Population projection models based on long-term trends in regeneration and tree survival can be used to predict the future stability of swamp forest species using water management. Population growth and regeneration of a foundational tree species in North American cypress swamps (Taxodium distichum) were compared in the Cache River watershed of southern Illinois USA over several decades. This study examined T. distichum population growth in several regional swamps within the Cache River watershed along a moisture gradient to model growth, stability, or decline based on data of life history stage transitions, using data from the following time periods: the 1990s, 2002–2011, and 2012–2022. Using data from the 1990s, T. distichum populations in Crawford Tract in Buttonland Swamp were projected to increase over time because of the high frequency of juvenile stages at elevations with growing season drawdown and winter flooding. The situation in Crawford Tract had changed by 2019–2021 because T. distichum seedlings had not transitioned (i.e., saplings and young trees were absent) although no old-growth trees had died during or after the 1990s. In other regional swamps, projection models were constructed over two decades (2006–2021) along a dry-to-wet flood gradient including Section 8 Woods, Deer Pond, Wildcat Bluff, Snake Hole, and Heron Pond. Populations in swamps on the drier end of the gradient had more support from juvenile stages. In a tree health survey in 2022 within the impounded interior of Buttonland Swamp (not Crawford Tract), seventy percent of the old-growth T. distichum trees were stressed or declining. Coupled with the fact that no seedlings or saplings were observed, the populations in the interior of the swamp were not stable. Many factors could further stress these ancient T. distichum including interactions of impoundment with chemicals, disease, and changes in air temperature and precipitation. Overall, the seedling abundance in these swamps increased in these environments from 2012 to 2021, except in the most flooded swamps. From a management perspective, drier conditions were more conducive to the success of the earlier life history stages of T. distichum in these old-growth forests.
Updating predictions of the response of high-profile, at-risk species to climate change and anthropogenic stressors is vital for informing effective conservation action. Here, we review two prior generations of Bayesian network probability models predicting changes in global polar bear (Ursus maritimus) population status, and provide a contemporary update based on recent research findings and sea-ice projections by newer climate models. We compare predictions of polar bear population response from all 3 models among four circumpolar Arctic ecoregions, using sea ice projections based on three IPCC greenhouse gas emissions scenarios (SSP2.6, 4.5, 8.5). Consistent with the previous two model generations, polar bears will continue to experience increasing probability of declining or greatly declining populations throughout the 21st century, varying by emission scenario. Populations within the Polar Basin Divergent Ice Ecoregion have the highest predicted probability of declines, but predictions were slightly less dire relative to the previous model generation. Most of the influence, denoted by model sensitivity analysis, is from expected degradation and loss of sea ice and reduced access to marine prey. The lack of terrestrial prey adequate to substitute for loss of access to marine prey, as well as human-caused bear morality associated with hunting and defense of life and property encountered when polar bears are increasingly forced ashore also contributed to predicted declines. Although some tidewater glacial fjords and other localized onshore resources may provide local refugia, their benefit is transient. Our findings continue to inform priorities for inventory, monitoring, and research needs, and suggest that similar updates to models of other at-risk species can capitalize on the comparison framework we present here.
Large multi-site trend studies provide an opportunity to evaluate progress of waterbodies towards water-quality goals across broad geographic areas. Such studies often aggregate the results of site-specific models and thus contend with evaluating each model for appropriate fit and statistical assumptions. We explored the use of four traditional machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) to perform these checks and estimate probabilities that an analyst would publish or reject a site-specific trend model from a multi-site study. We trained these “model-checking models” (MCMs) using a national study of over 6000 trend models and tested the MCMs using a smaller set of novel trend models. Although the MCMs did not perform well enough to bypass analyst review entirely, we found incorporating an MCM into a larger evaluation workflow can reduce the number of trend models needing an analyst review by more than half.
The lower Mississippi River Valley spans over 200,000 square kilometres in parts of seven states, encompassing areas of critical groundwater supplies, natural hazards, infrastructure, and low-lying coastal regions. From 2018 - 2022, the U.S. Geological Survey acquired over 82,000 line-kilometres of airborne electromagnetic, radiometric, and magnetic data over this region to provide comprehensive and systematic information about subsurface geologic and hydrologic properties that support multiple scientific and societal interests. Most of the data were acquired on a regional grid of west-east flight lines separated by 3 - 6 kilometres; however, several high-resolution inset grids with line spacing as close as 200 m were acquired in targeted areas of interest. Approximately 8,000 line-kilometres were acquired along streams and rivers to characterise the potential for surface water-groundwater connection, and another 6,000 line-kilometres were acquired along the Mississippi and Arkansas River levees to characterise this critical infrastructure. Here, we present a summary of the data along with several examples of how they are being used to inform regional groundwater model development, inferences of groundwater salinity, identification of faults in the New Madrid seismic zone, and levee infrastructure.
