Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.
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R.","contributorId":238115,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[],"preferred":false,"id":867163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solvik, Kylen 0000-0001-6537-1791","orcid":"https://orcid.org/0000-0001-6537-1791","contributorId":303316,"corporation":false,"usgs":false,"family":"Solvik","given":"Kylen","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":867164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nieuwlandt, Peter Joseph 0000-0002-8245-2873","orcid":"https://orcid.org/0000-0002-8245-2873","contributorId":303317,"corporation":false,"usgs":true,"family":"Nieuwlandt","given":"Peter","email":"","middleInitial":"Joseph","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":867165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prentiss, Mallory Annelle 0000-0002-0010-0744","orcid":"https://orcid.org/0000-0002-0010-0744","contributorId":303318,"corporation":false,"usgs":true,"family":"Prentiss","given":"Mallory","email":"","middleInitial":"Annelle","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":867166,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70246795,"text":"70246795 - 2023 - Fire modifies plant–soil feedbacks","interactions":[],"lastModifiedDate":"2023-07-19T11:49:57.43808","indexId":"70246795","displayToPublicDate":"2023-02-15T06:48:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fire modifies plant–soil feedbacks","docAbstract":"Although plant–soil feedbacks (interactions between plants and soils, often mediated by soil microbes, abbreviated as PSFs) are widely known to influence patterns of plant diversity at local and landscape scales, these interactions are rarely examined in the context of important environmental factors. Resolving the roles of environmental factors is important because the environmental context may alter PSF patterns by modifying the strength or even direction of PSFs for certain species. One important environmental factor that is increasing in scale and frequency with climate change is fire, though the influence of fire on PSFs remains essentially unexamined. By changing microbial community composition, fire may alter the microbes available to colonize the roots of plants and thus seedling growth post-fire. This has potential to change the strength and/or direction of PSFs, depending on how such changes in microbial community composition occur and the plant species with which the microbes interact. We examined how a recent fire altered PSFs of two leguminous, nitrogen-fixing tree species in Hawaiʻi. For both species, growing in conspecific soil resulted in higher plant performance (as measured by biomass production) than growing in heterospecific soil. This pattern was mediated by nodule formation, an important process for growth for legume species. Fire weakened PSFs for these species and therefore pairwise PSFs, which were significant in unburned soils, but were nonsignificant in burned soils. Theory suggests that positive PSFs such as those found in unburned sites would reinforce the dominance of species where they are locally dominant. The change in pairwise PSFs with burn status shows PSF-mediated dominance might diminish after fire. Our results demonstrate that fire can modify PSFs by weakening the legume-rhizobia symbiosis, which may alter local competitive dynamics between two canopy dominant tree species. These findings illustrate the importance of considering environmental context when evaluating the role of PSFs for plants.
Drivers of phytoplankton and zooplankton dynamics vary spatially and temporally in estuaries due to variation in hydrodynamic exchange and residence time, complicating efforts to understand controls on food web productivity. We conducted approximately monthly (2012–2019; n = 74) longitudinal sampling at 10 fixed stations along a freshwater tidal terminal channel in the San Francisco Estuary, California, characterized by seaward to landward gradients in water residence time, turbidity, nutrient concentrations, and plankton community composition. We used multivariate autoregressive state space (MARSS) models to quantify environmental (abiotic) and biotic controls on phytoplankton and mesozooplankton biomass. The importance of specific abiotic drivers (e.g., water temperature, turbidity, nutrients) and trophic interactions differed significantly among hydrodynamic exchange zones with different mean residence times. Abiotic drivers explained more variation in phytoplankton and zooplankton dynamics than a model including only trophic interactions, but individual phytoplankton–zooplankton interactions explained more variation than individual abiotic drivers. Interactions between zooplankton and phytoplankton were strongest in landward reaches with the longest residence times and the highest zooplankton biomass. Interactions between cryptophytes and both copepods and cladocerans were stronger than interactions between bacillariophytes (diatoms) and zooplankton taxa, despite contributing less biovolume in all but the most landward reaches. Our results demonstrate that trophic interactions and their relative strengths vary in a hydrodynamic context, contributing to food web heterogeneity within estuaries at spatial scales smaller than the freshwater to marine transition.
The U.S. Geological Survey (USGS) provides reliable science, data, information, and models (hereafter collectively referred to as “information”) to describe and understand the Earth. This information is used to minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect quality of life. USGS science informs public and private decisions, operations, and risk management in all major United States economic sectors, as defined by the Bureau of Economic Analysis, and provides critical information for natural resource and natural hazard management and stewardship decisions. Understanding the value of scientific information supports applications of USGS science in land- and water-management decisions, and better informs the public about the return on investment of USGS programs. USGS economists, social scientists, and physical scientists are engaged in collaborative efforts to advance methods to estimate the value of information (VOI) produced by the USGS. These efforts involve collaborating with an international community to develop and refine estimation methods, establish best practices to determine VOI, develop a study repository, and conduct projects to assess the VOI of specific information products and their application. This report focuses on economic valuation conducted by USGS specifically, although the methodology has much broader applicability within the U.S. government, academia, and beyond. Noneconomic valuation techniques for assessing the VOI also exist but are not addressed in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231011","usgsCitation":"Pindilli, E., Chiavacci, S., and Straub, C., 2023, The value of scientific information—An overview: U.S. Geological Survey Open-File Report 2023–1011, 5 p., https://doi.org/10.3133/ofr20231011.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146764","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":412985,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1011"},{"id":412987,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1011/ofr20231011.XML"},{"id":412950,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1011/ofr20231011.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1011"},{"id":412949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1011/coverthb.jpg"},{"id":412986,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1011/images/"}],"contact":"Science and Decisions Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Lacustrine carbonates in a 12.4-m-long core from Lower Pahranagat Lake (LPAH), southern Nevada, indicate that radiogenic isotopes of Sr and U (87Sr/86Sr and 234U/238U) preserve evidence of past variations in water sources and evolving hydrologic conditions. Sr and U isotope compositions in LPAH carbonates fall within the range defined by the three primary groundwater sources in Pahranagat Valley and reflect variable mixtures of those sources since the mid-Holocene. Compositions in the oldest sample (5.78 ka) closely match modern compositions of modern discharge from nearby springs, indicating that LPAH water was derived almost exclusively from the local volcanic aquifer. By ca. 5.3–5.2 ka, LPAH water compositions shifted sharply towards isotopic compositions observed in groundwater from the regional carbonate aquifer, indicating a marked increase in surface flow from high-volume springs discharging from the carbonate aquifer to the north. Sediments deposited between 3.08–1.06 ka indicate reduced contributions from the regional aquifer. A comparison of uranium- and oxygen-isotope values in LPAH carbonates suggests that wetter climate conditions favor increased supply from deeper, regional carbonate aquifers compared to drier conditions when contributions from shallower, local volcanic aquifers were more important.
