{"pageNumber":"233","pageRowStart":"5800","pageSize":"25","recordCount":165605,"records":[{"id":70246672,"text":"70246672 - 2023 - Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product","interactions":[],"lastModifiedDate":"2024-05-28T14:05:25.727146","indexId":"70246672","displayToPublicDate":"2023-10-20T09:04:28","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product","docAbstract":"

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Urban areas usually exhibit relatively warmer air and surface temperatures than surrounding non-urban lands, a phenomenon recognized as Surface Urban Heat Island (SUHI). As urban areas continue to develop and the climate continues to warm, it has become increasingly important to quantify and map the SUHI effect and learn how to mitigate it. To help meet the expanding need of analysis ready data for SUHI based studies, a methodology was developed to evaluate Land Surface Temperature (LST) using the Landsat Collection 1 Provisional Surface Temperature Science Product. The Landsat derived LST products were processed for 50 major cities throughout the Conterminous U.S. The SUHI product package includes per-pixel annual surface temperature, annual intensity, annual hotspot, and hotspot probability bands from 1985 to 2020.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2023 - 2023 IEEE international geoscience and remote sensing symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"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/IGARSS52108.2023.10282386","usgsCitation":"Mueller, C., Hussain, R., Xian, G.Z., Shi, H., and Arab, S., 2023, Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product, in IGARSS 2023 - 2023 IEEE international geoscience and remote sensing symposium, Pasadena, CA, July 16-21, 2023, p. 441-444, https://doi.org/10.1109/IGARSS52108.2023.10282386.","productDescription":"4 p.","startPage":"441","endPage":"444","ipdsId":"IP-153990","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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0000-0002-9948-1304","orcid":"https://orcid.org/0000-0002-9948-1304","contributorId":302266,"corporation":false,"usgs":false,"family":"Mueller","given":"Chase","affiliations":[],"preferred":false,"id":877861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hussain, Reza 0000-0002-5445-3027","orcid":"https://orcid.org/0000-0002-5445-3027","contributorId":301245,"corporation":false,"usgs":false,"family":"Hussain","given":"Reza","affiliations":[{"id":65343,"text":"KBR, Contractor to U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":877862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":877863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":877864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":877865,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":562,"text":"South 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.

