Unknown causes behind the loss of freshwater mussel populations have prompted population restoration as a tool to recover these imperiled species. However, water quality conditions that support mussel species within natural environments and potential causes of water quality impairment in systems with declining populations are typically unknown and may be critical knowledge needed before reintroducing mussels. Our objective was to relate the growth and survival of declining freshwater mussel populations of the Brook Floater Alasmidonta varicosa (Lamarck, 1819) to water quality parameters within 4 Massachusetts, USA, rivers containing extant populations. We deployed propagated age-1 and age-2 Brook Floater in contained systems (silos) for 1 growing season (June–October). Through biweekly sampling, we tracked the growth and survival of mussels then modeled their relationships with water quality variables. Mussels had a higher growth rate at a higher chlorophyll a (Chl a) value (2.82 µg/L) over the temperature range measured (biweekly mean = 16–26°C) when compared with lower Chl a values (0.61 and 1.17 µg/L). Age-1 mussel growth rate was negatively affected by low Chl a concentration (0.61 µg/L) across the temperature range, but age-2 mussel growth rate was not negatively affected until temperatures were above ~22°C. Na+ limited the growth rate of mussels, with the rate of change in growth rate for age-1 mussels greater than for age-2 mussels. Other cations (Mg2+, K+, and Ca2+)—potentially linked to road deicers—also negatively affected growth rate in all 4 rivers but may have had a greater impact on mussels in rivers with reduced growth rates from lower temperatures and Chl a. However, survival was uniformly high across all rivers, indicating water quality parameters may have sublethal but not lethal effects. Additional assessments for chronic water quality stressors along with changing land cover, land management, and climates are important considerations for restoration potential and the long-term persistence of populations.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/730247","usgsCitation":"Skorupa, A., Roy, A.H., Hazelton, P., Perkins, D., Timothy Warren, T., and Cheng, B.S., 2024, Food, water quality, and the growth of a freshwater mussel: Implications for population restoration: Freshwater Science, v. 43, no. 2, p. 107-123, https://doi.org/10.1086/730247.","productDescription":"7 p.","startPage":"107","endPage":"123","ipdsId":"IP-154695","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.92801492613863,\n 42.333950606928596\n ],\n [\n -71.9376914557071,\n 42.33232039224919\n ],\n [\n -73.077586638902,\n 42.32546816564172\n ],\n [\n -73.17435193458931,\n 42.06443189078277\n ],\n [\n -71.94349737344804,\n 42.081670744671186\n ],\n [\n -71.92801492613863,\n 42.333950606928596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.62300789778979,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.70452095814687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":340492,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter D.","contributorId":340493,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, David","contributorId":340494,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Timothy Warren, Timothy","contributorId":340495,"corporation":false,"usgs":false,"family":"Timothy Warren","given":"Timothy","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907297,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Brian S.","contributorId":340496,"corporation":false,"usgs":false,"family":"Cheng","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907298,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264807,"text":"70264807 - 2024 - Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","interactions":[],"lastModifiedDate":"2025-03-25T14:09:06.120979","indexId":"70264807","displayToPublicDate":"2024-04-05T08:59:18","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"displayTitle":"Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","title":"Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","docAbstract":"This field trip examines the results of integrated geologic studies of the 9 August 2020, Mw 5.1 earthquake near Sparta, North Carolina, USA. The earthquake generated ~4 km of coseismic surface rupture of the Little River fault and uplifted a surface area of ~11 km2. The Little River fault is a thrust fault oriented 110–130°/45–70°SW, and mapped fault segments are en echelon with scarp heights from <5–30 cm. The epicenter is in polydeformed rocks of the Ashe and Alligator Back Metamorphic Suites in the eastern Blue Ridge. Bedrock structure formed during multiple Paleozoic orogenies; the regional foliation strikes NE-SW and dips SE (mean orientation 063°/52°SE). Mapping identified late Paleozoic veins and shear zones, a regional joint set striking 330–340° and 250–240°, and brittle faults that cut the Paleozoic foliation. Brittle faults oriented similar to the Little River fault are mapped up to 4 km along strike from the coseismic rupture along Bledsoe Creek valley, and the combined length of the Little River fault system is ~8 km. Paleoseismic trenches across the Little River fault corroborate the reactivation of an older fault by the 2020 earthquake and reveal two events during late Pleistocene (<50 ka). Surficial mapping identified several terrace deposits, including a deposit along Bledsoe Creek that yielded a 26Al/10Be isochron burial age of 0.46 ± 0.13 Ma and overlies a brittle fault, thus constraining the timing of movement of the fault at that location. Paleoliquefaction studies document soft-sediment deformation features in alluvium that may represent paleoseismic events. Collectively, these results highlight long-lived paleoseismicity of the Blue Ridge and that the 9 August 2020 earthquake reactivated an older, suitably oriented brittle fault in the bedrock. The Little River fault is an example of a previously unknown but active fault lying outside of known seismic zones with demonstrated recurrence of paleo-ruptures, raising questions about the assumption that damaging earthquakes are limited to areas of ongoing background seismicity, which is counter to seismic hazard assessments in the eastern United States.
Bedrock mapping separates eastern Blue Ridge lithostratigraphy of the Lynchburg Group and Ashe and Alligator Back Metamorphic Suites into separate fault-bound packages juxtaposed over various 1.3–1.0 Ga basement rocks of the northern French Broad massif by the Gossan Lead fault.