","conferenceTitle":"AEM2023 8th International Airborne Electromagnetics Workshop","conferenceDate":"September 3-7, 2023","conferenceLocation":"Fitzroy Island, Queensland, Australia","language":"English","publisher":"Australian Society of Exploration Geophysicists","doi":"10.5281/zenodo.10052667","usgsCitation":"Minsley, B.J., Adams, R.F., Asquith, W.H., Burton, B.L., Hoogenboom, B.E., James, S.R., Killian, C.D., Knierim, K.J., Kress, W.H., Lindaman, M., Leaf, A.T., Rigby, J.R., and Traylor, J.P., 2023, System-scale airborne electromagnetic surveys in the lower Mississippi River Valley support multidisciplinary applications, AEM2023 8th International Airborne Electromagnetics Workshop, Fitzroy Island, Queensland, Australia, September 3-7, 2023, 5 p., https://doi.org/10.5281/zenodo.10052667.","productDescription":"5 p.","ipdsId":"IP-150848","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science 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aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886137,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":260894,"corporation":false,"usgs":true,"family":"Rigby","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886138,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Traylor, Jonathan P. 0000-0002-2008-1923 jtraylor@usgs.gov","orcid":"https://orcid.org/0000-0002-2008-1923","contributorId":5322,"corporation":false,"usgs":true,"family":"Traylor","given":"Jonathan","email":"jtraylor@usgs.gov","middleInitial":"P.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886139,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70273450,"text":"70273450 - 2023 - Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching","interactions":[],"lastModifiedDate":"2026-01-14T15:59:24.616609","indexId":"70273450","displayToPublicDate":"2023-10-30T08:53:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7169,"text":"Quaternary Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching","docAbstract":"A forest bed of tree stumps currently in the intertidal zone at Girdwood, south-central Alaska, records coseismic submergence during the penultimate great earthquake. Tree-ring samples from ten spruce stumps were crossdated to develop a 149-year-long ring-width chronology. Radiocarbon wiggle-matching found that single-ring ages from the chronology were offset 28 ± 7 years older than the IntCal20 calibration curve and that the last ring of the chronology dated as 1169 to 1189 CE (781–761 cal. yr. BP) at the 95% confidence level. Bark was observed on some stumps, six samples had the same year for the last growth ring, and so this wiggle-match date is also the best estimate of the date of the penultimate great earthquake. This date is in good agreement with a date for this event in a seismo-turbidite record from Skilak Lake but not with previous dates from Bayesian models of maximum- and minimum-limiting ages from coastal salt marshes. Reanalysis of the coastal salt marsh ages with the data grouped by area, context and material found that outer wood samples from stumps at coseismic submergence sites and a Bayesian limiting age model based on just herbaceous plant ages from Turnagain Arm and the Copper River area are both consistent with our wiggle-match date. Furthermore, coseismic emergence ages from Cape Suckling and Yakataga are older than the penultimate earthquake and so likely relate to an earlier uplift event in this eastern area. The rupture extent during the penultimate great earthquake appears to have been less than in the 1964 great earthquake and the interseismic interval between these two events was 785 ± 10 years.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2023.100142","usgsCitation":"Barclay, D.J., Haeussler, P., and Witter, R.C., 2023, Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching: Quaternary Science Advances, v. 13, 100142, 13 p., https://doi.org/10.1016/j.qsa.2023.100142.","productDescription":"100142, 13 p.","ipdsId":"IP-158222","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2023.100142","text":"Publisher Index Page"},{"id":498618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.97392172569602,\n 60.587785877878815\n ],\n [\n -156.97392172569602,\n 56.48596044935496\n ],\n [\n -140.95577716118677,\n 56.48596044935496\n ],\n [\n -140.95577716118677,\n 60.587785877878815\n ],\n [\n -156.97392172569602,\n 60.587785877878815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, David J 0009-0007-9629-3731","orcid":"https://orcid.org/0009-0007-9629-3731","contributorId":365136,"corporation":false,"usgs":false,"family":"Barclay","given":"David","middleInitial":"J","affiliations":[{"id":87054,"text":"SUNY Cortland, Cortland, NY","active":true,"usgs":false}],"preferred":false,"id":953743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":953744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":219962,"corporation":false,"usgs":true,"family":"Witter","given":"Robert","email":"rwitter@usgs.gov","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953745,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249787,"text":"ofr20231082 - 2023 - Annotated bibliography of scientific research on greater sage-grouse published from October 2019 to July 2022","interactions":[],"lastModifiedDate":"2023-12-14T20:58:32.054422","indexId":"ofr20231082","displayToPublicDate":"2023-10-27T16:20:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1082","displayTitle":"Annotated Bibliography of Scientific Research on Greater Sage-Grouse Published from October 2019 to July 2022","title":"Annotated bibliography of scientific research on greater sage-grouse published from October 2019 to July 2022","docAbstract":"Integrating recent scientific knowledge into management decisions supports effective natural resource management and can lead to better resource outcomes. However, finding and accessing scientific knowledge can be time consuming and costly. To assist in this process, the U.S. Geological Survey (USGS) created a series of annotated bibliographies on topics of management concern for western lands. Previously published reports introduced a methodology for preparing annotated bibliographies to facilitate integration of recent, peer-reviewed science into resource management decisions. Therefore, relevant text from those efforts is reproduced here and built on to incorporate new information. The greater sage-grouse (Centrocercus urophasianus; hereafter “GRSG”) has been a focus of scientific investigation and management action for the past two decades. The 2015 U.S. Fish and Wildlife Service listing determination of “not warranted” under the Endangered Species Act was in part a result of a large-scale collaborative effort to develop strategies to conserve GRSG populations and their habitat and to reduce threats to both. New scientific information augments existing knowledge and can help inform updates or modifications to existing plans for managing GRSG and sagebrush ecosystems. However, the sheer number of scientific publications can be a challenge for managers tasked with evaluating and determining the need for potential updates to existing planning documents. To assist in this process, the USGS has reviewed and summarized the scientific literature published since January 1, 2015. Our most recent GRSG literature summary was published in 2020 (Carter and others, 2020) and included products published through October 2, 2019. Here, we consider products published between October 2, 2019, and July 21, 2022. We compiled and summarized peer-reviewed journal articles, data products, and formal technical reports (such as U.S. Department of Agriculture Forest Service General Technical Reports and USGS Open-File Reports) on greater sage-grouse. We first systematically searched three reference databases