Species distribution models (SDMs) have been widely employed to evaluate species–environment relationships. However, when extrapolated over broad spatial scales or through time, these models decline in their predictive ability due to variation in how species respond to their environment. Many models assume species–environment relationships remain constant over space and time, hindering their ability to accurately forecast distributions. Therefore, there is growing recognition that models could be improved by accounting for spatio-temporal nonstationarity – a phenomenon wherein the factors governing ecological processes change over space or time. Here, we investigated nonstationarity in American pika (Ochotona princeps) relationships with climatic variables in the Rocky Mountains (USA). We first compared broad-scale differences in pika–climate patterns for occupancy and population density across the Southern, Central, and Northern Rockies. Next, we investigated within-ecoregion variation across four mountain ranges nested within the Northern Rockies. Lastly, we tested whether species–climate relationships changed over time within the Central Rockies ecoregion. Across all analyses, we found varying levels of nonstationarity among the climate metrics for both occupancy and density. Although we found general congruence in temperature metrics, which consistently had negative coefficients, and moisture metrics (e.g., relative humidity), which had positive coefficients, nonstationarity was greatest for summer and winter precipitation over both space and time. These results suggest that interpretations from one ecoregion should not be applied to other regions universally – especially when using precipitation metrics. The within-ecoregion analysis found much greater variation in the strength-of-relationship coefficients among the four mountain ranges, relative to the inter-regional analysis, possibly attributable to smaller sample sizes per mountain range. Lastly, the importance of several variables shifted through time from significant to insignificant in the temporal analysis. Our results collectively reveal the overall complexity underlying species–environment relationships. With rapidly shifting conditions globally, this work adds to the growing body of literature highlighting how issues of spatio-temporal nonstationarity can limit the accuracy, transferability, and reliability of models and that interpretations will likely be most robust at local to regional scales. Diagnosing, describing, and incorporating nonstationarity of species–climate relationships into models over space and time could serve as a pivotal step in creating more informative models.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1202610","usgsCitation":"Billman, P.D., Beever, E.A., Westover, M.L., and Ryals, D., 2023, Spatio-temporal variability in the strength, directionality, and relative importance of climate on occupancy and population densities in a philopatric mammal, the American pika (Ochotona princeps): Frontiers in Ecology and Evolution, v. 11, 1202610, 13 p., https://doi.org/10.3389/fevo.2023.1202610.","productDescription":"1202610, 13 p.","ipdsId":"IP-152205","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":442659,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1202610","text":"Publisher Index Page"},{"id":435244,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WB1EWC","text":"USGS data release","linkHelpText":"Climatic data associated with American-pika survey (2011-2021) locations in 3 regions of the Rocky Mountains"},{"id":420114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Billman, Peter D.","contributorId":311242,"corporation":false,"usgs":false,"family":"Billman","given":"Peter","email":"","middleInitial":"D.","affiliations":[{"id":67370,"text":"University of Connecticut, Dept. of Ecology and Evolution","active":true,"usgs":false}],"preferred":false,"id":881024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Westover, Marie L.","contributorId":274853,"corporation":false,"usgs":false,"family":"Westover","given":"Marie","email":"","middleInitial":"L.","affiliations":[{"id":48790,"text":"Dept. of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":881026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryals, Dylan K.","contributorId":328675,"corporation":false,"usgs":false,"family":"Ryals","given":"Dylan K.","affiliations":[{"id":78450,"text":"Dept. of Entomology, Purdue University, West Lafayette, IN","active":true,"usgs":false}],"preferred":false,"id":881027,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247704,"text":"70247704 - 2023 - Accuracy of finite fault slip estimates in subduction zone regions with topographic Green's functions and seafloor geodesy","interactions":[],"lastModifiedDate":"2023-08-14T12:25:52.922387","indexId":"70247704","displayToPublicDate":"2023-07-25T07:24:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy of finite fault slip estimates in subduction zone regions with topographic Green's functions and seafloor geodesy","docAbstract":"Until recently, the lack of seafloor geodetic instrumentation and the use of unrealistically simple, half-space based forward models have resulted in poor resolution of near-trench slip in subduction zone settings. Here, we use a synthetic framework to investigate the impact of topography and geodetic data distribution on coseismic slip estimates in various subduction zone settings. We calculate surface displacements in two synthetic topographic domains that have topography similar to that of Chile and Japan, respectively. We then attempt to image target slip distributions by using a Bayesian approach to solve for slip with two sets of Green's functions—one that accounts for topography and one that does not—and five sets of 50 or more observation points selected from the synthetic surface displacements. Three of these sets of observation points are entirely onland, and two include 5–10 seafloor geodetic sites. We find that the use of topographic Green's functions always improves inferred slip models, and with seafloor geodetic data, it enables an almost perfect recovery of a target slip model, even in the near-trench region. Critically, our results demonstrate that it would be impossible for non-topographic Green's functions to properly recover the true slip distribution, particularly in the near-trench region. We also perform a parameter study with approximately 4,000 slip models estimated using a least-square approach, and find that topographic Green's functions yield significantly more accurate slip models in cases where good data (well distributed and reasonably dense) are available, even in the absence of seafloor geodetic sites.
Breccia pipe deposits of the Grand Canyon region contain ore grade copper and uranium. Horn Creek is located near the Orphan Mine mineralized breccia pipe deposit and groundwater emerging from the bedrock in the headwaters of Horn Creek has the highest uranium concentrations in the region. Uranium decreases an order of magnitude between the groundwater at the top of the watershed and the groundwater emerging from the alluvial material lower in the watershed. Horn Creek water has low sulfur and uranium isotopic ratios which may suggest interaction with sulfide and uranium minerals found in mineralized breccia pipe deposits. Per- and polyfluoroalkyl substances (PFBA and PFBS) were found in low concentrations in groundwater from the bedrock and may be related to mining process materials or other anthropogenic activities. PHREEQC modeling suggests that water that is elevated in uranium emerging from the bedrock in the upper watershed may mix with other groundwater and atmospheric precipitation infiltrated into the alluvial material in the lower watershed. Tritium is elevated in Horn Creek groundwaters suggesting a component of modern water, some of which may have interacted with Orphan Mine workings. Additional studies could build on this understanding of chemistry changes in waters of Horn Creek to provide more direct evidence of contribution of water moving through the Orphan Mine.
