Corondi","given":"A","email":"","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":907611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Banfield, J.E.","contributorId":340856,"corporation":false,"usgs":false,"family":"Banfield","given":"J.E.","email":"","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":907612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallingford, B.D.","contributorId":340857,"corporation":false,"usgs":false,"family":"Wallingford","given":"B.D.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":907613,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stainbrook, D.P.","contributorId":340859,"corporation":false,"usgs":false,"family":"Stainbrook","given":"D.P.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":907614,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosenberry, C.S.","contributorId":340861,"corporation":false,"usgs":false,"family":"Rosenberry","given":"C.S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":907615,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buderman, F.E.","contributorId":340863,"corporation":false,"usgs":false,"family":"Buderman","given":"F.E.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":907616,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259714,"text":"70259714 - 2023 - An integrated framework for examining groundwater vulnerability in the Mekong River Delta region","interactions":[],"lastModifiedDate":"2024-10-19T13:06:25.936468","indexId":"70259714","displayToPublicDate":"2023-10-20T08:04:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"An integrated framework for examining groundwater vulnerability in the Mekong River Delta region","docAbstract":"

The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.

","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0292991","usgsCitation":"Powlen, K., Haider, S., Davis, K., Burkardt, N., Shah, S.D., Romanach, S., and Andersen, M.E., 2023, An integrated framework for examining groundwater vulnerability in the Mekong River Delta region: PLoS ONE, v. 10, no. 18, e0292991, 23 p., https://doi.org/10.1371/journal.pone.0292991.","productDescription":"e0292991, 23 p.","ipdsId":"IP-141532","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":467085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1371/journal.pone.0292991","text":"Publisher Index Page"},{"id":463038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cambodia, Vietnam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 102.82615287028528,\n 13.329686255248149\n ],\n [\n 102.82615287028528,\n 8.82420550041465\n ],\n [\n 107.28660208903472,\n 8.82420550041465\n ],\n [\n 107.28660208903472,\n 13.329686255248149\n ],\n [\n 102.82615287028528,\n 13.329686255248149\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"18","noUsgsAuthors":false,"publicationDate":"2023-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Powlen, Kathryn 0000-0002-9685-0063","orcid":"https://orcid.org/0000-0002-9685-0063","contributorId":328833,"corporation":false,"usgs":true,"family":"Powlen","given":"Kathryn","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haider, Saira M. 0000-0001-9306-3454","orcid":"https://orcid.org/0000-0001-9306-3454","contributorId":206253,"corporation":false,"usgs":true,"family":"Haider","given":"Saira","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":916415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Kyle W. 0000-0002-8723-0110","orcid":"https://orcid.org/0000-0002-8723-0110","contributorId":201549,"corporation":false,"usgs":true,"family":"Davis","given":"Kyle W.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkardt, Nina 0000-0002-9392-9251 burkardtn@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-9251","contributorId":2781,"corporation":false,"usgs":true,"family":"Burkardt","given":"Nina","email":"burkardtn@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":916417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shah, Sachin D. 0000-0002-5440-5535 sdshah@usgs.gov","orcid":"https://orcid.org/0000-0002-5440-5535","contributorId":194450,"corporation":false,"usgs":true,"family":"Shah","given":"Sachin","email":"sdshah@usgs.gov","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":916419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Andersen, Matthew E. 0000-0003-4115-5028 mandersen@usgs.gov","orcid":"https://orcid.org/0000-0003-4115-5028","contributorId":3190,"corporation":false,"usgs":true,"family":"Andersen","given":"Matthew","email":"mandersen@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":916420,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249633,"text":"70249633 - 2023 - Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island","interactions":[],"lastModifiedDate":"2023-10-20T12:15:39.718843","indexId":"70249633","displayToPublicDate":"2023-10-20T06:59:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2990,"text":"Pacific Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island","title":"Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island","docAbstract":"

The study of nocturnally active bats is difficult even for those species that seasonally congregate. This challenge is particularly acute for ‘ōpe‘ape‘a (Hawaiian hoary bat; Lasiurus semotus) because of its solitary foliage-roosting behavior. Yet surveys are essential for conservation and management of this endangered species and only land mammal endemic to the Hawaiian Islands. We surveyed for ‘ōpe‘ape‘a at 23 sites and a range of elevations (33–2,341 m) on Hawai‘i Island from May 2018 to August 2021. We captured 138 unique bats (37 female, 101 male) over 224 mist-netting events. We averaged 16 net-hours per bat capture, with peak captures 30–90 min after sunset. We marked all captured individuals in this study with identifying forearm bands and recaptures represented 7% of total captures (10 of 148). We developed generalized linear mixed models to examine the relationship of nightly bat captures by sex to elevation and time-of-year while accounting for variable sampling effort and repeated sampling in this study. Both males and females were captured at low and high elevations with peak capture rates occurring at approximately 930 m. The capture rate for females was highest during the reproductive season (May to September), whereas it was highest for males during the non-reproductive season (October to April). This study informs future fieldwork with a description of ‘ōpe‘ape‘a capture on Hawai‘i Island by sex, elevation, time-of-year and time-of-night, radio transmitter retention, and recapture frequency.

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Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

","language":"English","publisher":"Springer Nature","doi":"10.1038/s43247-023-01009-8","usgsCitation":"Massoud, E., Lee, H., Terando, A., and Wehner, M., 2023, Bayesian weighting of climate models based on climate sensitivity: Communications Earth & Environment, v. 4, 365, 8 p., https://doi.org/10.1038/s43247-023-01009-8.","productDescription":"365, 8 p.","ipdsId":"IP-148193","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":441830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-023-01009-8","text":"Publisher Index Page"},{"id":422127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2023-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Massoud, Elias","contributorId":331182,"corporation":false,"usgs":false,"family":"Massoud","given":"Elias","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":886878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Huikyo","contributorId":331183,"corporation":false,"usgs":false,"family":"Lee","given":"Huikyo","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":886879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":886880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wehner, Michael","contributorId":195292,"corporation":false,"usgs":false,"family":"Wehner","given":"Michael","email":"","affiliations":[],"preferred":false,"id":886881,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254447,"text":"70254447 - 2023 - Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations","interactions":[],"lastModifiedDate":"2024-05-24T11:53:28.457658","indexId":"70254447","displayToPublicDate":"2023-10-20T06:50:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17783,"text":"Nature, Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations","docAbstract":"

In 2016, the National Oceanic and Atmospheric Administration deployed the first iteration of an operational National Water Model (NWM) to forecast the water cycle in the continental United States. With many versions, an hourly, multi-decadal historic simulation is made available to the public. In all released to date, the files containing simulated streamflow contain a snapshot of model conditions across the entire domain for a single timestep which makes accessing  time series a technical and resource-intensive challenge. In the most recent release, extracting a complete streamflow time series for a single location requires managing 367,920 files (~16.2 TB). In this work we describe a reproducible process for restructuring a sequential set of NWM steamflow files for efficient time series access and provide restructured datasets for versions 1.2 (1993–2018), 2.0 (1993–2020), and 2.1 (1979–2022). These datasets have been made accessible via an OPeNDAP enabled THREDDS data server for public use and a brief analysis highlights the latest version of the model should not be assumed best for all locations. Lastly we describe an R package that expedites data retrieval with examples for multiple use-cases.