and three government databases using the search phrase “greater sage-grouse.” We refined the initial list of products by removing (1) duplicates, (2) publications not published as research, data products, or scientific review articles in peer-reviewed journals or as formal technical reports, and (3) products for which greater sage-grouse was not a research focus or the study did not present new data or findings about greater sage-grouse. We summarized each product using a consistent structure (background, objectives, methods, location, findings, and implications) and identified management topics addressed by each product; for example, species and population characteristics. We also identified projects that provided new geospatial data. The review process for this annotated bibliography included two initial internal colleague reviews of each summary, requesting input on each summary from an author of the original publication, and formal peer review. Our initial searches resulted in 221 total products, of which 147 met our criteria for inclusion. Across products summarized in the annotated bibliography, broad-scale habitat characteristics, behavior or demographics, site-scale habitat characteristics, habitat selection, and population estimates or targets were the most commonly addressed management topics. The online version of this bibliography, which will be available on the Science for Resource Managers tool (https://apps.usgs.gov/science-for-resource-managers), will be searchable by topic, location, and year, and will include links to each original publication. The studies compiled and summarized here may inform planning and management actions that seek to maintain and restore sagebrush landscapes and GRSG populations across the GRSG range.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231082","usgsCitation":"Teige, E.C., Maxwell, L.M., Jordan, S.E., Rutherford, T.K., Dietrich, E.I., Samuel, E.M., Stoneburner, A.L., Kleist, N.J., Meineke, J.K., Selby, L.B., Foster, A.C., and Carter, S.K., 2023, Annotated bibliography of scientific research on greater sage-grouse published from October 2019 to July 2022 (ver. 1.1, November 2023): U.S. Geological Survey Open-File Report 2023–1082, 122 p., https://doi.org/10.3133/ofr20231082.","productDescription":"ix, 122 p.","onlineOnly":"Y","ipdsId":"IP-152382","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":422491,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1082/versionHist.txt","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1082 version history"},{"id":422190,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1082/ofr20231082.pdf","text":"Report","size":"5.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1082"},{"id":422189,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1082/coverthb2.jpg"},{"id":422620,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1082"},{"id":422537,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1082/images"},{"id":422538,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1082/ofr20231082.xml"}],"edition":"Version 1.0: October 27, 2023; Version 1.1: November 13, 2023","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Metabolic rate is a key parameter in fish energy budgets that strongly influences the output of bioenergetics models. In this study, we tested the hypothesis that metabolic rate varies with growth history of age-1 largemouth bass Micropterus nigricans Cuvier, 1828. Two groups of fish were fed alternating maintenance or ad libitum rations of fathead minnow Pimephales promelas Rafinesque, 1820, so that over a 9-week period, initial and ending size of fish was similar. After 9 weeks, oxygen consumption was measured using static, closed respirometry. Although final body weight was similar between the two groups (means, 104–108 g), specific oxygen consumption for fish fed maintenance rations (0.094 mg O2 g−2 h−1) was 38% less than that measured for fish fed ad libitum (0.152 mg O2 g−2 h−1). Bioenergetics estimates of food consumption were similar to observed values for fish fed ad libitum (∼7% error), but for fish fed maintenance rations, the model overestimated food consumption by 65%. By accounting for changes in metabolic rate owing to reduced feeding, error in model estimates of food consumption was reduced. These findings shed new insight into factors associated with consumption-dependent error in bioenergetics models and highlight the importance of feeding history on metabolic rate of fish. Incorporating growth-dependent metabolism into bioenergetics models can improve model accuracy and allow fisheries biologists to make more informed decisions regarding fish growth and energetics.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjz-2023-0047","usgsCitation":"Ranney, S., Chipps, S.R., and Wahl, D., 2023, Effect of feeding history on metabolic rate of largemouth bass (Micropterus nigricans): implications for bioenergetics models: Canadian Journal of Zoology, v. 102, no. 3, p. 207-216, https://doi.org/10.1139/cjz-2023-0047.","productDescription":"10 p.","startPage":"207","endPage":"216","ipdsId":"IP-064668","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433015,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"102","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ranney, Steven H.","contributorId":342588,"corporation":false,"usgs":false,"family":"Ranney","given":"Steven H.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":910204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wahl, David H.","contributorId":342591,"corporation":false,"usgs":false,"family":"Wahl","given":"David H.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":910206,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251059,"text":"70251059 - 2023 - Using high-frequency monitoring data to quantify city-wide suspended-sediment load and evaluate TMDL goals","interactions":[],"lastModifiedDate":"2024-01-19T13:23:35.008001","indexId":"70251059","displayToPublicDate":"2023-10-26T07:21:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Using high-frequency monitoring data to quantify city-wide suspended-sediment load and evaluate TMDL goals","docAbstract":"Excess sediment is a common reason water bodies in the USA become listed as impaired resulting in total maximum daily loads (TMDL) that require municipalities to invest millions of dollars annually on management practices aimed at reducing suspended-sediment loads (SSLs), yet monitoring data are rarely used to quantify SSLs and track TMDL progress. A monitoring network was created to quantify the SSL from the City of Roanoke, Virginia, USA (CoR), to the Roanoke River and Tinker Creek and help guide TMDL assessment and implementation. Suspended-sediment concentrations were estimated between 2020 and 2022 from high-frequency turbidity data using surrogate linear-regression models. Sixty-one percent of the total three-year SSL resulted from five large storm events. The average suspended-sediment yield from the CoR (58.1 metric tons/km2/year) was similar to other urban watersheds in the Eastern United States; however, the yield was nearly five times larger than the TMDL allocation (12.2 metric tons/km2/year). The TMDL allocated load was modeled based on a predominantly forested reference watershed and may not be a practical target for highly impervious watersheds within the CoR. The TMDL model used daily input data which likely does not capture the full range of SSLs during storm events, particularly from flashy urban streams. The average SSL following the five large storm events doubled that of the CoR’s annual allocated load from the TMDL. The results of this study highlight the importance of using high-frequency monitoring data to accurately estimate SSLs and evaluate TMDLs in urban areas.