","language":"English","publisher":"Geological Society of London","doi":"10.1144/geochem2023-007","usgsCitation":"Beisner, K.R., Davidson, C., and Tillman, F.D., 2023, Anthropogenic influence on groundwater geochemistry in Horn Creek Watershed near the Orphan Mine in Grand Canyon National Park, Arizona, USA: Geochemistry: Exploration, Environment, Analysis, v. 23, no. 3, geochem2023-007, 14 p., https://doi.org/10.1144/geochem2023-007.","productDescription":"geochem2023-007, 14 p.","ipdsId":"IP-148025","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":442670,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/geochem2023-007","text":"Publisher Index Page"},{"id":435245,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X17FKG","text":"USGS data release","linkHelpText":"PHREEQC files for geochemical simulations in Horn Creek, Grand Canyon, AZ"},{"id":419348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park, Horn Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.20894969063656,\n 36.094735674145454\n ],\n [\n -112.21000073958996,\n 36.06653388307063\n ],\n [\n -112.13348437577403,\n 36.06653388307063\n ],\n [\n -112.13768857158801,\n 36.110530696949866\n ],\n [\n -112.20894969063656,\n 36.094735674145454\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, Collin","contributorId":317722,"corporation":false,"usgs":false,"family":"Davidson","given":"Collin","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":879134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879135,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247123,"text":"70247123 - 2023 - Patterns, drivers, and a predictive model of dam removal cost in the United States","interactions":[],"lastModifiedDate":"2023-12-01T21:14:06.647209","indexId":"70247123","displayToPublicDate":"2023-07-24T08:36:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Patterns, drivers, and a predictive model of dam removal cost in the United States","docAbstract":"Given the burgeoning dam removal movement and the large number of dams approaching obsolescence in the United States, cost estimating data and tools are needed for dam removal prioritization, planning, and execution. We used the list of removed dams compiled by American Rivers to search for publicly available reported costs for dam removal projects. Total cost information could include component costs related to project planning, dam deconstruction, monitoring, and several categories of mitigation activities. We compiled reported costs from 455 unique sources for 668 dams removed in the United States from 1965 to 2020. The dam removals occurred within 571 unique projects involving 1–18 dams. When adjusted for inflation into 2020 USD, cost of these projects totaled \\$1.522 billion, with per-dam costs ranging from $1 thousand (k) to \\$268.8 million (M). The median cost for dam removals was \\$157k, \\$823k, and \\$6.2M for dams that were< 5 m, between 5–10 m, and > 10 m in height, respectively. Geographic differences in total costs showed that northern states in general, and the Pacific Northwest in particular, spent the most on dam removal. The Midwest and the Northeast spent proportionally more on removal of dams less than 5 m in height, whereas the Northwest and Southwest spent the most on larger dam removals > 10 m tall. We used stochastic gradient boosting with quantile regression to model dam removal cost against potential predictor variables including dam characteristics (dam height and material), hydrography (average annual discharge and drainage area), project complexity (inferred from construction and sediment management, mitigation, and post-removal cost drivers), and geographic region. Dam height, annual average discharge at the dam site, and project complexity were the predominant drivers of removal cost. The final model had an R2 of 57% and when applied to a test dataset model predictions had a root mean squared error of $5.09M and a mean absolute error of \\$1.45M, indicating its potential utility to predict estimated costs of dam removal. We developed a R shiny application for estimating dam removal costs using customized model inputs for exploratory analyses and potential dam removal planning.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1215471","usgsCitation":"Duda, J.J., Jumani, S., Wieferich, D.J., Tullos, D.D., McKay, S.K., Randle, T.J., Jansen, A., Bailey, S., Jensen, B.L., Johnson, R.C., Wagner, E.J., Richards, K.B., Wenger, S., Walther, E.J., and Bountry, J.A., 2023, Patterns, drivers, and a predictive model of dam removal cost in the United States: Frontiers in Ecology and Evolution, v. 11, 1215471. 16 p., https://doi.org/10.3389/fevo.2023.1215471.","productDescription":"1215471. 16 p.","ipdsId":"IP-153157","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":442673,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1215471","text":"Publisher Index Page"},{"id":435246,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G8V371","text":"USGS data release","linkHelpText":"Compilation of cost estimates for dam removal projects in the United States"},{"id":419297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jumani, Suman 0000-0002-2292-7996","orcid":"https://orcid.org/0000-0002-2292-7996","contributorId":305995,"corporation":false,"usgs":false,"family":"Jumani","given":"Suman","email":"","affiliations":[{"id":66338,"text":"Network for Engineering with Nature, Georgia, USA","active":true,"usgs":false}],"preferred":false,"id":878957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true}],"preferred":true,"id":878958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tullos, Desiree D.","contributorId":176667,"corporation":false,"usgs":false,"family":"Tullos","given":"Desiree","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":878959,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKay, S. Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":878960,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Randle, Timothy J.","contributorId":90994,"corporation":false,"usgs":false,"family":"Randle","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":878961,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansen, Alvin","contributorId":317292,"corporation":false,"usgs":false,"family":"Jansen","given":"Alvin","email":"","affiliations":[{"id":68995,"text":"Technical Service Center, Bureau of Reclamation, Denver, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":878962,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Susan","contributorId":317293,"corporation":false,"usgs":false,"family":"Bailey","given":"Susan","email":"","affiliations":[{"id":68996,"text":"Engineer Research and Development Center - Environmental Laboratory, U.S. Army Corps of Engineers, Vicksburg, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":878963,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jensen, Benjamin Lorenz 0000-0003-1199-973X","orcid":"https://orcid.org/0000-0003-1199-973X","contributorId":306036,"corporation":false,"usgs":true,"family":"Jensen","given":"Benjamin","email":"","middleInitial":"Lorenz","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878964,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878965,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wagner, Ella J.","contributorId":306038,"corporation":false,"usgs":false,"family":"Wagner","given":"Ella","email":"","middleInitial":"J.","affiliations":[{"id":66358,"text":"Previously USGS, WFRC, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":878966,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Richards, Kyla Breanne 0000-0001-7504-6239","orcid":"https://orcid.org/0000-0001-7504-6239","contributorId":306039,"corporation":false,"usgs":true,"family":"Richards","given":"Kyla","email":"","middleInitial":"Breanne","affiliations":[{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true}],"preferred":true,"id":878967,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":878968,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Walther, Eric J.","contributorId":304288,"corporation":false,"usgs":false,"family":"Walther","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":878969,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bountry, Jennifer A.","contributorId":30114,"corporation":false,"usgs":false,"family":"Bountry","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":878970,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70255163,"text":"70255163 - 2023 - Conserving habitat for migratory ungulates: How wide is a migration corridor?","interactions":[],"lastModifiedDate":"2024-06-14T13:32:12.209183","indexId":"70255163","displayToPublicDate":"2023-07-23T08:23:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Conserving habitat for migratory ungulates: How wide is a migration corridor?","docAbstract":"Climate-driven permafrost thaw can release ancient carbon to the atmosphere, begetting further warming in a positive feedback loop. Polar ice core data and young radiocarbon ages of dissolved methane in thermokarst lakes have challenged the importance of this feedback, but field studies did not adequately account for older methane released from permafrost through bubbling. We synthesized panarctic isotope and emissions datasets to derive integrated ages of panarctic lake methane fluxes. Methane age in modern thermokarst lakes (3132 ± 731 years before present) reflects remobilization of ancient carbon. Thermokarst-lake methane emissions fit within the constraints imposed by polar ice core data. Younger, albeit ultimately larger sources of methane from glacial lakes, estimated here, lagged those from thermokarst lakes. Our results imply that panarctic lake methane release was a small positive feedback to climate warming, comprising up to 17% of total northern hemisphere sources during the deglacial period.