Loss and degradation of sagebrush rangelands due to an accelerated invasive annual grass-wildfire cycle and other stressors are significant management, conservation, and economic issues in the western United States. These sagebrush rangelands comprise a unique biome spanning 11 states, support over 350 wildlife species, and provide important ecosystem services that include stabilizing the economies of western communities. Impacts to sagebrush ecosystem processes over large areas due to the annual grass-wildfire cycle necessitated the development of a coordinated, science-based strategy for improving efforts to achieve long-term protection, conservation, and restoration of sagebrush rangelands, which was framed in 2015 under the Integrated Rangeland Fire Management Strategy (IRFMS). Central to this effort was the development of an Actionable Science Plan (Plan) that identified 37 priority science needs (Needs) for informing the actions proposed under the 5 topics (Fire, Invasives, Restoration, Sagebrush and Sage-Grouse, Climate and Weather) that were part of the collective focus of the IRFMS. Notable keys to this effort were identification of the Needs co-produced by managers and researchers, and a focus on resulting science being “actionable.”
Substantial investments aimed at fulfilling the Needs identified in the Plan have been made since its release in 2016. While the state of the science has advanced considerably, the extent to which knowledge gaps remain relative to identified Needs is relatively unknown. Moreover, new Needs have likely emerged since the original strategy as results from actionable science reveal new questions and possible (yet untested) solutions. A quantifiable assessment of the progress made on the original science Needs can identify unresolved gaps and new information that can help inform prioritization of future research efforts.
This report details a systematic literature review that evaluated how well peer-reviewed journal articles and formal technical reports published between January 1, 2015, and December 31, 2020, addressed nine needs (hereinafter, “Needs”) identified under the Sagebrush and Sage-Grouse topic in the Plan. The topic outlined research Needs broadly focused on understanding sagebrush rangelands and population dynamics important for the conservation and management of sage-grouse and other sagebrush-reliant wildlife species. We established the level of progress towards addressing each Need following a standardized set of criteria, and developed summaries detailing how research objectives nested within Needs identified in the Plan (‘Next Steps’) were either addressed well, partially addressed or remain outstanding (in other words, addressed poorly) in the literature through 2020. Our searches resulted in the inclusion of 333 science products that at least partially addressed a Need identified in the Sagebrush and Sage-Grouse topic. The Needs that were well and partially addressed included:
Needs addressed poorly included:
The information provided in this assessment will assist updating the Plan along with other science strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231010","collaboration":"Prepared in cooperation with Bureau of Land Management and the U.S. Fish and Wildlife Service","usgsCitation":"Holloran, M.J., Anthony, C.R., Ricca, M.A., Hanser, S.E., Phillips, S.L., Steblein, P.F., and Wiechman, L.A., 2023, Integrated rangeland fire management strategy actionable science plan completion assessment—Sagebrush and sage-grouse topic, 2015–20: U.S. Geological Survey Open-File Report 2023–1010, 49 p., https://doi.org/10.3133/ofr20231010.","productDescription":"v, 49 p.","onlineOnly":"Y","ipdsId":"IP-141294","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":412990,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1010/ofr20231010.pdf","text":"Report","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1010"},{"id":412989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1010/coverthb.jpg"},{"id":412992,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1010/images"},{"id":412993,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1010/ofr20231010.XML"},{"id":413029,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231004","text":"OFR 2023-1004 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Restoration topic, 2015–20"},{"id":413030,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231009","text":"OFR 2023-1009 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Fire topic, 2015–20"},{"id":499736,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114938.htm","linkFileType":{"id":5,"text":"html"}},{"id":418555,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231035","text":"OFR 2023-1035 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment— Climate and weather topic, 2015–20"},{"id":413028,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231003","text":"OFR 2023-1003 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Invasives topic, 2015–20"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.82114253002419,\n 48.983205061126796\n ],\n [\n -122.41445472125756,\n 48.983205061126796\n ],\n [\n -122.41445472125756,\n 34.5\n ],\n [\n -102.82114253002419,\n 34.5\n ],\n [\n -102.82114253002419,\n 48.983205061126796\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
2150 Centre Avenue, Bldg C
Fort Collins, CO 80526
Loss and degradation of sagebrush rangelands due to an accelerated invasive annual grass-wildfire cycle and other stressors are significant management, conservation, and economic issues in the western United States. These sagebrush rangelands comprise a unique biome spanning 11 states, support over 350 wildlife species, and provide important ecosystem services that include stabilizing the economies of western communities. Impacts to sagebrush ecosystem processes over large areas due to the annual grass-wildfire cycle necessitated the development of a coordinated, science-based strategy for improving efforts to achieve long-term protection, conservation, and restoration of sagebrush rangelands, which was framed in 2015 under the Integrated Rangeland Fire Management Strategy (IRFMS). Central to this effort was the development of an Actionable Science Plan (Plan) that identified 37 priority science needs (Needs) for informing the actions proposed under the 5 topics (Fire, Invasives, Restoration, Sagebrush and Sage-Grouse, Climate and Weather) that were part of the collective focus of the IRFMS. Notable keys to this effort were identification of the Needs co-produced by managers and researchers, and a focus on resulting science being “actionable.”