","language":"English","publisher":"University of Hawai'i Press","doi":"10.2984/77.1.1","usgsCitation":"Hoeh, J.P., Aguirre, A.A., Calderon, F.A., Casler, S.P., Ciarrachi, S.G., Courtot, K., Montoya-Aiona, K., Pinzari, C., and Gorresen, P., 2023, Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island: Pacific Science, v. 77, no. 1, p. 1-26, https://doi.org/10.2984/77.1.1.","productDescription":"26 p.","startPage":"1","endPage":"26","ipdsId":"IP-145607","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":422009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Hawai‘i Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.96842132885067,\n 19.33138539404075\n ],\n [\n -154.7897923836328,\n 19.485585611959735\n ],\n [\n -154.79887521135575,\n 19.551218990406824\n ],\n [\n -154.91089675327194,\n 19.591156680319756\n ],\n [\n -154.9684212594931,\n 19.645342013414833\n ],\n [\n -154.96236604101108,\n 19.696658591411236\n ],\n [\n -154.99264213342082,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.864747424330687\n ],\n [\n -155.19851956180733,\n 19.98998602165949\n ],\n [\n -155.41045220867588,\n 20.095223300447913\n ],\n [\n -155.56183267072475,\n 20.137866947170835\n ],\n [\n -155.6011915908574,\n 20.146394280939575\n ],\n [\n -155.78890336379806,\n 20.262887779216527\n ],\n [\n -155.87670403178655,\n 20.28276819224483\n ],\n [\n -155.90698012419628,\n 20.254366822413203\n ],\n [\n -155.9160629519192,\n 20.16344755159399\n ],\n [\n -155.89789729647333,\n 20.095223300447913\n ],\n [\n -155.84037272089478,\n 20.026969315439658\n ],\n [\n -155.85551076709956,\n 19.9871407929613\n ],\n [\n -155.91303534267828,\n 19.92453277061513\n ],\n [\n -155.94331143508802,\n 19.867594857310905\n ],\n [\n -155.99175318294365,\n 19.861899940222415\n ],\n [\n -156.03111210307634,\n 19.81348489371841\n ],\n [\n -156.070471023209,\n 19.79069623879731\n ],\n [\n -156.0734986324501,\n 19.71946068185811\n ],\n [\n -155.98569796446162,\n 19.588304345937644\n ],\n [\n -155.96147709053392,\n 19.46845951057206\n ],\n [\n -155.91000776811617,\n 19.34850848155353\n ],\n [\n -155.925902725301,\n 19.21704010756863\n ],\n [\n -155.94293303161635,\n 19.105507378038368\n ],\n [\n -155.88814437651806,\n 19.00626880146993\n ],\n [\n -155.81565527064834,\n 18.990692600887243\n ],\n [\n -155.72554212736438,\n 18.938198274908366\n ],\n [\n -155.69548950343082,\n 18.8925651150143\n ],\n [\n -155.6244303900727,\n 18.91275839274777\n ],\n [\n -155.55569553714562,\n 19.002395865451618\n ],\n [\n -155.52134277651828,\n 19.078471509831623\n ],\n [\n -155.48905706234626,\n 19.119349850631657\n ],\n [\n -155.44276140564918,\n 19.13124152882338\n ],\n [\n -155.3530735113342,\n 19.191279451469995\n ],\n [\n -155.265880055271,\n 19.23839493374041\n ],\n [\n -155.17278493527743,\n 19.23992550373882\n ],\n [\n -154.96842132885067,\n 19.33138539404075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"77","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoeh, Julia P. 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A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA’s Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017–2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91–98%) than for the CDL (79–93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.
","language":"English","publisher":"MDPI","doi":"10.3390/s23208595","usgsCitation":"Kraatz, S., Lamb, B.T., Hively, W.D., Jennewein, J., Gao, F., Cosh, M.H., and Siqueira, P., 2023, Comparing NISAR (using Sentinel-1), USDA/NASS CDL, and ground truth crop/non-crop areas in an urban agricultural region: Sensors, v. 23, no. 20, 8595, 26 p., https://doi.org/10.3390/s23208595.","productDescription":"8595, 26 p.","ipdsId":"IP-154886","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":441826,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s23208595","text":"Publisher Index Page"},{"id":424947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.95,\n 39.161296434160306\n ],\n [\n -76.95,\n 38.93946741714126\n ],\n [\n -76.8,\n 38.93946741714126\n ],\n [\n -76.8,\n 39.161296434160306\n ],\n [\n -76.95,\n 39.161296434160306\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"20","noUsgsAuthors":false,"publicationDate":"2023-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraatz, Simon","contributorId":333602,"corporation":false,"usgs":false,"family":"Kraatz","given":"Simon","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":893319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893321,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":893322,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":893323,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":893324,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Siqueira, Paul","contributorId":333605,"corporation":false,"usgs":false,"family":"Siqueira","given":"Paul","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":893325,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249728,"text":"70249728 - 2023 - Bayesian weighting of climate models based on climate sensitivity","interactions":[],"lastModifiedDate":"2023-10-26T11:54:44.46868","indexId":"70249728","displayToPublicDate":"2023-10-20T06:53:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian weighting of climate models based on climate sensitivity","docAbstract":"

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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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.

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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":70257394,"text":"70257394 - 2023 - Leading the charge: A qualitative case-study of leadership conditions in collaborative environmental governance structures","interactions":[],"lastModifiedDate":"2024-09-05T16:40:31.989078","indexId":"70257394","displayToPublicDate":"2023-10-19T11:36:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Leading the charge: A qualitative case-study of leadership conditions in collaborative environmental governance structures","docAbstract":"

Collaborative governance structures are increasingly common among natural resource managers. While studies have assessed the conditions under which collaborative action occurs, little emphasis has been placed on the role leadership may play in joint-jurisdictional systems. Management of species under the Endangered Species Act offers an opportunity to assess the collaboration of federal, state, and tribal resource agencies. The Gulf of Maine Distinct Population Segment of Atlantic salmon (Salmo salar) was managed under a structure called the Atlantic Salmon Recovery Framework (ASRF) from 2011 to 2019. Using the ASRF as a case study, we examined the influence of leadership approaches on perceived program efficacy, member buy-in, and experience through semi-structured interviews. Participant reflections revealed three major leadership themes that participants found inadequate: (1) shared goals, (2) transparency, and (3) trust. Collaborative approaches that foster these leadership conditions may increase adaptive capacity and the likelihood of sustained success in this, and other, environmental governance structures.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.119203","usgsCitation":"Flye, M.E., Sponarski, C.C., McGreavy, B., and Zydlewski, J.D., 2023, Leading the charge: A qualitative case-study of leadership conditions in collaborative environmental governance structures: Journal of Environmental Management, v. 348, 119203, 9 p., https://doi.org/10.1016/j.jenvman.2023.119203.","productDescription":"119203, 9 p.","ipdsId":"IP-119349","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":486919,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2023.119203","text":"Publisher Index Page"},{"id":433513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"348","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flye, Melissa. E.","contributorId":275664,"corporation":false,"usgs":false,"family":"Flye","given":"Melissa.","email":"","middleInitial":"E.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sponarski, Carly. C.","contributorId":342620,"corporation":false,"usgs":false,"family":"Sponarski","given":"Carly.","email":"","middleInitial":"C.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGreavy, Bridie","contributorId":275231,"corporation":false,"usgs":false,"family":"McGreavy","given":"Bridie","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":910224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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 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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":70249692,"text":"70249692 - 2023 - Leaf litter decomposition and detrital communities following the removal of two large dams on the Elwha River (Washington, USA)","interactions":[],"lastModifiedDate":"2023-10-25T13:22:27.176833","indexId":"70249692","displayToPublicDate":"2023-10-19T08:08:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14245,"text":"Frontiers of Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Leaf litter decomposition and detrital communities following the removal of two large dams on the Elwha River (Washington, USA)","docAbstract":"

