Diurnal basking (“sunning”) is common in many ectotherms and is generally thought to be a behavioural mechanism for thermoregulation. Recent studies have reported the occurrence of nocturnal basking in a few distantly-related species of freshwater turtles, but the true extent of this behaviour is unknown, and it may be underreported due to sampling biases (e.g., not surveying for turtles at night). Therefore, we initiated a global, collaborative effort to systematically document and quantify basking activity (diurnal and nocturnal) across a wide range of freshwater turtle species and locations. We conducted camera trap or manual surveys in North America, the Caribbean, Europe, Asia, Africa, the Seychelles, and Australia. We collected 873,111 trail camera photographs (25,273 hrs of search effort) and obtained data on 29 freshwater turtle species from seven families. Nocturnal basking was documented in 13 species, representing six families (Chelidae, Emydidae, Geoemydidae, Kinosternidae, Pelomedusidae, and Trionychidae), including representatives in Central America, Trinidad and Tobago, Africa, the Seychelles, Asia, and Australia. Nocturnal basking was restricted to tropical and sub-tropical locations, suggesting that environmental temperature plays a role in this behaviour. However, the primary factors driving nocturnal basking are yet to be determined and may vary geographically and by species. The frequency and duration of nocturnal basking varied among species and seasons, but nocturnal basking events were often substantially longer than diurnal events. This is the first study to document a widespread occurrence of nocturnal basking, and our results suggest that nocturnal basking may be a common, although overlooked, aspect of many species’ ecology.
Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.
To inform responsible energy development, it is important to understand the ecological effects of contamination events. Wastewaters, a common byproduct of oil and gas extraction, often contain high concentrations of sodium chloride (NaCl) and heavy metals (e.g., strontium and vanadium). These constituents can negatively affect aquatic organisms, but there is scarce information for how wastewaters influence potentially distinct microbiomes in wetland ecosystems. Additionally, few studies have concomitantly investigated effects of wastewaters on the habitat (water and sediment) and skin microbiomes of amphibians or relationships among these microbial communities. We sampled microbiomes of water, sediment, and skin of four larval amphibian species across a gradient of chloride contamination (0.04–17,500 mg/L Cl) in the Prairie Pothole Region of North America. We detected 3129 genetic phylotypes and 68 % of those phylotypes were shared among the three sample types. The most common shared phylotypes were Proteobacteria, Firmicutes, and Bacteroidetes. Salinity of wastewaters increased dissimilarity within all three microbial communities, but not the diversity or richness of water and skin microbial communities. Strontium was associated with lower diversity and richness of sediment microbial communities, but not those of water or amphibian skin, likely because metal deposition occurs in sediment when wetlands dry. Based on Bray Curtis distance matrices, sediment microbiomes were similar to those of water, but neither had substantial overlap with amphibian microbiomes. Species identity was the strongest predictor of amphibian microbiomes; frog microbiomes were similar but differed from that of the salamander, whose microbiome had the lowest richness and diversity. Understanding how effects of wastewaters on the dissimilarity, richness, and diversity of microbial communities also influence the ecosystem function of communities will be an important next step. However, our study provides novel insight into the characteristics of, and associations among, different wetland microbial communities and effects of wastewaters from energy production.
Xyleborini (Coleoptera: Curculionidae) beetles on the island of Kauaʻi, Hawaiʻi, are of interest due to their role in the fungal disease complex, rapid ʻōhiʻa death (ROD), and the unique radiation of endemic ambrosia beetles found across the Hawaiian archipelago. We investigated the status of RODassociated and native ambrosia beetles on Kauaʻi by rearing beetles from bolts collected from ROD-Ceratocystis infested ʻōhiʻa lehua (ʻōhiʻa; Metrosideros polymorpha) trees and trapping in mixed ʻōhiʻa forests. Beetles associated with ROD on Kauaʻi include Xyleborinus saxesenii, Xyleborus affinis, Xyleborus ferrugineus, Xyleborus perforans, Xylosandrus crassiusculus, and Crossotarsus externedentatus. Xyleborus perforans was most abundant in ʻōhiʻa bolts followed by Xyleborus ferrugineus. From trap captures, we identified new island records of the native Xyleborus beetles including Xyleborus dubiosus, Xyleborus oahuensis, and Xyleborus tantalus. Xylosandrus crassiusculus was most abundant in traps. Additional work could contribute to understanding and mitigating the spread of ROD on Kauaʻi and to documenting the endemic Xyleborus beetles.