Substantial investments aimed at fulfilling the Needs identified in the Plan have been made since its release in 2016. While the state of the science has advanced considerably, the extent to which knowledge gaps remain relative to identified Needs is relatively unknown. Moreover, new Needs have likely emerged since the original strategy as results from actionable science reveal new questions and possible (yet untested) solutions. A quantifiable assessment of the progress made on the original science Needs can identify unresolved gaps and new information that can help inform prioritization of future research efforts.
This report details a systematic literature review that evaluated how well peer-reviewed journal articles and formal technical reports published between January 1, 2015, and December 31, 2020, addressed eight needs (hereinafter known as “Needs”) identified under the Fire topic in the Plan, defined as any non-structure fire that occurs in vegetation or natural fuels, including wildfires and prescribed fires. The topic outlined research Needs broadly focused on understanding the mechanisms and management of threats posed to the maintenance of large, contiguous sagebrush rangelands by fire. We established the level of progress towards addressing each Need following a standardized set of criteria, and developed summaries detailing how research objectives nested within Needs identified in the Plan (‘Next Steps’) were either addressed well, partially addressed or remain outstanding (in other words., addressed poorly) in the literature through 2020. Our searches resulted in the inclusion of 156 science products that at least partially addressed a Need identified in the Fire topic. The Needs that were well and partially addressed included:
Needs addressed poorly included:
The information provided in this assessment will assist updating the Plan along with other science strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231009","collaboration":"Prepared in cooperation with Bureau of Land Management and the U.S. Fish and Wildlife Service","usgsCitation":"Holloran, M.J., Anthony, C.R., Ricca, M.A., Hanser, S.E., Phillips, S.L., Steblein, P.F., and Wiechman, L.A., 2023, Integrated rangeland fire management strategy actionable science plan completion assessment—Fire topic, 2015–20: U.S. Geological Survey Open-File Report 2023–1009, 31 p., https://doi.org/10.3133/ofr20231009.","productDescription":"vi, 31 p.","onlineOnly":"Y","ipdsId":"IP-141295","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":418552,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231035","text":"OFR 2023-1035 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment— Climate and weather topic, 2015–20"},{"id":413027,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231010","text":"OFR 2023-1010 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Sagebrush and sage-grouse topic, 2015–20"},{"id":413026,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231004","text":"OFR 2023-1004 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Restoration topic, 2015–20"},{"id":413025,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231003","text":"OFR 2023-1003 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment—Invasives topic, 2015–20"},{"id":413017,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1009/ofr20231009.XML"},{"id":413016,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1009/images"},{"id":413014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1009/ofr20231009.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1009"},{"id":413013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1009/coverthb.jpg"},{"id":418557,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231035","text":"OFR 2023-1035 —","description":"Related work","linkHelpText":"Integrated rangeland fire management strategy actionable science plan completion assessment— Climate and weather topic, 2015–20"},{"id":499734,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114345.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.82114253002419,\n 48.983205061126796\n ],\n [\n -122.41445472125756,\n 48.983205061126796\n ],\n [\n -122.41445472125756,\n 34.5\n ],\n [\n -102.82114253002419,\n 34.5\n ],\n [\n -102.82114253002419,\n 48.983205061126796\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
2150 Centre Avenue, Bldg C
Fort Collins, CO 80526
One of the most important considerations for acoustic telemetry study designs is detection probability between the transmitter and the receiver. Variation in environmental (i.e., wind and flow) and abiotic (i.e., bathymetry) conditions among aquatic systems can lead to differences in detection probability temporally or between systems. In this study we evaluate the effect of distance, receiver mount design, transmitter depth, and wind speed on detection probabilities of two models of acoustic transmitters in a mid-sized river. InnovaSea V16-6H (hereafter V16) and V13-1L (hereafter V13) tags were deployed in the James River, SD at 0.36 m (deep) and 2.29 m (V16 tag) or 1.98 m (V13 tag; shallow) above the benthic surface downstream of InnovaSea VR2W stationary receivers at distances of 100, 200, or 300 m. We used two receiver mount designs that included a fixed position within a PVC pipe on the downstream side of a bridge piling or a metal frame deployed in the middle of the river channel. Tags were deployed for 72 h at each location, and hourly detections were summarized. We evaluated downstream distance, receiver mount design, tag depth, and wind effects on tag detection using Bayesian logistic regression.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-022-00313-y","usgsCitation":"Carlson, T.L., LaBrie, L.A., Wesner, J., Chipps, S.R., Coulter, A., and Schall, B.J., 2023, Receiver mount design, transmitter depth, and wind speed affect detection probability of acoustic telemetry transmitters in a Missouri River tributary: Animal Biotelemetry, v. 11, no. 6, 10 p., https://doi.org/10.1186/s40317-022-00313-y.","productDescription":"10 p.","ipdsId":"IP-147364","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":444481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-022-00313-y","text":"Publisher Index Page"},{"id":433268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"James River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.3152938410847,\n 42.884709549840466\n ],\n [\n -97.3152938410847,\n 42.85752575088864\n ],\n [\n -97.2356156303545,\n 42.85752575088864\n ],\n [\n -97.2356156303545,\n 42.884709549840466\n ],\n [\n -97.3152938410847,\n 42.884709549840466\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Tanner L.","contributorId":342338,"corporation":false,"usgs":false,"family":"Carlson","given":"Tanner","email":"","middleInitial":"L.","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":910003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaBrie, Lindsey A. P.","contributorId":342339,"corporation":false,"usgs":false,"family":"LaBrie","given":"Lindsey","email":"","middleInitial":"A. P.","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":910004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wesner, Jeff S.","contributorId":342343,"corporation":false,"usgs":false,"family":"Wesner","given":"Jeff S.","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":910005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coulter, Alison A.","contributorId":342346,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":910007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schall, Benjamin J.","contributorId":342349,"corporation":false,"usgs":false,"family":"Schall","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":81859,"text":"South Dakota Department of Game","active":true,"usgs":false}],"preferred":false,"id":910008,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250963,"text":"70250963 - 2023 - Atmospheric radiocarbon for the period 1910 to 2021 recorded by annual plants","interactions":[],"lastModifiedDate":"2024-01-17T13:18:21.737873","indexId":"70250963","displayToPublicDate":"2023-02-13T07:16:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3225,"text":"Radiocarbon","active":true,"publicationSubtype":{"id":10}},"title":"Atmospheric radiocarbon for the period 1910 to 2021 recorded by annual plants","docAbstract":"We present a timeseries of 14CO2 for the period 1910–2021 recorded by annual plants collected in the southwestern United States, centered near Flagstaff, Arizona. This timeseries is dominated by five commonly occurring annual plant species in the region, which is considered broadly representative of the southern Colorado Plateau. Most samples (1910–2015) were previously archived herbarium specimens, with additional samples harvested from field experiments in 2015–2021. We used this novel timeseries to develop a smoothed local record with uncertainties for “bomb spike” 14C dating of recent terrestrial organic matter. Our results highlight the potential importance of local records, as we document a delayed arrival of the 1963–1964 bomb spike peak, lower values in the 1980s, and elevated values in the last decade in comparison to the most current Northern Hemisphere Zone 2 record. It is impossible to retroactively collect atmospheric samples, but archived annual plants serve as faithful scribes: samples from herbaria around the Earth may be an under-utilized resource to improve understanding of the modern carbon cycle.