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Michael","contributorId":336915,"corporation":false,"usgs":false,"family":"Johnson","given":"J.","email":"","middleInitial":"Michael","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":901387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":901388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clarke, Keith C.","contributorId":336916,"corporation":false,"usgs":false,"family":"Clarke","given":"Keith C.","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":901389,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pollak, Jon","contributorId":336918,"corporation":false,"usgs":false,"family":"Pollak","given":"Jon","email":"","affiliations":[{"id":80911,"text":"CUAHSI","active":true,"usgs":false}],"preferred":false,"id":901390,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263958,"text":"70263958 - 2023 - Regional crustal structure of Indonesia from receiver functions","interactions":[],"lastModifiedDate":"2025-03-04T14:09:43.092705","indexId":"70263958","displayToPublicDate":"2023-10-20T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Regional crustal structure of Indonesia from receiver functions","docAbstract":"

Characterizing the crustal structure of Indonesia is important to gain a better understanding of its geodynamic evolution and improve seismic hazard assessments in the area. However, a unified crustal model of the entire Indonesian region and its surroundings is lacking. We present new maps of crustal thickness and bulk Vp/Vs ratio in Indonesia and the surrounding area that are obtained using P-wave receiver functions at 36 seismic stations from several permanent regional networks. The measured crustal thickness varies from ∼24 km to ∼38 km. The thickest crust, ∼38 km, is beneath Flores Island, southern Maluku, and neighboring northernmost Australia, whereas the thinnest crust, ∼24 km, is found under eastern Malaysia. Thus, crustal thickness varies by ∼14 km (from ∼24 km to ∼38 km) despite the small changes in elevation at the measurement points. The Vp/Vs ratios are 1.79±\">±0.11, with high values (>1.85) found along the Banda-Sunda arc-trench system. We attribute these high values to: (1) the presence of mafic island arc and oceanic crust and (2) partial melting within this volcanic region, which causes a larger decrease in S-wave velocities compared with P-wave velocities. The comparison of the seismic properties of Indonesian island arc crust, particularly the Vp/Vs ratio, with laboratory measurements and the petrology of the exhumed Talkeetna island arc, Alaska, allows us to infer the crustal composition of Indonesian island arc crust.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2023.230033","usgsCitation":"Zhang, Y., and Mooney, W.D., 2023, Regional crustal structure of Indonesia from receiver functions: Tectonophysics, v. 865, 230033, 12 p., https://doi.org/10.1016/j.tecto.2023.230033.","productDescription":"230033, 12 p.","ipdsId":"IP-151963","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489974,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2023.230033","text":"Publisher Index 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Ying","contributorId":351735,"corporation":false,"usgs":false,"family":"Zhang","given":"Ying","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":929348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mooney, Walter D. 0000-0002-5310-3631 mooney@usgs.gov","orcid":"https://orcid.org/0000-0002-5310-3631","contributorId":3194,"corporation":false,"usgs":true,"family":"Mooney","given":"Walter","email":"mooney@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929349,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250885,"text":"70250885 - 2023 - Comparing methods to estimate feral burro abundance","interactions":[],"lastModifiedDate":"2024-01-10T16:06:43.747101","indexId":"70250885","displayToPublicDate":"2023-10-19T09:56:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Comparing methods to estimate feral burro abundance","docAbstract":"

Obtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter-based, simultaneous double-observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio-collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark-recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter-based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio-collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model-generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio-collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark-recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR-based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio-collars and mark-recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1495","usgsCitation":"Hennig, J., and Schoenecker, K., 2023, Comparing methods to estimate feral burro abundance: Wildlife Society Bulletin, v. 47, no. 4, e1495, 15 p., https://doi.org/10.1002/wsb.1495.","productDescription":"e1495, 15 p.","ipdsId":"IP-143673","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":441836,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1495","text":"Publisher Index Page"},{"id":435146,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZS081M","text":"USGS data release","linkHelpText":"Feral burro detections from aerial infrared surveys collected in Sinbad Herd Management Area, Utah, USA, from 2015-2016"},{"id":424281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Sinbad Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.64724760767687,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.90843748458232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hennig, Jacob D.","contributorId":333094,"corporation":false,"usgs":false,"family":"Hennig","given":"Jacob D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":891913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891914,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249642,"text":"70249642 - 2023 - Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","interactions":[],"lastModifiedDate":"2023-10-21T13:43:50.552769","indexId":"70249642","displayToPublicDate":"2023-10-19T08:39:55","publicationYear":"2023","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":"Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","docAbstract":"