Large-scale dam removals provide opportunities to restore river function in the long-term and are massive disturbances to riverine ecosystems in the short-term. The removal of two dams on the Elwha River (WA, USA) between 2011 and 2014 was the largest dam removal project to be completed by that time and has since resulted in major changes to channel dynamics, river substrates, in-stream communities, and the size and shape of the river delta. To assess ecosystem function across the restored Elwha watershed, we compared leaf litter decomposition at twenty sites: 1) four tributary sites not influenced by restoration activities; 2) four river sites downstream of the upper dam (Glines Canyon Dam); 3) four river sites within the footprint of the former Aldwell Reservoir upstream of the lower dam (Elwha Dam); 4) four river sites downstream of the lower dam; and 5) four lentic sites in the newly developing Elwha delta. Three major findings emerged: 1) decomposition rates differed among sections of the Elwha watershed, with slowest decomposition rates at the delta sites and fastest decomposition rates just downstream of the upper dam; 2) aquatic macroinvertebrate communities establishing in leaf litterbags differed significantly among sections of the Elwha watershed; and 3) aquatic fungal communities growing on leaf litter differed significantly among sections. Aquatic macroinvertebrate and fungal diversity were sensitive to differences in canopy cover, water chemistry, and river bottom sediments across sites, with a stronger relationship to elevation for aquatic macroinvertebrates. As the Elwha River undergoes recovery following the massive sediment flows associated with dam removal, we expect to see changes in leaf litter processing dynamics and shifts in litter-dependent decomposer communities (both fungal and invertebrate) involved in this key ecosystem process.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1231689","usgsCitation":"LeRoy, C.J., Morley, S.A., Duda, J.J., Zinck, A.A., Lamoureux, P.J., Pennell, C., Bailey, A., Oswell, C., Silva, M., Kamakawiwo’ole, B.K., Hartford, S., Van Der Hout, J., Peters, R., Mahan, R., Stapleton, J., Johnson, R.C., and Foley, M.M., 2023, Leaf litter decomposition and detrital communities following the removal of two large dams on the Elwha River (Washington, USA): Frontiers of Ecology and Evolution, v. 11, 1231689, 17 p., https://doi.org/10.3389/fevo.2023.1231689.","productDescription":"1231689, 17 p.","ipdsId":"IP-154059","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":441841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fevo.2023.1231689","text":"Publisher Index Page"},{"id":422096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.70384847912715,\n 48.18248970246785\n ],\n [\n -123.69747256054883,\n 47.89956988612926\n ],\n [\n -123.36728352594747,\n 47.903844451880445\n ],\n [\n -123.3573654303813,\n 48.18248970246785\n ],\n [\n -123.70384847912715,\n 48.18248970246785\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Carri J.","contributorId":331098,"corporation":false,"usgs":false,"family":"LeRoy","given":"Carri","email":"","middleInitial":"J.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morley, Sarah A.","contributorId":148956,"corporation":false,"usgs":false,"family":"Morley","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":17601,"text":"NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":886743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zinck, Alex A.","contributorId":331099,"corporation":false,"usgs":false,"family":"Zinck","given":"Alex","email":"","middleInitial":"A.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamoureux, Paris J.","contributorId":331100,"corporation":false,"usgs":false,"family":"Lamoureux","given":"Paris","email":"","middleInitial":"J.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pennell, Cameron","contributorId":331101,"corporation":false,"usgs":false,"family":"Pennell","given":"Cameron","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bailey, Ali","contributorId":331102,"corporation":false,"usgs":false,"family":"Bailey","given":"Ali","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oswell, Caitlyn","contributorId":331103,"corporation":false,"usgs":false,"family":"Oswell","given":"Caitlyn","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886749,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Silva, Mary","contributorId":331104,"corporation":false,"usgs":false,"family":"Silva","given":"Mary","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886750,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kamakawiwo’ole, Brandy K.","contributorId":331105,"corporation":false,"usgs":false,"family":"Kamakawiwo’ole","given":"Brandy","email":"","middleInitial":"K.