We reviewed studies of piscivorous colonial waterbird predation on juvenile salmonids to synthesize current knowledge of factors affecting fish susceptibility to avian predators. Specifically, we examined peer-reviewed publications and reports from academic, governmental, and nongovernmental agencies to identify commonalities and differences in susceptibility of salmonids to avian predation, with a focus on mark–recovery studies in the Columbia River basin. Factors hypothesized to influence salmonid susceptibility to avian predation were grouped into four general categories: (1) salmonid species and populations, (2) environmental factors, (3) prey density, predator density, and migration timing, and (4) prey characteristics. Our review focused on predation by Caspian terns Hydroprogne caspia, double-crested cormorants Nannopterum auritum, and gull species Larus spp. as these are the most well-studied avian predators of salmonids. Results indicated that predator–prey interactions varied across salmonid species and populations and species of avian predator. Inferences across studies supported multiple hypotheses regarding predator–prey dynamics, including environmental factors that influence prey exposure to predators (e.g., river flows, turbidity, alternative prey), variation in predator and prey abundances, predator characteristics (e.g., foraging behavior, colony location), and prey characteristics (e.g., fish length, condition). Mark–recovery studies of avian predation on fish populations have greatly improved our understanding of the factors affecting fish susceptibility to avian predation, the relative contributions of abiotic and biotic factors to predation susceptibility, and the extent to which avian predation affects fish survival and the viability of prey populations. Future studies that jointly model predation and survival and the factors affecting those processes will further broaden our understanding of predator–prey dynamics and directly evaluate the effects of predation on prey population dynamics.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10862","usgsCitation":"Hostetter, N.J., Evans, A.F., Payton, Q., Roby, D., Lyons, D., and Collis, K., 2023, A review of factors affecting the susceptibility of juvenile salmonids to avian predation, v. 43, no. 1, p. 244-256, https://doi.org/10.1002/nafm.10862.","productDescription":"13 p.","startPage":"244","endPage":"256","ipdsId":"IP-145263","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10862","text":"Publisher Index Page"},{"id":433156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Allen F.","contributorId":171691,"corporation":false,"usgs":false,"family":"Evans","given":"Allen","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":908261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Payton, Quinn","contributorId":149990,"corporation":false,"usgs":false,"family":"Payton","given":"Quinn","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":908262,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roby, Daniel D. 0000-0001-9844-0992","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":272249,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyons, Donald E.","contributorId":20119,"corporation":false,"usgs":true,"family":"Lyons","given":"Donald E.","affiliations":[],"preferred":false,"id":908264,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collis, Ken","contributorId":149991,"corporation":false,"usgs":false,"family":"Collis","given":"Ken","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":908265,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241138,"text":"70241138 - 2023 - Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform","interactions":[],"lastModifiedDate":"2023-03-13T11:54:13.751883","indexId":"70241138","displayToPublicDate":"2023-02-12T06:51:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform","docAbstract":"Much remains unknown about variation in pathogen transmission across the geographic range of a free-ranging fish or animal species and about the influence of movement (associated with husbandry practices or animal behavior) on pathogen transmission. Salmonid hatcheries are an ideal system in which to study these processes. Salmonid hatcheries are managed for endangered species recovery, supplementation of threatened or at-risk fish stocks, support of fisheries, and ecosystem stability. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus of significant concern to salmon aquaculture. Landscape IHNV transmission dynamics previously had been estimated only for salmonid hatcheries in the Lower Columbia River Basin (LCRB). The objectives of this study were to estimate IHNV transmission dynamics in a unique geographic region, the Snake River Basin (SRB), and to quantitatively estimate the effect of model coproduction on inference because previous assessments of coproduction have been qualitative. In contrast to the LCRB, the SRB has hatchery complexes consisting of a main hatchery and ≥1 satellite facility. Knowledge about hatchery complexes was held by a subset of project researchers but would not have been available to project modelers without coproduction. Project modelers generated and tested multiple versions of Bayesian susceptible-exposedinfected models to realistically represent the SRB and estimate the effect of coproduction. Models estimated the frequency of transmission routes, route-specific infection probabilities, and infection probabilities for combinations of salmonid hosts and IHNV lineages. Model results indicated that in the SRB, avoiding exposure to IHNV-positive adult salmonids is the most important action to prevent juvenile infections. Migrating adult salmonids exposed juvenile cohort-sites most frequently, and the infection probability was greatest following exposure to migrating adults. Without coproduction, the frequency of exposure by migrating adults would have been overestimated by 70 cohort-sites, and the infection probability following exposure to migrating adults would have been underestimated by∼0.09. The coproduced model had less uncertainty in the infection probability if no transmission route could be identified (Bayesian credible interval (BCI) width = 0.12) compared to the model without coproduction (BCI width = 0.34). Evidence for virus lineage MD specialization on steelhead and rainbow trout (both Oncorhynchus mykiss) was apparent without model coproduction. In the SRB, we found a greater probability of virus lineage UC infection in Chinook salmon (Oncorhynchus tshawytscha) compared to in O. mykiss, whereas in the LCRB, UC more clearly exhibited a generalist approach. Coproduction influenced estimates