","language":"English","publisher":"ScholarSpace","doi":"10125/110083","usgsCitation":"Roy, K., Dunkle, E., Manandhar, R., Clark, M., Magnacca, K.N., Harshman, K., and Peck, R., 2023, Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae and Platypodinae) associated with rapid ohia death and mixed Metrosideros polymorpha forests on the Island of Kauai, Hawaii: Proceedings of the Hawaiian Entomological Society, v. 56, p. 61-71, https://doi.org/10125/110083.","productDescription":"11 p.","startPage":"61","endPage":"71","ipdsId":"IP-169929","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":482910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kauai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.81907120710895,\n 22.25305341906116\n ],\n [\n -159.81907120710895,\n 21.856904765473885\n ],\n [\n -159.2742849994599,\n 21.856904765473885\n ],\n [\n -159.2742849994599,\n 22.25305341906116\n ],\n [\n -159.81907120710895,\n 22.25305341906116\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roy, Kylle 0000-0002-7993-9031","orcid":"https://orcid.org/0000-0002-7993-9031","contributorId":213271,"corporation":false,"usgs":true,"family":"Roy","given":"Kylle","email":"","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":929665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunkle, Ellen 0000-0002-7081-0717","orcid":"https://orcid.org/0000-0002-7081-0717","contributorId":244898,"corporation":false,"usgs":false,"family":"Dunkle","given":"Ellen","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":929666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manandhar, Roshan","contributorId":332580,"corporation":false,"usgs":false,"family":"Manandhar","given":"Roshan","affiliations":[{"id":79499,"text":"University of Hawaiʻi at Mānoa, College of Tropical Agriculture and Human Resources","active":true,"usgs":false}],"preferred":false,"id":929667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Michelle","contributorId":332581,"corporation":false,"usgs":false,"family":"Clark","given":"Michelle","affiliations":[{"id":79500,"text":"U.S. Fish and Wild Life Service, Pacific Islands Fish and Wildlife Office","active":true,"usgs":false}],"preferred":false,"id":929668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Magnacca, Karl N.","contributorId":173504,"corporation":false,"usgs":false,"family":"Magnacca","given":"Karl","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":929669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harshman, Kalli","contributorId":332582,"corporation":false,"usgs":false,"family":"Harshman","given":"Kalli","affiliations":[{"id":79501,"text":"State of Hawai’i Department of Land and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":929670,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":929671,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242042,"text":"70242042 - 2023 - Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","interactions":[],"lastModifiedDate":"2023-04-07T16:56:20.79635","indexId":"70242042","displayToPublicDate":"2023-04-04T11:47:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","title":"Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","docAbstract":"One of the key factors in obtaining precise and accurate 230Th ages of corals, especially for corals with ages less than a few thousand years, is the correction for non-radiogenic 230Th based on an initial 230Th/232Th value (230Th/232Th0). Studies that consider coral 230Th/232Th0 values in intertidal environments are limited, and it is in these environments that corals have Th concentrations 100–1000 times greater than open-ocean corals. Here we present 66 reconstructed U–Th isochrons of modern and Holocene Porites lutea and lobata corals from throughout the Sumatran forearc islands (SFI). Mean square of weighted deviates (MSWD) is used to evaluate the variabilities of 28 location-specific 230Th/232Th0 values before the regional mean. Results show no significant spatial difference between the means of 3.1 ± 3.2 × 10−6 for the northern SFI and 4.7 ± 1.3 × 10−6 for the southern SFI. Using the weighted mean of 4.3 ± 2.5 × 10−6 for all islands has the advantage that no data need be subjectively excluded in the calculation of reliable 230Th ages, where a local initial value is unknown. There were no clear centennial to millennial time-scale changes in 230Th/232Th0 in the past 2500 years. This study, therefore, suggests a reliable value of initial 230Th/232Th ratio in intertidal environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2023.108005","usgsCitation":"Chiang, H., Philibosian, B.E., Meltzner, A.J., Wu, C., Shen, C., Edwards, R.L., Chuang, C., Suwargadi, B.W., and Natawidjaja, D.H., 2023, Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals: Quaternary Science Reviews, v. 307, 108005, 11 p., https://doi.org/10.1016/j.quascirev.2023.108005.","productDescription":"108005, 11 p.","ipdsId":"IP-125955","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2023.108005","text":"Publisher Index Page"},{"id":415422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sumatran forearc islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94,\n 3.5\n ],\n [\n 94,\n -3.5\n ],\n [\n 103,\n -3.5\n ],\n [\n 103,\n 3.5\n ],\n [\n 94,\n 3.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"307","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chiang, Hong-Wei","contributorId":193421,"corporation":false,"usgs":false,"family":"Chiang","given":"Hong-Wei","email":"","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":868669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chung-Che","contributorId":193422,"corporation":false,"usgs":false,"family":"Wu","given":"Chung-Che","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chuan-Chou","contributorId":193424,"corporation":false,"usgs":false,"family":"Shen","given":"Chuan-Chou","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence 0000-0002-7027-5881","orcid":"https://orcid.org/0000-0002-7027-5881","contributorId":223143,"corporation":false,"usgs":false,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":868672,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chuang, Chih-Kai","contributorId":303947,"corporation":false,"usgs":false,"family":"Chuang","given":"Chih-Kai","email":"","affiliations":[{"id":30216,"text":"National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868673,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Suwargadi, Bambang W.","contributorId":150205,"corporation":false,"usgs":false,"family":"Suwargadi","given":"Bambang","email":"","middleInitial":"W.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Natawidjaja, Danny H.","contributorId":150204,"corporation":false,"usgs":false,"family":"Natawidjaja","given":"Danny","email":"","middleInitial":"H.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243078,"text":"70243078 - 2023 - 22 years of aquatic plant spatiotemporal dynamics in the upper Mississippi River","interactions":[],"lastModifiedDate":"2023-04-28T11:43:52.690731","indexId":"70243078","displayToPublicDate":"2023-04-04T06:41:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"22 years of aquatic plant spatiotemporal dynamics in the upper Mississippi River","docAbstract":"Since its introduction to volcanology in the mid-2000 s, the SO2 camera has become an important instrument for the acquisition of accurate and high time-resolution SO2 emission rates, aiding in hazard assessment and volcanological research. However, with the exception of a few locations (Stromboli, Etna, Kīlauea), hitherto the majority of measurements have been made on discrete field campaigns, which provide only brief snapshots into a volcano’s activity. Here, we present the development of a new, low-cost, low-power SO2 camera for permanent deployment on volcanoes, facilitating long-term, quasi-continuous (daylight hours only) measurements. We then discuss preliminary datasets from Lascar and Kīlauea volcanoes, where instruments are now in continuous operation. Further proliferation of such instrumentation has the potential to greatly improve our understanding of the transient nature of volcanic activity, as well as aiding volcano monitoring/eruption forecasting.
The Blackfeet Nation seeks an increased scientific understanding of the water resources within the Blackfeet Indian Reservation of northwestern Montana. Hydrologic information is needed to better inform water-management decisions as the Blackfeet Nation implements the Blackfeet Water Rights Compact, initiates new water-use projects, and improves the Blackfeet Irrigation Project.