The U.S. Geological Survey is working with Federal land management agencies to develop a series of science syntheses to support environmental effects analyses that agencies conduct to comply with the National Environmental Policy Act (NEPA). This report synthesizes science information about the potential effects of noise from oil and gas development on North American ungulates and small mammals, including rodents and leporids. We conducted a structured search of published scientific literature to find information about noise levels produced during oil and gas development, methods for analyzing sound propagation, the effects of noise on ungulates and small mammals, and measures to reduce noise emissions. We organized the sections of this synthesis to align with standard elements of NEPA analyses. We found that oil and gas development is a common source of human-caused noise on public lands and includes noise sources such as heavy construction and drilling machinery, long-term production machinery, truck traffic, and aircraft. Common techniques for predicting potential noise include field data collection using a sound level meter, inference from previously published data, and sound propagation modeling. A substantial body of research shows that human-caused noise can affect wildlife health and behavior, with variation in sensitivity to noise among species. Studies have shown consistent, detectable effects of noise on ungulates, but the amount of literature on ungulates is very small, and additional research could improve our understanding of differences in effects among species, seasons, and individual indicators of fitness. Several species of small mammals are dependent on audible signals for predator detection and communication, and noise has been shown to affect their vigilance and foraging behavior. However, other studies have documented no effects to rodents in noisy areas, and the effects of noise on small mammals may differ by species and study system. Techniques suggested in the literature for reducing noise emissions include sound barriers, seasonal and daily timing restrictions, traffic control measures, and siting infrastructure to take advantage of natural sound barriers. Public land managers can use this report by incorporating it by reference in NEPA documentation, as supplemental information, or as a general reference for literature about the effects of noise from oil and gas development on ungulates and small mammals.
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The recreational fishery for salmonine species in Lake Michigan (lake trout, Chinook salmon, coho salmon, steelhead, and brown trout) is largely maintained through stocking. Decisions about how many of each species to stock require an understanding of how to maintain a sustainable balance of predators (salmonine species) to prey (alewife) in the lake. The current models used to make these decisions can estimate the ratio of Chinook salmon to alewife in the lake. However, the Lake Michigan Committee’s new stocking strategy aims to incorporate the other salmonine species into this predator-prey ratio. We used structured decision making to evaluate potential stocking strategies. We worked with fishery stakeholders and members of the Lake Michigan Committee, conducted participatory modeling to forecast outcomes of stocking scenarios using updated information on fish movement and feeding, and evaluated the risk of these stocking strategies. Most of the stocking practices we evaluated resulted in a high risk of large declines in alewife abundance, negatively affecting future salmon fisheries. The forecasts were substantially more pessimistic than those resulting from a similar analysis conducted a decade earlier, apparently due to more recent alewife assessments indicating lower alewife productivity (recruits per spawner). Alewife recruitment dynamics is an area of substantial uncertainty, with apparently large consequences for management; decision makers on Lake Michigan would benefit from greater understanding of alewife recruitment dynamics to reduce this uncertainty when accounting for risks.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Robinson, K.F., Jones, M.L., Clark, R., Roth, B., Jonas, J., Tsehaye, I., Kornis, M.S., Turschak, B., O’Keefe, D., and Brenton, B., 2023, Updated decision analysis to inform multi-species salmonine management in Lake Michigan: Cooperator Science Series FWS/CSS-154-2023, 23 p.","productDescription":"23 p.","ipdsId":"IP-158445","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431814,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/updated-decision-analysis-inform-multi-species-salmonine-management-lake-michigan"},{"id":433625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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Jory","contributorId":195544,"corporation":false,"usgs":false,"family":"Jonas","given":"Jory","affiliations":[{"id":6983,"text":"Michigan DNR","active":true,"usgs":false}],"preferred":false,"id":907531,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tsehaye, Iyob","contributorId":106801,"corporation":false,"usgs":true,"family":"Tsehaye","given":"Iyob","email":"","affiliations":[],"preferred":false,"id":907532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kornis, Matthew S.","contributorId":201252,"corporation":false,"usgs":false,"family":"Kornis","given":"Matthew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":907533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Turschak, Benjamin A.","contributorId":340768,"corporation":false,"usgs":false,"family":"Turschak","given":"Benjamin A.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907534,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O’Keefe, Daniel","contributorId":340769,"corporation":false,"usgs":false,"family":"O’Keefe","given":"Daniel","email":"","affiliations":[{"id":37753,"text":"Michigan Sea Grant","active":true,"usgs":false}],"preferred":false,"id":907535,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brenton, Brian","contributorId":340770,"corporation":false,"usgs":false,"family":"Brenton","given":"Brian","email":"","affiliations":[{"id":81664,"text":"Brenton Consulting, LLC","active":true,"usgs":false}],"preferred":false,"id":907536,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249996,"text":"70249996 - 2023 - Construction and modification of debris-flow alluvial fans as captured in the geomorphic and sedimentary record: Examples from the western Sangre de Cristo Mountains, south-central Colorado","interactions":[],"lastModifiedDate":"2023-11-12T14:15:31.35482","indexId":"70249996","displayToPublicDate":"2023-10-25T08:03:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5198,"text":"Geological Society of America Special Papers ","active":true,"publicationSubtype":{"id":10}},"title":"Construction and modification of debris-flow alluvial fans as captured in the geomorphic and sedimentary record: Examples from the western