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886751,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hartford, Sorrel","contributorId":331106,"corporation":false,"usgs":false,"family":"Hartford","given":"Sorrel","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886752,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Van Der Hout, Jacqueline","contributorId":331107,"corporation":false,"usgs":false,"family":"Van Der Hout","given":"Jacqueline","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886753,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peters, Roger","contributorId":219502,"corporation":false,"usgs":false,"family":"Peters","given":"Roger","affiliations":[],"preferred":false,"id":886754,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mahan, Rebecca","contributorId":331108,"corporation":false,"usgs":false,"family":"Mahan","given":"Rebecca","email":"","affiliations":[{"id":79121,"text":"Clallam County Department of Community Development, Port Angeles, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886755,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Stapleton, Justin","contributorId":241974,"corporation":false,"usgs":false,"family":"Stapleton","given":"Justin","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":886756,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886757,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Foley, Melissa M.","contributorId":316727,"corporation":false,"usgs":false,"family":"Foley","given":"Melissa","email":"","middleInitial":"M.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":886758,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70252521,"text":"70252521 - 2023 - Use of physical blockers to control invasive red swamp crayfish in burrows","interactions":[],"lastModifiedDate":"2024-03-27T12:06:47.68054","indexId":"70252521","displayToPublicDate":"2023-10-19T07:05:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Use of physical blockers to control invasive red swamp crayfish in burrows","docAbstract":"The red swamp crayfish Procambarus clarkii is native to the southeast United States\nbut has successfully invaded nearly every continent around the world. Although physical,\nbiological, and chemical controls are employed to reduce or eliminate populations\nin open-water systems, terrestrial burrows provide a potential refuge from aquatic\ncontrol treatments. We conducted burrow trials to test whether two physical blocker\ntreatments would kill P. clarkii in their burrows. Bentonite clay (a sealing agent) and\nexpanding foam (an insulating sealant) were each applied to 37 crayfish burrows,\nand 36 burrows served as treatment controls (i.e., 110 total burrows). Burrows were\nexcavated 48 hr after the application of the physical blockers to assess the status of\ncrayfish in treated and control burrows. There was 74% mortality of crayfish in\noccupied burrows treated with bentonite clay, 62% in burrows treated with expanding\nfoam, and 6% mortality in control burrows. We believe bentonite clay should continue\nto be field-tested; however, because expanding foam is toxic to aquatic organisms and\nis expected to persist in the environment, we do not believe it is a suitable physical\nblocker for the control of invasive crayfish in burrows. Bentonite clay applications\nlikely will not need permits, will mitigate damage to banks and levees caused by\nburrowing crayfish, and can be used with other control agents such as pesticides.\nHowever, the use of physical blockers may be limited at field sites that have burrows\nwith complex morphologies. We believe the use of bentonite clay to control invasive\ncrayfish in terrestrial burrows will provide resource managers with an effective tool\nfor their integrative pest management programs.","language":"English","publisher":"Reabic","doi":"10.3391/mbi.2023.14.4.09","usgsCitation":"Bates, B.L., Allert, A., Wildhaber, M.L., and Stoeckel, J., 2023, Use of physical blockers to control invasive red swamp crayfish in burrows: Management of Biological Invasions, v. 14, no. 4, p. 709-729, https://doi.org/10.3391/mbi.2023.14.4.09.","productDescription":"21 p.","startPage":"709","endPage":"729","ipdsId":"IP-153209","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":441843,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.4.09","text":"Publisher Index Page"},{"id":435147,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96V08D0","text":"USGS data release","linkHelpText":"Crayfish morphometric measurements, burrow attributes and occupancy in response to physical burrow barriers"},{"id":427139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bates, Benjamin Lee 0000-0001-5142-8881","orcid":"https://orcid.org/0000-0001-5142-8881","contributorId":330857,"corporation":false,"usgs":true,"family":"Bates","given":"Benjamin","email":"","middleInitial":"Lee","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allert, Ann 0000-0001-7063-8016 aallert@usgs.gov","orcid":"https://orcid.org/0000-0001-7063-8016","contributorId":178200,"corporation":false,"usgs":true,"family":"Allert","given":"Ann","email":"aallert@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoeckel, Jim","contributorId":299806,"corporation":false,"usgs":false,"family":"Stoeckel","given":"Jim","email":"","affiliations":[],"preferred":false,"id":897401,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249598,"text":"fs20233043 - 2023 - Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","interactions":[],"lastModifiedDate":"2026-02-09T17:47:45.10106","indexId":"fs20233043","displayToPublicDate":"2023-10-18T16:01:18","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":"2023-3043","displayTitle":"Hydrologic Investigations of Green Infrastructure by the Central Midwest Water Science Center","title":"Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","docAbstract":"