The U.S. Geological Survey and the Blackfeet Water Department began cooperating in 2019 to design and implement a hydrologic data-collection program. The program is being implemented in phases that include discrete and continuous discharge measurements of streams and canals, installation, operation of streamgages, groundwater-level monitoring, and database management. Data collected will be used to characterize current hydrologic conditions on the reservation and will act as a baseline for comparison as Blackfeet Nation water projects are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233012","collaboration":"Prepared in cooperation with the Blackfeet Water Department","usgsCitation":"Lawlor, S.M., Caldwell, R.R., Bartos, T.T., and Price, B., 2023, U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana: U.S. Geological Survey Fact Sheet 2023–3012, 4 p., https://doi.org/10.3133/fs20233012.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141394","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":414914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3012/coverthb2.jpg"},{"id":414918,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3012/images"},{"id":414919,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":415102,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233012/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":499662,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114650.htm","linkFileType":{"id":5,"text":"html"}},{"id":414917,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3012"}],"country":"United States","state":"Montana","otherGeospatial":"Blackfeet Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.92382337824559,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 49.00115786005651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Within the Gulf of Alaska, in the North Pacific Ocean, three major events - both natural and human-caused – resulted in large-scale ecosystem changes during the last 50 years.
","language":"English","publisher":"Open Access Government","doi":"10.56367/OAG-038-10678","usgsCitation":"Suryan, R.M., Lindeberg, M., Arimitsu, M.L., Esler, D., Coletti, H., Hopcroft, R., and Pegau, W.S., 2023, Gulf watch Alaska: Long-term research and monitoring in the Gulf of Alaska: Open Access Government, v. 38, no. 1, p. 468-469, https://doi.org/10.56367/OAG-038-10678.","productDescription":"2 p.","startPage":"468","endPage":"469","ipdsId":"IP-148894","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443972,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.56367/oag-038-10678","text":"Publisher Index Page"},{"id":415847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.4399134359869,\n 52.574048355723704\n ],\n [\n -135.17789540456553,\n 56.85438463524653\n ],\n [\n -139.82767703845167,\n 59.79957173693737\n ],\n [\n -147.2251844144387,\n 61.1241382977862\n ],\n [\n -151.09164861277324,\n 58.87023875501092\n ],\n [\n -151.2988466630248,\n 60.668331578783324\n ],\n [\n -153.50284160357148,\n 59.11972555702502\n ],\n [\n -158.91367720917896,\n 55.710837343186455\n ],\n [\n -162.81404039809868,\n 54.85301697024397\n ],\n [\n -132.4399134359869,\n 52.574048355723704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Suryan, Robert M. 0000-0003-0755-8317","orcid":"https://orcid.org/0000-0003-0755-8317","contributorId":221852,"corporation":false,"usgs":false,"family":"Suryan","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40443,"text":"Oregon State University, NOAA","active":true,"usgs":false}],"preferred":false,"id":869688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindeberg, Mandy","contributorId":195895,"corporation":false,"usgs":false,"family":"Lindeberg","given":"Mandy","email":"","affiliations":[],"preferred":false,"id":869689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":869690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":869691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coletti, Heather","contributorId":258849,"corporation":false,"usgs":false,"family":"Coletti","given":"Heather","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":869692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hopcroft, Russell","contributorId":258854,"corporation":false,"usgs":false,"family":"Hopcroft","given":"Russell","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":869693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pegau, W. Scott","contributorId":304200,"corporation":false,"usgs":false,"family":"Pegau","given":"W.","email":"","middleInitial":"Scott","affiliations":[{"id":13600,"text":"Prince William Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":869694,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242647,"text":"70242647 - 2023 - Population dynamics and harvest management of eastern mallards","interactions":[],"lastModifiedDate":"2023-06-09T15:15:15.702993","indexId":"70242647","displayToPublicDate":"2023-04-03T07:03:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics and harvest management of eastern mallards","docAbstract":"Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision-making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2-season banding and recovery data, and hunter-harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub-model for survival including estimating the degree of harvest additivity and any age-specific trends. The top survival sub-model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.
The development of sample processing techniques that recover a broad suite of pesticides from solid matrices, while mitigating coextracted matrix interferences, and reducing processing time is beneficial for high throughput analyses. The objective of this study was to evaluate the effectiveness of an automated extraction system for pesticide analyses in solid environmental samples. An Energized Dispersive Guided Extraction (EDGE) system was used to evaluate two different extraction solvents in optimizing the extraction of 210 pesticides and pesticide transformation products. A graphitized carbon cleanup step was implemented, and three elution solvents were evaluated separately for analyte recoveries. Recoveries between 70 and 130% were achieved for 167 compounds in a test soil using acetonitrile as an extraction solvent and carbon cleanup with acetonitrile and dichloromethane elutions. Nine field samples (soil, sediment, and biosolids) were extracted using the newly developed method and were compared with a previously validated pressurized liquid extraction (PLE) method using an Accelerated Solvent Extraction (ASE) system. Concentrations obtained from the two methods were comparable (linear R2 > 0.999), suggesting similar performance between the EDGE and PLE extractions in complex matrices. The new method provided slightly better sensitivities in comparison to the PLE method, ranging from 0.09 to 2.56 ng g−1. The method presented here significantly reduces extraction setup and runtimes while also minimizing the volume of carcinogenic solvents (e.g., dichloromethane) used in the laboratory and presents a sensitive multiresidue method for a wide range of pesticides in solid matrices.
Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.