Functional trait approaches are common in ecology, but a lack of clear hypotheses on how traits relate to environmental gradients (i.e., trait–niche relationships) often makes uncovering mechanisms difficult. Furthermore, measures of community functional structure differ in their implications, yet inferences are seldom compared among metrics. Community-weighted mean trait values (CWMs), a common measure, are largely driven by the most common species and thus do not reflect community-wide trait–niche relationships per se. Alternatively, trait–niche relationships can be estimated across a larger group of species using hierarchical joint species distribution models (JSDMs), quantified by a parameter Γ. We investigated how inferences about trait–niche relationships are affected by the choice of metric. Using deadwood-dependent (saproxylic) beetles in fragmented Finnish forests, we followed a protocol for investigating trait–niche relationships by (1) identifying environmental filters (climate, forest age, and deadwood volume), (2) relating these to an ecological function (dispersal ability), and (3) identifying traits related to this function (wing morphology). We tested 18 hypothesized dispersal relationships using both CWM and Γ estimates across these environmental gradients. CWMs were more likely than Γ to show support for trait–niche relationships. Up to 13% of species' realized niches were explained by dispersal traits, but the directions of effects were consistent with fewer than 11%–39% of our 18 trait–niche hypotheses (depending on the metric used). This highlights the difficulty in connecting morphological traits and ecological functions in insects, despite the clear conceptual link between landscape connectivity and flight-related traits. Caution is thus warranted in hypothesis development, particularly where apparent trait–function links are less clear. Inferences differ when CWMs versus Γ estimates are used, necessitating the choice of a metric that reflects study questions. CWMs help explain the effects of environmental gradients on community trait composition, whereas the effects of traits on species' niches are better estimated using hierarchical JSDMs.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10588","usgsCitation":"Burner, R.C., Stephan, J., Drag, L., Potterf, M., Birkemoe, T., Siitonen, J., Müller, J., Ovaskainen, O., Sverdrup-Thygeson, A., and Snäll, T., 2023, Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles: Ecology and Evolution, v. 13, no. 10, e10588, 14 p., https://doi.org/10.1002/ece3.10588.","productDescription":"e10588, 14 p.","ipdsId":"IP-151508","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":441838,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10588","text":"Publisher Index Page"},{"id":422033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Finland","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[28.59193,69.06478],[28.44594,68.36461],[29.97743,67.6983],[29.05459,66.94429],[30.21765,65.80598],[29.54443,64.94867],[30.44468,64.20445],[30.03587,63.55281],[31.51609,62.86769],[31.13999,62.35769],[30.21111,61.78003],[28.07,60.50352],[26.25517,60.42396],[24.49662,60.05732],[22.86969,59.84637],[22.29076,60.39192],[21.32224,60.72017],[21.54487,61.70533],[21.05921,62.60739],[21.53603,63.18974],[22.44274,63.81781],[24.73051,64.90234],[25.39807,65.11143],[25.29404,65.53435],[23.90338,66.00693],[23.56588,66.39605],[23.53947,67.93601],[21.97853,68.61685],[20.64559,69.10625],[21.24494,69.37044],[22.35624,68.84174],[23.66205,68.89125],[24.73568,68.64956],[25.68921,69.09211],[26.17962,69.8253],[27.73229,70.16419],[29.01557,69.76649],[28.59193,69.06478]]]},\"properties\":{\"name\":\"Finland\"}}]}","volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Burner, Ryan C. 0000-0002-7314-9506","orcid":"https://orcid.org/0000-0002-7314-9506","contributorId":304152,"corporation":false,"usgs":true,"family":"Burner","given":"Ryan","email":"","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":886564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephan, Jörg G.","contributorId":331036,"corporation":false,"usgs":false,"family":"Stephan","given":"Jörg G.","affiliations":[{"id":79096,"text":"SLU Swedish Species Information Centre, Swedish University of Agricultural Sciences, Uppsala, Sweden","active":true,"usgs":false}],"preferred":false,"id":886565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drag, Lukas 0000-0002-6002-9214","orcid":"https://orcid.org/0000-0002-6002-9214","contributorId":304151,"corporation":false,"usgs":false,"family":"Drag","given":"Lukas","email":"","affiliations":[{"id":65984,"text":"University of Würzburg","active":true,"usgs":false}],"preferred":false,"id":886566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Potterf, Mária","contributorId":331037,"corporation":false,"usgs":false,"family":"Potterf","given":"Mária","affiliations":[{"id":79097,"text":"Department of Life Science Systems, Technical University of Munich, Freising, Bavaria, Germany","active":true,"usgs":false}],"preferred":false,"id":886567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birkemoe, Tone","contributorId":304154,"corporation":false,"usgs":false,"family":"Birkemoe","given":"Tone","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":886568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siitonen, Juha","contributorId":331038,"corporation":false,"usgs":false,"family":"Siitonen","given":"Juha","email":"","affiliations":[{"id":29898,"text":"Natural Resources Institute Finland (Luke), Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":886569,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Müller, Jörg","contributorId":331039,"corporation":false,"usgs":false,"family":"Müller","given":"Jörg","affiliations":[{"id":79099,"text":"Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Rauhenebrach, Germany","active":true,"usgs":false}],"preferred":false,"id":886570,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ovaskainen, Otso 0000-0001-9750-4421","orcid":"https://orcid.org/0000-0001-9750-4421","contributorId":304157,"corporation":false,"usgs":false,"family":"Ovaskainen","given":"Otso","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":886571,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sverdrup-Thygeson, Anne 0000-0002-3122-2250","orcid":"https://orcid.org/0000-0002-3122-2250","contributorId":304161,"corporation":false,"usgs":false,"family":"Sverdrup-Thygeson","given":"Anne","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":886572,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Snäll, Tord","contributorId":331040,"corporation":false,"usgs":false,"family":"Snäll","given":"Tord","affiliations":[{"id":79096,"text":"SLU Swedish Species Information Centre, Swedish University of Agricultural Sciences, Uppsala, Sweden","active":true,"usgs":false}],"preferred":false,"id":886573,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259657,"text":"70259657 - 2023 - Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i","interactions":[],"lastModifiedDate":"2024-10-19T13:24:47.508554","indexId":"70259657","displayToPublicDate":"2023-10-19T08:23:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i","docAbstract":"

Sustained-yield hunting of introduced ungulates in the Hawaiian Islands often conflicts with the conservation of native species, but there is little reliable data to guide effective management. European mouflon sheep (Ovis musimon; mouflon) and axis deer (Axis axis; deer) were introduced on the island of Lāna‘i to provide additional hunting opportunities. Managers will require better information regarding the ecological associations of introduced ungulate species, relative to the habitats occupied, to resolve longstanding conflicts between native species conservation and sustained-yield hunting on islands. To address this information need, we modeled sheep and deer ecological associations, habitat-use, and suitability using data obtained from an intensive aerial survey completed in 2013 and temporally matching environmental data. In habitat suitability models evaluated by Receiver Operating Characteristic (ROC) metrics, predictor importance in a generalized linear model (GLM) of deer decreased in the following order: afternoon cloud cover, topographic slope, mean annual precipitation (MAP), elevation, normalized difference vegetation index (NDVI), and bare soil index. In a random GLM model of mouflon, predictor importance decreased in the following order: afternoon cloud cover, deer habitat suitability, NDVI, bare soil index, topographic slope, elevation, and MAP. Mouflon were restricted to lower elevation arid slopes, whereas deer were more broadly distributed throughout upland environments of the island. The presence of deer was also an important predictor for mouflon distribution, although mouflon was not an important predictor of deer, suggesting asymmetrical competition. Removal of the more abundant deer population may lead to an increase in abundance and distribution of mouflon without containment. This work represents the first habitat suitability analysis for all nonnative ungulates on any entire Hawaiian island. Our results are applicable to other islands where conflicts may arise with introduced ungulates, sustained-yield hunting, and native species conservation.