Sangre de Cristo Mountains, south-central Colorado","docAbstract":"Debris-flow alluvial fans are iconic features of dynamic landscapes and are hypothesized to record tectonic and climatic change. Here, we highlight their complex formation and evolution through an exemplary suite of Quaternary debris-flow alluvial fans emanating from the western range front of the Sangre de Cristo Mountains in south-central Colorado, USA. To evaluate the constructive and modifying processes that produce fan form and the associated sedimentary signatures, we applied a combined geomorphologic and sedimentologic approach using sedimentary facies analysis, soils mapping, high-resolution topographic data, and luminescence geochronology to document timing of fan construction and modification. We explored two subsets of fans in the study area: a southern set sourced from the extensively glaciated drainages of the Blanca Peak massif, and a northern set from the unglaciated drainages south of Great Sand Dunes National Park. Both sets of fans have: (1) active and successively abandoned surfaces that show evolving degradation of primary features through modification by secondary processes, (2) associated facies that display distinct characteristics representative of primary depositional and secondary modifying sedimentary processes, and (3) evidence of primary debris flow with subsequent modification by secondary processes. We found that surface geomorphology and facies assemblages in exposed alluvial-fan deposits represent sediment transport processes on both active and abandoned lobes. The link between fan surface morphologies and the sedimentary facies of their deposits provides a basis for an evolutionary process–based interpretation of debris-flow alluvial-fan geomorphology and provides a better understanding of complexities in buried paleosurfaces (intraformational progressive unconformities), surficial deformation, and landform development as recorded in debris-flow fan deposits in the sedimentary record.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2023.2561(01)","usgsCitation":"Nicovich, S., Schmitt, J., Gray, H., Klinger, R.E., and Mahan, S.A., 2023, Construction and modification of debris-flow alluvial fans as captured in the geomorphic and sedimentary record: Examples from the western Sangre de Cristo Mountains, south-central Colorado: Geological Society of America Special Papers , v. 561, 48 p., https://doi.org/10.1130/2023.2561(01).","productDescription":"48 p.","ipdsId":"IP-144536","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":441768,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2023.2561(01)","text":"Publisher Index Page"},{"id":435138,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96162BF","text":"USGS data release","linkHelpText":"Data Release for Luminescence: Construction and modification of debris-flow alluvial fans as captured in the geomorphic and sedimentary record: examples from the western Sangre de Cristo Mountains, south-central Colorado"},{"id":422521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Sangre de Cristo Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.01583616224015,\n 38.75219123538736\n ],\n [\n -107.01583616224015,\n 37.01816107244015\n ],\n [\n -104.81857053724035,\n 37.01816107244015\n ],\n [\n -104.81857053724035,\n 38.75219123538736\n ],\n [\n -107.01583616224015,\n 38.75219123538736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"561","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nicovich, Sylvia","contributorId":210054,"corporation":false,"usgs":false,"family":"Nicovich","given":"Sylvia","affiliations":[{"id":38060,"text":"Department of Earth Sciences, Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":887922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmitt, James","contributorId":256655,"corporation":false,"usgs":false,"family":"Schmitt","given":"James","email":"","affiliations":[{"id":38060,"text":"Department of Earth Sciences, Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":887923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":887924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klinger, Ralph E.","contributorId":172929,"corporation":false,"usgs":false,"family":"Klinger","given":"Ralph","email":"","middleInitial":"E.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":887925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":887926,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251200,"text":"70251200 - 2023 - Model-based surveillance system design under practical constraints with application to white-nose syndrome","interactions":[],"lastModifiedDate":"2024-01-29T12:21:40.355884","indexId":"70251200","displayToPublicDate":"2023-10-25T06:18:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1573,"text":"Environmental and Ecological Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Model-based surveillance system design under practical constraints with application to white-nose syndrome","docAbstract":"Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful prevention and control of pathogens requires knowledge of the current scope and severity of disease, as well as the ability to forecast future disease dynamics. Assessment of the current situation as well as prediction of the future conditions, rely on spatially referenced information regarding the presence or absence of a pathogen, and the prevalence of the pathogen in the population. In particular, knowledge about the location of the disease front is foundational for deploying disease countermeasures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future. We also devise an optimal surveillance system design at the disease front that takes into account the practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for the pathogen that causes white-nose syndrome.
Water clarity, defined in this study using measurements of the downwelling diffuse light attenuation coefficient (Kd) and turbidity, is an important indicator of lake trophic status and ecosystem health. We used in-situ measurements to evaluate existing semi-analytical models for Kd and turbidity, developed a regional turbidity model based on spectral shape, and evaluated the spatial and temporal trends in Lake Washington from 2013 to 2022 using Landsat-8/9 Operational Land Imager (OLI). We found no significant trends from 2013 to 2022 in Kd or turbidity when both the annual and full datasets were considered. In addition to the spring peak lasting from April through June, autumn Kd peaks were present at all sites, a pattern consistent with seasonal chlorophyll a and zooplankton concentrations. There existed no autumn peak in the monthly turbidity dataset, and the spring peak occurred two months before the Kd peak, nearly mirroring seasonal variability in the Cedar River discharge rates over the same period. The Kd and turbidity algorithms were thus each more sensitive to different sources of water clarity variability in Lake Washington.