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\"}}]}","volume":"120","issue":"15","noUsgsAuthors":false,"publicationDate":"2023-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schliep, Erin M.","contributorId":349360,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"North, Joshua S.","contributorId":349361,"corporation":false,"usgs":false,"family":"North","given":"Joshua S.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":924259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kundel, Holly","contributorId":341087,"corporation":false,"usgs":false,"family":"Kundel","given":"Holly","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Custer, Christopher A.","contributorId":341088,"corporation":false,"usgs":false,"family":"Custer","given":"Christopher A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruzich, Jenna K.","contributorId":349365,"corporation":false,"usgs":false,"family":"Ruzich","given":"Jenna K.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hansen, Gretchen J.A.","contributorId":349370,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924263,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260468,"text":"70260468 - 2023 - Protocol for the reintroduction of California red-legged frogs to Santa Monica Mountains National Recreation Area","interactions":[],"lastModifiedDate":"2024-11-05T15:42:08.317008","indexId":"70260468","displayToPublicDate":"2023-04-01T11:00:56","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SAMO/NRR—2023/2507","title":"Protocol for the reintroduction of California red-legged frogs to Santa Monica Mountains National Recreation Area","docAbstract":"Once common and widespread in Southern California, California red-legged frogs (Rana draytonii) began declining sometime in the middle of the 20th century. They were listed as threatened under the Endangered Species Act in 1996. Three small and isolated populations remained in Los Angeles and Ventura Counties by the start of the 21st century. The nearest population of California red-legged frogs to Santa Monica Mountains National Recreation Area is critically small, located 15 km to the north, yet there is evidence of persistence, including successful reproduction each year it has been measured. A potential solution to alleviate small population size and isolation is to reintroduce a species back to habitable historical locations nearby. In 2011, we initiated a project to reintroduce California red-legged frogs back to the Santa Monica Mountains, where historical records showed they were once widespread. We developed a procedure to transfer partial egg masses into tadpole rearing pens located within streams determined to be suitable for the species. This translocation protocol outlines our procedure and results for the first five years of the project. It is our hope that this protocol will guide and inform similar conservation efforts for California red-legged frogs in other parts of their range as well as other amphibian conservation efforts throughout the world.
","language":"English","publisher":"National Park Service","doi":"10.36967/2297287","collaboration":"NPS; USFWS","usgsCitation":"Delaney, K., Mendelsohn, M., Wenner, S.M., Backlin, A.R., Gallegos, E., Fisher, R., and Riley, S.P., 2023, Protocol for the reintroduction of California red-legged frogs to Santa Monica Mountains National Recreation Area: Natural Resource Report NPS/SAMO/NRR—2023/2507, vi, 24 p., https://doi.org/10.36967/2297287.","productDescription":"vi, 24 p.","ipdsId":"IP-159471","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":463595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Monica Mountains National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.12614115387291,\n 34.21071593478666\n ],\n [\n -118.864112231819,\n 34.21071593478666\n ],\n [\n -118.864112231819,\n 33.9964212451746\n ],\n [\n -118.12614115387291,\n 33.9964212451746\n ],\n [\n -118.12614115387291,\n 34.21071593478666\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, Kathleen Semple","contributorId":269389,"corporation":false,"usgs":false,"family":"Delaney","given":"Kathleen Semple","affiliations":[{"id":55965,"text":"NPS - Santa Monica Mountains National Recreation Area","active":true,"usgs":false}],"preferred":false,"id":917760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mendelsohn, Mark","contributorId":345864,"corporation":false,"usgs":false,"family":"Mendelsohn","given":"Mark","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":917761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wenner, Sarah M.","contributorId":345865,"corporation":false,"usgs":false,"family":"Wenner","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":917762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Backlin, Adam R. 0000-0001-5618-8426 abacklin@usgs.gov","orcid":"https://orcid.org/0000-0001-5618-8426","contributorId":3802,"corporation":false,"usgs":true,"family":"Backlin","given":"Adam","email":"abacklin@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Elizabeth 0000-0002-8402-2631 egallegos@usgs.gov","orcid":"https://orcid.org/0000-0002-8402-2631","contributorId":1528,"corporation":false,"usgs":true,"family":"Gallegos","given":"Elizabeth","email":"egallegos@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Riley, Seth P.D.","contributorId":345869,"corporation":false,"usgs":false,"family":"Riley","given":"Seth","email":"","middleInitial":"P.D.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":917766,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70246314,"text":"70246314 - 2023 - Flood-frequency analysis in the Midwest: Addressing potential nonstationarity of annual peak-flow records","interactions":[],"lastModifiedDate":"2023-07-19T15:37:03.166943","indexId":"70246314","displayToPublicDate":"2023-04-01T10:32:41","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":16356,"text":"AASHTO Hydrolink","active":true,"publicationSubtype":{"id":30}},"title":"Flood-frequency analysis in the Midwest: Addressing potential nonstationarity of annual peak-flow records","docAbstract":"Flood-frequency analysis is essential in numerous water-resource management applications, including critical structure design and flood-plain mapping. A basic assumption within Bulletin 17C [1], the standardized guidelines for conducting flood-frequency analysis, is that basins without major hydrologic alterations, such as regulation or urbanization, exhibit stationary statistical properties of the distribution of annual peak streamflow. That is, the mean, variance, and skew are constant over time and the peak-flow record is a representative sample of the population of future floods [1]. In recent decades, better understanding of long-term climatic persistence and concerns about climate and land-use change have caused the assumption of stationarity in peak-flow records to be reexamined [2, 3, 4, 5]. Under nonstationary conditions, the long-term distributional properties (mean, variance, and/or skew) of peak-flow series change one or more times, either gradually or abruptly. Nonstationarities may be attributed to one source, but are often a result of a mixture of drivers, making detection and attribution of nonstationarities challenging [6, 7, 8]. Failure to incorporate observed trends and abrupt changes into flood-frequency analysis may result in a poor representation of the true flood risk. Bulletin 17C currently offers no guidance on how to account for nonstationarities when estimating floods and acknowledges the benefit additional flood frequency studies that incorporate changing climate or basin characteristics into the analysis would provide[1].","language":"English","publisher":"American Association of State Highway and Transportation Officials","usgsCitation":"Marti, M.K., Ryberg, K.R., and Levin, S., 2023, Flood-frequency analysis in the Midwest: Addressing potential nonstationarity of annual peak-flow records: AASHTO Hydrolink, no. 22, p. 9-11.","productDescription":"3 p.","startPage":"9","endPage":"11","ipdsId":"IP-146874","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419152,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://transportation.org/design/technical-committees/hydrology-and-hydraulics/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Midwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.63079880812552,\n 39.981338894898926\n ],\n [\n -80.305088787779,\n 42.407786545262155\n ],\n [\n -82.78313999112669,\n 41.8642895150752\n ],\n [\n -81.94361722979289,\n 43.7411297751668\n ],\n [\n -84.57946169161875,\n 46.783497345807575\n ],\n [\n -88.02687384801146,\n 48.18911170131872\n ],\n [\n -90.78141289084317,\n 48.05054435475736\n ],\n [\n -94.73142386151744,\n 48.96813415136279\n ],\n [\n -96.0701137437052,\n 49.34965225487991\n ],\n [\n -118.36279683132679,\n 49.044880788818546\n ],\n [\n -114.80117084034327,\n 34.5056159266112\n ],\n [\n -106.83709671015015,\n 33.4475379407086\n ],\n [\n -89.0414291409545,\n 33.06253925797563\n ],\n [\n -81.8240905040015,\n 37.13731159053141\n ],\n [\n -80.63079880812552,\n 39.981338894898926\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876792,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270840,"text":"70270840 - 2023 - Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","interactions":[],"lastModifiedDate":"2026-03-16T14:53:10.311939","indexId":"70270840","displayToPublicDate":"2023-04-01T09:35:57","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","docAbstract":"To predict future status of the northwestern pond turtle (Actinemys marmorata) and southwestern pond turtle (Actinemys pallida) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was
included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation.
