Quantifying how contaminants change across life cycles of species that undergo metamorphosis is critical to assessing organismal risk, particularly for consumers. Pond-breeding amphibians can dominate aquatic animal biomass as larvae and are terrestrial prey as juveniles and adults. Thus, amphibians can be vectors of mercury exposure in both aquatic and terrestrial food webs. However, it is still unclear how mercury concentrations are affected by exogenous (e.g., habitat or diet) vs endogenous factors (e.g., catabolism during hibernation) as amphibians undergo large diet shifts and periods of fasting during ontogeny. We measured total mercury (THg), methylmercury (MeHg), and isotopic compositions (δ 13C, δ15N) in boreal chorus frogs (Pseudacris maculata) across five life stages in two Colorado (USA) metapopulations. We found large differences in concentrations and percent MeHg (of THg) among life stages. Frog MeHg concentrations peaked during metamorphosis and hibernation coinciding with the most energetically demanding life cycle stages. Indeed, life history transitions involving periods of fasting coupled with high metabolic demands led to large increases in mercury concentrations. The endogenous processes of metamorphosis and hibernation resulted in MeHg bioamplification, thus decoupling it from the light isotopic proxies of diet and trophic position. These step changes are not often considered in conventional expectations of how MeHg concentrations within organisms are assessed.
Climate change and human–wildlife conflict are both pressing challenges for biodiversity conservation and human well-being in the Anthropocene. Climate change is a critical yet underappreciated amplifier of human–wildlife conflict, as it exacerbates resource scarcity, alters human and animal behaviours and distributions, and increases human–wildlife encounters. We synthesize evidence of climate-driven conflicts occurring among ten taxonomic orders, on six continents and in all five oceans. Such conflicts disrupt both subsistence livelihoods and industrial economies and may accelerate the rate at which human–wildlife conflict drives wildlife declines. We introduce a framework describing distinct environmental, ecological and sociopolitical pathways through which climate variability and change percolate via complex social–ecological systems to influence patterns and outcomes of human–wildlife interactions. Identifying these pathways allows for developing mitigation strategies and proactive policies to limit the impacts of human–wildlife conflict on biodiversity conservation and human well-being in a changing climate.
Groundwater uranium (U) concentrations have been measured above the U.S. EPA maximum contaminant level (30 μg/L) in many U.S. aquifers, including in areas not associated with anthropogenic contamination by milling or mining. In addition to carbonate, nitrate has been correlated to uranium groundwater concentrations in two major U.S. aquifers. However, to date, direct evidence that nitrate mobilizes naturally occurring U from aquifer sediments has not been presented. Here, we demonstrate that the influx of high-nitrate porewater through High Plains alluvial aquifer silt sediments bearing naturally occurring U(IV) can stimulate a nitrate-reducing microbial community capable of catalyzing the oxidation and mobilization of U into the porewater. Microbial reduction of nitrate yielded nitrite, a reactive intermediate, which was further demonstrated to abiotically mobilize U from the reduced alluvial aquifer sediments. These results indicate that microbial activity, specifically nitrate reduction to nitrite, is one mechanism driving U mobilization from aquifer sediments in addition to previously described bicarbonate-driven desorption from mineral surfaces, such as Fe(III) oxides.
Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2827","usgsCitation":"Barrile, G., Augustine, D.J., Porensky, L., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E.A., and Davidson, A.D., 2023, A big data–model integration approach for predicting epizootics and population recovery in a keystone species: Ecological Applications, v. 33, no. 4, e2827, 23 p., https://doi.org/10.1002/eap.2827.","productDescription":"e2827, 23 p.","ipdsId":"IP-142779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2827","text":"Publisher Index Page"},{"id":465980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.72151393816772,\n 49.06842429079816\n ],\n [\n -106.74407678276975,\n 44.935250564946244\n ],\n [\n -105.02441274593369,\n 40.78587530801761\n ],\n [\n -105.18708495048385,\n 35.23907808129579\n ],\n [\n -110.74352872625728,\n 31.51616164533567\n ],\n [\n -101.12343743446304,\n 28.915776651344572\n ],\n [\n -100.73547916940133,\n 38.76206177537206\n ],\n [\n -100.82267130820097,\n 44.32291601997803\n ],\n [\n -101.89899715272732,\n 47.61230649548153\n ],\n [\n -111.72151393816772,\n 49.06842429079816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":922769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":922771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duchardt, Courtney J.","contributorId":347959,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":922772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":922773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartway, Cynthia R.","contributorId":347961,"corporation":false,"usgs":false,"family":"Hartway","given":"Cynthia R.","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":922774,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":922775,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922776,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davidson, Ana D. 0000-0003-4701-5923","orcid":"https://orcid.org/0000-0003-4701-5923","contributorId":304176,"corporation":false,"usgs":false,"family":"Davidson","given":"Ana","email":"","middleInitial":"D.","affiliations":[{"id":65991,"text":"CNHP","active":true,"usgs":false}],"preferred":false,"id":922777,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70266441,"text":"70266441 - 2023 - Lake Erie hypoxia spatial and temporal dynamics present challenges for assessing progress toward water quality goals","interactions":[],"lastModifiedDate":"2025-05-07T18:29:08.000023","indexId":"70266441","displayToPublicDate":"2023-02-27T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Lake Erie hypoxia spatial and temporal dynamics present challenges for assessing progress toward water quality goals","docAbstract":"Seasonal hypolimnetic hypoxia has been documented in Lake Erie’s central basin since the 1950s. Ship-based surveys to monitor hypoxia have been conducted since the 1980s, but they occur at a relatively low frequency and focus on the deeper areas of the central basin. To better document the seasonal development of stratification and the consequent occurrence of hypoxia, we deployed eight moorings, in both nearshore-shallow areas and offshore-deep areas of the central basin, equipped with temperature and oxygen sensors at multiple depths, that recorded temperature and oxygen concentrations every 10 minutes. Results from 2017-2019 reveal that hypoxia occurs as early as July in the shallower areas west of, and around the southern perimeter of the central basin, but does not occur until August or September in the deeper central basin. Hypoxia is intermittent in the shallower perimeter areas; whereas in the deeper areas, hypoxia can persist into October, often progressing to anoxia. The intra and interannual differences in the spatial and temporal extent of hypoxia indicate that an extensive monitoring program will be necessary to more accurately assess progress toward reducing the extent of hypoxia pursuant to the lake ecosystem objectives of the 2012 Great Lakes Water Quality Agreement.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.02.008","usgsCitation":"Stow, C., Rowe, M., Godwin, C., Mason, L., Alsip, P., Kraus, R., Johengen, T., and Constant, S., 2023, Lake Erie hypoxia spatial and temporal dynamics present challenges for assessing progress toward water quality goals: Journal of Great Lakes Research, v. 49, no. 5, p. 981-992, https://doi.org/10.1016/j.jglr.2023.02.008.","productDescription":"12 p.","startPage":"981","endPage":"992","ipdsId":"IP-140878","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.02.008","text":"Publisher Index Page"},{"id":485516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, New York, Ohio, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.45746247368997,\n 42.19332204270543\n ],\n [\n -83.58088952321158,\n 41.37751990998936\n ],\n [\n -81.36793483137687,\n 41.36610477953545\n ],\n [\n -79.12723694634781,\n 42.41574902379864\n ],\n [\n -78.7500162167698,\n 43.007471194229566\n ],\n [\n -81.13424345938826,\n 42.76252432461877\n ],\n [\n -82.2940150129951,\n 42.35073159163453\n ],\n [\n -83.45746247368997,\n 42.19332204270543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stow, Craig A.","contributorId":354602,"corporation":false,"usgs":false,"family":"Stow","given":"Craig A.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":935968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowe, Mark D.","contributorId":354603,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark D.