The water management system within developed communities includes stormwater, wastewater, and drinking-water sources and sinks. Each water management system component provides critical services that support public health in these areas. Stormwater can be quite variable and difficult to manage in developed communities because the amount of stormwater that must be routed through a developed area depends on changing land cover and variable precipitation. In addition to flooding concerns, stormwater also is a major cause of water contamination in developed communities because it carries contaminants such as trash, bacteria, heavy metals, and sediments to local waterways. Historically, communities have managed stormwater with gray infrastructure such as street gutters, culverts, sewer systems, and tunnels. Although these structures efficiently capture and route stormwater to a local waterway or treatment plant, they do not filter any contaminants. Furthermore, many older communities have combined storm sewer and sanitary sewer systems. These combined systems result in an excessive amount of wastewater to be treated before being released into receiving water or the untreated waters are released directly to receiving waters during storms.  

Many communities are now incorporating green infrastructure stormwater mitigating solutions—pervious surfaces (allows water through), grassed swales, bioretention basins, and rain gardens—into their stormwater-management systems. Green infrastructure can absorb and filter stormwater where it falls by taking advantage of natural soil and plant storage and filtration capabilities. Thus, green infrastructure projects can potentially reduce the amount of stormwater and the concentration and transport of contaminants. Increasing green infrastructure in a developed community may reduce the requirements for new storm sewer infrastructure, improve the water quality of nearby waterways, and enhance aesthetics. 

The U.S. Geological Survey has partnered with several cooperators to quantify the effects of green infrastructure projects in several developed communities throughout the central Midwest. As part of these green infrastructure projects, the U.S. Geological Survey Central Midwest Water Science Center and cooperators installed, calibrated, and monitored equipment to measure hydrologic responses (including flooding and water movement) and selected water-quality constituents in developed communities.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233043","usgsCitation":"Atkinson, A.A., Heimann, D.C., and Bailey, C.R., 2023, Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center: U.S. Geological Survey Fact Sheet 2023–3043, 4 p., https://doi.org/10.3133/fs20233043.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147766","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499695,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115581.htm","linkFileType":{"id":5,"text":"html"}},{"id":499694,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115580.htm","linkFileType":{"id":5,"text":"html"}},{"id":421980,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.pdf","text":"Report","size":"2.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3043"},{"id":421979,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3043/images"},{"id":421978,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.XML","text":"XML"},{"id":421976,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3043/coverthb.jpg"},{"id":421981,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233043/full","text":"HTML","linkFileType":{"id":5,"text":"html"}}],"contact":"

Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL  61801

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-10-18","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Atkinson, Allison A. 0009-0001-7572-0729 aatkinson@usgs.gov","orcid":"https://orcid.org/0009-0001-7572-0729","contributorId":330979,"corporation":false,"usgs":true,"family":"Atkinson","given":"Allison","email":"aatkinson@usgs.gov","middleInitial":"A.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey, Clinton R. 0000-0003-3951-2268 cbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-3951-2268","contributorId":5457,"corporation":false,"usgs":true,"family":"Bailey","given":"Clinton","email":"cbailey@usgs.gov","middleInitial":"R.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":70256600,"text":"70256600 - 2023 - Assessing potential spawning locations of Silver Chub in Lake Erie","interactions":[],"lastModifiedDate":"2024-08-23T16:05:33.325572","indexId":"70256600","displayToPublicDate":"2023-10-18T10:57:20","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":"Assessing potential spawning locations of Silver Chub in Lake Erie","docAbstract":"

Objective

Silver Chub Macrhybopsis storeriana, a predominately riverine species throughout its native range, exists within Lake Erie as the only known lake population. Its population declined in the 1950s and never fully recovered. Canada has listed Silver Chub in the Great Lakes–St. Lawrence River as endangered and has initiated a recovery plan that recognized the identification of spawning areas as a critical component to inform Silver Chub's recovery potential.

Methods

We investigated potential spawning locations of Silver Chub using capture records, otolith microchemistry, and daily age analysis. Lapillus otolith Sr:Ca ratios from 27 age-0 Silver Chub were used to identify potential spawning areas. Daily ages estimated from lapilli were used to calculate hatch dates, which then were compared with capture data of adults and river flows to further inform potential spawning areas.

Result

The Detroit River (and its nearshore area) was all but ruled out as a potential spawning location. The Maumee, Portage, and Sandusky rivers or their nearshore areas were all possible spawning locations. Projected hatch dates spanned the end of May through the end of June and occurred across a wide range of flows, although some peaks in hatch dates corresponded to flow peaks, indicating recruitment is potentially enhanced by high flows.