The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Species status assessment report for Northwestern Pond Turel (Actinemys marmorata) and Southwestern Pond Turtle (Actinemys pallida)","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Gregory, K.M., and McGowan, C.P., 2023, Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida ) (version 1.1), 43 p.","productDescription":"43 p.","ipdsId":"IP-146555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494872,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/node/5110701"},{"id":501175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Kaili M.","contributorId":360548,"corporation":false,"usgs":false,"family":"Gregory","given":"Kaili","middleInitial":"M.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":947202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":947203,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257346,"text":"70257346 - 2023 - Landscape transcriptomics as a tool for addressing global change effects across diverse species","interactions":[],"lastModifiedDate":"2024-08-28T16:42:00.761546","indexId":"70257346","displayToPublicDate":"2023-04-01T09:29:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Landscape transcriptomics as a tool for addressing global change effects across diverse species","docAbstract":"Landscape transcriptomics is an emerging field studying how genome-wide expression patterns reflect dynamic landscape-scale environmental drivers, including habitat, weather, climate, and contaminants, and the subsequent effects on organismal function. This field is benefitting from advancing and increasingly accessible molecular technologies, which in turn are allowing the necessary characterization of transcriptomes from wild individuals distributed across natural landscapes. This research is especially important given the rapid pace of anthropogenic environmental change and potential impacts that span levels of biological organization. We discuss three major themes in landscape transcriptomic research: connecting transcriptome variation across landscapes to environmental variation, generating and testing hypotheses about the mechanisms and evolution of transcriptomic responses to the environment, and applying this knowledge to species conservation and management. We discuss challenges associated with this approach and suggest potential solutions. We conclude that landscape transcriptomics has great promise for addressing fundamental questions in organismal biology, ecology, and evolution, while providing tools needed for conservation and management of species.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13796","usgsCitation":"Keagy, J., Drummond, C.P., Gilbert, K.J., Christina Grozinger, Hamilton, J., Hines, H.M., Lasky, J., Logan, C.A., Sawers, R., and Wagner, T., 2023, Landscape transcriptomics as a tool for addressing global change effects across diverse species: Molecular Ecology Resources, 16 p., https://doi.org/10.1111/1755-0998.13796.","productDescription":"16 p.","ipdsId":"IP-147507","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.13796","text":"Publisher Index Page"},{"id":433254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Keagy, Jason","contributorId":342368,"corporation":false,"usgs":false,"family":"Keagy","given":"Jason","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Chloe P.","contributorId":342369,"corporation":false,"usgs":false,"family":"Drummond","given":"Chloe","email":"","middleInitial":"P.","affiliations":[{"id":25495,"text":"Mount Holyoke College","active":true,"usgs":false}],"preferred":false,"id":910028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilbert, Kadeem J.","contributorId":342370,"corporation":false,"usgs":false,"family":"Gilbert","given":"Kadeem","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christina Grozinger","contributorId":342371,"corporation":false,"usgs":false,"family":"Christina Grozinger","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hamilton, Jill","contributorId":342372,"corporation":false,"usgs":false,"family":"Hamilton","given":"Jill","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910031,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hines, Heather M.","contributorId":342373,"corporation":false,"usgs":false,"family":"Hines","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910032,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lasky, Jesse","contributorId":342374,"corporation":false,"usgs":false,"family":"Lasky","given":"Jesse","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910033,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Logan, Cheryl A.","contributorId":342375,"corporation":false,"usgs":false,"family":"Logan","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":81516,"text":"California State University Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":910034,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sawers, Ruairidh","contributorId":342376,"corporation":false,"usgs":false,"family":"Sawers","given":"Ruairidh","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910035,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910036,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70266579,"text":"70266579 - 2023 - Automated soft pressure sensor array-based sea lamprey detection using machine learning","interactions":[],"lastModifiedDate":"2025-05-09T14:14:32.73205","indexId":"70266579","displayToPublicDate":"2023-04-01T09:10:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Automated soft pressure sensor array-based sea lamprey detection using machine learning","docAbstract":"Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.