","language":"English","publisher":"MPDI","doi":"10.3390/rs15205055","usgsCitation":"Schulien, J.A., Code, T.J., DeGasperi, C.L., Beauchamp, D., Tonus Ellis, A., and Litt, A.H., 2023, Annual and inter-annual variability in the diffuse attenuation coefficient and turbidity in an urbanized Washington lake from 2013 to 2022 assessed using Landsat-8/9: Remote Sensing, v. 15, no. 20, 5055, 19 p., https://doi.org/10.3390/rs15205055.","productDescription":"5055, 19 p.","ipdsId":"IP-158607","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":441806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15205055","text":"Publisher Index Page"},{"id":422837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.29668187426911,\n 47.76219367672758\n ],\n [\n -122.2936983290256,\n 47.49342533323215\n ],\n [\n -122.1703784589545,\n 47.49342533323215\n ],\n [\n -122.19822488122844,\n 47.75617656843582\n ],\n [\n -122.29668187426911,\n 47.76219367672758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"20","noUsgsAuthors":false,"publicationDate":"2023-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Schulien, Jennifer A.","contributorId":331715,"corporation":false,"usgs":false,"family":"Schulien","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":79272,"text":"Schulien Consulting","active":true,"usgs":false}],"preferred":false,"id":888561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Code, Tessa Julianne 0000-0003-1481-020X","orcid":"https://orcid.org/0000-0003-1481-020X","contributorId":331687,"corporation":false,"usgs":true,"family":"Code","given":"Tessa","email":"","middleInitial":"Julianne","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeGasperi, Curtis L.","contributorId":257393,"corporation":false,"usgs":false,"family":"DeGasperi","given":"Curtis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":888563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tonus Ellis, Arielle","contributorId":331716,"corporation":false,"usgs":false,"family":"Tonus Ellis","given":"Arielle","email":"","affiliations":[{"id":36795,"text":"University of Washington, School of Aquatic and Fishery Sciences","active":true,"usgs":false}],"preferred":false,"id":888565,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Litt, Arni H.","contributorId":331717,"corporation":false,"usgs":false,"family":"Litt","given":"Arni","email":"","middleInitial":"H.","affiliations":[{"id":36795,"text":"University of Washington, School of Aquatic and Fishery Sciences","active":true,"usgs":false}],"preferred":false,"id":888566,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249725,"text":"70249725 - 2023 - Climate-induced shifts in grassland bird nesting phenology have implications for grassland management","interactions":[],"lastModifiedDate":"2023-10-26T11:52:39.435755","indexId":"70249725","displayToPublicDate":"2023-10-21T06:47:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Climate-induced shifts in grassland bird nesting phenology have implications for grassland management","docAbstract":"Grasslands are among the most impacted ecosystems globally. In the midcontinent of North America, a > 80% loss of grasslands has made their conservation a major priority for resource managers. Grassland ecosystems evolved under periodic disturbances; consequently, grassland management often involves regular actions such as grazing, haying, or burning to maintain ecosystem integrity. The timing of such practices has direct implications on grassland ecology, agricultural economics, and survival and fecundity of grassland nesting birds (hereafter grassland birds). We conducted a meta-analysis on the nesting phenology of grassland birds throughout North America, focusing on nest-survival literature. We constructed a well-fitting model to predict median date of expected nest departure (hereafter fledge date) for grassland birds across the midcontinent. Predictions from our model demonstrate considerable spatial variation in median nesting phenology that is predictable using a spatially explicit spring phenology index. Median fledge dates were 8 or 13 days earlier in years of extreme weather conditions (dry or wet, respectively) than in years of average conditions. Species that generally nest in taller vegetation tended to have later median nest phenologies than those using shorter vegetation. Our results incorporate the most rigorous information available in the literature on nesting phenology of 36 grassland bird species and improve information available to managers about nesting phenology of grassland birds in the midcontinent of North America. Our predictions approximate the day when one-half of the nesting efforts would be complete for a given area within the midcontinent grasslands and can inform management and conservation decisions about the timing of management actions in grassland ecosystems.
We present the validation methodology and results of Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS). The DSWx-HLS product is the first of the DSWx suite, comprised of products each which map water from Earth Observation optical and SAR satellites. We detail the generation of high-resolution (3 m) validation datasets from a globally-stratified sample of dry, moderate, and wet sites. We provide the precise accounting of the classification metrics used to verify the Observational Products for End-users from Remote Sensing Analysis (OPERA) project requirements. We also report broader classification metrics across the validation datasets considered. OPERA performs validation in the public domain to ensure that the validation activities are transparent and reproducible. The resulting validation datasets and provisional OPERA products are publicly available; the software used for validation is also open-source.
","conferenceTitle":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 16-21, 2023","conferenceLocation":"Pasadena, CA","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS52108.2023.10283397","usgsCitation":"Arena, N., Bato, G., Bekaert, D., Bonnema, M., Chan, S., Chapman, B., Jones, J., Handwerger, A., Lewandowski, A., Marshak, C., Sangha, S., and Venkataramani, K., 2023, Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, July 16-21, 2023, p. 2723-2726, https://doi.org/10.1109/IGARSS52108.2023.10283397.","productDescription":"4 p.","startPage":"2723","endPage":"2726","ipdsId":"IP-153954","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arena, Nicholas","contributorId":336167,"corporation":false,"usgs":false,"family":"Arena","given":"Nicholas","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bato, Grace","contributorId":336168,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekaert, David","contributorId":336169,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew","contributorId":336170,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chan, Steven","contributorId":336171,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Bruce","contributorId":336172,"corporation":false,"usgs":false,"family":"Chapman","given":"Bruce","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":900093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handwerger, Alexander L.","contributorId":336174,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander L.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900094,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lewandowski, Alex","contributorId":336176,"corporation":false,"usgs":false,"family":"Lewandowski","given":"Alex","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900095,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marshak, Charlie","contributorId":336178,"corporation":false,"usgs":false,"family":"Marshak","given":"Charlie","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900096,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sangha, Simran","contributorId":336183,"corporation":false,"usgs":false,"family":"Sangha","given":"Simran","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900097,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Venkataramani, Karthik","contributorId":336185,"corporation":false,"usgs":false,"family":"Venkataramani","given":"Karthik","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900098,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70256488,"text":"70256488 - 2023 - Change-point models for identifying behavioral transitions in wild animals","interactions":[],"lastModifiedDate":"2024-08-07T15:40:00.947336","indexId":"70256488","displayToPublicDate":"2023-10-20T10:36:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Change-point models for identifying behavioral transitions in wild animals","docAbstract":"Animal behavior can be difficult, time-consuming, and costly to observe in the field directly. Innovative modeling methods, such as hidden Markov models (HMMs), allow researchers to infer unobserved animal behaviors from movement data, and implementations often assume that transitions between states occur multiple times. However, some behavioral shifts of interest, such as parturition, migration initiation, and juvenile dispersal, may only occur once during an observation period, and HMMs may not be the best approach to identify these changes. We present two change-point models for identifying single transitions in movement behavior: a location-based change-point model and a movement metric-based change-point model. We first conducted a simulation study to determine the ability of these models to detect a behavioral transition given different amounts of data and the degree of behavioral shifts. We then applied our models to two ungulate species in central Pennsylvania that were fitted with global positioning system collars and vaginal implant transmitters to test hypotheses related to parturition behavior. We fit these models in a Bayesian framework and directly compared the ability of each model to describe the parturition behavior across species. Our simulation study demonstrated that successful change point estimation using either model was possible given at least 12 h of post-change observations and 15 min fix interval. However, our models received mixed support among deer and elk in Pennsylvania due to behavioral variation between species and among individuals. Our results demonstrate that when the behavior follows the dynamics proposed by the two models, researchers can identify the timing of a behavioral change. Although we refer to detecting parturition events, our results can be applied to any behavior that results in a single change in time.