Conclusion

Silver Chub spawning period and hypothesized spawning rivers or lacustuaries overlap those of invasive Grass Carp Ctenopharyngodon idella, creating a need to jointly consider Grass Carp control efforts with conservation of Silver Chub when assessing management alternatives. Further research on spawning guild and the use of rivers themselves or nearshore areas influenced by rivers as spawning areas are required to maximize potential for conservation and recovery of Silver Chub.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10870","usgsCitation":"McKenna, J.R., Bowen, A., Farver, J.R., Long, J.M., Miner, J.G., Stott, N.D., and Kocovsky, P.M., 2023, Assessing potential spawning locations of Silver Chub in Lake Erie: North American Journal of Fisheries Management, v. 43, no. 5, p. 1166-1179, https://doi.org/10.1002/nafm.10870.","productDescription":"14 p.","startPage":"1166","endPage":"1179","ipdsId":"IP-139223","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":441847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10870","text":"Publisher Index Page"},{"id":433107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n 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]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Jorden R.","contributorId":341316,"corporation":false,"usgs":false,"family":"McKenna","given":"Jorden","email":"","middleInitial":"R.","affiliations":[{"id":81722,"text":"Lake Erie Biological Station","active":true,"usgs":false}],"preferred":false,"id":908227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowen, Anjanette","contributorId":341317,"corporation":false,"usgs":false,"family":"Bowen","given":"Anjanette","affiliations":[{"id":81723,"text":"US Fish and Wildlife Service Alpena Fish and Wildlife Conservation Office","active":true,"usgs":false}],"preferred":false,"id":908228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farver, John R.","contributorId":341318,"corporation":false,"usgs":false,"family":"Farver","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908230,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miner, Jeffrey G.","contributorId":341319,"corporation":false,"usgs":false,"family":"Miner","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908231,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stott, Nathan D.","contributorId":341320,"corporation":false,"usgs":false,"family":"Stott","given":"Nathan","email":"","middleInitial":"D.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908232,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":908233,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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":70249795,"text":"70249795 - 2023 - Florida Kingsnake (Lampropeltis floridana) consumes a juvenile Burmese Python (Python molurus bivitattus) in southern Florida","interactions":[],"lastModifiedDate":"2023-10-28T13:09:17.645288","indexId":"70249795","displayToPublicDate":"2023-10-18T08:06:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3263,"text":"Reptiles & Amphibians","active":true,"publicationSubtype":{"id":10}},"title":"Florida Kingsnake (Lampropeltis floridana) consumes a juvenile Burmese Python (Python molurus bivitattus) in southern Florida","docAbstract":"The Burmese python (Python molurus bivittatus) is an invasive constrictor established across southern Florida. These snakes are dietary generalists with large home ranges and broad habitat requirements and their introduction has had severe impacts on native species and ecosystems in the region. We describe the first observation of a Florida kingsnake (Lampropeltis floridana) that consumed a hatchling Burmese python.","language":"English","publisher":"International Reptile Conservation Foundation","doi":"10.17161/randa.v30i1.19971","usgsCitation":"Crawford, P.F., Torres, J.A., Guzy, J.C., Currylow, A.F., McBride, L.M., Anderson, G.E., McCollister, M.F., Romagosa, C.M., Yackel Adams, A.A., and Hart, K., 2023, Florida Kingsnake (Lampropeltis floridana) consumes a juvenile Burmese Python (Python molurus bivitattus) in southern Florida: Reptiles & Amphibians, v. 30, no. 1, e19971, 3 p., https://doi.org/10.17161/randa.v30i1.19971.","productDescription":"e19971, 3 p.","ipdsId":"IP-151311","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":887092,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":887093,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"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":70269403,"text":"70269403 - 2023 - High potential but low achievement: Frequent disturbance constrains the light use efficiency of river ecosystems","interactions":[],"lastModifiedDate":"2025-07-22T14:48:02.96331","indexId":"70269403","displayToPublicDate":"2023-10-18T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"High potential but low achievement: Frequent disturbance constrains the light use efficiency of river ecosystems","docAbstract":"