","largerWorkTitle":"Geology and geologic hazards of the Blue Ridge: Field excursions for the 2024 GSA southeastern section meeting, Asheville, North Carolina, USA","language":"English","publisher":"Geological Society of America","doi":"10.1130/2024.0067(03)","usgsCitation":"Merschat, A.J., Carter, M.W., Lynn, A., Stewart, K., Figueiredo, P., Odom, W.E., McAleer, R.J., Vazquez, J.A., Powell, N.E., and Holm-Denoma, C.S., 2024, Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA, chap. of Geology and geologic hazards of the Blue Ridge: Field excursions for the 2024 GSA southeastern section meeting, Asheville, North Carolina, USA, v. 67, p. 69-106, https://doi.org/10.1130/2024.0067(03).","productDescription":"38 p.","startPage":"69","endPage":"106","ipdsId":"IP-177390","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":483771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Sparta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.20449414311922,\n 36.5814103989881\n ],\n [\n -81.2095938748342,\n 36.44322251293683\n ],\n [\n -81.04130272824062,\n 36.44322251293683\n ],\n [\n -81.04275979444436,\n 36.57614511920512\n ],\n [\n -81.20449414311922,\n 36.5814103989881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":931956,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science 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mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":931776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynn, Ashley S.","contributorId":352582,"corporation":false,"usgs":false,"family":"Lynn","given":"Ashley S.","affiliations":[{"id":24614,"text":"North Carolina Geological Survey","active":true,"usgs":false}],"preferred":false,"id":931777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Kevin G.","contributorId":352583,"corporation":false,"usgs":false,"family":"Stewart","given":"Kevin G.","affiliations":[{"id":84275,"text":"University of North Carolina-Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":931778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Figueiredo, Paula","contributorId":248217,"corporation":false,"usgs":false,"family":"Figueiredo","given":"Paula","affiliations":[{"id":49830,"text":"North Carolina University","active":true,"usgs":false}],"preferred":false,"id":931779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":931782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Powell, Nicholas Edwin 0000-0003-3654-8759","orcid":"https://orcid.org/0000-0003-3654-8759","contributorId":304622,"corporation":false,"usgs":true,"family":"Powell","given":"Nicholas","email":"","middleInitial":"Edwin","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931783,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 cholm-denoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":2442,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher","email":"cholm-denoma@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":931784,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70263160,"text":"70263160 - 2024 - Morbidity in California giant salamander (Dicamptodon ensatus Eschscholtz, 1833) caused by Euryhelmis sp. Poche, 1926 (Trematoda: Heterophyiidae)","interactions":[],"lastModifiedDate":"2025-01-30T15:13:06.533454","indexId":"70263160","displayToPublicDate":"2024-04-05T08:04:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Morbidity in California giant salamander (Dicamptodon ensatus Eschscholtz, 1833) caused by Euryhelmis sp. Poche, 1926 (Trematoda: Heterophyiidae)","docAbstract":"In the fall of 2021, California Department of Fish and Wildlife reported larval and adult California giant salamanders (Dicamptodon ensatus Eschscholtz, 1833) with skin lesions at multiple creeks in Santa Clara and Santa Cruz Counties, California, USA. Field signs in both stages included rough, lumpy textured skin, and larvae with tails that were disproportionately long, flat, wavy, and flaccid. Presence of large-bodied larvae suggested delayed metamorphosis, with some larvae having cloudy eyes and suspected blindness. To determine the cause of the disease, three first-of-the-year salamanders from one location were collected, euthanized with 20% benzocaine, and submitted for necropsy to the U.S. Geological Survey, National Wildlife Health Center. Upon gross examination, all salamanders were emaciated with no internal fat stores, and had multiple pinpoint to 1.5-mm diameter raised nodules in the skin over the body, including the head, gills, dorsum, ventrum, all four limbs, and the tail; one also had nodules in the oral cavity and tongue. Histologically all salamanders had multiple encysted metacercariae in the dermis, subcutis, and skeletal muscles of the head, body, and tail that were often associated with granulomatous and granulocytic inflammation and edema. A small number of encysted meta cercariae or empty cysts were present in the gills with minimal inflammation, and rarely in the kidney with no associated inflammation. Morphology of live metacercariae (Trematoda: Heterophyiidae), and sequencing of the 28S rRNA gene identified a species of Euryhelmis (Poche, 1926). Artificial digestion of a 1.65 g, decapitated, eviscerated carcass yielded 773 metacercariae, all of similar size and morphology as the live specimens. Based on these findings, the poor body condition of these salamanders was concluded to be due to heavy parasite burden. Environmental factors such as drought, increased temperature, and overcrowded conditions may be exacerbating parasite infections in these populations of salamander.
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Jaimie L. 0000-0002-2305-6862 jaimiemiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2305-6862","contributorId":272836,"corporation":false,"usgs":false,"family":"Miller","given":"Jaimie","email":"jaimiemiller@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":925700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Lawrence","contributorId":350328,"corporation":false,"usgs":false,"family":"Erickson","given":"Lawrence","affiliations":[{"id":83717,"text":"Independent Contractor, Boulder Creek, CA","active":true,"usgs":false}],"preferred":false,"id":925701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fork, Susanne","contributorId":350329,"corporation":false,"usgs":false,"family":"Fork","given":"Susanne","affiliations":[{"id":40430,"text":"Elkhorn Slough National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":925702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roderick, Constance 0000-0001-8330-8024","orcid":"https://orcid.org/0000-0001-8330-8024","contributorId":215346,"corporation":false,"usgs":true,"family":"Roderick","given":"Constance","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cole, Rebecca A. 0000-0003-2923-1622 rcole@usgs.gov","orcid":"https://orcid.org/0000-0003-2923-1622","contributorId":2873,"corporation":false,"usgs":true,"family":"Cole","given":"Rebecca","email":"rcole@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925705,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252769,"text":"ofr20241020 - 2024 - Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan","interactions":[],"lastModifiedDate":"2024-04-05T13:51:41.672255","indexId":"ofr20241020","displayToPublicDate":"2024-04-05T07:43:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1020","displayTitle":"Using Structured Decision Making to Assess Management Alternatives to Inform the 2024 Update of the Minnesota Invasive Carp Action Plan","title":"Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan","docAbstract":"This report summarizes the results of a structured decision making process started by the Minnesota Department of Natural Resources to develop and evaluate various invasive carp management strategies to inform a 2024 update of the Minnesota Invasive Carp Action Plan. The Minnesota Department of Natural Resources invited State, Federal, Tribal, and nongovernmental organization partners to participate in online and in-person workshops to elicit concerns and perform an expert-based assessment of potential invasive carp management strategies to address those concerns. The participatory group divided into two subgroups: the values team and the technical team. The values team specified 12 management objectives that captured the major interest group concerns. The technical and values teams developed 18 different invasive carp management strategies to compare. The technical team then scored each strategy in terms of how well it met each management objective. The values team weighted the management objectives to assess where tradeoffs might need to be made. The results of this process captured the wide range of partner concerns and technical opinions using a transparent, repeatable, and rigorous method. The analyses suggest that tradeoffs between management efficacy and cost are likely. Furthermore, considerable uncertainty exists among the technical experts regarding which strategy is likely to be most effective or cost-efficient. Followup analyses were done to assess how resolving uncertainty among experts could affect decision making and guide future monitoring efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241020","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources","usgsCitation":"Post van der Burg, M., and Colvin, M.E., 2024, Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan: U.S. Geological Survey Open-File Report 2024–1020, 19 p., https://doi.org/10.3133/ofr20241020.","productDescription":"Report: vi, 19 p; 1 Table","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-161101","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":427385,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241020/full"},{"id":427383,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1020/ofr20241020.XML"},{"id":427384,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1020/images/"},{"id":427382,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2024/1020/downloads/","text":"Table 1.1"},{"id":427381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1020/ofr20241020.