Habitat and corridor mapping are key components of many conservation programs. Grizzly bear populations in the continental US are fragmented and connectivity among federal recovery areas is a conservation goal. Building on recent work, we modeled movements to predict areas of connectivity, using integrated step selection functions (iSSFs) developed from GPS-collared grizzly bears (F = 46, M = 19) in the Northern Continental Divide Ecosystem (NCDE). We applied iSSFs in a >300,000 km2 area including the NCDE, Cabinet–Yaak (CYE), Bitterroot (BE), and Greater Yellowstone (GYE) Ecosystems. First, we simulated directed movements (randomized shortest paths with 3 levels of exploration) between start and end nodes across populations. Second, we simulated undirected movements from start nodes in the NCDE, CYE, or GYE (no predetermined end nodes). We summarized and binned results as classes 1 (lowest relative predicted use) – 10 (highest relative predicted use) and evaluated predictions using 127 outlier grizzly bear locations. Connectivity pathways were primarily associated with mountainous areas and secondarily with river and stream courses in open valleys. Values at outlier locations indicated good model fit and mean classes at outlier locations (≥7.4) and Spearman rank correlations (≥0.87) were highest for undirected simulations and directed simulations with the highest level of exploration. Our resulting predictive maps can facilitate on-the-ground application of this research for prioritizing habitat conservation, human-bear conflict mitigation, and transportation planning. Additionally, our overall modeling approach has utility for myriad species and conservation applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110199","usgsCitation":"Sells, S.N., Costello, C., Lukacs, P., Roberts, L., and Vinks, M., 2023, Predicted connectivity pathways between grizzly bear ecosystems in western Montana: Biological Conservation, v. 284, 110199, 14 p., https://doi.org/10.1016/j.biocon.2023.110199.","productDescription":"110199, 14 p.","ipdsId":"IP-147522","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":442688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2023.110199","text":"Publisher Index Page"},{"id":429885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.95053391508199,\n 44.43718455079485\n ],\n [\n -111.00373928161369,\n 44.654761054970265\n ],\n [\n -111.03818726935785,\n 45.07387870212517\n ],\n [\n -108.85212263919709,\n 45.04832534471231\n ],\n [\n -108.66626782395645,\n 49.03152143147685\n ],\n [\n -116.13232385049233,\n 48.92723714245324\n ],\n [\n -116.03762573536355,\n 47.98615190263325\n ],\n [\n -115.57692588139909,\n 47.339026847952084\n ],\n [\n -114.54184801583628,\n 46.55145022278339\n ],\n [\n -114.48564988684791,\n 45.576847377601254\n ],\n [\n -113.90424702051097,\n 45.6284093642955\n ],\n [\n -112.95053391508199,\n 44.43718455079485\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"284","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sells, Sarah Nelson 0000-0003-4859-7160","orcid":"https://orcid.org/0000-0003-4859-7160","contributorId":302377,"corporation":false,"usgs":true,"family":"Sells","given":"Sarah","email":"","middleInitial":"Nelson","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costello, C.M.","contributorId":338295,"corporation":false,"usgs":false,"family":"Costello","given":"C.M.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":903097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lukacs, P.M.","contributorId":338298,"corporation":false,"usgs":false,"family":"Lukacs","given":"P.M.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":903098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, L.L.","contributorId":338301,"corporation":false,"usgs":false,"family":"Roberts","given":"L.L.","email":"","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":903099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vinks, M.A.","contributorId":338305,"corporation":false,"usgs":false,"family":"Vinks","given":"M.A.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":903100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247092,"text":"70247092 - 2023 - Prolonged drought in a northern California coastal region suppresses wildfire impacts on hydrology","interactions":[],"lastModifiedDate":"2024-09-16T16:43:11.061729","indexId":"70247092","displayToPublicDate":"2023-07-21T09:31:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Prolonged drought in a northern California coastal region suppresses wildfire impacts on hydrology","docAbstract":"Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e. burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought-prone, wildfire-adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub-watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre-and-post wildfire hydrology with a rainfall-runoff hydrological model. Even though these successive wildfires burned 1-50% of each sub-watershed (1-30% at moderate/high severity), we found little evidence of wildfire-related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations - model simulations). Our findings that post-wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR034206","usgsCitation":"Newcomer, M.E., Underwood, J.C., Murphy, S.F., Ulrich, C., Schram, T., Maples, S.R., Pena, J., Siirila-Woodburn, E.R., Trotta, M., Jasperse, J., Seymour, D., and Hubbard, S., 2023, Prolonged drought in a northern California coastal region suppresses wildfire impacts on hydrology: Water Resources Research, v. 59, no. 8, e2022WR034206, 23 p., https://doi.org/10.1029/2022WR034206.","productDescription":"e2022WR034206, 23 p.","ipdsId":"IP-147415","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":442702,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr034206","text":"Publisher Index Page"},{"id":419247,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Russian River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.58166191175413,\n 39.66438164966229\n ],\n [\n -123.52362681286694,\n 38.86220749551856\n ],\n [\n -122.91949120418995,\n 38.952501194302044\n ],\n [\n -123.01324021008476,\n 39.6265634050871\n ],\n [\n -123.58166191175413,\n 39.66438164966229\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Newcomer, Michelle E.","contributorId":317249,"corporation":false,"usgs":false,"family":"Newcomer","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":68983,"text":"Lawrence Berkeley National Laboratory, Earth & Environmental Sciences Area","active":true,"usgs":false}],"preferred":false,"id":878842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Jennifer C. 0000-0002-2702-0410 jcunder@usgs.gov","orcid":"https://orcid.org/0000-0002-2702-0410","contributorId":294555,"corporation":false,"usgs":true,"family":"Underwood","given":"Jennifer","email":"jcunder@usgs.gov","middleInitial":"C.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":878843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":878844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ulrich, Craig","contributorId":317250,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","affiliations":[{"id":68983,"text":"Lawrence Berkeley National Laboratory, Earth & Environmental Sciences Area","active":true,"usgs":false}],"preferred":false,"id":878845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schram, Todd","contributorId":317251,"corporation":false,"usgs":false,"family":"Schram","given":"Todd","email":"","affiliations":[{"id":68984,"text":"Sonoma Water, Santa Rosa, California","active":true,"usgs":false}],"preferred":false,"id":878846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maples, Stephen R.","contributorId":317252,"corporation":false,"usgs":false,"family":"Maples","given":"Stephen","email":"","middleInitial":"R.","affiliations":[{"id":68984,"text":"Sonoma Water, Santa Rosa, California","active":true,"usgs":false}],"preferred":false,"id":878847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pena, Jasquelin","contributorId":317253,"corporation":false,"usgs":false,"family":"Pena","given":"Jasquelin","email":"","affiliations":[{"id":68983,"text":"Lawrence Berkeley National Laboratory, Earth & Environmental Sciences Area","active":true,"usgs":false}],"preferred":false,"id":878848,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Siirila-Woodburn, Erica R.","contributorId":317254,"corporation":false,"usgs":false,"family":"Siirila-Woodburn","given":"Erica","email":"","middleInitial":"R.","affiliations":[{"id":68983,"text":"Lawrence Berkeley National Laboratory, Earth & Environmental Sciences Area","active":true,"usgs":false}],"preferred":false,"id":878849,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Trotta, Marcus","contributorId":317255,"corporation":false,"usgs":false,"family":"Trotta","given":"Marcus","affiliations":[{"id":68984,"text":"Sonoma Water, Santa Rosa, California","active":true,"usgs":false}],"preferred":false,"id":878850,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jasperse, Jay","contributorId":317256,"corporation":false,"usgs":false,"family":"Jasperse","given":"Jay","affiliations":[{"id":68984,"text":"Sonoma Water, Santa Rosa, California","active":true,"usgs":false}],"preferred":false,"id":878851,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Seymour, Donald","contributorId":317257,"corporation":false,"usgs":false,"family":"Seymour","given":"Donald","affiliations":[{"id":68984,"text":"Sonoma Water, Santa Rosa, California","active":true,"usgs":false}],"preferred":false,"id":878852,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hubbard, Susan S.","contributorId":317258,"corporation":false,"usgs":false,"family":"Hubbard","given":"Susan S.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":878853,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70246679,"text":"sir20235042 - 2023 - Selenium hazards in the Salton Sea environment—Summary of current knowledge to inform future wetland management","interactions":[],"lastModifiedDate":"2026-03-06T21:38:07.222047","indexId":"sir20235042","displayToPublicDate":"2023-07-20T14:20:47","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5042","displayTitle":"Selenium Hazards in the Salton Sea Environment—Summary of Current Knowledge to Inform Future Wetland Management","title":"Selenium hazards in the Salton Sea environment—Summary of current knowledge to inform future wetland management","docAbstract":"Quaternary marine and continental shales in the western United States are sources of selenium that can be loaded into the aquatic environment through mining, agricultural, and energy production processes. The mobilization of selenium from shales through agricultural irrigation has been recognized since the 1930s; however, discovery of deformities in birds and other wildlife using agricultural habitats during the 1980s spurred studies to determine the extent and effects of the contamination. Through these early studies, researchers determined that biota in the Salton Sea drainage basin was at risk from legacy selenium contamination in the Colorado River watershed.