The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.
The study of nocturnally active bats is difficult even for those species that seasonally congregate. This challenge is particularly acute for ‘ōpe‘ape‘a (Hawaiian hoary bat; Lasiurus semotus) because of its solitary foliage-roosting behavior. Yet surveys are essential for conservation and management of this endangered species and only land mammal endemic to the Hawaiian Islands. We surveyed for ‘ōpe‘ape‘a at 23 sites and a range of elevations (33–2,341 m) on Hawai‘i Island from May 2018 to August 2021. We captured 138 unique bats (37 female, 101 male) over 224 mist-netting events. We averaged 16 net-hours per bat capture, with peak captures 30–90 min after sunset. We marked all captured individuals in this study with identifying forearm bands and recaptures represented 7% of total captures (10 of 148). We developed generalized linear mixed models to examine the relationship of nightly bat captures by sex to elevation and time-of-year while accounting for variable sampling effort and repeated sampling in this study. Both males and females were captured at low and high elevations with peak capture rates occurring at approximately 930 m. The capture rate for females was highest during the reproductive season (May to September), whereas it was highest for males during the non-reproductive season (October to April). This study informs future fieldwork with a description of ‘ōpe‘ape‘a capture on Hawai‘i Island by sex, elevation, time-of-year and time-of-night, radio transmitter retention, and recapture frequency.
","language":"English","publisher":"University of Hawai'i Press","doi":"10.2984/77.1.1","usgsCitation":"Hoeh, J.P., Aguirre, A.A., Calderon, F.A., Casler, S.P., Ciarrachi, S.G., Courtot, K., Montoya-Aiona, K., Pinzari, C., and Gorresen, P., 2023, Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island: Pacific Science, v. 77, no. 1, p. 1-26, https://doi.org/10.2984/77.1.1.","productDescription":"26 p.","startPage":"1","endPage":"26","ipdsId":"IP-145607","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":422009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Hawai‘i Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.96842132885067,\n 19.33138539404075\n ],\n [\n -154.7897923836328,\n 19.485585611959735\n ],\n [\n -154.79887521135575,\n 19.551218990406824\n ],\n [\n -154.91089675327194,\n 19.591156680319756\n ],\n [\n -154.9684212594931,\n 19.645342013414833\n ],\n [\n -154.96236604101108,\n 19.696658591411236\n ],\n [\n -154.99264213342082,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.864747424330687\n ],\n [\n -155.19851956180733,\n 19.98998602165949\n ],\n [\n -155.41045220867588,\n 20.095223300447913\n ],\n [\n -155.56183267072475,\n 20.137866947170835\n ],\n [\n -155.6011915908574,\n 20.146394280939575\n ],\n [\n -155.78890336379806,\n 20.262887779216527\n ],\n [\n -155.87670403178655,\n 20.28276819224483\n ],\n [\n -155.90698012419628,\n 20.254366822413203\n ],\n [\n -155.9160629519192,\n 20.16344755159399\n ],\n [\n -155.89789729647333,\n 20.095223300447913\n ],\n [\n -155.84037272089478,\n 20.026969315439658\n ],\n [\n -155.85551076709956,\n 19.9871407929613\n ],\n [\n -155.91303534267828,\n 19.92453277061513\n ],\n [\n -155.94331143508802,\n 19.867594857310905\n ],\n [\n -155.99175318294365,\n 19.861899940222415\n ],\n [\n -156.03111210307634,\n 19.81348489371841\n ],\n [\n -156.070471023209,\n 19.79069623879731\n ],\n [\n -156.0734986324501,\n 19.71946068185811\n ],\n [\n -155.98569796446162,\n 19.588304345937644\n ],\n [\n -155.96147709053392,\n 19.46845951057206\n ],\n [\n -155.91000776811617,\n 19.34850848155353\n ],\n [\n -155.925902725301,\n 19.21704010756863\n ],\n [\n -155.94293303161635,\n 19.105507378038368\n ],\n [\n -155.88814437651806,\n 19.00626880146993\n ],\n [\n -155.81565527064834,\n 18.990692600887243\n ],\n [\n -155.72554212736438,\n 18.938198274908366\n ],\n [\n -155.69548950343082,\n 18.8925651150143\n ],\n [\n -155.6244303900727,\n 18.91275839274777\n ],\n [\n -155.55569553714562,\n 19.002395865451618\n ],\n [\n -155.52134277651828,\n 19.078471509831623\n ],\n [\n -155.48905706234626,\n 19.119349850631657\n ],\n [\n -155.44276140564918,\n 19.13124152882338\n ],\n [\n -155.3530735113342,\n 19.191279451469995\n ],\n [\n -155.265880055271,\n 19.23839493374041\n ],\n [\n -155.17278493527743,\n 19.23992550373882\n ],\n [\n -154.96842132885067,\n 19.33138539404075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"77","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoeh, Julia P. 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Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. Marcos","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":886520,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249728,"text":"70249728 - 2023 - Bayesian weighting of climate models based on climate sensitivity","interactions":[],"lastModifiedDate":"2023-10-26T11:54:44.46868","indexId":"70249728","displayToPublicDate":"2023-10-20T06:53:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian weighting of climate models based on climate sensitivity","docAbstract":"Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.