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 20241020"},{"id":427380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1020/coverthb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","otherGeospatial":"Upper Mississippi River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.28863011626855,\n 36.967503821513446\n ],\n [\n -87.94391643617755,\n 40.58553678179527\n ],\n [\n -85.83165826135074,\n 41.39856936972541\n ],\n [\n -86.26582742157316,\n 41.79170899467056\n ],\n [\n -87.56529202081786,\n 41.69802278026626\n ],\n [\n -88.28550154004203,\n 43.465559915400405\n ],\n [\n -89.81947003232136,\n 43.27701839489646\n ],\n [\n -88.441517663658,\n 45.88964868854825\n ],\n [\n -92.89455996568974,\n 46.6688703992705\n ],\n [\n -93.90294960750411,\n 46.954074901936195\n ],\n [\n -93.89057371275759,\n 47.45968753805238\n ],\n [\n -95.66287916799946,\n 47.30832878269692\n ],\n [\n -96.0833744059119,\n 45.445266641443965\n ],\n [\n -97.25196294733773,\n 45.73968681198525\n ],\n [\n -97.57385808711409,\n 45.5014351606913\n ],\n [\n -95.14621163333918,\n 43.21030588453809\n ],\n [\n -95.56457215116306,\n 42.47496837126678\n ],\n [\n -91.70810834882887,\n 38.50319929325272\n ],\n [\n -89.94905595458137,\n 36.68584673279122\n ],\n [\n -89.28863011626855,\n 36.967503821513446\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
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8711 37th Street Southeast
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Many freshwater lakes are groundwater flow-through systems. Although lakes commonly are considered to be sinks for nitrogen inputs, relatively little is known about carbon and nitrogen export from lakes to groundwater. The current study focused on lake-bottom biogeochemical processes accompanying the transport of nitrogen, dissolved oxygen (O2), and dissolved organic carbon (DOC) during lake-water recharge from a groundwater flow-through lake. Lake-water and porewater (15–100 cm below lakebed) samples were collected along transects within the lake downwelling zone. Infiltrating porewater O2 and DOC concentrations decreased with depth while nitrate (NO3−) concentrations increased, indicating nitrification of organic matter within the profiles. The depth of NO3− production and transport was seasonally dependent. In winter, NO3− and O2 were exported beyond 100-cm depth; whereas in summer, shallow nitrification zones were underlain by deeper NO3− reduction zones, and diel patterns of O2 and NO3− penetration depths were observed. Microbial community compositions and stable isotope profiles (δ15N[NO3−], δ18O[NO3−], δ18O[O2]) were consistent with apparent C–N–O reaction stoichiometries indicating O2 reduction and nitrification in shallower porewater, followed by varying NO3− reduction at depth. Maximum porewater NO3− concentrations (∼10–20 μM) were limited by infiltrating O2 concentrations and C/N ratios of reacting organic matter. Lake-water level variations caused changes in shoreline position and porewater velocities, while variations in lake-water temperature, DOC, and O2 contributed to changes in reaction rates and depth of O2 and NO3− penetration into the lakebed. The quality of groundwater recharged by lake water reflected temporally and spatially varying physical and biogeochemical processes in the sediment porewater.
Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.
Carbon dioxide (CO2) is gaining interest as a tool to combat aquatic invasive species, including zebra mussels (Dreissena polymorpha). However, the effects of water chemistry on CO2 efficacy are not well described. We conducted five trials in which we exposed adult zebra mussels to a range of CO2 in water with adjusted total hardness and specific conductance. We compared dose–responses and found differences in lethal concentration to 50% of organisms (LC50) estimates ranging from 108.3 to 179.3 mg/L CO2 and lethal concentration to 90% of organisms (LC90) estimates ranging from 163.7 to 216.6 mg/L CO2. We modeled LC50 and LC90 estimates with measured water chemistry variables from the trials. We found sodium (Na+) concentration to have the strongest correlation to changes in the LC50 and specific conductance to have the strongest correlation to changes in the LC90. Our results identify water chemistry as an important factor in considering efficacious CO2 concentrations for zebra mussel control. Additional research into the physiological responses of zebra mussels exposed to CO2 may be warranted to further explain mode of action and reported selectivity. Further study could likely develop a robust and relevant model to refine CO2 applications for a wider range of water chemistries. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
An understanding of oceanographic conditions and processes important to marine animal ecology is fundamental to the development of effective management and conservation actions. Longfin Smelt (Spirinchus thaleichthys) is a pelagic forage fish found in coastal and estuarine waters along the Pacific coast of North America from Alaska to central California. Substantial population declines in California’s San Francisco Estuary, where Longfin Smelt are protected under California’s Endangered Species Act, have prompted extensive study of estuarine factors associated with the decline. However, coastal factors that affect up to two-thirds of the Longfin Smelt life cycle are poorly understood and may be important drivers of population dynamics. We compiled coastal observations from numerous sources to estimate the range-wide coastal marine distribution of Longfin Smelt and assess habitat factors affecting distribution in the northeast Pacific Ocean. Based on maximum entropy species distribution models, Longfin Smelt distribution was correlated with depth, distance from the nearest estuary, sea surface temperature, and sea surface chlorophyll. Longfin Smelt were found in shallow, higher productivity coastal waters closer to estuaries, with depth and temperature the most consistent factors influencing distribution. Habitat suitability was highly variable at the southern extent of the range, particularly off the California coast, and was largely driven by habitat contractions associated with warm-water conditions. Study results provide insights into the habitat and range-wide distribution of an at-risk estuarine-reliant forage fish and are the first step toward identifying processes that affect the marine portion of the Longfin Smelt life cycle.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
He‘eia and ‘Ioleka‘a Streams in the He‘eia watershed on O‘ahu, Hawai‘i, receive substantial discharge from dike-impounded groundwater. Previous studies indicated that groundwater withdrawals from the watershed affect streamflow. Resource managers and users seek information that can be used to balance the needs of competing uses of groundwater and streamflow in the watershed.
In this study, analyses of historical streamflow and withdrawal data indicate that when groundwater withdrawals from Haiku Tunnel (a groundwater development tunnel built in the 1940s in the watershed) of 1.73–1.87 million gallons per day (Mgal/d) were introduced in the first few decades of the tunnel’s operation, base flow at a gage on He‘eia Stream decreased by 1.37–1.40 Mgal/d. Changes in rainfall during this period were not sufficient to account for the changes in base flow. The tunnel withdrawal also affected ‘Ioleka‘a Stream, but the effect was less. In the 1980s, average withdrawal from the tunnel decreased by 0.73–1.00 Mgal/d and base flow at the He‘eia streamgage increased by 0.15–0.21 Mgal/d; a concurrent rainfall increase may partly account for the base-flow increase. Withdrawal from another well (Haiku well) starting in the late 1980s had a much smaller effect than the tunnel did on flow at the He‘eia streamgage.
Numerical groundwater-model simulations indicate that shutting down withdrawals from Haiku Tunnel and Haiku well would increase base flows in streams inside and outside of the He‘eia watershed. Simulated shutdown of 0.35 Mgal/d withdrawal from Haiku well caused base flow of streams in the He‘eia watershed to increase by 0.09 Mgal/d or 26 percent of the withdrawal reduction, and shutdown of 0.60 Mgal/d withdrawal from Haiku Tunnel caused base flow of streams within the watershed to increase by 0.12 Mgal/d or 20 percent of withdrawal reduction. Shutdown of a combined 0.95 Mgal/d withdrawal from the tunnel and well caused base flow of streams within the watershed to increase by 0.22 Mgal/d or 23 percent of the withdrawal reduction.