We rarely consider light limitation in ecosystem productivity, yet light limitation is a major constraint on river autotrophy. Because the light that reaches benthic autotrophs must first pass through terrestrial vegetation and an overlying water column that can be loaded with sediments or colored organic material, there is strong selection for river autotrophs to have high light use efficiencies (LUEs), that is, the efficiency at which light energy is converted to biomass. In contrast to prior studies that have estimated river LUE on single days, we calculated continuous LUE over more than 6 full years for 64 free-flowing rivers across the United States. This dataset represents the largest compilation of continuous estimates of daily rates of gross primary productivity (GPP) and daily light inputs from which we calculated daily estimates of LUE. Early estimates of LUE in rivers found that clearwater springs with stable flows could achieve LUEs of 4%, much higher than LUEs reported for terrestrial plants. We found that 53% of the rivers in our dataset have LUEs that exceed 4% on at least one day of their time series. Because of the high variability in daily LUE, measurements taken on any given day may misrepresent a river ecosystem's annual LUE. Though most rivers share a high potential, the mean annual LUE of all rivers in our dataset is much lower, only 0.5%. We found that rivers with more variable flow regimes had lower annual LUEs, which indicates that LUE is constrained by hydrologic disturbances that remove, bury, or shade autotrophic biomass. Comparisons of LUE across ecosystems allow us to reframe our view of rivers, by recognizing the high efficiency with which they convert light to biomass compared with lentic, marine, and terrestrial ecosystems.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4659","usgsCitation":"Thellman, A., Savoy, P., and Bernhardt, E., 2023, High potential but low achievement: Frequent disturbance constrains the light use efficiency of river ecosystems: Ecosphere, v. 14, no. 10, e4659, 9 p., https://doi.org/10.1002/ecs2.4659.","productDescription":"e4659, 9 p.","ipdsId":"IP-151660","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4659","text":"Publisher Index Page"},{"id":492731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n 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lakes","interactions":[],"lastModifiedDate":"2024-08-12T14:56:10.530072","indexId":"70256500","displayToPublicDate":"2023-10-17T09:48:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18328,"text":"Frontiers in Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Fish life-history traits predict abundance-occupancy patterns in artificial lakes","docAbstract":"

Life-history traits of a species have been postulated as a factor in abundance and occupancy patterns. Understanding how traits contribute to the ubiquity and rarity of taxa can facilitate the development of effective conservation policy by establishing a connection between species requirements and resource. The goal was to evaluate fish assemblages in artificial lakes for evidence of the abundance-occupancy patterns reported in natural environments and, if evident, to explore if observed patterns of abundance and occupancy could be attributed to species traits. Fish abundance and occupancy were estimated over 1990–2018 in 22 artificial lakes impounded within the Tennessee River basin, USA. Consistent with reports for many other taxonomic groups in natural environments, there was a positive association amidst 114 fish species between abundance and occupancy in artificial lakes (R2 = 0.78). This result indicates that the fish assemblages that develop in these anthropized environments follow the fundamental abundance-occupancy patterns uncovered in natural environments, despite assemblages having been disfigured by the dramatic rearrangement of habitats brought by impoundment. Moreover, a redundancy analysis focusing mostly on reproductive and habitat traits adequately predicted abundance-occupancy patterns of fish assemblages in artificial lakes (R2 = 0.69). Species abundance-occupancy is influenced by the interplay between life-history traits and habitat availability, even in artificial lakes, and by extension, possibly other artificial ecosystems.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2023.1270939","usgsCitation":"Miranda, L.E., 2023, Fish life-history traits predict abundance-occupancy patterns in artificial lakes: Frontiers in Freshwater Science, v. 1, 1270939, 9 p., https://doi.org/10.3389/ffwsc.2023.1270939.","productDescription":"1270939, 9 p.","ipdsId":"IP-155049","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":441861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/ffwsc.2023.1270939","text":"Publisher Index Page"},{"id":432486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabame, Georgia, Kentucky, Mississippi, North Carolina, Tennessee, Virginia","otherGeospatial":"Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.71853058052089,\n 36.417865012804256\n ],\n [\n -88.01805823954074,\n 37.068175622572426\n ],\n [\n -88.5039106745598,\n 36.97105864199936\n ],\n [\n -88.16082450902296,\n 36.19663882041334\n ],\n [\n -88.32274922263527,\n 34.72002280226771\n ],\n [\n -85.82977660734407,\n 34.31592129522848\n ],\n [\n -84.85626355348249,\n 34.81392356154679\n ],\n [\n -84.3271421583876,\n 35.07256459313197\n ],\n [\n -83.4163366401158,\n 34.40943382347814\n ],\n [\n -81.57660967502278,\n 35.63205440342975\n ],\n [\n -80.94496328446938,\n 36.7865912817942\n ],\n [\n -81.34468565872093,\n 36.99713207425954\n ],\n [\n -82.10618545728924,\n 36.61910300314784\n ],\n [\n -84.22694404251676,\n 36.05281390065235\n ],\n [\n -84.53649174113569,\n 35.51939544911066\n ],\n [\n -87.71853058052089,\n 36.417865012804256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationDate":"2023-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907688,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70249484,"text":"cir1513 - 2023 - Woods Hole Coastal and Marine Science Center—2022 annual report","interactions":[],"lastModifiedDate":"2023-12-14T21:00:32.188771","indexId":"cir1513","displayToPublicDate":"2023-10-17T08:40:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1513","displayTitle":"Woods Hole Coastal and Marine Science Center—2022 Annual Report","title":"Woods Hole Coastal and Marine Science Center—2022 annual report","docAbstract":"