In 2016, the National Oceanic and Atmospheric Administration deployed the first iteration of an operational National Water Model (NWM) to forecast the water cycle in the continental United States. With many versions, an hourly, multi-decadal historic simulation is made available to the public. In all released to date, the files containing simulated streamflow contain a snapshot of model conditions across the entire domain for a single timestep which makes accessing time series a technical and resource-intensive challenge. In the most recent release, extracting a complete streamflow time series for a single location requires managing 367,920 files (~16.2 TB). In this work we describe a reproducible process for restructuring a sequential set of NWM steamflow files for efficient time series access and provide restructured datasets for versions 1.2 (1993–2018), 2.0 (1993–2020), and 2.1 (1979–2022). These datasets have been made accessible via an OPeNDAP enabled THREDDS data server for public use and a brief analysis highlights the latest version of the model should not be assumed best for all locations. Lastly we describe an R package that expedites data retrieval with examples for multiple use-cases.
Characterizing the crustal structure of Indonesia is important to gain a better understanding of its geodynamic evolution and improve seismic hazard assessments in the area. However, a unified crustal model of the entire Indonesian region and its surroundings is lacking. We present new maps of crustal thickness and bulk Vp/Vs ratio in Indonesia and the surrounding area that are obtained using P-wave receiver functions at 36 seismic stations from several permanent regional networks. The measured crustal thickness varies from ∼24 km to ∼38 km. The thickest crust, ∼38 km, is beneath Flores Island, southern Maluku, and neighboring northernmost Australia, whereas the thinnest crust, ∼24 km, is found under eastern Malaysia. Thus, crustal thickness varies by ∼14 km (from ∼24 km to ∼38 km) despite the small changes in elevation at the measurement points. The Vp/Vs ratios are 1.79
Obtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter-based, simultaneous double-observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio-collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark-recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter-based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio-collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model-generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio-collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark-recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR-based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio-collars and mark-recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1495","usgsCitation":"Hennig, J., and Schoenecker, K., 2023, Comparing methods to estimate feral burro abundance: Wildlife Society Bulletin, v. 47, no. 4, e1495, 15 p., https://doi.org/10.1002/wsb.1495.","productDescription":"e1495, 15 p.","ipdsId":"IP-143673","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":441836,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1495","text":"Publisher Index Page"},{"id":435146,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZS081M","text":"USGS data release","linkHelpText":"Feral burro detections from aerial infrared surveys collected in Sinbad Herd Management Area, Utah, USA, from 2015-2016"},{"id":424281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Sinbad Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.64724760767687,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.90843748458232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hennig, Jacob D.","contributorId":333094,"corporation":false,"usgs":false,"family":"Hennig","given":"Jacob D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":891913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891914,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249642,"text":"70249642 - 2023 - Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","interactions":[],"lastModifiedDate":"2023-10-21T13:43:50.552769","indexId":"70249642","displayToPublicDate":"2023-10-19T08:39:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","docAbstract":"Functional trait approaches are common in ecology, but a lack of clear hypotheses on how traits relate to environmental gradients (i.e., trait–niche relationships) often makes uncovering mechanisms difficult. Furthermore, measures of community functional structure differ in their implications, yet inferences are seldom compared among metrics. Community-weighted mean trait values (CWMs), a common measure, are largely driven by the most common species and thus do not reflect community-wide trait–niche relationships per se. Alternatively, trait–niche relationships can be estimated across a larger group of species using hierarchical joint species distribution models (JSDMs), quantified by a parameter Γ. We investigated how inferences about trait–niche relationships are affected by the choice of metric. Using deadwood-dependent (saproxylic) beetles in fragmented Finnish forests, we followed a protocol for investigating trait–niche relationships by (1) identifying environmental filters (climate, forest age, and deadwood volume), (2) relating these to an ecological function (dispersal ability), and (3) identifying traits related to this function (wing morphology). We tested 18 hypothesized dispersal relationships using both CWM and Γ estimates across these environmental gradients. CWMs were more likely than Γ to show support for trait–niche relationships. Up to 13% of species' realized niches were explained by dispersal traits, but the directions of effects were consistent with fewer than 11%–39% of our 18 trait–niche hypotheses (depending on the metric used). This highlights the difficulty in connecting morphological traits and ecological functions in insects, despite the clear conceptual link between landscape connectivity and flight-related traits. Caution is thus warranted in hypothesis development, particularly where apparent trait–function links are less clear. Inferences differ when CWMs versus Γ estimates are used, necessitating the choice of a metric that reflects study questions. CWMs help explain the effects of environmental gradients on community trait composition, whereas the effects of traits on species' niches are better estimated using hierarchical JSDMs.
Sustained-yield hunting of introduced ungulates in the Hawaiian Islands often conflicts with the conservation of native species, but there is little reliable data to guide effective management. European mouflon sheep (Ovis musimon; mouflon) and axis deer (Axis axis; deer) were introduced on the island of Lāna‘i to provide additional hunting opportunities. Managers will require better information regarding the ecological associations of introduced ungulate species, relative to the habitats occupied, to resolve longstanding conflicts between native species conservation and sustained-yield hunting on islands. To address this information need, we modeled sheep and deer ecological associations, habitat-use, and suitability using data obtained from an intensive aerial survey completed in 2013 and temporally matching environmental data. In habitat suitability models evaluated by Receiver Operating Characteristic (ROC) metrics, predictor importance in a generalized linear model (GLM) of deer decreased in the following order: afternoon cloud cover, topographic slope, mean annual precipitation (MAP), elevation, normalized difference vegetation index (NDVI), and bare soil index. In a random GLM model of mouflon, predictor importance decreased in the following order: afternoon cloud cover, deer habitat suitability, NDVI, bare soil index, topographic slope, elevation, and MAP. Mouflon were restricted to lower elevation arid slopes, whereas deer were more broadly distributed throughout upland environments of the island. The presence of deer was also an important predictor for mouflon distribution, although mouflon was not an important predictor of deer, suggesting asymmetrical competition. Removal of the more abundant deer population may lead to an increase in abundance and distribution of mouflon without containment. This work represents the first habitat suitability analysis for all nonnative ungulates on any entire Hawaiian island. Our results are applicable to other islands where conflicts may arise with introduced ungulates, sustained-yield hunting, and native species conservation.