","language":"English","publisher":"Berryman Insititute","doi":"10.26077/be03-b519","usgsCitation":"Hess, S.C., Brinck, K., Leopold, C.R., Muise, J., and Sprague, J., 2023, Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i: Human-Wildlife Interactions, v. 17, no. 2, 14 p., https://doi.org/10.26077/be03-b519.","productDescription":"14 p.","ipdsId":"IP-134608","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":463043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Lāna‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.25852174158157,\n 21.072831141997597\n ],\n [\n -157.25852174158157,\n 20.56423494187817\n ],\n [\n -156.5879617183619,\n 20.56423494187817\n ],\n [\n -156.5879617183619,\n 21.072831141997597\n ],\n [\n -157.25852174158157,\n 21.072831141997597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hess, Steven C.","contributorId":176679,"corporation":false,"usgs":false,"family":"Hess","given":"Steven","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":916161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":916162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Christina R","contributorId":345275,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","email":"","middleInitial":"R","affiliations":[{"id":82536,"text":"0000-0003-0499-3196","active":true,"usgs":false}],"preferred":false,"id":916163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muise, Jacob","contributorId":240997,"corporation":false,"usgs":false,"family":"Muise","given":"Jacob","email":"","affiliations":[{"id":48185,"text":"KIA Hawaii","active":true,"usgs":false}],"preferred":false,"id":916164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprague, Jonathan","contributorId":240998,"corporation":false,"usgs":false,"family":"Sprague","given":"Jonathan","email":"","affiliations":[{"id":48186,"text":"Pulama Lana‘i","active":true,"usgs":false}],"preferred":false,"id":916165,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241162,"text":"70241162 - 2023 - Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","interactions":[],"lastModifiedDate":"2023-11-08T11:48:36.011532","indexId":"70241162","displayToPublicDate":"2023-10-18T11:44:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","docAbstract":"

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-23-3261-2023","usgsCitation":"Patton, A., Luna, L., Roering, J.J., Jacobs, A., Korup, O., and Mirus, B., 2023, Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA: Natural Hazards and Earth System Sciences, v. 23, no. 10, p. 3261-3284, https://doi.org/10.5194/nhess-23-3261-2023.","productDescription":"24 p.","startPage":"3261","endPage":"3284","ipdsId":"IP-148647","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":441845,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-23-3261-2023","text":"Publisher Index Page"},{"id":422429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Sitka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.5083890264902,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 57.18995904083906\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Annette","contributorId":303028,"corporation":false,"usgs":false,"family":"Patton","given":"Annette","email":"","affiliations":[{"id":65615,"text":"Sitka Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":866314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luna, Lisa","contributorId":303029,"corporation":false,"usgs":false,"family":"Luna","given":"Lisa","email":"","affiliations":[{"id":52955,"text":"University of Potsdam","active":true,"usgs":false}],"preferred":false,"id":866315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roering, Josh J.","contributorId":303030,"corporation":false,"usgs":false,"family":"Roering","given":"Josh","email":"","middleInitial":"J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":866316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":866317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":866318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866319,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249600,"text":"70249600 - 2023 - Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019","interactions":[],"lastModifiedDate":"2023-10-20T13:19:06.53518","indexId":"70249600","displayToPublicDate":"2023-10-18T09:23:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019","docAbstract":"

Wet deposition monitoring is a critical part of the long-term monitoring of acid deposition, which aims to assess the ecological impact of anthropogenic emissions of SO2 and NOx. In North America, long-term wet deposition has been monitored through two national networks: the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program (NADP), for Canada and the USA, respectively. In order to assess the comparability of measurements from the two networks, collocated measurements have been made at two sites, one in each country, since 1986 (Sirois et al., in Environmental Monitoring and Assessment, 62, 273–303, 2000; Wetherbee et al., in Environmental Monitoring and Assessment, 1995–2004, 2010). In this study, we compared the measurements from NADP and CAPMoN instrumentation at the collocated sites at the Pennsylvania State University (Penn State), USA, from 1989 to 2016, and Frelighsburg, Quebec, Canada, from 2002 to 2019. We also included in the study the collocated daily-vs-weekly measurements by the CAPMoN network during 1999–2001 and 2016–2017 in order to evaluate the differences in wet concentration of ions due to sampling frequency alone. The study serves as an extension to two previous CAPMoN-NADP inter-comparisons by Sirois et al. (Environmental Monitoring and Assessment, 62, 273–303, 2000) and Wetherbee et al., in (Environmental Monitoring and Assessment, 1995–2004, 2010). At the Penn State University site, for 1986–2019, CAPMoN was higher than NADP for all ions, in terms of weekly concentration, precipitation-weighted annual mean concentration, and annual wet deposition. The precipitation-weighted annual mean concentrations were higher for SO42− (2%), NO3− (12%), NH4+ (16%), H+ (6%), and base cations and Cl− (11–15%). For annual wet deposition, CAPMoN was higher for SO4−2, NO3−, NH4+ and H+ (5–17%), and base cations and Cl− (12–17%) during 1986–2019. At the Frelighsburg site, NADP changed the sample collector in October 2011. For 2002–2011, the relative differences at the Frelighsburg site were positive and similar in magnitude to those at the Penn State site. For 2012–2019, the precipitation-weighted annual mean concentrations were 5–27% lower than NADP, except for H+, which was 23% higher. The change in sample collector by NADP had the largest effect on between-network biases. The comparisons of daily-vs-weekly measurements conducted by the CAPMoN network during 1999–2001 and 2016–2017 show that the weekly measurements were higher than the daily measurements by 1–3% for SO42−, NO3−, and NH4+; 3–9% for Ca2+, Mg2+, Na+, and Cl−; 10–24% for K+; and lower for H+ by 8–30% in terms of precipitation-weighted mean concentration. Thus, differences in sampling frequencies did not contribute to the systematically higher CAPMoN measurements. Understanding the biases in the data for these networks is important for interpretation of continental scale deposition models and transboundary comparison of wet deposition trends.

","language":"English","publisher":"Springer Link","doi":"10.1007/s10661-023-11771-z","usgsCitation":"Feng, J., Cole, A., Wetherbee, G.A., and Banwait, K., 2023, Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019: Environmental Monitoring and Assessment, v. 195, 1333, 34 p., https://doi.org/10.1007/s10661-023-11771-z.","productDescription":"1333, 34 p.","ipdsId":"IP-153496","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":441851,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-023-11771-z","text":"Publisher Index Page"},{"id":422000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"195","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Feng, Jian","contributorId":330980,"corporation":false,"usgs":false,"family":"Feng","given":"Jian","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, Amanda","contributorId":330981,"corporation":false,"usgs":false,"family":"Cole","given":"Amanda","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":215100,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":886402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banwait, Kulbir","contributorId":330982,"corporation":false,"usgs":false,"family":"Banwait","given":"Kulbir","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252214,"text":"70252214 - 2023 - Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","interactions":[],"lastModifiedDate":"2024-03-20T11:53:06.811606","indexId":"70252214","displayToPublicDate":"2023-10-18T06:50:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","docAbstract":"

Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ad04b8","usgsCitation":"Piliouras, A., Jones, B.M., Clevenger, T., Gibbs, A.E., and Rowland, J.C., 2023, Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast: Environmental Research Letters, v. 18, 114050, 10 p., https://doi.org/10.1088/1748-9326/ad04b8.","productDescription":"114050, 10 p.","ipdsId":"IP-141537","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441857,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad04b8","text":"Publisher Index Page"},{"id":426794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.82972953158225,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 72.33792024202972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2023-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Piliouras, Anastasia","contributorId":334927,"corporation":false,"usgs":false,"family":"Piliouras","given":"Anastasia","email":"","affiliations":[{"id":80287,"text":"Department of Geosciences, Pennsylvania State University, University Park, PA","active":true,"usgs":false}],"preferred":false,"id":896945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M.","contributorId":305542,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":896946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clevenger, Tabatha","contributorId":334928,"corporation":false,"usgs":false,"family":"Clevenger","given":"Tabatha","email":"","affiliations":[{"id":80288,"text":"Department of Earth Science and Geography, Vassar College, Poughkeepsie, NY","active":true,"usgs":false}],"preferred":false,"id":896947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowland, Joel C.","contributorId":169046,"corporation":false,"usgs":false,"family":"Rowland","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":896949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257232,"text":"70257232 - 2023 - Advances in wildlife abundance estimation using pedigree reconstruction","interactions":[],"lastModifiedDate":"2024-08-14T11:42:08.512608","indexId":"70257232","displayToPublicDate":"2023-10-18T06:38:48","publicationYear":"2023","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":"Advances in wildlife abundance estimation using pedigree reconstruction","docAbstract":"

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose (Alces americanus) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10650","usgsCitation":"Rosenblatt, E., Creel, S., Gieder, K., Murdoch, J., and Donovan, T.M., 2023, Advances in wildlife abundance estimation using pedigree reconstruction: Ecology and Evolution, v. 13, no. 10, e10650, 18 p., https://doi.org/10.1002/ece3.10650.","productDescription":"e10650, 18 p.","ipdsId":"IP-139722","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441859,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10650","text":"Publisher Index Page"},{"id":432645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":342124,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creel, Scott","contributorId":342128,"corporation":false,"usgs":false,"family":"Creel","given":"Scott","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":909738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gieder, Katherina","contributorId":342131,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":909739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James","contributorId":342134,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909740,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909741,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256170,"text":"70256170 - 2023 - The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.","interactions":[],"lastModifiedDate":"2024-07-26T00:13:41.320242","indexId":"70256170","displayToPublicDate":"2023-10-16T19:11:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7143,"text":"Geochemistry, Geophysics, and Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.","docAbstract":"

To determine the post-40 ka slip-rate along the Mojave section of the San Andreas Fault (MSAF) we re-analyze the sedimentary record preserved where Little Rock (LR) Creek flows across the fault. At this location, interaction between the northeast-flowing stream and right-lateral fault has resulted in the abandonment and preservation of 11 strath terraces and one paleo-floodplain in the downstream trailing corner of the river, two of which are also preserved upstream to provide cross-fault matches. A new model of fault-induced river deflection, together with standard terrace riser restoration, yields strike-slip displacements of 1,140 ± 160 m for the older terrace and 360 ± 70 m for the younger one. When combined with new 10Be dating and reinterpretation of prior measurements the displaced terraces yield right-lateral slip-rates of 27.7+6.9/−3.5 and 26.8+3.4/−3.0 mm/yr over the last 23 k.y. and last 40 k.y., where uncertainties are at 95% credible intervals. These new rate determinations are consistent with independent late Holocene estimates, indicating that the long-term rate of strain accumulation along the MSAF is relatively fast and does not vary significantly when averaged over timescales of 15–20 k.y. Using our new model of stream deflection, we find that the fluvial sequence was emplaced in two distinct periods, each characterized by a temporally stable but markedly different deflected river geometry. Each period coincides with a distinct stage of erosive power along LR Creek determined from independent paleoclimate proxies. Importantly, application of the new river-deflection model allows strike-slip displacements to be determined in the absence of upstream piercing points.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC010869","usgsCitation":"Moulin, A., Cowgill, E., Scharer, K., McPhillips, D., and Heimsath, A., 2023, The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.: Geochemistry, Geophysics, and Geosystems, v. 24, no. 10, e2023GC010869, 40 p., https://doi.org/10.1029/2023GC010869.","productDescription":"e2023GC010869, 40 p.","ipdsId":"IP-154087","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc010869","text":"Publisher Index Page"},{"id":431456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.8640320695366,\n 34.6946394144248\n ],\n [\n -117.8640320695366,\n 33.13569579634151\n ],\n [\n -115.44703988203676,\n 33.13569579634151\n ],\n [\n -115.44703988203676,\n 34.6946394144248\n ],\n [\n -117.8640320695366,\n 34.6946394144248\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Moulin, Adrien","contributorId":340360,"corporation":false,"usgs":false,"family":"Moulin","given":"Adrien","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":906968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowgill, Eric","contributorId":192850,"corporation":false,"usgs":false,"family":"Cowgill","given":"Eric","affiliations":[],"preferred":false,"id":906969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McPhillips, Devin 0000-0003-1987-9249","orcid":"https://orcid.org/0000-0003-1987-9249","contributorId":217362,"corporation":false,"usgs":true,"family":"McPhillips","given":"Devin","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heimsath, Arjun","contributorId":340361,"corporation":false,"usgs":false,"family":"Heimsath","given":"Arjun","email":"","affiliations":[{"id":12431,"text":"ASU","active":true,"usgs":false}],"preferred":false,"id":906972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252802,"text":"70252802 - 2023 - The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture","interactions":[],"lastModifiedDate":"2024-04-05T14:44:12.872874","indexId":"70252802","displayToPublicDate":"2023-10-16T09:42:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture","docAbstract":"

In the absence of documented surface rupture during the 1 September 1886 Charleston earthquake, there has been considerable speculation about the location and mechanism of the causative fault. We use an inferred coseismic offset of the South Carolina Railroad and additional numerical constraints to develop an elastic deformation model—a west‐dipping fault following strands of two previously identified faults. The constraints are consistent with a blind rupture with 6.5 ± 0.3 m of dextral slip and 2 ± 0.5 m of reverse slip below 450 m depth. We propose that repeated slip on this fault has raised the Penholoway Marine Terrace >6 m since ∼770 ka. The inferred coseismic slip on the fault in an Mw 7.3 earthquake is consistent with the distribution of damage in 1886.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230022","usgsCitation":"Bilham, R., and Hough, S.E., 2023, The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture: The Seismic Record, v. 3, no. 4, p. 278-288, https://doi.org/10.1785/0320230022.","productDescription":"11 p.","startPage":"278","endPage":"288","ipdsId":"IP-152926","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230022","text":"Publisher Index Page"},{"id":427514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Charleston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.24634165513044,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 33.088760020667124\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bilham, Roger","contributorId":225117,"corporation":false,"usgs":false,"family":"Bilham","given":"Roger","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":898271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":898272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256086,"text":"70256086 - 2023 - BatTool: Projecting bat populations facing multiple stressors using a demographic model","interactions":[],"lastModifiedDate":"2024-07-19T11:57:51.992521","indexId":"70256086","displayToPublicDate":"2023-10-16T06:55:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"BatTool: Projecting bat populations facing multiple stressors using a demographic model","docAbstract":"

Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface (https://rconnect.usgs.gov/battool) facilitating user-defined scenario analyses, and its intended uses and limitations (Wiens et al., US Geol Surv Data Release 2022; Wiens et al., US Geol Surv Softw Release 2022). We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.