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":935969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Godwin, Casey M.","contributorId":354604,"corporation":false,"usgs":false,"family":"Godwin","given":"Casey M.","affiliations":[{"id":84640,"text":"Cooperative Institute for Great Lakes Research","active":true,"usgs":false}],"preferred":false,"id":935970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mason, Lacey A.","contributorId":354605,"corporation":false,"usgs":false,"family":"Mason","given":"Lacey A.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":935971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alsip, Peter","contributorId":354606,"corporation":false,"usgs":false,"family":"Alsip","given":"Peter","affiliations":[{"id":84640,"text":"Cooperative Institute for Great Lakes Research","active":true,"usgs":false}],"preferred":false,"id":935972,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":935973,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johengen, Thomas","contributorId":354609,"corporation":false,"usgs":false,"family":"Johengen","given":"Thomas","affiliations":[{"id":37753,"text":"Michigan Sea Grant","active":true,"usgs":false}],"preferred":false,"id":935974,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Constant, Stephen A.","contributorId":354610,"corporation":false,"usgs":false,"family":"Constant","given":"Stephen A.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":935975,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70241408,"text":"70241408 - 2023 - Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","interactions":[],"lastModifiedDate":"2023-03-17T11:42:04.586294","indexId":"70241408","displayToPublicDate":"2023-02-26T06:39:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","docAbstract":"Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.
Fish that exhibit high foraging activity or bold behavior can be particularly vulnerable to angling. If these traits are heritable, selection through harvest can drive phenotypic change, eventually rendering a target population less vulnerable to angling and consequently impacting the quality of the fishery. In this study, we used parental-based tags to investigate whether vulnerability to angling might be heritable in steelhead trout (Oncorhynchus mykiss) spawned at a hatchery in western Oregon, USA. We found modest evidence to support the hypothesis that vulnerability to angling is a heritable trait in steelhead. However, our data unexpectedly revealed that steelhead collected with in-river traps produced nearly twice as many adult offspring as steelhead collected by anglers. This difference in adult-to-adult production is explained in part through lower egg-to-fry survival of steelhead produced with angler-caught broodstock, possibly related to collection stress and greater time in captivity experienced by angler-caught broodstock. Our findings suggest that managers could improve broodstock fitness and program efficiencies by preferentially spawning fish collected with traps, and limiting use of broodstock collected by anglers. Additional research is needed to identify mechanisms contributing to higher juvenile mortality of steelhead produced with angler-caught broodstock.
Diet plasticity is a common behavior exhibited by piscivores to sustain predator biomass when preferred prey biomass is reduced. Invasive piscivore diet plasticity could complicate suppression success; thus, understanding invasive predator consumption is insightful to meeting conservation targets. Here, we determine if diet plasticity exists in an invasive apex piscivore and whether plasticity could influence native species recovery benchmarks and invasive species suppression goals. We compared diet and stable isotope signatures of invasive lake trout and native Yellowstone cutthroat trout (cutthroat trout) from Yellowstone Lake, Wyoming, U.S.A. as a function of no, low-, moderate-, and high-lake trout density states. Lake trout exhibited plasticity in relation to their density; consumption of cutthroat trout decreased 5-fold (diet proportion from 0.89 to 0.18) from low- to high-density state. During the high-density state, lake trout switched to amphipods, which were also consumed by cutthroat trout, resulting in high diet overlap (Schoener’s index value, D = 0.68) between the species. As suppression reduced lake trout densities (moderate-density state), more cutthroat trout were consumed (proportion of cutthroat trout = 0.42), and diet overlap was released between the species (D = 0.30). A shift in lake trout δ13C signatures from the high- to the moderate-density state also corroborated increased consumption of cutthroat trout and lake trout diet plasticity. Observed declines in lake trout are not commensurate with expected cutthroat trout recovery due to lake trout diet plasticity. The abundance of the native species in need of conservation may take longer to recover due to the diet plasticity of the invasive species. The changes observed in diet, diet overlap, and isotopes associated with predator suppression provides more insight into conservation and suppression dynamics than using predator and prey biomass alone. By understanding these dynamics, we can better prepare conservation programs for potential feedbacks caused by invasive species suppression.