The model simulations and analyses of streamflow data demonstrate that, climate changes notwithstanding, reducing or shutting down withdrawal from Haiku Tunnel has not in the past, and will not in the future, restore base flow to predevelopment rates. The nearly pristine condition that existed prior to the construction of the Haiku Tunnel no longer exists because other large-producing tunnels and wells near the He‘eia watershed have since begun withdrawing water from the same dike-impounded aquifer. Reduction or shutdown of withdrawals from the wells and tunnel in the He‘eia watershed cannot restore streamflow to predevelopment rates if withdrawals from all other wells and tunnels continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245020","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Izuka, S.K., Kāne, H.L., and Rotzoll, K., 2024, Groundwater and surface-water interactions in the He‘eia watershed, O‘ahu, Hawai‘i—Insights from analysis of historical data and numerical groundwater-model simulations: U.S. Geological Survey Scientific Investigations Report 2024–5020, 22 p., https://doi.org/10.3133/sir20245020.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149791","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499449,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116223.htm","linkFileType":{"id":5,"text":"html"}},{"id":427359,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5020/sir20245020.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427358,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5020/covrthb.jpg"},{"id":427357,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91JM5FZ","text":"USGS Data Release","description":"Rotzoll, K., 2024, MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P91JM5FZ.","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii"}],"country":"United States","state":"Hawaii","otherGeospatial":"He‘eia Watershed, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.841667,\n 21.441667\n ],\n [\n -157.841667,\n 21.391667\n ],\n [\n -157.791667,\n 21.391667\n ],\n [\n -157.791667,\n 21.441667\n ],\n [\n -157.841667,\n 21.441667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America’s ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America.
We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature.
We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou (Rangifer tarandus), elk (Cervus canadensis), and white-tailed deer (Odocoileus virginianus) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat.
The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn (Antilocapra americana) and mountain goats (Oreamnos americanus), and on ungulates in the eastern U.S. and Mexico.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-024-00331-8","usgsCitation":"Malpeli, K., Endyke, S.C., Weiskopf, S.R., Thompson, L., Johnson, C.G., Kurth, K.A., and Carlin, M.A., 2024, Existing evidence on the effects of climate variability and climate change on ungulates in North America: A systematic map: Environmental Evidence, v. 13, 8, 21 p., https://doi.org/10.1186/s13750-024-00331-8.","productDescription":"8, 21 p.","ipdsId":"IP-159301","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439938,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-024-00331-8","text":"Publisher Index Page"},{"id":434994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EMV7HO","text":"USGS data release","linkHelpText":"Catalogue of the literature assessing climate effects on ungulates in North America 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Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":221497,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Ciara G.","contributorId":271273,"corporation":false,"usgs":false,"family":"Johnson","given":"Ciara","email":"","middleInitial":"G.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":898183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kurth, Katherine Anne 0000-0002-6883-8307","orcid":"https://orcid.org/0000-0002-6883-8307","contributorId":334177,"corporation":false,"usgs":true,"family":"Kurth","given":"Katherine","email":"","middleInitial":"Anne","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlin, Maxfield A.","contributorId":335367,"corporation":false,"usgs":false,"family":"Carlin","given":"Maxfield","email":"","middleInitial":"A.","affiliations":[{"id":37275,"text":"none","active":true,"usgs":false}],"preferred":false,"id":898185,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254886,"text":"70254886 - 2024 - Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","interactions":[],"lastModifiedDate":"2024-06-10T15:45:06.315386","indexId":"70254886","displayToPublicDate":"2024-04-04T10:36:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","title":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","docAbstract":"Ungulates commonly select habitat with higher forage biomass and or nutritional quality to improve body condition and fitness. However, predation risk can alter ungulate habitat selection and foraging behavior and may affect their nutritional condition. Ungulates often choose areas with lower predation risk, sometimes sacrificing higher quality forage. This forage–predation risk trade-off can be important for life history strategies and influences individual nutritional condition and population vital rates. We used GPS collar data from adult female mule deer (Odocoileus hemionus) and mountain lions (Puma concolor) to model mule deer habitat selection in relation to forage conditions, stalking cover and predation risk from mountain lions to determine if a forage-predation risk trade-off existed for mule deer in central New Mexico. We also examined mountain lion kill sites and mule deer foraging locations to assess trade-offs at a finer scale. Forage biomass and protein content were inversely correlated with horizontal visibility, hence associated with higher stalking cover for mountain lions, suggesting a forage-predation risk trade-off for mule deer. Mule deer habitat selection was influenced by forage biomass and protein content at the landscape and within home range spatial scales, with forage protein being related to habitat selection during spring and summer and forage biomass during winter. However, mule deer selection for areas with better foraging conditions was constrained by landscape-scale encounter risk for mountain lions, such that increasing encounter risk was associated with diminished selection for areas with better foraging conditions. Mule deer also selected for areas with higher visibility when mountain lion predation risk was higher. Mountain lion kill sites were best explained by decreasing horizontal visibility and available forage protein, suggesting that deer may be selecting for forage quality at the cost of predation risk. A site was 1.5 times more likely to be a kill site with each 1-meter decrease in visibility (i.e., increased stalking cover). Mule deer selection of foraging sites was related to increased forage biomass, further supporting the potential for a trade-off scenario. Mule deer utilized spatio-temporal strategies and risk-conditional behavior to reduce predation risk, and at times selected suboptimal foraging areas with lower predation risk.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1121439","usgsCitation":"Cain, J.W., Kay, J.H., Liley, S.G., and Gedir, J.V., 2024, Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk: Frontiers in Ecology and Evolution, v. 12, 1121439, 17 p., https://doi.org/10.3389/fevo.2024.1121439.","productDescription":"1121439, 17 p.","ipdsId":"IP-148026","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2024.1121439","text":"Publisher Index Page"},{"id":429764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cibola National Forest, Gallinas Mountains area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.59348062065897,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.340783560738174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kay, Jacob H.","contributorId":337909,"corporation":false,"usgs":false,"family":"Kay","given":"Jacob","email":"","middleInitial":"H.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liley, Stewart G.","contributorId":337910,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","email":"","middleInitial":"G.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gedir, Jay V.","contributorId":337911,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":902779,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252794,"text":"70252794 - 2024 - Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","interactions":[],"lastModifiedDate":"2024-04-05T15:19:37.198461","indexId":"70252794","displayToPublicDate":"2024-04-04T10:14:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","docAbstract":"Introduction: Information on spatial patterns of water depth in river channels is valuable for numerous applications, but such data can be difficult to obtain via traditional field methods. Ongoing developments in remote sensing technology have enabled various image-based approaches for mapping river bathymetry; this study evaluated the potential to retrieve depth from multispectral images acquired by an uncrewed aircraft system (UAS).