that depended on transmission routes, which operated differently at main hatcheries and satellite sites within hatchery complexes. Hatchery complexes are found outside of the SRB and are not specific to salmonid hatcheries alone. There is great potential for coproduction and modeling spatial contact networks to advance understanding about infectious disease transmission in complex production systems and surrounding free-ranging animal populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.117415","usgsCitation":"Mattheiss, J.P., Breyta, R., Kurath, G., LaDeau, S.L., Paez, D.J., and Ferguson, P.F., 2023, Coproduction and modeling spatial contact networks prevent bias about infectious hematopoietic necrosis virus transmission for Snake River Basin salmonids: Journal of Environmental Management, v. 334, 117415, 15 p., https://doi.org/10.1016/j.jenvman.2023.117415.","productDescription":"117415, 15 p.","ipdsId":"IP-141347","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":419245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.65408985827867,\n 46.96204590471055\n ],\n [\n -119.40893704245391,\n 42.213418016305525\n ],\n [\n -112.05454040913827,\n 42.2038651769914\n ],\n [\n -112.2736150561004,\n 47.00960829169131\n ],\n [\n -119.65408985827867,\n 46.96204590471055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"334","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mattheiss, Jeffrey P.","contributorId":317260,"corporation":false,"usgs":false,"family":"Mattheiss","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[{"id":36722,"text":"Department of Biological Sciences, University of Alabama, Box 870344, Tuscaloosa, AL 35487","active":true,"usgs":false}],"preferred":false,"id":878857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breyta, Rachel","contributorId":150355,"corporation":false,"usgs":false,"family":"Breyta","given":"Rachel","affiliations":[],"preferred":false,"id":878858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurath, Gael 0000-0003-3294-560X","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":220175,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":878860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878861,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferguson, Paige F. B.","contributorId":317261,"corporation":false,"usgs":false,"family":"Ferguson","given":"Paige","email":"","middleInitial":"F. B.","affiliations":[{"id":36722,"text":"Department of Biological Sciences, University of Alabama, Box 870344, Tuscaloosa, AL 35487","active":true,"usgs":false}],"preferred":false,"id":878862,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243039,"text":"70243039 - 2023 - Framework for facilitating mangrove recovery after hurricanes on Caribbean islands","interactions":[],"lastModifiedDate":"2023-09-06T16:08:19.597675","indexId":"70243039","displayToPublicDate":"2023-02-11T07:24:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Framework for facilitating mangrove recovery after hurricanes on Caribbean islands","docAbstract":"Mangrove ecosystems in the Caribbean are frequently exposed to hurricanes, leading to structural and regenerative change that elicit calls for recovery action. For those mangroves unaffected by human modifications, recovery can occur naturally. Indeed, observable natural recovery after hurricanes is the genesis of the “disturbance adaptation” classification for mangroves; while structural legacies exist, unaltered stands often regenerate and persist. However, among the >7,000 islands, islets, and cays that make up the Caribbean archipelago, coastal alterations to support development affect mechanisms for regeneration, sediment distribution, tidal water conveyance, and intertidal mangrove transgression, imposing sometimes insurmountable barriers to natural post-hurricane recovery. We use a case study approach to suggest that actions to facilitate recovery of mangroves on Caribbean islands (and similar settings globally) may be more effective when focusing on ameliorating pre-existing anthropogenic stressors. Actions to clean debris, collect mangrove propagules, and plant seedlings are noble endeavors, but can be costly and fall short of achieving recovery goals in isolation without careful consideration of pre-hurricane stress. We update a procedural framework that considers six steps to implementing “Ecological Mangrove Restoration” (EMR), and we apply them specifically to hurricane recovery. If followed, EMR may expedite actions by suggesting immediate damage assessment focused on hydrogeomorphic mangrove type, hydrology, and previous anthropogenic (or natural) influence. Application of EMR may help to improve mangrove recovery success following catastrophic storms, and reduce guesswork, delays, and monetary inefficiencies.
Conservation planning and decision-making can be enhanced by ecological models that reliably transfer to times and places beyond those where models were developed. Transferrable models can be especially helpful for species of conservation concern, such as grizzly bears (Ursus arctos). Currently, only four grizzly bear populations remain in the contiguous United States. We evaluated transferability of previously derived individual-based, integrated step selection functions (iSSFs) developed from GPS-collared grizzly bears in the Northern Continental Divide Ecosystem by applying them within the nearby Selkirk (SE), Cabinet-Yaak (CYE), and Greater Yellowstone Ecosystems (GYE). We simulated 100 replicates of 5000 steps for each iSSF in each ecosystem, summarized relative use into 10 equal-area classes for each sex, and overlaid GPS locations from bears in the SE, CYE, and GYE on resulting maps. Spearman rank correlations between numbers of locations and class rank were ≥ 0.96 within each study area, indicating models were highly predictive of grizzly bear space use in these nearby populations. Assessment of models using smaller subsets of data in space and time demonstrated generally high predictive accuracy for females. Although generally high across space and time, predictive accuracy for males was low within some watersheds and in summer within the SE and CYE, potentially due to seasonal effects, vegetation, and food assemblage differences. Altogether, these results demonstrated high transferability of our models to landscapes in the Northern Rocky Mountains, suggesting they may be used to evaluate habitat suitability and connectivity throughout the region to benefit conservation planning.
Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2 and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 and r = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.