To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Coupled paleomagnetic and geochronologic data derived from mafic dykes provide valuable records of continental movement. To reconstruct the Proterozoic paleogeographic history of Peninsular India, we report paleomagnetic directions and U-Pb zircon ages from twenty-nine mafic dykes in the Eastern Dharwar Craton near Hyderabad. Paleomagnetic analysis yielded clusters of directional data that correspond to dyke swarms at 2.37 Ga, 2.22 Ga, 2.08 Ga, 1.89–1.86 Ga, 1.79 Ga, and a previously undated dual polarity magnetization. We report new positive baked contact tests for the 2.08 Ga swarm and the 1.89–1.86 Ga swarm(s), and a new inverse baked contact test for the 2.08 Ga swarm. Our results promote the 2.08 Ga Dharwar Craton paleomagnetic pole (43.1° N, 184.5° E; A95 = 4.3°) to a reliability score of R = 7 and suggest a position for the Dharwar Craton at 1.79 Ga based on a virtual geomagnetic pole (VGP) at 33.0° N, 347.5° E (a95 = 16.9°, k = 221, N = 2). The new VGP for the Dharwar Craton provides support for the union of the Dharwar, Singhbhum, and Bastar Cratons in the Southern India Block by at least 1.79 Ga. Combined new and published northeast-southwest moderate-steep dual polarity directions from Dharwar Craton dykes define a new paleomagnetic pole at 20.6° N, 233.1° E (A95 = 9.2°, N = 18; R = 5). Two dykes from this group yielded 1.05–1.01 Ga 207Pb/206Pb zircon ages and this range is taken as the age of the new paleomagnetic pole. A comparison of the previously published poles with our new 1.05–1.01 Ga pole shows India shifting from equatorial to higher (southerly) latitudes from 1.08 Ga to 1.01 Ga as a component of Rodinia.
The objective of the work presented in this report is to develop equations that can be used to extend the base flow record at multiple partial-record streamgaging stations at Acadia National Park in eastern coastal Maine based on nearby continuous-record streamgaging stations. Daily mean streamflow values at U.S. Geological Survey continuous-record streamgaging station Otter Creek near Bar Harbor, Maine (station 01022840) had stronger correlations with instantaneous measurements during base flow conditions from 2006 to 2020 at 14 partial-record streamgaging stations at Acadia National Park than the other four continuous-record streamgaging stations tested for use as index stations. Index stations are continuous-record stations on hydrologically similar streams that have the potential to be used to extend the record at the partial-record station. Base flow is that part of streamflow that is sustained primarily by groundwater discharge. It is not attributable to direct precipitation or melting snow. Five of the partial-record stations had strong correlations with Otter Creek (correlation coefficient greater than 0.90) and relatively low root mean square errors (from 0.04 to 0.19). An additional four partial-record stations had fair correlations with Otter Creek (correlation coefficient from 0.79 to 0.9) and relatively low root mean square errors (from 0.05 to 0.19). For these 10 stations, maintenance of variance extension type 1 (MOVE.1) record extension equations computed in this report provide a reasonable method for extending the partial record, estimating summer monthly means and medians, and estimating daily mean streamflow values at these sites on days with no streamflow (discharge) measurements. Four of the partial-record stations have weak correlations (less than 0.78) or high root mean square error values (greater than 9) or both, indicating that record extension techniques are not appropriate for these partial-record stations using currently [2022] available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225126","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lombard, P.J., 2023, Estimating streamflow for base flow conditions at partial-record streamgaging stations at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2022–5126, 13 p., https://doi.org/10.3133/sir20225126.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-143769","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":413317,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZP8XHG","text":"USGS data release","linkHelpText":"Data and code to support MOVE.1 regression equations for streamflow at partial-record streamgaging stations at Acadia National Park, Maine:"},{"id":413315,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.XML"},{"id":413312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5126/coverthb.jpg"},{"id":413313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5126"},{"id":413316,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5126/images/"},{"id":413864,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225126/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5126"},{"id":500481,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114380.