","language":"English","publisher":"British Ecological Society","doi":"10.1186/s12862-023-02159-1","usgsCitation":"Wiens, A.M., Schorg, A., Szymanski, J., and Thogmartin, W.E., 2023, BatTool: Projecting bat populations facing multiple stressors using a demographic model: Methods in Ecology and Evolution, v. 23, 61, 16 p., https://doi.org/10.1186/s12862-023-02159-1.","productDescription":"61, 16 p.","ipdsId":"IP-132438","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":441872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12862-023-02159-1","text":"Publisher Index Page"},{"id":431237,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schorg, Amber","contributorId":333055,"corporation":false,"usgs":false,"family":"Schorg","given":"Amber","email":"","affiliations":[{"id":68344,"text":"U.S. Fish and Wildlife Service (USFWS)","active":true,"usgs":false}],"preferred":false,"id":906645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":906646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906647,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249590,"text":"70249590 - 2023 - Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","interactions":[],"lastModifiedDate":"2023-10-18T11:59:10.643012","indexId":"70249590","displayToPublicDate":"2023-10-16T06:55:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","docAbstract":"

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

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Vermont","active":true,"usgs":false}],"preferred":false,"id":886351,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70250400,"text":"70250400 - 2023 - Science to support conservation action in a large river system: The Willamette River, Oregon, USA","interactions":[],"lastModifiedDate":"2023-12-07T12:58:26.054885","indexId":"70250400","displayToPublicDate":"2023-10-14T06:52:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17103,"text":"Water Biology and Security","active":true,"publicationSubtype":{"id":10}},"title":"Science to support conservation action in a large river system: The Willamette River, Oregon, USA","docAbstract":"

Management and conservation efforts that support the recovery and protection of large rivers are daunting, reflecting the complexity of the challenge and extent of effort (in terms of policy, economic investment, and spatial extent) needed to afford measurable change. These large systems have generally experienced intensive development and regulation, compromising their capacity to respond to disturbances such as climate change or wildfire. Functionally, large river and basin management require insights gained from social, ecological, geophysical, and hydrological sciences. This multi-disciplinary perspective can unveil the integrated relationship between a river network's biotic community and seasonally variable environmental conditions that are often influenced by human activities. Large rivers and their basins are constantly changing due to anthropogenic influences and as climate modifies patterns of temperature and precipitation. Because of these factors, the state of knowledge must advance to address changing conditions. The Willamette River, in western Oregon, USA, is a prime example of a basin that has experienced significant degradation and investment in rehabilitation in recent decades. Innovative science has facilitated development of fine-scale, spatially extensive datasets and models that can generate targeted conservation and rehabilitation actions that are prioritized across the entire river network. This prioritization allows investment decisions to be driven by site-specific conditions while simultaneously considering potentials for ecological improvement. Here, we review hydrologic, geomorphic, ecologic, and social conditions in the Willamette River basin through time—including pre-settlement, river development, and contemporary periods—and offer a future vision for consideration. Currently, detailed information about fish populations and habitat, hydrologic conditions, geomorphology, water quality, and land use can be leveraged to make informed decisions about protection, rehabilitation, and development. The time is ripe for strategic management and goal development for the entire Willamette River, and these efforts can be informed by comprehensive science realized through established institutions (e.g., public agencies, non-profit watershed groups, Tribes, and universities) focused on conservation and management. The approaches to science and social-network creation that were pioneered in the Willamette River basin offer insights into the development of comprehensive conservation-based planning that could be implemented in other large river systems globally.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.watbs.2023.100203","usgsCitation":"Flitcroft, R.L., Whitman, L., White, J., Wallick, J., Stratton Garvin, L.E., Smith, C., Plotnikoff, R., Mulvey, M., Kock, T.J., Jones, K., Gruendike, P., Gombert, C., Giannico, G., Dutterer, A., Brown, D.G., Barrett, H., and Hughes, R.M., 2023, Science to support conservation action in a large river system: The Willamette River, Oregon, USA: Water Biology and Security, v. 2, no. 4, 100203, 16 p., https://doi.org/10.1016/j.watbs.2023.100203.","productDescription":"100203, 16 p.","ipdsId":"IP-148710","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":441883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watbs.2023.100203","text":"Publisher Index Page"},{"id":423291,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70885566657823,\n 46.467292298881915\n ],\n [\n -124.70885566657823,\n 43.55562581742163\n ],\n [\n -121.35802558845327,\n 43.55562581742163\n ],\n [\n -121.35802558845327,\n 46.467292298881915\n ],\n [\n -124.70885566657823,\n 46.467292298881915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flitcroft, Rebecca L. 0000-0003-3341-996X","orcid":"https://orcid.org/0000-0003-3341-996X","contributorId":172180,"corporation":false,"usgs":false,"family":"Flitcroft","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":889772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitman, Luke","contributorId":290613,"corporation":false,"usgs":false,"family":"Whitman","given":"Luke","email":"","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":889773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":889774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallick, J. 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2023 - Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams","interactions":[],"lastModifiedDate":"2023-11-22T16:06:40.787482","indexId":"70250123","displayToPublicDate":"2023-10-13T09:55:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams","docAbstract":"

Objective

In the Pacific Northwest (USA), Pacific salmon Oncorhynchus spp. populations have been declining significantly for decades, prompting stakeholders to respond with a variety of conservation and restoration measures. One such measure being considered in the Skagit River basin (Washington, USA) is the introduction of steelhead Oncorhynchus mykiss (anadromous Rainbow Trout) above the impassable Gorge, Diablo, and Ross dams to bolster their populations. Because freshwater growth is key to survival at subsequent life stages, we evaluated current trends in size and growth of Rainbow Trout among key tributaries to Gorge, Diablo, and Ross reservoirs using empirical data collection and bioenergetics modeling.

Methods

For nine candidate streams, a bioenergetics model was used to assess how temperature and prey consumption affected growth performance of Rainbow Trout between annuli 1 and 2, and 2 and 3. Thermal scenarios were created to evaluate how fish growth responded to temperature variability while total annual consumption was constrained within empirical growth estimates. We then compared these results to back-calculated size thresholds established by size-at-age observed in wild steelhead adults that returned to the Skagit River below the dams.

Result

Of the streams proposed for introductions, there was one instance (McMillan Creek) in the nominal simulations where growth met or exceeded the size at annulus 2 or 3 of a returning adult steelhead (24.9 g at annulus 2 and 50.3 g at annulus 3). Modeled growth under different thermal scenarios showed that colder temperatures (0.1–10.7°C, Canyon Creek) produced higher growth than under the nominal or warm scenarios (2.0–15.3°C, Canyon Creek), as well as one additional tributary where size at annulus 2 or 3 (±2 SE) was comparable to the threshold established by adult steelhead below the dams (Big Beaver Creek, annulus 3).