The Salton Sea and its surrounding managed and unmanaged wetlands provide vital inland habitat and trophic support for diverse assemblages of resident and migratory wildlife, and understanding regional selenium hazards for these trust species is a priority for many Federal and State agencies. The modern Salton Sea is a shallow, landlocked saline lake in Riverside and Imperial Counties (not shown) of California that is sustained by irrigation return and perennial river inflow. Changes in water transfer agreements under the 2003 Quantification Settlement Agreement (QSA) have resulted in reduced irrigation flow, declining lake levels, and the evolution of unmanaged wetlands in areas where drains and rivers no longer reach the Salton Sea. These wetlands provide additional habitat for some species of concern, but their potential to increase selenium hazards for trust species is largely unknown.
From the 1980s to 2020, efforts to document selenium contamination and effects throughout the region have resulted in a considerable amount of selenium data from the Salton Sea and its surrounding drainage basin; however, no long-term (greater than 20 years), consistent sampling program has been established, and all data have been collected by different entities using a variety of protocols and analytical techniques. This lack of coordination has been previously documented in regional management plans and has led to difficulty in reliably assessing selenium hazards in the Salton Sea environment. This report provides a summary of the available disparate selenium information collected from water, sediment, and biota in the Salton Sea region since the 1980s and to identify data gaps that need to be filled to understand the potential effects of selenium on species of concern, including federally endangered desert pupfish (Cyprinodon macularius) and Yuma Ridgway’s Rail (Rallus obsoletus yumanensis; formerly Yuma Clapper Rail, Rallus longirostris yumanensis).
Available data from the Salton Sea drainage basin show that water from the Colorado River has the lowest selenium concentration of all surface water sources. All other surface water flowing into the Salton Sea has elevated selenium concentrations due to evaporation and evapotranspiration that occurs in agricultural fields and associated water delivery infrastructure or leaching of selenium from irrigated farmland soils. The Salton Sea has lower selenium concentrations because of various biogeochemical processes that recycle selenium into the sediment or volatilize it to the atmosphere; however, these mechanisms are not well defined, and it is not clear if selenium cycling will change in response to possible changes in the oxidation state of the Salton Sea bottom waters as water levels decline. Agricultural drains have the highest average selenium concentrations, but few drains have been sampled since changes in irrigation practices have occurred (due to the 2003 QSA). Groundwater selenium concentrations are variable; some wells south of the Salton Sea have selenium concentrations as high as 300 micrograms per liter (µg/L), whereas selenium concentrations are below detection in other wells. Groundwater and surface-water geothermal discharge zones around the margins of the Salton Sea and in unmanaged wetlands have not been studied in detail, and published selenium measurements are not available for these surface features.
Selenium concentrations in the sediment of the Salton Sea drainage basin are highest in wetland particulate organic matter and the Salton Sea lakebed, indicating that removal of selenium from the water to the sediment has been a primary mechanism for keeping selenium concentrations low in the water column. Sediment selenium concentrations in wetlands are lower than in the Salton Sea but higher than inflowing drains and rivers, indicating the lentic wetland sites also may be important sinks for selenium because of biogeochemical processes. Sediment selenium data have not been collected in agricultural drains since changes in irrigation practices occurred (due to the 2003 QSA), and it is unknown if selenium sequestration from the water column has changed in these systems.
We divided biological data into broad taxonomic categories, including primary producers, invertebrates, herpetofauna, mammals, fishes, and birds to facilitate evaluation of selenium concentrations and spatiotemporal trends observed in the Salton Sea. Overall, selenium concentrations were substantially greater in algae samples compared to all vascular plant samples combined. Median selenium concentrations in several invertebrate taxa (Chironomidae, Formicidae, Corixidae, Corbiculidae and Nereididae, and Decapoda) exceeded the maximum suggested dietary threshold of 3.0–4.0 micrograms per gram (µg/g) dry weight (dw) for predators consuming invertebrates in aquatic food webs. The greatest number of samples were collected from fish, and selenium distributions among species and locations showed that the range for most samples was lower than the U.S. Environmental Protection Agency selenium criterion for aquatic life (8.5 µg/g dw whole body, 11.3 µg/g dw fillets). The median selenium concentrations for whole body fish were below the selenium criterion in most locations, except for bairdiella (Bairdiella icistia) from the Salton Sea and irrigation drains, a few individual tilapia spp. (family Cichlidae, including genera Tilapia, Oreochromis, and their hybrids) from the river and river outlets, and several western mosquitofish (Gambusia affinis) and sailfin molly (Poecilia latipinna) from irrigation drain outlets. For avian samples combined among years and locations, median selenium concentrations in livers from all families except waders and Ibis (family Threskiornithidae) were higher than levels expected to cause selenium toxicosis (10–20 µg/g dw), and all median egg concentrations were above or near 6.0 μg/g dw, which is a conservative threshold value for reproductive impairment.
Most knowledge gaps we identified for water, sediment, and biota were interrelated, and the use of integrated approaches to address knowledge gaps can provide greater insight into the drivers behind selenium hazards. Integrated water, sediment, and biota studies could help identify cost-effective management solutions that serve multiple purposes. A comprehensive analysis of the hydrology, biogeochemistry, and food-web processes in wetlands and other habitats can inform predictive models to identify drivers of selenium bioavailability, uptake from the environment and subsequent trophic transfer, ultimately forming the basis for experimental habitat management manipulations to minimize selenium hazards to wildlife. Furthermore, a comprehensive, long-term sampling and analytical laboratory plan would enable comparison of data among different entities that are sampling at the Salton Sea. Such efforts are well suited to help fill knowledge gaps that preclude understanding of selenium hazards and future management options for biota using Salton Sea habitats, including newly formed wetlands throughout the region.