Some animal species are responding to climate change by altering the timing of events like mating and migration. Such behavioral plasticity can be adaptive, but it is not always. Polar bears (Ursus maritimus) from the southern Beaufort Sea subpopulation have mostly remained on ice year-round, but as the climate warms and summer sea ice declines, a growing proportion of the subpopulation is summering ashore. The triggers of this novel behavior are not well understood. Our study uses a parametric time-to-event model to test whether biological and/or time-varying environmental variables thought to influence polar bear movement and habitat selection also drive decisions to swim ashore. We quantified the time polar bears spent occupying offshore sea ice of varying ice concentrations. We evaluated variations in the ordinal date bears moved to land with respect to local environmental conditions such as sea ice concentration and wind across 10 years (2005–2015). Results from our study suggest that storm events (i.e., sustained high wind speeds) may force polar bears from severely degraded ice habitat and catalyze seasonal movements to land. Unlike polar bears long adapted to complete summer ice melt, southern Beaufort Sea bears that summer ashore appear more tolerant of poor-quality sea ice habitat and are less willing to abandon it. Our findings provide a window into emergent, climatically mediated behavior in an Arctic marine mammal vulnerable to rapid habitat decline.
Groundwater quality in the north Sacramento Valley (NSV) was studied in the Redding–Red Bluff shallow aquifer study unit (referred to as the NSV shallow aquifer or NSV-SA) as part of the Priority Basin Project (PBP) of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in Shasta and Tehama Counties and included two physiographic study areas: (1) the Redding area to the north and (2) the Red Bluff area to the south. The study was focused on groundwater resources used for domestic drinking-water supply, which are mostly drawn from shallower parts of aquifer systems than those of groundwater resources used for public drinking-water supply in the same area. This assessment characterized the quality of ambient groundwater in the aquifer before filtration or treatment, rather than the quality of drinking water delivered to the tap.
The water-quality evaluation in this study has three components: (1) a status assessment, which characterized the quality of the groundwater resources used for domestic supply for 2018–19, in reference to state and national benchmarks; (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources; and (3) a comparison between the groundwater resources used for domestic supply and those used for public supply in the region.
The status assessment was based on data collected from 50 sites sampled by the U.S. Geological Survey for the GAMA-PBP in 2018–19. To provide context for the measured concentrations of groundwater constituents compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality, relative concentrations (RCs) of groundwater constituents were calculated as the concentration in a sample divided by the respective benchmark. Health-based benchmarks include regulatory and non-regulatory human-health benchmarks such as a maximum contaminant level, notification level, or health-based screening level. Aesthetic-based benchmarks are regulatory or non-regulatory non-health-based benchmarks that can affect the color or taste of water. A grid-based method was used to estimate the proportions of the groundwater resources used for domestic drinking wells that have water-quality constituents below (low), approaching (moderate, greater than half the benchmark), or above (high) benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA-PBP study areas.
NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Remotely sensed surface temperature (ST) has been widely used to monitor and assess landscape thermal conditions, hydrologic modeling, and surface energy balance. Landsat thermal sensors have continuously measured the Earth surface thermal radiance since August 1982. The thermal radiance measurements are atmospherically compensated and converted to Landsat STs and delivered as part of the U.S. Geological Survey Landsat Collection 1 U.S. Analysis Ready Data; however, the low satellite revisit cycles combined with the presence of clouds and cloud shadows reduce the number of valid retrievals. This reduction can limit the ability to monitor annual or seasonal variations in the surface thermal budget. These factors reduce the ability to use the temperature data to fit time series for historical trend analysis to match background climate variations. In this study, we implemented an approach that uses linear harmonic least absolute shrinkage and selection operator regression models to fill gaps because of clouds, shadows, and coarse temporal resolution. The gap-filled data provide increased temporal density of Landsat ST records. The gap-filled Landsat ST, therefore, can allow for an improved monitoring of annual, seasonal, or even monthly landscape thermal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231006","usgsCitation":"Xian, G., Shi, H., Arab, S., Mueller, C., Hussain, R., Sayler, K., and Howard, D., 2023, Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm: U.S. Geological Survey Open-File Report 2023–1006, 15 p., https://doi.org/10.3133/ofr20231006.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-144337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1006/images"},{"id":412872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1006"},{"id":412880,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1006/coverthb.jpg"},{"id":499732,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114340.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.9318883744094,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 34.338976979151155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Introduction: To sustain black bear (Ursus americanus) populations, wildlife managers should understand the coupled socio-ecological systems that influence acceptance capacity for bears.
Method: In a study area encompassing a portion of New York State, we spatially matched datasets from three sources: human-bear conflict reports between 2006 and 2018, estimates of local bear density in 2017–2018, and responses to a 2018 property owner survey (n=1,772). We used structural equation modeling to test hypothesized relationships between local human-bear conflict, local bear density, and psychological variables.
Results: The final model explained 57% of the variance in acceptance. The effect of bear population density on acceptance capacity for bears was relatively small and was mediated by a third variable: perception of proximity to the effects of human-bear interactions. The variables that exerted a direct effect on acceptance were perception of bear-related benefits, perception of bear-related risks, perceived proximity to effects of human-bear interactions, and being a hunter. Perception of bear-related benefits had a greater effect on acceptance than perception of bear-related risks. Perceived proximity to effects of human-bear interactions was affected by local bear density, but also was affected by social trust. Increased social trust had nearly the same effect on perceived proximity as decreased bear density. Social trust had the greatest indirect effect on acceptance of any variable in the model.