Methods: More specifically, we produced depth maps for a 4 km reach of a clear-flowing, relatively shallow river using an established spectrally based algorithm, Optimal Band Ratio Analysis. To assess accuracy, we compared image-derived estimates to direct measurements of water depth. The field data were collected by wading and from a boat equipped with an echo sounder and used to survey cross sections and a longitudinal profile. We partitioned our study area along the Sacramento River, California, USA, into three distinct sub-reaches and acquired a separate image for each one. In addition to the typical, self-contained, per-image depth retrieval workflow, we also explored the possibility of exporting a relationship between depth and reflectance calibrated using data from one site to the other two sub-reaches. Moreover, we evaluated whether sampling configurations progressively more sparse than our full field survey could still provide sufficient calibration data for developing robust depth retrieval models.
Results: Our results indicate that under favorable environmental conditions like those observed on the Sacramento River during low flow, accurate, precise depth maps can be derived from images acquired by UAS, not only within a sub-reach but also across multiple, adjacent sub-reaches of the same river.
Discussion: Moreover, our findings imply that the level of effort invested in obtaining field data for calibration could be significantly reduced. In aggregate, this investigation suggests that UAS-based remote sensing could facilitate highly efficient, cost-effective, operational mapping of river bathymetry at the reach scale in clear-flowing streams.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1305991","usgsCitation":"Legleiter, C.J., and Harrison, L.R., 2024, Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images: Frontiers in Remote Sensing, v. 5, 1305991, 16 p., https://doi.org/10.3389/frsen.2024.1305991.","productDescription":"1305991, 16 p.","ipdsId":"IP-156864","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1305991","text":"Publisher Index Page"},{"id":434995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEXVAR","text":"USGS data release","linkHelpText":"Multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, acquired September 14-16, 2021"},{"id":427518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 40\n ],\n [\n -122.5,\n 39\n ],\n [\n -121.75,\n 39\n ],\n [\n -121.75,\n 40\n ],\n [\n -122.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":898243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252803,"text":"70252803 - 2024 - Apparent non-double-couple components as artifacts of moment tensor inversion","interactions":[],"lastModifiedDate":"2024-04-05T15:09:16.834651","indexId":"70252803","displayToPublicDate":"2024-04-04T10:05:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"Apparent non-double-couple components as artifacts of moment tensor inversion","docAbstract":"Compilations of earthquake moment tensors from global and regional catalogs find pervasive non-double-couple (NDC) components with a mean deviation from a double-couple (DC) source of around 20%. Their distributions vary only slightly with magnitude, faulting mechanism, or geologic environments. This consistency suggests that for most earthquakes, especially smaller ones whose rupture processes are expected to be simpler, the NDC components are largely artifacts of the moment tensor inversion procedure. This possibility is also supported by the fact that NDC components for individual earthquakes with Mw<6.5 are only weakly correlated between catalogs. We explore this possibility by generating synthetic seismograms for the double-couple components of earthquakes around the world using one Earth model and inverting them with a different Earth model. To match the waveforms with a different Earth model, the inversion changes the mechanisms to include a substantial NDC component while largely preserving the fault geometry (DC component). The resulting NDC components have a size and distribution similar to those reported for the earthquakes in the Global Centroid Moment Tensor (GCMT) catalog. The fact that numerical experiments replicate general features of the pervasive NDC components reported in moment tensor catalogs implies that these components are largely artifacts of the inversions not adequately accounting for the effects of laterally varying Earth structure.
","language":"English","publisher":"Seismica","doi":"10.26443/seismica.v3i1.1157","usgsCitation":"Rosler, B., Stein, S., Ringler, A.T., and Vackar, J., 2024, Apparent non-double-couple components as artifacts of moment tensor inversion: Seismica, v. 3, no. 1, 1157, 11 p., https://doi.org/10.26443/seismica.v3i1.1157.","productDescription":"1157, 11 p.","ipdsId":"IP-140079","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v3i1.1157","text":"Publisher Index Page"},{"id":427517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosler, Boris","contributorId":335403,"corporation":false,"usgs":false,"family":"Rosler","given":"Boris","email":"","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":898275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vackar, Jiri","contributorId":335404,"corporation":false,"usgs":false,"family":"Vackar","given":"Jiri","email":"","affiliations":[{"id":80396,"text":"The Czech Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":898276,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252873,"text":"70252873 - 2024 - Preliminary implications of viscoelastic ray theory for anelastic seismic tomography models","interactions":[],"lastModifiedDate":"2024-06-03T14:58:44.548945","indexId":"70252873","displayToPublicDate":"2024-04-04T07:02:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary implications of viscoelastic ray theory for anelastic seismic tomography models","docAbstract":"The recent developments in general viscoelastic ray theory provide a rigorous mathematical framework for anelastic seismic tomography. They provide closed‐form solutions of forward ray‐tracing and simple inverse problems for anelastic horizontal and spherical layered media with material gradients. They provide ray‐tracing computation algorithms valid for all angles of incidence that account for changes in wave speed, attenuation, and trajectory of anelastic P and S body waves induced by anelastic boundaries. They account for theoretical predictions that seismic waves refract as inhomogeneous waves across anelastic boundaries for all angles of incidence, which in turn accounts for energy carried by plane waves along seismic boundaries at head wave critical angles and wide‐angle refracted (WAR) ray paths that are not predicted by elastic models. Exact viscoelastic ray‐tracing numerical results for various models provide examples that illustrate the effects of anelastic boundaries on the travel times and amplitudes of seismic waves. They show the effects are strongly dependent on angle of incidence. For near‐critical and wide angles of incidence the anelastic effects on travel times and amplitudes can be large and are not explained by elastic ray theory, but the effects on travel times can be relatively small and difficult to distinguish from those for elastic media for pre‐near‐critical angles of incidence. The results for some models indicate that reflected anelastic WAR waves may be observable at the surface and possibly account for some prominent seismic arrivals not explained by elasticity. These preliminary results suggest that the application of exact viscoelastic ray‐tracing computation algorithms to exploration and teleseismic data sets can reveal new insights regarding the properties and distribution of anelastic materials in the Earth.
From 2018 through 2020, the U.S. Geological Survey, in cooperation with the Air Force Civil Engineering Center, conducted an integrated study of the Fountain Creek alluvial aquifer located near Colorado Springs, Colorado. The objective of the study was to characterize hydrologic conditions for the alluvial aquifer pertinent to the potential for transport of solutes. Specific goals of this report were to characterize the groundwater hydrology of the area, to quantify groundwater and surface-water interactions, to estimate hydraulic properties of the aquifer using aquifer testing, and to complete numerical simulations of groundwater flow.