All data compiled for this report are available in two U.S. Geological Survey data releases: Groover and others (2022) for water and sediment samples and De La Cruz and others (2022) for biological samples. The data releases include all publicly available data for selenium concentrations in water, sediment, and biological samples collected in and around the Salton Sea, including the Coachella and Imperial Valleys. The data releases also include previously unpublished data.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Riverbank groundwater discharge faces are spatially extensive areas of preferential seepage that are exposed to air at low river flow. Some conceptual hydrologic models indicate discharge faces represent the spatial convergence of highly variable age and length groundwater flowpaths, while others indicate greater consistency in source groundwater characteristics. Our detailed field investigation of preferential discharge points nested across mainstem riverbank discharge faces was accomplished by: (1) leveraging new temperature-based recursive estimation (extended Kalman Filter) modelling methodology to evaluate seasonal, diurnal, and event-driven groundwater flux patterns, (2) developing a multi-parameter toolkit based on readily measured attributes to classify the general source groundwater flowpath depth and flowpath length scale, and, (3) assessing whether preferential flow points across discharge faces tend to represent common or convergent groundwater sources. Five major groundwater discharge faces were mapped along the Farmington River, CT, United States using thermal infrared imagery. We then installed vertical temperature profilers directly into 39 preferential discharge points for 4.5 months to track vertical discharge flux patterns. Monthly water chemistry was also collected at the discharge points along with one spatial synoptic of stable isotopes of water and dissolved radon gas. We found pervasive evidence of shallow groundwater sources at the upstream discharge faces along a wide valley section with deep bedrock, as primarily evidenced by pronounced diurnal discharge flux patterns. Discharge flux seasonal trends and bank storage transitions during large river flow events provided further indication of shallow, local sources. In contrast, downstream discharge faces associated with near surface cross cutting bedrock exhibited deep and regional source flowpath characteristics such as more stable discharge patterns and temperatures. However, many neighbouring points across discharge faces had similar discharge flux patterns that differed in chloride and radon concentrations, indicating the additional effects of localized flowpath heterogeneity overprinting on larger scale flowpath characteristics.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14939","usgsCitation":"Haynes, A., Briggs, M., Moore, E., Jackson, K., Knighton, J., Rey, D., and Helton, A., 2023, Shallow and local or deep and regional? Inferring source groundwater characteristics across mainstem riverbank discharge faces: Hydrological Processes, v. 37, no. 7, e14939, 19 p., https://doi.org/10.1002/hyp.14939.","productDescription":"e14939, 19 p.","ipdsId":"IP-151076","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":442704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14939","text":"Publisher Index Page"},{"id":419349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Farmington River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.716667,\n 41.933\n ],\n [\n -72.8333,\n 41.933\n ],\n [\n -72.8333,\n 41.7833\n ],\n [\n -72.716667,\n 41.7833\n ],\n [\n -72.716667,\n 41.933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Haynes, Adam","contributorId":216657,"corporation":false,"usgs":false,"family":"Haynes","given":"Adam","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":879120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":879121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Eric","contributorId":216658,"corporation":false,"usgs":false,"family":"Moore","given":"Eric","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":879122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Kevin","contributorId":317715,"corporation":false,"usgs":false,"family":"Jackson","given":"Kevin","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":879123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knighton, James","contributorId":317716,"corporation":false,"usgs":false,"family":"Knighton","given":"James","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":879124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":879125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Helton, Ashley","contributorId":219741,"corporation":false,"usgs":false,"family":"Helton","given":"Ashley","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":879126,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247423,"text":"70247423 - 2023 - Adjacent and downstream effects of forest harvest on the distribution and abundance of larval headwater stream amphibians in the Oregon Coast Range","interactions":[],"lastModifiedDate":"2023-08-04T12:27:36.129329","indexId":"70247423","displayToPublicDate":"2023-07-20T07:25:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Adjacent and downstream effects of forest harvest on the distribution and abundance of larval headwater stream amphibians in the Oregon Coast Range","docAbstract":"Forest harvest is a primary landscape-scale management action affecting riparian forests. Although concerns about impacts of forest harvest on stream amphibians is generally limited to areas adjacent to harvest, there is a paucity of information regarding potential downstream effects of forest harvest on these species. We designed a before-after, control-impact (BACI) experiment to quantify potential impacts of clearcut logging that included 12-m buffers or smaller variable-width buffers on the distribution and abundance of headwater stream amphibians in adjacent and downstream areas. We sampled larval coastal tailed frogs (Ascaphus truei), coastal giant salamanders (Dicamptodon tenebrosus), and Columbia torrent salamanders (Rhyacotriton kezeri) across 3,915 sampling occasions that spanned 13 study reaches in 2008–2011 (pre-harvest) and 2013–2016 (post-harvest) as part of the Trask River Watershed Study in the Oregon Coast Range, U.S.A. We analyzed these data using occupancy models to estimate occupancy and (when possible) relative abundance, while accounting for various sources of imperfect detection. All species exhibited reduced occupancy adjacent to clearcuts with variable-width buffers (odds ratios [ORs] ranged = 0.24–0.48), and these negative impacts were not always diminished when increasing the buffer size to 12 m (ORs ranged = 0.20–3.56). Dicamptodon tenebrosus was the only species to have occupancy impacted in downstream areas, and this negative impact was related to clearcut logging with uniform 12-m buffers (OR = 0.60). This species was also the only species to have abundance negatively impacted by forest harvest in downstream areas (OR = 0.41 with uniform 12-m buffers, OR = 0.38 with variable-width buffers), albeit impacts to abundance were not evaluated for R. kezeri. Ascaphus truei abundance increased in areas downstream of clearcut logging with uniform 12-m buffers (OR = 2.92). Although we found the direction and magnitude of responses varied by species, our study confirms that clearcut logging can have negative impacts on amphibians that inhabit the adjacent stream areas. Perhaps more importantly, we also found that forest harvest can have negative effects on stream amphibians downstream of the harvested area and that increasing the buffer size to 12 m did not necessarily diminish these impacts in adjacent and downstream areas. Altogether, our study provides a nuanced picture of adjacent and downstream effects of forest harvest on three endemic headwater stream amphibians, and our findings demonstrate that forest management practices should consider downstream effects on aquatic taxa when assessing the impact of harvesting trees near headwater streams.
Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short-distance local movement state and a long-distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long-distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long-distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long-distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long-distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.
Compressional-wave seismic velocity (velocity) and bulk density (density) data were compiled from published sources for rock suites and earth materials that are significant for geophysical modeling of the Mesoproterozoic Midcontinent Rift System in the Lake Superior region. The data include laboratory measurements of outcrop and drill core samples, seismic refraction studies, and a sonic log from a 1.5-kilometer-deep exploration well. Rock suites of the Midcontinent Rift System include basalts of the Mesoproterozoic Keweenawan Supergroup, Oronto Group sedimentary rocks (divided into arenaceous versus argillaceous units), and several sedimentary formations overlying the Oronto Group that have been correlated across the area. Intrusive units include diabase, gabbro, and felsic igneous rocks. Other geologic units important for geophysical modeling in the Lake Superior region include Archean crystalline crust, Paleoproterozoic metasedimentary and crystalline rocks, lower Mesoproterozoic sedimentary rocks, and Holocene to Pleistocene surficial deposits.