Discussion: Findings suggest wildlife agencies could maintain public acceptance for bears through an integrated approach that combines actions to address bear-related perceptions and social trust along with active management of bear populations.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2023.1041393","usgsCitation":"Siemer, W., Lauber, T., Stedman, R., Hurst, J., Sun, C., Fuller, A.K., Hollingshead, N., Belant, J., and Kellner, K., 2023, Perception and trust influence acceptance for black bears more than bear density or conflicts: Frontiers in Conservation Science, v. 4, 1041393, 13 p., https://doi.org/10.3389/fcosc.2023.1041393.","productDescription":"1041393, 13 p.","ipdsId":"IP-147449","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2023.1041393","text":"Publisher Index Page"},{"id":466002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.92499168267652,\n 40.760363749644455\n ],\n [\n -73.65063699090229,\n 40.98264101294393\n ],\n [\n -73.6996393039949,\n 41.10089280193728\n ],\n [\n -73.49391688239375,\n 41.2484207881225\n ],\n [\n -73.56250536378525,\n 41.3073292248944\n ],\n [\n -73.48423008439727,\n 42.05372989136586\n ],\n [\n -73.5233960109858,\n 42.126434934459525\n ],\n [\n -73.41562767810787,\n 42.34405165012723\n ],\n [\n -73.83681626701608,\n 42.54652252735795\n ],\n [\n -74.44841929328119,\n 42.6495653802757\n ],\n [\n -75.7909273246029,\n 43.03044245093176\n ],\n [\n -76.43773682001827,\n 43.50144007856014\n ],\n [\n -77.04532365530125,\n 43.25213523900416\n ],\n [\n -78.07429641881241,\n 43.3804649394846\n ],\n [\n -79.06407311309604,\n 43.2521232638133\n ],\n [\n -79.02485730522619,\n 42.98027715144707\n ],\n [\n -78.90726018672747,\n 42.90136300879922\n ],\n [\n -79.08365221595503,\n 42.69283803617958\n ],\n [\n -79.75001378472541,\n 42.331662242956355\n ],\n [\n -79.76953018691533,\n 42.01940417603805\n ],\n [\n -75.3404244202763,\n 41.98299557666223\n ],\n [\n -75.07586228888928,\n 41.75679559693819\n ],\n [\n -75.0464736918644,\n 41.515116258185316\n ],\n [\n -74.78190029599845,\n 41.44169802010788\n ],\n [\n -74.66431063355593,\n 41.3681952398467\n ],\n [\n -73.93913471760371,\n 41.02160483993919\n ],\n [\n -73.92499168267652,\n 40.760363749644455\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Siemer, William F.","contributorId":348063,"corporation":false,"usgs":false,"family":"Siemer","given":"William F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauber, T. Bruce","contributorId":348064,"corporation":false,"usgs":false,"family":"Lauber","given":"T. Bruce","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stedman, Richard C.","contributorId":348065,"corporation":false,"usgs":false,"family":"Stedman","given":"Richard C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hurst, Jeremy E.","contributorId":348066,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":922916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Catherine C.","contributorId":348067,"corporation":false,"usgs":false,"family":"Sun","given":"Catherine C.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":922917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hollingshead, Nicholas A.","contributorId":348068,"corporation":false,"usgs":false,"family":"Hollingshead","given":"Nicholas A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Belant, Jerrold L.","contributorId":348069,"corporation":false,"usgs":false,"family":"Belant","given":"Jerrold L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kellner, Kenneth III","contributorId":348070,"corporation":false,"usgs":false,"family":"Kellner","given":"Kenneth","suffix":"III","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922921,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241144,"text":"70241144 - 2023 - Decoupling of species and plant communities of the U.S. Southwest: A CCSM4 climate scenario example","interactions":[],"lastModifiedDate":"2023-03-13T12:14:35.489863","indexId":"70241144","displayToPublicDate":"2023-02-08T07:10:15","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":"Decoupling of species and plant communities of the U.S. Southwest: A CCSM4 climate scenario example","docAbstract":"Climate change is predicted to alter the current climate suitability under which plant species and communities occur. Predictions of change have focused on individual species or entire communities, but theory indicates plants will not respond uniformly to climate change within or between communities. We developed models of the current climate suitability (the baseline) of 66 plant species characteristic of 29 plant communities of the arid Southwest, made predictions of climate suitability for the species under two climate change scenarios for the years 2041–2060 (Community Climate System Model version 1.4 [CCSM4] global climate model [GCM], Representative Concentration Pathway [RCP] 4.5 and 8.5 scenarios), and calculated changes in suitability between the future scenarios and baseline for each species. Climate change exposure for the entire community was then evaluated as the composite change of the predicted future climate suitability of the communities' characteristic species. Loss of 25% or more of favorable climate suitability was predicted for 39 (RCP4.5) and 51 (RCP8.5) species within their communities. The proportion of the study area with all species in a community having unfavorable suitability was 17.9% (RCP4.5) and 21.3% (RCP8.5) compared to 6.2% for baseline. We show that suitable climates for species within a plant community are not expected to be a single community-wide trajectory, but rather changes in climate suitability will be unique to the species and not experienced uniformly across the extant communities. This decoupling of plant species within their traditional plant communities may lead to a cascade of unanticipated ecological responses and unprecedented challenges to resource management. Our study results can inform hypotheses of the future successional track of plant communities, characteristic species, and the decisions resource managers must make for management.