Synoptic groundwater-level elevation measurements completed throughout this study, and as part of other U.S. Geological Survey programs between 1994 and 2020, indicate groundwater-level elevations fluctuate on annual and interannual timeframes. Groundwater-level fluctuations likely were caused by temporally variable groundwater recharge and discharge components in the area, with many wells showing maximum groundwater-level elevations during the winter months (November through March). From an interannual perspective, groundwater-level fluctuations appear to have reached maximum values during 2000 to 2003, decreased during 2003 to 2006, and remained relatively constant since that time, with the exception of several wells which have displayed rising groundwater-level elevations since 2018. Spatial evaluation of groundwater-level elevations indicates groundwater flow is generally from northeast to southwest within the vicinity of several alluvial paleochannels occurring along the northeastern margin of the aquifer. Within the center of the aquifer along Fountain Creek, groundwater flow is generally from north to south, approximately paralleling surface-water flow. To quantitatively understand the potential effect of groundwater recharge and groundwater pumping on fluctuations in groundwater-level elevation, a statistical transfer-function-noise model was applied. Results of the statistical model indicate throughout most of the aquifer, fluctuations were primarily the result of recharge seasonality. In the main stem of the aquifer where groundwater pumping wells were more concentrated, however, groundwater-level elevation fluctuations were also attributable to groundwater pumping through time.
Three-dimensional evaluation of the aquifer geometry near Fountain Creek was combined with synoptic streamflow measurement and accounting of stream gains and losses to evaluate groundwater and surface-water interactions in the study area. Streamflow gain or loss calculations indicate Fountain Creek both gains from and loses flow to the alluvial aquifer, and gaining or losing reaches of the stream may be partially controlled by the depth to bedrock near the stream. Reaches with streamflow gains tend to coincide with areas where the estimated depth to bedrock is decreasing, meaning the alluvial aquifer is likely thinning in these areas and groundwater-flow paths may be converging and discharging groundwater to the stream. Losing reaches tended to coincide with locally greater depth to bedrock where the alluvial aquifer is likely thicker and has greater storage potential for surface water lost from Fountain Creek.
Results of aquifer testing indicate hydraulic conductivity, estimated from slug tests and single-well pumping tests, ranged from 0.32 to 1,410 feet per day (ft/d) and 4.13 to 664 ft/d, respectively. These results are similar to the range of values from previous aquifer tests in the study area. Hydraulic conductivities from aquifer testing for this study were generally greater than the estimates of previous slug tests and had a mean value less than the estimates from previous pumping tests. Spatial evaluation of aquifer testing results indicates hydraulic conductivity tends to be greater in the main stem of the alluvial aquifer and lower in paleochannels upgradient from the main stem of the aquifer. The spatial variation in hydraulic conductivity may be attributed to the geomorphologic processes that formed the alluvial aquifer. Compacted sediment in the paleochannels has not been potentially transported sufficient distance to cause grain-size sorting, resulting in a poorly sorted deposit and lower hydraulic conductivities. In the central portion of the alluvial aquifer, near Fountain Creek, the sediments have been transported farther from their source areas and are likely better sorted, removing finer grained sediments that would cause lower hydraulic conductivity.
A numerical groundwater-flow model was calibrated for the Fountain Creek alluvial aquifer for 2000–19 to simulate water-budget components, groundwater-flow directions, and groundwater-flow paths. The model simulated precipitation recharge, groundwater and surface-water interactions, evapotranspiration, high-volume groundwater pumping by pumping wells, and external inflows and outflows occurring along the boundaries of the alluvial aquifer. Model calibration was completed using manual and automated approaches, the latter of which assisted in quantifying model results sensitivity to input parameters. The calibrated model corresponds well with groundwater-level elevation observations, with a mean residual (observed minus simulated groundwater-level elevation) equal to −0.60 feet. Simulated groundwater base flow to streams was typically within 10 percent of base flow estimated by independent methods. Groundwater and surface-water interactions represented the largest water-budget components of the aquifer, with the second largest groundwater discharge component coming from pumping wells. Groundwater and surface-water interactions represent both the largest gain and loss terms in the water budget, because these interactions differ spatially, meaning in some areas of the model domain groundwater is being recharged by streams, whereas in other areas, groundwater is discharged to streams. Estimates of advective groundwater-flow paths indicate pumping wells may capture groundwater recharged from losing streams and groundwater that flows into the main stem of the alluvial aquifer from paleochannels.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Flood-flow estimates were computed at over 5,000 Federal Emergency Management Agency (FEMA) flood insurance study (FIS) flow locations across Pennsylvania for the 1-percent annual exceedance probability flood event (1-percent AEP). Depending on a point of interest’s proximity to a streamgage, weighting techniques may be applied to obtain flood-flow estimates for ungaged flow locations using observed peak-flow data from a nearby streamgage. Following the U.S. Geological Survey’s (USGS) published guidance, stream segments were identified where the drainage-area ratio method could be leveraged. Using updated regional regression equations and recently published flood-flow estimates at USGS streamgage locations following USGS Bulletin 17C guidelines, weighted and transferred flood flows were computed, where appropriate. For locations not applicable for the drainage-area ratio method, regression equations were used to compute flood-flow estimates. These flood-flow estimates were then compared to FEMA FIS 1-percent AEP flood-flow estimates. Percentage-difference values were computed for 3,599 FIS flow locations determined to be suitable for analysis, finding that USGS-derived flood-flow estimates were consistently lower than FEMA FIS flood-flow estimates with a statewide median percentage difference of −10.1 percent. The dataset was normally distributed with a standard deviation of 45.7 percent. Allegheny County was found to have 74 FIS flow locations with percentage-difference values greater than or equal to 67 percent or less than or equal to −67 percent. The flood-flow region in which Allegheny County is contained, Region 2, had a median percentage-difference value of −39 percent. Although removed from the final analysis, flow locations with drainage-area values above the recommended threshold for regression-based estimation (about 1,000 square miles [mi2]) were observed to have consistently higher percentage-difference values; a reminder of the limitations of use for regression-based flood-flow estimates. This report, the comparisons within, and a companion data release are intended to serve as tools to FEMA in assisting with the ongoing assessment of FIS flow locations across Pennsylvania.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235133","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Weaver, M.R., Stuckey, M.H., Colgin, J.E., and Roland, M.A., 2024, Estimation and comparison of 1-percent annual exceedance probability flood flows at Federal Emergency Management Agency flood insurance study flow locations across Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2023–5133, 33 p., https://doi.org/10.3133/sir20235133.","productDescription":"Report: viii, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151288","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":427275,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FG2MF2","text":"USGS data release","linkHelpText":"USGS-derived 1-percent 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road,