Empirical velocity-density relations for each rock suite were determined by comparing the compiled data to published relations, such as the Nafe-Drake curve, Gardner’s relation, and best-fit equations developed for different rock types from laboratory studies. Graphical representations of these velocity-density relations provide a way to easily understand how velocity and density differ between tectonic settings and by rock type. Overlaps in velocity and density ranges for different geologic units are significant and have especially important implications for geologic interpretation of seismic data. Important examples include similar velocities but differing densities for argillaceous Oronto Group versus units overlying the Oronto Group and arenaceous Oronto Group versus basalt of the Keweenawan Supergroup. Similar densities but differing velocities were found for diabase versus gabbro. In addition, expected velocity ranges by rock type show that high-velocity intervals (6.9–7.1 kilometers per second) interpreted as basalt in previous seismic-reflection studies more likely indicate diabase or gabbro, suggesting that these interpretations may warrant additional consideration.
Center Director, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March) of 2023. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was a Landsat 8 safehold anomaly. On January 26, 2023, the Global Positioning System (GPS) onboard Landsat 8 became invalid because the GPS fault tripped. Later that same day, the GPS was reinitialized, but a Field of View 1 fault trip occurred early the next morning, causing the observatory to go into Earth Point Safe mode, which put the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) into safehold. Once it was safe to reactivate the sensors, the OLI was transitioned to operational status late on January 27 and TIRS was reactivated early on January 28. Additional information about the Landsat 8 safehold anomaly is here: https://www.usgs.gov/landsat-missions/news/landsat-8-recovers-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231050","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2023: U.S. Geological Survey Open-File Report 2023–1050, 39 p., https://doi.org/10.3133/ofr20231050.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-152817","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":419162,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":419161,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1050/images/"},{"id":419158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1050/coverthb.jpg"},{"id":419181,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231050/full"},{"id":419160,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1050/ofr20231050.XML"},{"id":419159,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1050/ofr20231050.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1050"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
New geophysical profiles across the central Dead Sea Transform (DST) near the Sea of Galilee, Israel, and surrounding highlands, augmented by static stress modeling, allow us to study continental transform plate deformation. The DST separates a ∼10 km thick sedimentary column above a thinned (16–23 km) crust to the west from a ∼7 km column above a ∼30-km thick crust to the east. Crustal thinning starts under the DST, as observed also farther south, indicating that the DST is indeed located along the boundary between the Arabian plate and its continental margin. Moho step here is gradual. The DST's eastern shoulder dips westward toward the DST unlike the upward flexed shoulder observed farther south, perhaps delineating the northern limit of a thinner and hotter lithosphere. The shape of the Sea of Galilee is modeled as an asymmetric pull-apart basin formed by a left-lateral stepover of 2.6 km between slightly divergent and underlapping strike-slip fault strands dipping 70° to the west. Reflection data indicate that these strands are not connected. Several fault traces within the Sea of Galilee have previously been suggested to carry part of the relative plate motion. However, given slip along the main DST faults, Coulomb stress will increase only on fault portions in the northern part of the lake, in accord with the geographical distribution of seismicity, suggesting that these faults are likely secondary. Mismatch between the DST strand locations in the geophysical profiles and the subsidence model, may reflect temporal changes in fault geometry.
To determine the most relevant climatic and economic factors driving water demand for Providence, Rhode Island, and to further the understanding of human interactions with water availability, linear regression models were developed to estimate single-family and multifamily residential, commercial, and industrial water demand for the service area of Providence Water for 2014–21. Monthly water use delivery data were provided by Providence Water. An array of climatic and economic data, the drought index, and binary variables to represent seasonal water use and the onset of the coronavirus (COVID–19) were investigated as possible explanatory variables for the water demand models. The water demand model with the best fit with the least amount of error was the single-family residential water demand followed in descending order of accuracy by the commercial, multifamily residential, and industrial water demand. Seasonal variables were significant in all models, and the COVID–19 binary variable was significant in the commercial and industrial models. One or two economic variables were significant in all models and one climatic variable was significant in all models except the commercial model.
Overall residential, commercial, and industrial water demand in the Providence, Rhode Island, service area has decreased during the study period most likely because of widescale drought conditions and policies designed to improve water efficiencies. The linear regression models developed for single-family and multifamily residential, commercial, and industrial water use explained 94, 85, 91, and 77 percent, respectively, of the variability in monthly water use. Multifamily residential water demand displayed a less distinct seasonal trend than that observed for single-family residential customers, likely because multifamily homes tend to use less water outdoors. The commercial water-demand model included no climatic variables, one economic variable, the COVID–19 pandemic variable, and the high and low water use seasonal variables—the latter two variables indicating the importance of seasonal fluctuations in water use. The COVID–19 pandemic and a concomitant State executive order had the immediate effect of severely reducing commercial water use. The industrial water-demand model did not perform as well as the other models because industrial water delivery data display a greater range of values, both seasonally and for the overall study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235057","collaboration":"Prepared in cooperation with the Rhode Island Water Resources Board","usgsCitation":"Stagnitta, T.J., and Medalie, L., 2023, Assessment of factors that influence human water demand for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2023–5057, 18 p., https://doi.org/10.3133/sir20235057.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-142026","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":419046,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5057/coverthb.jpg"},{"id":500938,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114971.htm","linkFileType":{"id":5,"text":"html"}},{"id":419051,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91H5QOY","text":"USGS data release","linkHelpText":"Data for regression models to estimate water use in Providence, Rhode Island, 2014–2021"},{"id":419050,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5057/images/"},{"id":419049,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5057/sir20235057.XML"},{"id":419048,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235057/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 20023-5057"},{"id":419047,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5057/sir20235057.pdf","text":"Report","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 20023-5057"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.496115199193,\n 41.87371310909353\n ],\n [\n -71.496115199193,\n 41.785379633702576\n ],\n [\n -71.37573267926174,\n 41.785379633702576\n ],\n [\n -71.37573267926174,\n 41.87371310909353\n ],\n [\n -71.496115199193,\n 41.87371310909353\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Beginning in September 2010, the U.S. Geological Survey installed continuous water-quality monitors at several streamgages across Indiana as part of a network of supergages to meet cooperator information needs. Two types (or models) of water-quality monitors deployed at each site measured and recorded water temperature, dissolved oxygen, specific conductance, pH, and turbidity every 15 minutes during the study period. Associated discrete water samples were collected at regular intervals and analyzed for concentrations of suspended sediment and total phosphorus. Surrogate regression models were developed between the continuously measured turbidity values and turbidity values in the associated samples to compute continuous concentrations and loads of suspended sediment and total phosphorus. Starting in November 2018, the original extended deployment system monitors were replaced with the newest model of multiparameter water-quality monitors and were equipped with turbidity smart sensors because the older monitors were phased out of production. The updated monitor and smart sensor yield different but relatable turbidity values.