","language":"English","publisher":"IEEE","doi":"10.1109/JSEN.2023.3249625","usgsCitation":"Shi, H., Mei, Y., González-Afanador, I., Chen, C., Miehls, S.M., Holbrook, C., Sepulveda, N., and Tan, X., 2023, Automated soft pressure sensor array-based sea lamprey detection using machine learning: IEEE Sensors Journal, v. 23, p. 7546-7557, https://doi.org/10.1109/JSEN.2023.3249625.","productDescription":"12 p.","startPage":"7546","endPage":"7557","ipdsId":"IP-147136","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang","contributorId":354871,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mei, Yu","contributorId":354872,"corporation":false,"usgs":false,"family":"Mei","given":"Yu","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"González-Afanador, Ian","contributorId":354873,"corporation":false,"usgs":false,"family":"González-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Claudia","contributorId":354874,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936606,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo","contributorId":354875,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936607,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248495,"text":"70248495 - 2023 - The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?","interactions":[],"lastModifiedDate":"2023-09-15T13:40:34.465244","indexId":"70248495","displayToPublicDate":"2023-04-01T08:29:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?","docAbstract":"Lower Cretaceous marine sedimentary rocks, deposited in shallow shelf and basin settings and unconformity-bound, are well exposed in southwest Alaska. Collections of Early Cretaceous fossils from across western Alaska show that similar and coeval Lower Cretaceous clastic rocks are widely distributed though only locally exposed. Volcanic rocks become an important part of the Lower Cretaceous sequence in the Yukon-Koyukuk basin where they have been interpreted to represent a mobile intra-oceanic island arc, the Koyukuk terrane, that collided with Arctic Alaska to form the Brooks Range orogen. The volcanic rocks are chemically unlike Aleutian arc rocks but share compositional characteristics with spatially related, mid-Cretaceous alkaline intrusive rocks. The volcanic-bearing sequence was also deposited on an angular unconformity, includes both shallow shelf and basin depositional settings, and is unconformably overlain by mid-Cretaceous clastic rocks. The volcanic rocks are therefore considered part of the Lower Cretaceous sequence now identified across western Alaska. In this interpretation, the Lower Cretaceous volcanic rocks are an initial expression of the mid-Cretaceous tectonic regime that included extensional exhumation and subsidence, crustal and upper mantle melting, and high temperature metamorphism in the hinterland of the Brooks Range orogen. The Cretaceous heating that led to hinterland crust and upper mantle change may have been caused by deep mantle disturbances in a post-subduction setting. This interpretation has implications for the timing of contractional orogenesis, the location and nature of the related continental borderland, and the tectonic setting for development of the Anguyucham and related oceanic terranes.","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjes-2022-0041","usgsCitation":"Hudson, T.L., Blodgett, R., and Wilson, F.H., 2023, The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?: Canadian Journal of Earth Sciences, v. 60, no. 4, p. 422-441, https://doi.org/10.1139/cjes-2022-0041.","productDescription":"20 p.","startPage":"422","endPage":"441","ipdsId":"IP-126283","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":420829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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Challenge","docAbstract":"The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2616","usgsCitation":"Thomas, R.Q., Boettiger, C., Carey, C.C., Dietze, M., Johnson, L.R., Kenney, M.A., McLachlan, J.S., Peters, J.A., Sokol, E.R., Weltzin, J., Willson, A., and Woelmer, W., 2023, The NEON Ecological Forecasting Challenge: Frontiers in Ecology and the Environment, v. 21, no. 3, p. 112-113, https://doi.org/10.1002/fee.2616.","productDescription":"2 p.","startPage":"112","endPage":"113","ipdsId":"IP-145337","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":443983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2616","text":"Publisher Index Page"},{"id":415161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boettiger, Carl","contributorId":201833,"corporation":false,"usgs":false,"family":"Boettiger","given":"Carl","affiliations":[{"id":36267,"text":"Dept of Environmental Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":868550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":868551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":868552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Leah R.","contributorId":139035,"corporation":false,"usgs":false,"family":"Johnson","given":"Leah","email":"","middleInitial":"R.","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":868553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kenney, Melissa A.","contributorId":202414,"corporation":false,"usgs":false,"family":"Kenney","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":868554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLachlan, Jason S.","contributorId":245535,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peters, Jody A.","contributorId":303908,"corporation":false,"usgs":false,"family":"Peters","given":"Jody","email":"","middleInitial":"A.","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sokol, Eric R.","contributorId":303909,"corporation":false,"usgs":false,"family":"Sokol","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":55597,"text":"National Ecological Observatory Network","active":true,"usgs":false}],"preferred":false,"id":868557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":868558,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Willson, Alyssa","contributorId":303910,"corporation":false,"usgs":false,"family":"Willson","given":"Alyssa","email":"","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868559,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Woelmer, Whitney M.","contributorId":303911,"corporation":false,"usgs":false,"family":"Woelmer","given":"Whitney M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868560,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70244297,"text":"70244297 - 2023 - Habitat use by breeding waterbirds in relation to tidal marsh restoration in the San Francisco Bay estuary","interactions":[],"lastModifiedDate":"2023-06-13T13:22:07.509614","indexId":"70244297","displayToPublicDate":"2023-04-01T08:14:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use by breeding waterbirds in relation to tidal marsh restoration in the San Francisco Bay estuary","docAbstract":"Near-Earth Objects (NEOs) are asteroids and comets that orbit the Sun, but have orbits that can bring them into Earth’s neighborhood—within 30 million miles of Earth’s orbit. Planetary defense is “applied planetary science” to address the NEO impact risks on Earth.
This National Preparedness Strategy and Action Plan for Near-Earth Objects and Planetary Defense (2023 Planetary Defense Strategy) updates the United States’ first comprehensive Near-Earth Object Preparedness Strategy and Action Plan, released in 2018. The 2023 Planetary Defense Strategy builds on existing efforts by Federal Departments and Agencies to address the hazard of near-Earth object impacts, includes evaluation of where progress has been made since 2018, and focuses future work on planetary defense across the U.S. government. The 2023 Planetary Defense Strategy maintains and updates the core five goals of the 2018 Strategy and Action Plan, while adding a sixth goal to improve governance and interagency coordination on planetary defense issues across Federal Departments and Agencies for the decade ahead.
The 2023 Planetary Defense Strategy focuses on six goals:
• Goal 1: Enhance NEO detection, tracking, and characterization capabilities.
• Goal 2: Improve NEO modeling, prediction, and information integration.
• Goal 3: Develop technologies for NEO deflection and disruption missions.