New Cumberland, PA 17070
Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16071264","usgsCitation":"Geisz, J.K., Wernette, P., and Esselman, P., 2024, Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning: Remote Sensing, v. 16, no. 7, 1264, 29 p., https://doi.org/10.3390/rs16071264.","productDescription":"1264, 29 p.","ipdsId":"IP-152592","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":439948,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16071264","text":"Publisher Index Page"},{"id":434996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N32CV7","text":"USGS data release","linkHelpText":"Autonomously Collected Benthic Imagery for Substrate Prediction, Lake Michigan 2020-2021"},{"id":433724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.90204822016032,\n 42.674502983776904\n ],\n [\n -87.65489307014272,\n 42.76669269166209\n ],\n [\n -86.26871023108912,\n 45.83189647310439\n ],\n [\n -86.71489901589742,\n 45.86861211740754\n ],\n [\n -87.71542062998205,\n 44.51147169699496\n ],\n [\n -88.08342598535049,\n 43.233909567544146\n ],\n [\n -87.90204822016032,\n 42.674502983776904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.61587060463187,\n 45.628545449737345\n ],\n [\n -84.87703701182424,\n 45.79764011104598\n ],\n [\n -85.13854737456055,\n 45.75496839185476\n ],\n [\n -85.7086983940175,\n 44.97892485981876\n ],\n [\n -85.62680073631381,\n 44.81109395544547\n ],\n [\n -84.61587060463187,\n 45.628545449737345\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Geisz, Joseph K. 0000-0001-6783-7057","orcid":"https://orcid.org/0000-0001-6783-7057","contributorId":342270,"corporation":false,"usgs":false,"family":"Geisz","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":912943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":912944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":912945,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252786,"text":"70252786 - 2024 - Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys","interactions":[],"lastModifiedDate":"2024-04-05T14:33:25.164806","indexId":"70252786","displayToPublicDate":"2024-04-03T09:27:19","publicationYear":"2024","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":"Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys","docAbstract":"Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0288121","usgsCitation":"Ke, T., Yu, S.X., Koneff, M.D., Fronczak, D.L., Fara, L., Harrison, T., Landolt, K.L., Hlavacek, E., Lubinski, B.R., and White, T., 2024, Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys: PLoS ONE, v. 19, no. 4, e0288121, 19 p., https://doi.org/10.1371/journal.pone.0288121.","productDescription":"e0288121, 19 p.","ipdsId":"IP-154866","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":439949,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0288121","text":"Publisher Index Page"},{"id":434997,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CBZQV1","text":"USGS data release","linkHelpText":"Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery"},{"id":427513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Wisconsin","county":"Manitowoc County","otherGeospatial":"Lake Michigan, Nantucket Shoals area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.86205441323496,\n 44.34056668991383\n ],\n [\n -87.86205441323496,\n 43.85488754500619\n ],\n [\n -87.2738483494078,\n 43.85488754500619\n ],\n [\n -87.2738483494078,\n 44.34056668991383\n ],\n [\n -87.86205441323496,\n 44.34056668991383\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.86598676810571,\n 41.22419472396538\n ],\n [\n -69.86598676810571,\n 41.64346958731832\n ],\n [\n -70.45153895220938,\n 41.64346958731832\n ],\n 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D.","contributorId":191128,"corporation":false,"usgs":false,"family":"Koneff","given":"Mark","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":898212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fronczak, David L.","contributorId":191560,"corporation":false,"usgs":false,"family":"Fronczak","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":898213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fara, Luke J. 0000-0002-1143-4395","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":202973,"corporation":false,"usgs":true,"family":"Fara","given":"Luke J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":898214,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, Travis 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":335378,"corporation":false,"usgs":false,"family":"Harrison","given":"Travis","affiliations":[{"id":80387,"text":"Upper Midwest Environmental Sciences Center, Former Employee","active":true,"usgs":false}],"preferred":false,"id":898215,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Landolt, Kyle Lawrence 0000-0002-6738-8586","orcid":"https://orcid.org/0000-0002-6738-8586","contributorId":298782,"corporation":false,"usgs":true,"family":"Landolt","given":"Kyle","email":"","middleInitial":"Lawrence","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":898216,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hlavacek, Enrika 0000-0002-9872-2305","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":297184,"corporation":false,"usgs":false,"family":"Hlavacek","given":"Enrika","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":898217,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lubinski, Brian R.","contributorId":177523,"corporation":false,"usgs":false,"family":"Lubinski","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":898218,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"White, Timothy","contributorId":236917,"corporation":false,"usgs":false,"family":"White","given":"Timothy","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":true,"id":898219,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70252763,"text":"70252763 - 2024 - Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials","interactions":[],"lastModifiedDate":"2024-05-20T15:29:15.542173","indexId":"70252763","displayToPublicDate":"2024-04-03T09:13:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials","docAbstract":"While the recent incursion of highly pathogenic avian influenza into North America has resulted in notable losses to the commercial poultry industry, the mechanism by which virus enters commercial poultry houses is still not understood. One theorized mechanism is that waterfowl shed virus into the environment surrounding poultry farms, such as into retention ponds, and is then transmitted into poultry houses via bridge species. Little is known about if and when wild waterfowl use these retention ponds, leading to uncertainty regarding the potential significance of this interface. To quantify the use of retention ponds on commercial poultry farms by wild waterfowl, we surveyed 12 such ponds across Somerset and Dorchester counties, Maryland, USA. This region was chosen due to the high level of poultry production and its importance for migratory waterfowl. Surveys consisted of recording waterfowl visible on the retention ponds from public roadways at least once per week from 20 September 2022–31 March 2023. Throughout the course of this study, we observed a total of nine species of waterfowl using retention ponds on commercial poultry farms at nine of 12 sites. The number of waterfowl observed at retention ponds varied notably throughout the course of our survey period, with values generally following trends of fall migration within each species indicating that resident birds were not the only individuals to utilize these habitats. Additionally, waterfowl use was highest at sites with little vegetation immediately surrounding the pond, and lowest when ponds were surrounded by trees. Our data suggest that retention ponds on commercial poultry farms present a notable interface for waterfowl to introduce avian influenza viruses to farm sites. However, additional testing and surveys could provide further insight into whether it may be possible to reduce the use of these habitats by wild waterfowl through vegetative management as preliminarily reported here.