The 2022 annual report of the U.S. Geological Survey Woods Hole Coastal and Marine Science Center highlights accomplishments of 2022, includes a list of 2022 publications, and summarizes the work of the center, as well as the work of each of its science groups. This product allows readers to gain a general understanding of the focus areas of the center’s scientific research and learn more about specific projects and progress made throughout 2022, all while enjoying photographs taken in various environments and laboratories, and applicable maps and figures.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1513","isbn":"978-1-4113-4536-2","usgsCitation":"Ernst, S., 2023, Woods Hole Coastal and Marine Science Center—2022 annual report: U.S. Geological Survey Circular 1513, 36 p., https://doi.org/10.3133/cir1513.","productDescription":"iv, 36 p.","numberOfPages":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-151468","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":421834,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1513/cir1513.XML"},{"id":421833,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1513/images/"},{"id":421832,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1513/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1513"},{"id":421831,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1513/cir1513.pdf","text":"Report","size":"8.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1513"},{"id":421830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1513/coverthb.jpg"}],"contact":"

Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2023-10-17","noUsgsAuthors":false,"publicationDate":"2023-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ernst, Sara 0000-0001-7825-3209","orcid":"https://orcid.org/0000-0001-7825-3209","contributorId":215923,"corporation":false,"usgs":true,"family":"Ernst","given":"Sara","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885899,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70252187,"text":"70252187 - 2023 - Respiratory acclimation of tropical forest roots in response to in situ experimental warming and hurricane disturbance","interactions":[],"lastModifiedDate":"2024-03-19T11:44:29.944909","indexId":"70252187","displayToPublicDate":"2023-10-17T06:41:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Respiratory acclimation of tropical forest roots in response to in situ experimental warming and hurricane disturbance","docAbstract":"

Climate projections predict higher temperatures and more frequent hurricanes in the tropics. Tropical plants subjected to these stresses may respond by acclimating their physiology. We investigated tropical forest root respiration in response to in situ experimental warming and hurricane disturbance in eastern Puerto Rico. We measured mass-normalized root specific respiration, root biomass, and root traits at the Tropical Responses to Altered Climate Experiment (TRACE), where understory vegetation is warmed + 4 °C above ambient. Our measurements span 5 years, including before and after two major hurricanes, to quantify root contributions to ecosystem carbon fluxes. Experimental warming did not affect root specific respiration at a standard temperature of 25° (RSR25, mean = 3.89 nmol CO2 g−1 s−1) or the temperature sensitivity of root respiration (Q10, mean = 1.75), but did result in decreased fine-root biomass, thereby decreasing area-based estimations of ecosystem-level root respiration in warmed plots by ~ 35%. RSR25 of newer roots, which increased with increasing root nitrogen, showed greater rates 6 months after the hurricanes, but subsequently decreased after 12 months. Root specific respiration did not acclimate to higher temperatures, based on lack of adjustments in either Q10 or RSR25 in the warmed plots; however, decreased root biomass indicates the root contribution to soil carbon dioxide efflux was overall lower with warming. Lower root biomass may also limit nutrient and water uptake, having potential negative effects on carbon assimilation. Our results show that warming and hurricane disturbance have strong potential to affect tropical forest roots, as well as ecosystem carbon fluxes.

","language":"English","publisher":"Springer","doi":"10.1007/s10021-023-00880-y","usgsCitation":"Tunison, R., Wood, T.E., Reed, S., and Cavaleri, M.A., 2023, Respiratory acclimation of tropical forest roots in response to in situ experimental warming and hurricane disturbance: Ecosystems, v. 27, p. 168-184, https://doi.org/10.1007/s10021-023-00880-y.","productDescription":"17 p.","startPage":"168","endPage":"184","ipdsId":"IP-155528","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":426764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","noUsgsAuthors":false,"publicationDate":"2023-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tunison, Rob","contributorId":334894,"corporation":false,"usgs":false,"family":"Tunison","given":"Rob","email":"","affiliations":[{"id":80283,"text":"College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, USA","active":true,"usgs":false}],"preferred":false,"id":896867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Tana E.","contributorId":202372,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[{"id":36399,"text":"International Institute of Tropical Forestry, USDA Forest Service, Rio Piedras, PR","active":true,"usgs":false}],"preferred":false,"id":896868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":896869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":896870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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}]}} ]}