• Goal 4: Increase international cooperation on NEO preparation.
• Goal 5: Strengthen and routinely exercise NEO impact emergency procedures and action protocols.
• Goal 6: Improve U.S. governance of planetary defense through new interagency collaboration.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","usgsCitation":"Daniels, M., Johnson, L., Kommel, R., Besha, P., Brody, P., Conole, K., Fast, K., Fernandez, A., Gaume, R., Greenaugh, K., Guglietta, R., Howard, D., Hu, G., Joseph, C., Keuker-Murphy, B.G., Lewis, L., Millard, L., Mozer, J., Poster, D., Titus, T.N., and Vanderley, A., 2023, National preparedness strategy & action plan for potentially hazardous near-Earth objects and planetary defense, iv, 34 p.","productDescription":"iv, 34 p.","ipdsId":"IP-150721","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":415223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415217,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.whitehouse.gov/wp-content/uploads/2023/04/2023-NSTC-National-Preparedness-Strategy-and-Action-Plan-for-Near-Earth-Object-Hazards-and-Planetary-Defense.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniels, Matthew","contributorId":303927,"corporation":false,"usgs":false,"family":"Daniels","given":"Matthew","email":"","affiliations":[{"id":65936,"text":"Assistant Director, White House OSTP","active":true,"usgs":false}],"preferred":false,"id":868644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Lindley","contributorId":303928,"corporation":false,"usgs":false,"family":"Johnson","given":"Lindley","email":"","affiliations":[{"id":65937,"text":"Planetary Defense Officer, NASA","active":true,"usgs":false}],"preferred":false,"id":868645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kommel, Renata","contributorId":303929,"corporation":false,"usgs":false,"family":"Kommel","given":"Renata","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Besha, Patrick","contributorId":303930,"corporation":false,"usgs":false,"family":"Besha","given":"Patrick","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brody, Perry","contributorId":303931,"corporation":false,"usgs":false,"family":"Brody","given":"Perry","email":"","affiliations":[{"id":65939,"text":"DOC/NOAA","active":true,"usgs":false}],"preferred":false,"id":868648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conole, Kevin","contributorId":303932,"corporation":false,"usgs":false,"family":"Conole","given":"Kevin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868649,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fast, Kelly","contributorId":303933,"corporation":false,"usgs":false,"family":"Fast","given":"Kelly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868650,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fernandez, Angelo","contributorId":303934,"corporation":false,"usgs":false,"family":"Fernandez","given":"Angelo","email":"","affiliations":[{"id":65941,"text":"JCS","active":true,"usgs":false}],"preferred":false,"id":868651,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaume, Ralph","contributorId":303935,"corporation":false,"usgs":false,"family":"Gaume","given":"Ralph","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868652,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Greenaugh, Kevin","contributorId":303936,"corporation":false,"usgs":false,"family":"Greenaugh","given":"Kevin","email":"","affiliations":[{"id":34199,"text":"DOE","active":true,"usgs":false}],"preferred":false,"id":868653,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Guglietta, Ryan","contributorId":303937,"corporation":false,"usgs":false,"family":"Guglietta","given":"Ryan","email":"","affiliations":[{"id":65943,"text":"State","active":true,"usgs":false}],"preferred":false,"id":868654,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Howard, Diane","contributorId":303938,"corporation":false,"usgs":false,"family":"Howard","given":"Diane","email":"","affiliations":[{"id":65944,"text":"NSpC","active":true,"usgs":false}],"preferred":false,"id":868655,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hu, Grace","contributorId":303939,"corporation":false,"usgs":false,"family":"Hu","given":"Grace","email":"","affiliations":[{"id":65945,"text":"OMB","active":true,"usgs":false}],"preferred":false,"id":868656,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Joseph, Christine","contributorId":303940,"corporation":false,"usgs":false,"family":"Joseph","given":"Christine","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":868657,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Keuker-Murphy, Brig Gen Traci","contributorId":303941,"corporation":false,"usgs":false,"family":"Keuker-Murphy","given":"Brig","email":"","middleInitial":"Gen Traci","affiliations":[{"id":65946,"text":"USSPACECOM","active":true,"usgs":false}],"preferred":false,"id":868658,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lewis, L.A.","contributorId":303942,"corporation":false,"usgs":false,"family":"Lewis","given":"L.A.","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":868659,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Millard, Lindsay","contributorId":303943,"corporation":false,"usgs":false,"family":"Millard","given":"Lindsay","email":"","affiliations":[{"id":65947,"text":"OSD(R&E)","active":true,"usgs":false}],"preferred":false,"id":868660,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mozer, Joel","contributorId":303944,"corporation":false,"usgs":false,"family":"Mozer","given":"Joel","email":"","affiliations":[{"id":65948,"text":"DOD/USSF","active":true,"usgs":false}],"preferred":false,"id":868661,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Poster, Dianne","contributorId":303945,"corporation":false,"usgs":false,"family":"Poster","given":"Dianne","email":"","affiliations":[{"id":65949,"text":"DOC","active":true,"usgs":false}],"preferred":false,"id":868662,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":868663,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Vanderley, Ashley","contributorId":303946,"corporation":false,"usgs":false,"family":"Vanderley","given":"Ashley","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868664,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70242034,"text":"70242034 - 2023 - Low estradiol production of non-laying whooping cranes (Grus americana) is associated with the failure of small follicles to enter follicular hierarchy","interactions":[],"lastModifiedDate":"2023-04-12T14:42:48.623808","indexId":"70242034","displayToPublicDate":"2023-04-01T06:50:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"title":"Low estradiol production of non-laying whooping cranes (Grus americana) is associated with the failure of small follicles to enter follicular hierarchy","docAbstract":"For endangered species managed ex situ, production of offspring is a key factor to ensure healthy and self-sustaining populations. However, current breeding goals for the whooping crane (Grus americana) are impeded by poor reproduction. Our study sought to better understand mechanisms regulating ovarian function in ex situ managed whooping cranes and the regulatory function of the hypothalamic-pituitary-gonadal (HPG) axis in relation to follicle formation and egg laying. To characterize hormonal regulation of follicular development and ovulation, we collected weekly blood samples from six female whooping cranes during two breeding seasons, for a total of 11 reproductive cycles. The plasma samples were assessed for follicle stimulating hormone, luteinizing hormone, estradiol, and progesterone and the yolk precursors vitellogenin and very low-density lipoprotein. Ultrasonographic examination of the ovary was conducted at the time of blood collection. Preovulatory follicles (>12 mm) were present in laying cycles (n = 6) but absent in non-laying cycles (n = 5). The patterns of plasma hormone and yolk precursor concentrations corresponded to the stage of follicle development. Specifically, gonadotropin and yolk precursors concentrations increased as follicles transitioned from the non-yolky to yolky stage but did not increase further as the follicle advanced to preovulatory and ovulatory stages. Estrogen and progesterone concentrations increased as follicle size increased and reached peak concentrations (P < 0.05) when follicles developed to ovulatory and preovulatory stages, respectively. While overall mean circulating gonadotropin, progesterone, and yolk precursor concentrations did not differ for laying versus non-laying cycles, mean plasma estradiol in laying cycles was significantly higher than that in non-laying cycles. In summary, the findings suggested that disruption of mechanisms regulating follicle recruitment is likely responsible for the oviposition failure of the captive female whooping crane.