Conclusion

These results suggest Rainbow Trout growth is most limited by prey availability in the examined upper Skagit tributaries.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10944","usgsCitation":"Jensen, B.L., Johnson, R.C., Duda, J.J., Ostberg, C.O., Code, T.J., Mclean, J.H., Stenberg, K.D., Larsen, K., Hoy, M.S., and Beauchamp, D., 2023, Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams: North American Journal of Fisheries Management, v. 43, no. 5, p. 1427-1446, https://doi.org/10.1002/nafm.10944.","productDescription":"20 p.","startPage":"1427","endPage":"1446","ipdsId":"IP-147915","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":422838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Center","active":true,"usgs":true}],"preferred":true,"id":888482,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249676,"text":"70249676 - 2023 - An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau","interactions":[],"lastModifiedDate":"2023-10-24T13:40:35.956101","indexId":"70249676","displayToPublicDate":"2023-10-12T08:27:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau","docAbstract":"

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

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Liliang","contributorId":331073,"corporation":false,"usgs":false,"family":"Ren","given":"Liliang","email":"","affiliations":[],"preferred":false,"id":886701,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wang, Yanfen","contributorId":265955,"corporation":false,"usgs":false,"family":"Wang","given":"Yanfen","email":"","affiliations":[{"id":54838,"text":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China","active":true,"usgs":false}],"preferred":false,"id":886692,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70249500,"text":"ofr20231002 - 2023 - The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective","interactions":[],"lastModifiedDate":"2026-02-10T21:24:41.808706","indexId":"ofr20231002","displayToPublicDate":"2023-10-11T11:03:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered 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Rattlesnake Knoll is a small, 30-meter-high mound of igneous breccia in the center of Spring Valley, east-central Nevada. In the past, researchers have disagreed as to whether the unusual-looking outcrop is intrusive or volcanic. The breccia possesses a normal magnetic polarity, but this is not apparent in aeromagnetic survey data. These data instead show that the knoll lies within a small aeromagnetic low that partially overlaps the extent of a small gravity high. The small gravity anomaly associated with the knoll, combined with an initial, limited ground magnetic survey taken at the knoll, indicates that the knoll rocks extend northward in the subsurface. A second, more extensive ground magnetic traverse was also done north of the knoll. Taking into consideration these new survey data and preexisting data, a two and one-half dimensional modeling program based on Webring (1985) was used to produce a geophysical model that accounts for gravity and magnetic properties, satisfies available geologic information, and conforms to current estimates of basin thickness. This model and the field observations support the interpretation that the knoll consists of gently west-dipping beds of Tertiary volcanic flow breccia, mudflow breccia, and conglomerate.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231002","usgsCitation":"Mankinen, E.A., Rowley, P.D., and McKee, E.H., 2023, The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective: U.S. Geological Survey Open-File Report 2023–1002, 13 p., https://doi.org/10.3133/ofr20231002.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-133281","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":435149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WL97XY","text":"USGS data release","linkHelpText":"Ground magnetic data, Spring Valley, White Pine County, Nevada"},{"id":421859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1002/covrthb_.jpg"},{"id":421860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1002/ofr20231002.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499729,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115506.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","otherGeospatial":"Spring Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.36,\n 39.06\n ],\n [\n -114.36,\n 39.00\n ],\n [\n -114.24,\n 39.00\n ],\n [\n -114.24,\n 39.06\n ],\n [\n -114.36,\n 39.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-10-11","noUsgsAuthors":false,"publicationDate":"2023-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mankinen, Edward A. 0000-0001-7496-2681 emank@usgs.gov","orcid":"https://orcid.org/0000-0001-7496-2681","contributorId":1054,"corporation":false,"usgs":true,"family":"Mankinen","given":"Edward","email":"emank@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":885962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowley, Peter D.","contributorId":27435,"corporation":false,"usgs":true,"family":"Rowley","given":"Peter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":885963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKee, Edwin H. mckee@usgs.gov","contributorId":3728,"corporation":false,"usgs":true,"family":"McKee","given":"Edwin","email":"mckee@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":885964,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249499,"text":"ofr20231060 - 2023 - Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California","interactions":[],"lastModifiedDate":"2023-10-12T10:55:46.983978","indexId":"ofr20231060","displayToPublicDate":"2023-10-11T10:11:03","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1060","displayTitle":"Application of the Stream Salmonid Simulator (S3) Model to Assess Fall Chinook Salmon (Oncorhynchus tshawytscha) Production in the American River, California","title":"Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California","docAbstract":"

Executive Summary

Anadromous fish returning to the lower American River are restricted to 36 kilometers of free-flowing river between Nimbus Dam and American River’s confluence with the Sacramento River, California. Salmon in the American River provide an important freshwater recreational fishery. However, annual salmon production in the American River in recent years has been low relative to the mid-1990s (Surface Water Resources, Inc., 2001). To investigate the low production of fall-run Chinook salmon (Oncorhynchus tshawytscha), the Bureau of Reclamation requested that the U.S. Geological Survey apply the Stream Salmonid Simulator (S3) model to the population of fall-run Chinook salmon on the American River.

The American River was chosen among seven candidate Sacramento Basin rivers for S3 application. The American River was selected because of its management and public interest, recently low anadromous fish production, and rich time series of key demographic data needed for S3 application. Data that were not available, however, were empirical estimates on juvenile salmon habitat suitability in the American River. Therefore, a large component of applying S3 to the American River was devoted to the estimation of juvenile salmon habitat suitability and capacity. This entailed snorkeling the lower American River for 3 weeks in March 2021 during the early out-migration period for juvenile Chinook salmon. These efforts were fruitful and showed that the typically small fish (<55 millimeters) in the American River preferred much shallower depths than predicted by habitat suitability criteria derived from the literature for this population. Having empirical estimates on juvenile salmon in the American River provided a solid foundation from which to simulate the population using the S3 model.

The S3 model is a spatially explicit population model that runs on a daily time step to simulate redd superimposition, egg maturation, fry emergence and the subsequent growth, survival, and emigration of juvenile Chinook salmon from the river. The key features of this model relevant to this report include (1) a temperature-dependent bioenergetics model driving daily growth rates; (2) density-dependent dynamics that are influenced by the effect of flow on suitable habitat area; and (3) within-year habitat, river flow, and water temperature effects specific to spawning, egg incubation, and fry, parr, and smolt life stages. We used estimates of spawning escapement and geo-referenced redd locations to quantify the spatial and temporal distribution of female spawners for brood years 2014–19. These estimates of female spawners initiate the simulation of each year’s juvenile salmon emergence and emigration over a spatial domain extending from Nimbus Dam to the river’s confluence with the Sacramento River.