Turbidity data collected concurrently by the two sensors from November 2018 to December 2021 were compared and analyzed to quantify the relation between them at six supergage sites in northwestern Indiana and one site in the town of Zionsville in central Indiana. Ordinary least squares regression was used to calculate site-specific conversion factors so that turbidity data from the newer monitors can be used in published surrogate models based on the older monitor data. Regression analyses explained approximately 98 percent of the variation in turbidity readings between the two sensors. From these analyses, conversion factors were developed that may be applied to older turbidity readings to calculate near real-time concentrations of phosphorus and suspended sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235077","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management, Iroquois River Conservancy District, Kankakee River Basin and Yellow River Basin Development Commission, and the Town of Zionsville","usgsCitation":"Messner, M.L., Perkins, M.K., and Bunch, A.R., 2023, Comparison of turbidity sensors at U.S. Geological Survey supergages in Indiana from November 2018 to December 2021: U.S. Geological Survey Scientific Investigations Report 2023–5077, 13 p., https://doi.org/10.3133/sir20235077.","productDescription":"Report: iv, 13 p.; Dataset","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129902","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":501040,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114970.htm","linkFileType":{"id":5,"text":"html"}},{"id":419012,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","linkHelpText":"- U.S. Geological Survey water data for the nation"},{"id":419011,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5077/images/"},{"id":419010,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5077/sir20235077.XML"},{"id":419009,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5077"},{"id":419008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5077/sir20235077.pdf","text":"Report","size":"3.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5077"},{"id":419007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5077/coverthb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Kankakee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.47825888394271,\n 41.7055943665263\n ],\n [\n -87.47825888394271,\n 40.75718260396678\n ],\n [\n -86.31031406433961,\n 40.75718260396678\n ],\n [\n -86.31031406433961,\n 41.7055943665263\n ],\n [\n -87.47825888394271,\n 41.7055943665263\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
Rapid and simple estimation of the mass eruption rate (MER) from column height is essential for real-time volcanic hazard management and reconstruction of past explosive eruptions. Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1.0), we explore empirical MER-height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER-height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics-based models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL102633","usgsCitation":"Aubry, T.J., Engwell, S., Bonadonna, C., Mastin, L.G., Carazzo, G., Van Eaton, A.R., Jessop, D.E., Grainger, R.G., Scollo, S., Taylor, I.A., Jellinek, A.M., Schmidt, A., Biass, S., and Gouhier, M., 2023, New insights into the relationship between mass eruption rate and volcanic column height based on the IVESPA dataset: Geophysical Research Letters, v. 50, no. 14, e2022GL102633, 12 p., https://doi.org/10.1029/2022GL102633.","productDescription":"e2022GL102633, 12 p.","ipdsId":"IP-152764","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":442739,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl102633","text":"Publisher Index Page"},{"id":422333,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"14","noUsgsAuthors":false,"publicationDate":"2023-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Aubry, Thomas J.","contributorId":331321,"corporation":false,"usgs":false,"family":"Aubry","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":887344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engwell, Samantha 0000-0001-7719-6257","orcid":"https://orcid.org/0000-0001-7719-6257","contributorId":251719,"corporation":false,"usgs":false,"family":"Engwell","given":"Samantha","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":887345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonadonna, Costanza","contributorId":199721,"corporation":false,"usgs":false,"family":"Bonadonna","given":"Costanza","email":"","affiliations":[],"preferred":false,"id":887346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":887347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carazzo, Guillaume","contributorId":260384,"corporation":false,"usgs":false,"family":"Carazzo","given":"Guillaume","email":"","affiliations":[{"id":52575,"text":"CNRS, Paris, France","active":true,"usgs":false}],"preferred":false,"id":887348,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":887349,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jessop, David E.","contributorId":331322,"corporation":false,"usgs":false,"family":"Jessop","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":79186,"text":"Universite Clermont-Avergne","active":true,"usgs":false}],"preferred":false,"id":887350,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grainger, Roy G.","contributorId":331323,"corporation":false,"usgs":false,"family":"Grainger","given":"Roy","email":"","middleInitial":"G.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":887351,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scollo, Simona","contributorId":260385,"corporation":false,"usgs":false,"family":"Scollo","given":"Simona","email":"","affiliations":[{"id":27605,"text":"INGV, Catania, Italy","active":true,"usgs":false}],"preferred":false,"id":887352,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Taylor, Isabelle A","contributorId":260386,"corporation":false,"usgs":false,"family":"Taylor","given":"Isabelle","email":"","middleInitial":"A","affiliations":[{"id":30742,"text":"University of Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":887353,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jellinek, A. Mark","contributorId":54364,"corporation":false,"usgs":true,"family":"Jellinek","given":"A.","email":"","middleInitial":"Mark","affiliations":[],"preferred":false,"id":887354,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Schmidt, Anja","contributorId":260391,"corporation":false,"usgs":false,"family":"Schmidt","given":"Anja","email":"","affiliations":[{"id":52574,"text":"University of Cambridge, UK","active":true,"usgs":false}],"preferred":false,"id":887355,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Biass, Sebastien","contributorId":331324,"corporation":false,"usgs":false,"family":"Biass","given":"Sebastien","affiliations":[{"id":25472,"text":"University of Geneva","active":true,"usgs":false}],"preferred":false,"id":887356,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gouhier, Mathieu","contributorId":260388,"corporation":false,"usgs":false,"family":"Gouhier","given":"Mathieu","email":"","affiliations":[{"id":29878,"text":"Université Clermont Auvergne, Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":887357,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70247692,"text":"70247692 - 2023 - Water quality impacts of climate change, land use, and population growth in the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2023-12-20T17:47:57.414032","indexId":"70247692","displayToPublicDate":"2023-07-18T08:53:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Water quality impacts of climate change, land use, and population growth in the Chesapeake Bay watershed","docAbstract":"The 2010 Chesapeake Bay Total Maximum Daily Load was established for the water quality and ecological restoration of the Chesapeake Bay. In 2017, the latest science, data, and modeling tools were used to develop revised Watershed Implementation Plans (WIPs). In this article, we examine the vulnerability of the Chesapeake Bay watershed to the combined pressures of climate change and growth in population, agricultural intensity, and economic activity for the 60-year period 1995–2055. The results will be used to revise WIPs, as needed, to account for expected increases in loads. Assessing changes relative to 1995 for the years 2025, 2035, 2045, and 2055, mean annual precipitation increases of 3.11%, 4.21%, 5.34%, and 6.91%, respectively, air temperature increases of 1.12, 1.45, 1.84, and 2.12°C, respectively, and potential evapotranspiration increases of 3.36%, 4.43%, 5.54%, and 6.35%, respectively, are projected. Population in the watershed is expected to grow by 3.5 million between 2025 and 2055. Watershed model results show incremental increases in streamflow (2.3%–6.2%), nitrogen (2.6%–10.8%), phosphorus (4.5%–26.7%), and sediment (3.8%–18.8%) loads to the tidal Bay due to climate change. Growth in population, agricultural intensity, development, and economic activity resulted in relatively smaller increases in loads compared to climate change.