The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d−1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.
Fall bottom trawl (fall BT) and lakewide acoustic (AC) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The fall BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m at fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., Alewife (Alosa pseudoharengus), Bloater (Coregonus hoyi), Rainbow Smelt (Osmerus mordax), Deepwater Sculpin (Myoxocephalus thompsonii), Slimy Sculpin (Cottus cognatus), Round Goby (Neogobius melanostomus), Ninespine Stickleback (Pungitius pungitius)] as well as age-0 Yellow Perch (Perca flavescens) and large (> 350 mm) Burbot (Lota lota). The AC survey has been conducted each late summer/early fall since 2004, and the 2022 survey consisted of 26 transects [570 km total (354 miles)] covering bottom depths ranging from 5 to 255 m and 37 midwater trawl tows above bottom depths ranging 5 to 232 m; this survey estimates densities of three prey fish species (i.e., Alewife, Bloater, and Rainbow Smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks. In spring of 2022, an additional spring bottom trawl survey (spring BT) was implemented across six of the transects sampled in the fall and sites ranged in depth from 9 to 236 m. The goal of the spring BT was to explore seasonal differences in biomass density and distributions of key prey species, mostly notably Alewife.
Total prey fish biomass density from the spring BT was 2.1 kg/ha. For the AC survey, total biomass density of prey fish equaled 6.2 kg/ha, 37% higher than the long-term average (2004-2021) of 4.5 kg/ha and 0.43 kg/ha higher than the 2021 estimate. For the fall BT, total biomass density of prey fish equaled 8.7 kg/ha, the highest value since 2013 and 21% higher than average value from 20042021 (6.8 kg/ha). The 2022 fall BT biomass density was still well below the average over the entirety of the time series (1973-2021; 34.3 kg/ha). Over the period both surveys have been conducted (2004-2021), total biomass density has trended downward in the fall BT (despite a high 2022 estimate) and remained relatively stable in the AC survey.
Bloater was the dominant species (by biomass) among prey fishes in both the spring and fall BT, while the AC survey reported co-dominance of Bloater and Alewife. Mean biomass of yearling and older (YAO) Alewife was 0.38 kg/ha in the spring BT, 3.0 kg/ha in the AC survey, and 0.10 in the fall BT. Alewife were aggregated in deepwater habitats in the spring of 2022 (> 110 m). Since 2014, catchability of YAO Alewives for the fall BT has been substantially lower than the AC survey. Results of the 2022 spring BT do not suggest that catchability is substantially higher in the spring than the fall.
Comparing the acoustic estimate to previous years, YAO Alewife biomass was 40% higher than the average from 2004-2021. An age-7 fish was recorded for the first time since 2009. Despite the rare catches of older fish, the Alewife age distribution still appears truncated, with age-1 fish as the most represented age class in all three surveys. Numeric density of age-0 Alewife from the AC survey was 7 fish/ha in 2022, which is the third lowest in the time series and well below the longterm mean of 452 fish/ha. Biomass density of large (≥120 mm) Bloater was 2.7 kg/ha in the AC survey and 4.4 kg/ha in the fall BT - each at least an order of magnitude lower than what was estimated by the fall BT between 1981 and 1998. Following a record high year in 2021 (1,037 fish/ha), the numeric density of small (<120 mm) Bloater was only 15 fish/ha in the AC survey.
Meanwhile, small Bloater density estimated in the fall BT was 261 fish/ha, the highest value since 1990 and likely partially reflective of a large 2021 year-class. Biomass density of large Rainbow Smelt (≥90 mm) was 0.29 kg/ha in the AC survey and 0.12 kg/ha in the fall BT survey, continuing the trend of low Rainbow Smelt biomass that has been observed since 2001. Numeric density of small (<90 mm) Rainbow Smelt was 21 fish/ha in the AC survey and 2.7 fish/ha in the fall BT, indicating a weak year-class. All four prey fish species sampled only by the fall BT indicated below average biomass densities. Deepwater Sculpin biomass density was estimated at 0.41 kg/ha, which makes 12 of the past 13 years when biomass was <1 kg/ha. Slimy Sculpin was estimated at 0.10 kg/ha, the highest estimate since 2016 but still only 25% of the long-term average. Round Goby was estimated at 1.3 kg/ha, above the average biomass of 0.82 kg/ha since 2008 but similar to intermittent high values observed throughout the dataset. Ninespine Stickleback density was 1.5 fish/ha. Burbot biomass remained near record low levels, and no age-0 Yellow Perch were caught, indicating a weak Yellow Perch year-class in 2022.
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