Using weekly estimates of juvenile salmon abundance and size (fork length) that passed the Watt Avenue fish trap (river kilometer 14.7), we calibrated the S3 model by estimating three key demographic parameters for each year, y: (1) Sy, the average daily survival probability, (2) M0y, the intercept for density-dependence in movement, representing the average daily probability of remaining in a habitat at zero abundance, and (3) Cy, the average daily proportion of maximum consumption. These parameters were obtained by minimizing the Mallow’s distance (Lupu and others, 2017) between distributions of weekly abundances and sizes of fish at the traps and weekly simulated abundances and sizes (by S3). Investigation of model fit showed excellent agreement between simulated annual abundances and the abundance of fish passing the fish trap. However, when we compared weekly abundances at the fish trap, S3 under-predicted peaks and over-predicted troughs in the time series of weekly abundances at the fish trap. Thus, some unknown within-year effects have yet to be identified and incorporated in the S3 model. Identifying these important effects and incorporating them in the S3 model would help explain the lack of fit between estimated and simulated weekly abundances.

We estimated parameters for 6 years that included a wide range of female spawner abundances (3,057–10,753) and water year types (Critical–Wet). We contrast our estimated parameters to the corresponding number of female spawners and the water year type for the Sacramento Valley. By happenstance, years having higher annual spawner abundances concurred with Critical to Dry water year types. Estimates of survival trended lower with higher spawner abundances and Critical to Dry conditions. In contrast, the extremely wet water year of 2017 had the lowest M0y, suggesting less density-dependence in fish movement, and the lowest Cy, suggesting lower average consumption in this year. When this high-flow year was excluded, a trend towards higher probabilities of fish remaining in a habitat at low abundance and lower proportions of maximum consumption was apparent from Critical to Wet conditions, but only 5 years of data were included. Except for 2017, daily proportions of maximum consumption were relatively high (Cy > 0.83), suggesting that fish were feeding at reasonably high proportions relative to the expected maximum consumption as defined by the “Wisconsin” bioenergetics model (Stewart and Ibarra, 1991).

Survival estimates from fry emergence to outmigration at the Sacramento River confluence were generally low when integrated over time. The highest daily survival probability was Sy = 0.93 in 2019, or 50 percent total mortality after 10 days. In contrast, our lowest daily survival probability was Sy = 0.74 in 2015, or 95 percent total mortality after 10 days. Consequently, even our highest estimated daily survival probability might be considered low. This is especially true given that Sy was estimated over a relatively short distance (<14.7 kilometers) from emergence to the Watt Avenue fish trap. Several factors, including our assumed and relatively high daily egg survival rate of 0.9975, could influence juvenile survival estimates. For example, an egg survival rate of 0.9975 results in 3-percent total mortality after 10 days. Egg mortality estimates used in S3 calibration were approximated from egg survivorship studies in the Yakima River, Washington (Johnson and others, 2012), and remains one of the greater uncertainties in S3 when estimating survival across life stages. By including bona fide estimates of egg survival in S3 simulations, the validity of the S3’s current daily egg survival rate could be assessed specifically for the American River. Tagging studies also could provide S3 with direct estimates of juvenile survival and movement; survival during egg incubation then could be estimated indirectly via model fitting.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231060","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., Hatton, T.W., Smith, C.D., and Hannon, J.M., 2023, Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California: U.S. Geological Survey Open-File Report 2023–1060, 35 p., https://doi.org/10.3133/ofr20231060.","productDescription":"ix, 35 p.","onlineOnly":"Y","ipdsId":"IP-141661","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":421858,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.XML"},{"id":421857,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1060/images"},{"id":421856,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1060"},{"id":421855,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1060"},{"id":421854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1060/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"American River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5064051133351,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.727216763718815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016

","tableOfContents":"","publishedDate":"2023-10-11","noUsgsAuthors":false,"publicationDate":"2023-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":885957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":885958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":885959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":7915,"corporation":false,"usgs":true,"family":"Smith","given":"Collin D.","email":"cdsmith@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":885960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hannon, John M.","contributorId":330804,"corporation":false,"usgs":false,"family":"Hannon","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":885961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249935,"text":"70249935 - 2023 - Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science","interactions":[],"lastModifiedDate":"2023-12-04T17:25:25.241762","indexId":"70249935","displayToPublicDate":"2023-10-11T06:43:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science","docAbstract":"

Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness-based single-linear regression equations, water-effect ratios (WERs), biotic ligand models (BLMs), and multiple-linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness-based versus MLR-based criteria equations. The WER approach was shown to be inherently highly biased. The hardness-based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site-specific testing would be needed to determine reliable Cu criteria for such settings. The hardness-based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness-criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR-based USEPA-style chronic criterion appears to be more generally protective of ecosystems than other models.

","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5736","usgsCitation":"Mebane, C.A., 2023, Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science: Environmental Toxicology and Chemistry, v. 42, no. 12, p. 2529-2563, https://doi.org/10.1002/etc.5736.","productDescription":"35 p.","startPage":"2529","endPage":"2563","ipdsId":"IP-139187","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":441904,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5736","text":"Publisher Index Page"},{"id":422417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887754,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70249403,"text":"fs20233044 - 2023 - LANDFIRE","interactions":[],"lastModifiedDate":"2023-10-10T21:18:58.443597","indexId":"fs20233044","displayToPublicDate":"2023-10-10T15:02:29","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3044","displayTitle":"LANDFIRE","title":"LANDFIRE","docAbstract":"Landscape Fire and Resource Management Planning Tools (LANDFIRE) is a key national geospatial data source for strategic fire and resource management planning and analysis. LANDFIRE is the first complete, nationally consistent collection of more than 25 geospatial layers, databases, and ecological models at a 30-meter resolution that describe disturbance, vegetation, fire, and fuel characteristics. Because fires do not stop at ownership borders, LANDFIRE products by design support cross-boundary planning, management, and operations across all lands of the conterminous United States (CONUS), Alaska, Hawaii, and insular areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233044","usgsCitation":"Long, J.L., and Hatten, T.D., 2023, LANDFIRE: U.S. Geological Survey Fact Sheet 2023–3044, 4 p., https://doi.org/10.3133/fs20233044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-146927","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501266,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XVKXRL","text":"USGS data release","linkHelpText":"LANDFIRE 2024 Update (ver. 1.1, March 2026)"},{"id":421688,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.XML","linkFileType":{"id":8,"text":"xml"}},{"id":421689,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233044/full","linkFileType":{"id":5,"text":"html"}},{"id":421687,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3044"},{"id":421694,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3044/images/"},{"id":421686,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3044/coverthb.jpg"}],"contact":"

LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-10-10","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Jennifer L. 0000-0002-0698-2303","orcid":"https://orcid.org/0000-0002-0698-2303","contributorId":330641,"corporation":false,"usgs":false,"family":"Long","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":63244,"text":"KBR Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":885493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, Timothy D. 0000-0003-3413-4325","orcid":"https://orcid.org/0000-0003-3413-4325","contributorId":291959,"corporation":false,"usgs":false,"family":"Hatten","given":"Timothy D.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":885494,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}