","language":"English","publisher":"Hindawi","doi":"10.1155/2024/3022927","usgsCitation":"Sullivan, J.D., McDonough, A., Lescure, L., and Prosser, D., 2024, Identifying an understudied interface: Preliminary evaluation of the use of retention ponds on commercial poultry farms by wild waterfowl: Transboundary and Emerging Diseases, v. 2024, 3022927, 9 p., https://doi.org/10.1155/2024/3022927.","productDescription":"3022927, 9 p.","ipdsId":"IP-156554","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":439953,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2024/3022927","text":"Publisher Index Page"},{"id":434998,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U7QISZ","text":"USGS data release","linkHelpText":"Data describing the use of retention ponds on commercial poultry facilities on Delmarva by wild waterfowl"},{"id":427619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","noUsgsAuthors":false,"publicationDate":"2024-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":898427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonough, Ayla","contributorId":332811,"corporation":false,"usgs":false,"family":"McDonough","given":"Ayla","email":"","affiliations":[{"id":78934,"text":"Akima","active":true,"usgs":false}],"preferred":false,"id":898428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lescure, Lauren","contributorId":335066,"corporation":false,"usgs":false,"family":"Lescure","given":"Lauren","affiliations":[{"id":27609,"text":"Contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":898429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":898430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253057,"text":"70253057 - 2024 - Quantifying and evaluating strategies to decrease carbon dioxide emissions generated from tourism to Yellowstone National Park","interactions":[],"lastModifiedDate":"2024-04-18T12:11:12.119066","indexId":"70253057","displayToPublicDate":"2024-04-03T07:08:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16703,"text":"PLOS Climate","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying and evaluating strategies to decrease carbon dioxide emissions generated from tourism to Yellowstone National Park","docAbstract":"The tourism industry needs strategies to reduce emissions and hasten the achievement of global carbon dioxide (CO2) emission reduction targets. Using a case study approach, we estimated CO2 emissions related to park tourism in Yellowstone National Park (USA) generated from transit to and from the park, transit within the park, accommodations, and park operations. Results indicate tourism to Yellowstone National Park produces an estimated 1.03 megaton (1.03 billion kg) of CO2-equivalent emissions annually, with an average of 479 kg CO2 per visitor. Almost 90% of these emissions were attributable to transit to and from the destination, while 5% were from transit within the park, 4% from overnight accommodations, and about 1% from other park operations (e.g., visitor centers, museums, shops, restaurants, etc.). Visitors who fly only made up about 35% of all visitors, but produced 72% of the emissions related to transit to and from the park. Future scenarios that alter transit to and from the park can reduce emissions the most; this includes a greater proportion of local or regional visitors, fewer visitors flying, and increased fuel efficiency of vehicles. The method developed in this work, and applied specifically to Yellowstone National Park, can be adopted elsewhere and used to help decision makers evaluate the effectiveness of potential emission reduction strategies.
Weather, climate, and climate change all effect outdoor recreation and tourism, and will continue to cause a multitude of effects as the climate warms. We conduct a systematic literature review to better understand how weather, climate, and climate change affect outdoor recreation and nature-based tourism across the United States. We specifically explore how the effects differ by recreational activity, and how visitors and supply-side tourism operators perceive these effects and risks. The 82 papers reviewed show the complex ways in which weather, climate, and climate change may affect outdoor recreation, with common themes being an extended season to participate in warm-weather activities, a shorter season to participate in snow-dependent activities, and larger negative effects to activities that depend on somewhat consistent precipitation levels (e.g., snow-based recreation, water-based recreation, fishing). Nature-based tourists perceive a variety of climate change effects on tourism, and some recreationists have already changed their behavior as a result of climate change. Nature-based tourism suppliers are already noticing a wide variety of climate change effects, including shifts in seasonality of specific activities and visitation overall. Collectively, this review provides insights into our current understanding of climate change and outdoor recreation and opportunities for future research.
Declines in bumble bee species range and abundances are documented across multiple continents and have prompted the need for research to aid species recovery and conservation. The rusty patched bumble bee (Bombus affinis) is the first federally listed bumble bee species in North America. We conducted a range-wide population genetics study of B. affinis from across all extant conservation units to inform conservation efforts. To understand the species’ vulnerability and help establish recovery targets, we examined population structure, patterns of genetic diversity, and population differentiation. Additionally, we conducted a site-level analysis of colony abundance to inform prioritizing areas for conservation, translocation, and other recovery actions. We find substantial evidence of population structuring along an east-to-west gradient. Putative populations show evidence of isolation by distance, high inbreeding coefficients, and a range-wide male diploidy rate of ~15%. Our results suggest the Appalachians represent a genetically distinct cluster with high levels of private alleles and substantial differentiation from the rest of the extant range. Site-level analyses suggest low colony abundance estimates for B. affinis compared to similar datasets of stable, co-occurring species. These results lend genetic support to trends from observational studies, suggesting that B. affinis has undergone a recent decline and exhibit substantial spatial structure. The low colony abundances observed here suggest caution in overinterpreting the stability of populations even where B. affinis is reliably detected interannually. These results help delineate informed management units, provide context for the potential risks of translocation programs, and help set clear recovery targets for this and other threatened bumble bee species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jisesa/ieae041","usgsCitation":"Mola, J., Pearse, I.S., Boone, M., Evans, E., Hepner, M.J., Jean, R., Kochanski, J., Nordmeyer, C., Runquist, E., Smith, T.A., Strange, J., Watson, J., and Koch, J.B., 2024, Range-wide genetic analysis of an endangered bumble bee (Bombus affinis, Hymenoptera: Apidae) reveals population structure, isolation by distance, and low colony abundance: Journal of Insect Science, v. 24, no. 2, 19, 12 p., https://doi.org/10.1093/jisesa/ieae041.","productDescription":"19, 12 p.","ipdsId":"IP-160165","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439960,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jisesa/ieae041","text":"Publisher Index Page"},{"id":434999,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13CHTK8","text":"USGS data 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U.S. Fish and Wildlife Service, Minnesota-Wisconsin Ecological Services Field Office","active":true,"usgs":false}],"preferred":false,"id":898850,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Tamara A.","contributorId":257977,"corporation":false,"usgs":false,"family":"Smith","given":"Tamara","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":898851,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Strange, Jaime","contributorId":335615,"corporation":false,"usgs":false,"family":"Strange","given":"Jaime","email":"","affiliations":[{"id":80406,"text":"Ohio State","active":true,"usgs":false}],"preferred":false,"id":898852,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Watson, Jay","contributorId":335443,"corporation":false,"usgs":false,"family":"Watson","given":"Jay","email":"","affiliations":[{"id":80407,"text":"Winsconsin DNR","active":true,"usgs":false}],"preferred":false,"id":898853,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Koch, Jonathan B","contributorId":237988,"corporation":false,"usgs":false,"family":"Koch","given":"Jonathan","email":"","middleInitial":"B","affiliations":[{"id":47671,"text":"University of Hawai'i, Hilo","active":true,"usgs":false}],"preferred":false,"id":898854,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70256184,"text":"70256184 - 2024 - The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population","interactions":[],"lastModifiedDate":"2024-07-26T00:07:03.307419","indexId":"70256184","displayToPublicDate":"2024-04-02T19:05:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population","docAbstract":"Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO = 0.65) was higher in the recipient population than predicted had no augmentation occurred (HO = 0.58) but less than what was predicted by our model (HO = 0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype–environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.