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0000-0002-2737-2379","orcid":"https://orcid.org/0000-0002-2737-2379","contributorId":306024,"corporation":false,"usgs":false,"family":"Hinson","given":"Kyle","email":"","middleInitial":"E.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":880058,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Claggett, Peter 0000-0002-5335-2857","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":238920,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":880059,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248826,"text":"70248826 - 2023 - Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States","interactions":[],"lastModifiedDate":"2023-09-22T13:52:11.321375","indexId":"70248826","displayToPublicDate":"2023-07-18T08:49:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States","docAbstract":"Sinkholes in karst and pseudokarst regions threaten infrastructure, property, and lives. We mapped closed depressions in karst and pseudokarst regions of the conterminous United States (U.S.) from 10-m-resolution elevation data using high-performance computing, and then created a heuristic additive model of sinkhole susceptibility that also included nationally consistent data for factors related to geology, soils, precipitation extremes, and development. Maps identify potential sinkhole hotspots based on current conditions and projections for 50 years into the future (the years 2070–2079) based on climate change and urban development scenarios. Areas characterized as having either high or very high sinkhole susceptibility contain 94%–99% of known or probable sinkhole locations from three U.S. state databases. States and counties with the highest amounts and percentages of land in zones of highest sinkhole susceptibility are identified. Projected changes in extreme precipitation and development did not substantially change current hotspots of highest sinkhole susceptibility. Results provide a uniform index of sinkhole potential that can support national planning, instead of existing assessments produced through various methods within individual states or smaller areas.
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nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":883803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":883804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":883805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jeanne M. 0000-0001-7549-9270 jmjones@usgs.gov","orcid":"https://orcid.org/0000-0001-7549-9270","contributorId":4676,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","email":"jmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":883806,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248825,"text":"70248825 - 2023 - Modeling non-structural strategies to reduce pedestrian evacuation times for mitigating local tsunami threats in Guam","interactions":[],"lastModifiedDate":"2023-09-22T11:57:25.815889","indexId":"70248825","displayToPublicDate":"2023-07-16T06:51:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Modeling non-structural strategies to reduce pedestrian evacuation times for mitigating local tsunami threats in Guam","docAbstract":"Reducing the potential for loss of life from local tsunamis is challenging for emergency managers given the need for self-protective behavior of at-risk individuals within brief windows of time to evacuate. There has been considerable attention paid to discussing the use of tsunami vertical-evacuation structures for areas where there may be insufficient time to evacuate. This strategy may not be feasible or needed for at-risk populations in island communities for multiple reasons. We examine the influence of three non-structural interventions (reducing departure delays, increasing travel speeds, and managing vegetation to create new paths) that may improve the evacuation potential for at-risk individuals in island communities and use the United States territory of Guam as our case study. We model pedestrian travel times out of a modeled inundation zone for a local tsunami generated by a Mw 8.3 earthquake within the Mariana subduction zone. Evacuation-modeling results indicate that reducing departure delays has a larger impact than increasing travel speeds or creating evacuation corridors through heavy brush on reducing the number of at-risk individuals with insufficient time to evacuate. Travel times to safety are less than wave-arrival times for almost all at-risk individuals in the tsunami-hazard zone if one assumes all three interventions are implemented.
Increasingly frequent megafires are dramatically altering landscapes and critical habitats around the world. Across the western United States, megafires have become an almost annual occurrence, but the implication of these fires for the conservation of native wildlife remains relatively unknown. Woodland savannas are among the world's most biodiverse ecosystems and provide important food and structural resources to a variety of wildlife, but they are threatened by megafires. Despite this, the great majority of fire impact studies have only been conducted in coniferous forests. Understanding the resistance and resilience of wildlife assemblages following these extreme perturbations can help inform future management interventions that limit biodiversity loss due to megafire. We assessed the resistance of a woodland savanna mammal community to the short-term impacts of megafire using camera trap data collected before, during, and after the fire. Specifically, we utilized a 5-year camera trap data set (2016–2020) from the Hopland Research and Extension Center to examine the impacts of the 2018 Mendocino Complex Fire, California's largest recorded wildfire at the time, on the distributions of eight observed mammal species. We used a multispecies occupancy model to quantify the effects of megafire on species' space use, to assess the impact on species size and diet groups, and to create robust estimates of fire's impacts on species diversity across space and time. Megafire had a negative effect on the detection of certain mammal species, but overall, most species showed high resistance to the disturbance and returned to detection and site use levels comparable to unburned sites by the end of the study period. Following megafire, species richness was higher in burned areas that retained higher canopy cover relative to unburned and burned sites with low canopy cover. Fire management that prevents large-scale canopy loss is critical to providing refugia for vulnerable species immediately following fire in oak woodlands, and likely other mixed-forest landscapes.
This report provides analysis to extend the 2040 forecasts of polar bear (Ursus maritimus) land use for the southern Beaufort and Chukchi Sea populations presented in a recent publication (Rode and others, 2022) through the year 2065. To inform long-term polar bear management considerations, we provide point-estimate forecasts and 95-percent prediction intervals of the proportion of polar bear populations summering onshore for 21 days or more (≥) and their duration onshore every 5 years from 2040 to 2065. Because sea-ice projections based on earth system models show greater divergence with emission scenarios after 2040, we have provided forecasts for three greenhouse gas emission scenarios, SSP1-2.6, SSP2-4.5 and SSP5-8.5, compared to the two emission scenarios used in Rode and others (2022). Our forecasting methods estimated that 61–97 percent of polar bears in the southern Beaufort Sea and 80–100 percent of polar bears in the Chukchi Sea populations may summer onshore for ≥21 days by 2065. Forecasts of mean duration onshore were 105–158 days for polar bears in the southern Beaufort Sea and 111–178 days in the Chukchi Sea populations by 2065. Sea ice conditions projected to occur by 2065 could alter the current patterns of bear behavior from what has been observed over the past 30 years. As a result, these extended projections are associated with a higher degree of uncertainty than estimates through 2040, especially under the SSP5-8.5 scenario.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231048","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rode, K.D., Douglas, D.C., Atwood, T.C., and Wilson, R.R., Forecasts of polar bear (Ursus maritimus) land use in the southern Beaufort and Chukchi Seas, 2040–65: U.S. Geological Survey Open-File Report 2023–1048, 7 p., https://doi.org/10.3133/ofr20231048.","productDescription":"Report: vi, 7 p.; Data Release","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-148069","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":418953,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211048/full"},{"id":418955,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1048/images"},{"id":418954,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1048/ofr20231048.xml"},{"id":418956,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XEOBWV","text":"Polar bear Continuous Time-Correlated Random Walk (CTCRW) location data derived from satellite location data, Chukchi and Beaufort Seas, July-November 1985-2017","description":"Rode, K. D., Douglas, D. C., Atwood, T. C., Durner, G. M., Wilson, R. R., Bromaghin, J. F., Pagano, A. M. and Simac, K. S., 2022, Polar bear Continuous Time-Correlated Random Walk (CTCRW) location data derived from satellite location data, Chukchi and Beaufort Seas, July-November 1985-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9XEOBWV."},{"id":418951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1048/covrthb.jpg"},{"id":418952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1048/ofr20231048.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508