In this study, we explore whether the Parker and Baltay (2022) site response models for the Los Angeles (LA) basin region can improve ground‐motion forecasts in the U.S. Geological Survey ShakeAlert earthquake early warning system (hereafter ShakeAlert). We implement the peak ground acceleration and peak ground velocity site response models of Parker and Baltay (2022) in ShakeAlert via the earthquake information to ground‐motion (hereafter eqinfo2GM) module, which predicts ground motions from the estimated earthquake parameters of magnitude, rupture length, and location. The nonergodic site response models for the greater LA area were developed using ground motions from 414 M 3–7.3 earthquakes in southern California. We test nonergodic ground‐motion forecasts for five earthquakes in the LA area: the 1994 M 6.7 Northridge earthquake, the 2008 M 5.4 Chino Hills earthquake, the 2019 M 7.1 Ridgecrest earthquake, the 2020 M 4.5 South El Monte earthquake, and a synthetic M 7.8 earthquake on the southern San Andreas fault from the ShakeOut scenario, which was the basis of a statewide emergency response exercise. From the test results, we find that with the nonergodic site response applied, ShakeAlert not only alerts larger areas but can also result in longer warning times in LA region. In addition, the modified Mercalli intensity (MMI) ground‐motion predictions generated by the ShakeAlert eqinfo2GM module are improved in accuracy when compared with the corresponding ShakeMap ground‐truth MMI when the nonergodic site response model is applied.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220145","usgsCitation":"Lin, R., Parker, G.A., McGuire, J., and Baltay Sundstrom, A.S., 2023, Applications of nonergodic site response models to ShakeAlert case studies in the Los Angeles area: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1324-1343, https://doi.org/10.1785/0120220145.","productDescription":"20 p.","startPage":"1324","endPage":"1343","ipdsId":"IP-142361","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.38078299074152,\n 34.43090673360663\n ],\n [\n -119.38078299074152,\n 33.385811767084306\n ],\n [\n -116.79288709511383,\n 33.385811767084306\n ],\n [\n -116.79288709511383,\n 34.43090673360663\n ],\n [\n -119.38078299074152,\n 34.43090673360663\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Rongrong 0000-0002-6234-2183","orcid":"https://orcid.org/0000-0002-6234-2183","contributorId":305696,"corporation":false,"usgs":true,"family":"Lin","given":"Rongrong","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873573,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240218,"text":"sim3501 - 2023 - Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California","interactions":[],"lastModifiedDate":"2026-02-19T17:50:10.38879","indexId":"sim3501","displayToPublicDate":"2023-02-07T12:49:09","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3501","displayTitle":"Colored Shaded-Relief Bathymetric Map and Orthomosaic from Structure-from-Motion Quantitative Underwater Imaging Device with Five Cameras of the Lake Tahoe Floor, California","title":"Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California","docAbstract":"This two-sheet publication displays a high-resolution colored shaded-relief bathymetric map (sheet 1) and orthomosaic (sheet 2) of part of the Lake Tahoe floor in California generated from a U.S. Geological Survey towed surface vehicle with multiple downward-looking underwater cameras. The system is named the Structure-from-Motion Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). The cameras were synchronized with each other and with a survey-grade Global Navigation Satellite System. A total of 42,939 photographs were collected with nearly complete overlapping coverage of an area approximately 250 meters by 250 meters. A digital terrain model and an orthomosaic were generated from the overlapping photographs using Structure-from-Motion and photogrammetry techniques. Gaps are present in the bathymetry data owing to data-collection or -processing artifacts. These two sheets display the very fine details of the lake floor mapped using SQUID-5.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3501","usgsCitation":"Hatcher, G.A., Warrick, J.A., and Dartnell, P., 2022, Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California: U.S. Geological Survey Scientific Investigations Map 3501, 2 sheets, scale 1:700, https://doi.org/10.3133/sim3501.","productDescription":"2 Sheets: 35.00 × 34.00 inches; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-139653","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":412576,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9934I6U","text":"USGS data release","description":"USGS data release","linkHelpText":"Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412577,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V44ZYS","text":"USGS data release","description":"USGS data release","linkHelpText":"Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3501/coverthb.jpg"},{"id":412574,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet1.pdf","text":"Sheet 1","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 1"},{"id":412575,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet2.pdf","text":"Sheet 2","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 2"},{"id":500207,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114342.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.23690229714447,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 39.300192154621016\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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Research on Bluegills, Lepomis macrochirus R., is abundant but typically focuses on water bodies with similar environmental conditions. We assessed Bluegill density, relative abundance (catch per unit effort [CPUE] by electrofishing), growth, and size structure in 60 lakes with wide-ranging surface areas (2–12,412 ha), trophic states (oligotrophic–hypereutrophic), and macrophyte abundances (0.3–100 percent of lake volume inhabited [PVI]) across Florida, USA. Bluegill density and CPUE increased with lake productivity and decreased with macrophyte abundance. Bluegill growth increased with lake productivity and CPUE of stock-length Florida Bass, Micropterus floridanus L., a Bluegill predator. Bluegill size structure increased with lake productivity and decreased with Bluegill density. Results indicate that Bluegill fisheries with abundant individuals of quality size (≥150 mm) require productive (>25 μg/L chlorophyll-a concentration) lakes with moderate to high macrophyte coverage (PVI 50–100), abundant stock-length Florida Bass (>40 fish/h of electrofishing), and Bluegill densities <300 fish/ha. This study provides an approach to predict Bluegill population demographics based on abiotic and biotic factors, establish fisheries management expectations, and develop regional and lake-specific management tools.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8020100","usgsCitation":"Carlson, A.K., and Hoyer, M.V., 2023, Bluegill population demographics as related to abiotic and biotic factors in Florida lakes: Fishes, v. 8, no. 2, 100, 19 p., https://doi.org/10.3390/fishes8020100.","productDescription":"100, 19 p.","ipdsId":"IP-139048","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444546,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8020100","text":"Publisher Index Page"},{"id":432951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":10,\"properties\":{\"name\":\"Florida\",\"nation\":\"USA 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Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoyer, Mark V.","contributorId":340952,"corporation":false,"usgs":false,"family":"Hoyer","given":"Mark","email":"","middleInitial":"V.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":907727,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70254881,"text":"70254881 - 2023 - An evaluation of multistate occupancy models for estimating relative abundance and population trends","interactions":[],"lastModifiedDate":"2024-06-11T16:40:06.367815","indexId":"70254881","displayToPublicDate":"2023-02-07T11:37:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of multistate occupancy models for estimating relative abundance and population trends","docAbstract":"Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110303","usgsCitation":"Steen, V., Duarte, A., and Peterson, J., 2023, An evaluation of multistate occupancy models for estimating relative abundance and population trends: Ecological Modelling, v. 478, 110303, 9 p., https://doi.org/10.1016/j.ecolmodel.2023.110303.","productDescription":"110303, 9 p.","ipdsId":"IP-144901","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2023.110303","text":"Publisher Index Page"},{"id":429891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"478","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":902764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":902765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}