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.16726259768689,\n 44.32624433734378\n ],\n [\n -68.17739845831488,\n 44.36973371484888\n ],\n [\n -68.21954229987337,\n 44.38307924264697\n ],\n [\n -68.23981402112935,\n 44.41090441296549\n ],\n [\n -68.28035746364178,\n 44.41928748605292\n ],\n [\n -68.29422758871185,\n 44.406712425764226\n ],\n [\n -68.33050330043281,\n 44.35371506667144\n ],\n [\n -68.38971806515461,\n 44.35562227826236\n ],\n [\n -68.40305472387581,\n 44.31937464393428\n ],\n [\n -68.39025153150348,\n 44.27890346604781\n ],\n [\n -68.3390387620142,\n 44.21661505589944\n ],\n [\n -68.3112985118746,\n 44.217762064180675\n ],\n [\n -68.2883594588743,\n 44.24566571236582\n ],\n [\n -68.30329651664208,\n 44.285396004838105\n ],\n [\n -68.24514868461804,\n 44.281958867796675\n ],\n [\n -68.1982036459197,\n 44.29532438221068\n ],\n [\n -68.16726259768689,\n 44.32662596339111\n ],\n [\n -68.16726259768689,\n 44.32624433734378\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
Land management priorities and decisions may result in population declines for non-target wildlife species. In the western United States, large-scale removal of conifer from sagebrush ecosystems (Artemisia spp.) is occurring to recover greater sage-grouse (Centrocercus urophasianus) populations and may result in pinyon jay (Gymnorhinus cyanocephalus) habitat loss. Jay populations have experienced long-term declines, due to unknown causes, resulting in a recent petition for listing under the Endangered Species Act of 1973. We developed a Bayesian hierarchical model of jay abundance, using 13 years of point count data (2008–2020) collected across the western United States, to estimate regional population trends, model habitat requirements, assess conifer removal effects on jays, and generate hypotheses regarding jay population declines. Our model included climate and landcover covariates and regional trends in pinyon jay density. We applied our modeled habitat relationships to map predicted pinyon jay density, given 2008 and 2020 resource conditions, and map density changes from 2008 to 2020. Our results indicate pinyon jay populations are declining within Bird Conservation Region 16. Jay density was positively associated with sagebrush cover, Palmer Drought Severity Index, and pinyon-juniper cover. Conversely, jay populations were negatively associated with Normalized Difference Vegetation Index (NDVI). We found higher pinyon jay densities within locations possessing both sagebrush and pinyon-juniper cover; conditions characteristic of phase I and II conifer encroachment which are preferentially targeted for conifer removal to restore sagebrush communities. Conifer removal, if conducted at locations with high pinyon jay densities, is therefore likely to negatively affect jay abundance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.109959","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2023, A hidden cost of single species management: Habitat-relationships reveal potential negative effects of conifer removal on a non-target species: Biological Conservation, v. 280, 109959, 10 p., https://doi.org/10.1016/j.biocon.2023.109959.","productDescription":"109959, 10 p.","ipdsId":"IP-138764","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444374,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2023.109959","text":"Publisher Index Page"},{"id":435435,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NIG4UW","text":"USGS data release","linkHelpText":"Predicted Pinyon Jay (Gymnorhinus cyanocephalus) densities across the western United States, 2008-2020"},{"id":413855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, North Dakota, South Dakota, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.41395617933225,\n 35.71066116858752\n ],\n [\n -111.67021215517931,\n 35.905346739347536\n ],\n [\n -108.77088674515161,\n 36.96389200169858\n ],\n [\n -101.86862285960521,\n 37.125401887525115\n ],\n [\n -101.51491491061196,\n 37.52404629916971\n ],\n [\n -101.90911987227537,\n 41.291485987900245\n ],\n [\n -103.26229497324951,\n 42.46761717574853\n ],\n [\n -101.97643578414241,\n 43.25420173811844\n ],\n [\n -102.55180019651098,\n 49.041860323717856\n ],\n [\n -117.14265326477982,\n 49.014048521848\n ],\n [\n -116.95141239209565,\n 46.12283190981066\n ],\n [\n -116.941510874859,\n 40.99815769875613\n ],\n [\n -120.0102098874165,\n 38.93737098892768\n ],\n [\n -120.02540765639762,\n 38.10974034171085\n ],\n [\n -115.41395617933225,\n 35.71066116858752\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"280","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":865861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241942,"text":"70241942 - 2023 - Changes in mangrove blue carbon under elevated atmospheric CO2","interactions":[],"lastModifiedDate":"2023-03-31T13:41:21.573249","indexId":"70241942","displayToPublicDate":"2023-02-23T08:38:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5075,"text":"Ecosystem Health and Sustainability","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Changes in mangrove blue carbon under elevated atmospheric CO2","title":"Changes in mangrove blue carbon under elevated atmospheric CO2","docAbstract":"While there is consensus that blue carbon ecosystems, such as mangroves, have an important role in mitigating some aspects of global climate change, little is known about mangrove carbon cycling under elevated atmospheric CO2 concentrations (eCO2). Here, we review studies in order to identify pathways for how eCO2 might influence mangrove ecosystem carbon cycling. In general, eCO2 alters plant productivity, species community composition, carbon fluxes, and carbon deposition in ways that enhance mangrove carbon storage with eCO2. As a result, a negative feedback to climate change exists whereby eCO2 adds to mangrove’s ability to sequester additional carbon, which in turn reduces the rate by which CO2 builds. Furthermore, eCO2 affects warming and sea-level rise (SLR) through alternate pathways, which coinfluence the mangrove response in both antagonistic (i.e., warming = greater carbon loss to decomposition) and synergistic (i.e., SLR = greater soil carbon burial) ways. eCO2 is projected to become a more prominent driver in the future before reaching a steady state. However, given the complexity of the interactions of biological and environmental factors with eCO2, long-term field observations and in situ simulation experiments can help to better understand the mechanisms for proper model initialization to predict future changes in mangrove carbon sequestration.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.34133/ehs.0033","usgsCitation":"Gu, X., Qiao, P., Krauss, K., Lovelock, C.E., Adams, J.B., Chapman, S.K., Jennerjahn, T.C., Lin, Q., and Chen, L., 2023, Changes in mangrove blue carbon under elevated atmospheric CO2: Ecosystem Health and Sustainability, v. 9, 0033, 12 p., https://doi.org/10.34133/ehs.0033.","productDescription":"0033, 12 p.","ipdsId":"IP-146217","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/ehs.0033","text":"Publisher Index Page"},{"id":415007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gu, Xiaoxuan","contributorId":296950,"corporation":false,"usgs":false,"family":"Gu","given":"Xiaoxuan","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qiao, Peiyang","contributorId":303861,"corporation":false,"usgs":false,"family":"Qiao","given":"Peiyang","email":"","affiliations":[{"id":47617,"text":"Xiamen University, China","active":true,"usgs":false}],"preferred":false,"id":868299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":222378,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":868301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Janine B.","contributorId":303863,"corporation":false,"usgs":false,"family":"Adams","given":"Janine","email":"","middleInitial":"B.","affiliations":[{"id":65919,"text":"Nelson Mandela University (South Africa)","active":true,"usgs":false}],"preferred":false,"id":868302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Samantha K.","contributorId":303864,"corporation":false,"usgs":false,"family":"Chapman","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":868303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jennerjahn, Tim C.","contributorId":303865,"corporation":false,"usgs":false,"family":"Jennerjahn","given":"Tim","email":"","middleInitial":"C.","affiliations":[{"id":65921,"text":"Leibniz Centre for Tropical Marine Research (ZMT), Germany","active":true,"usgs":false}],"preferred":false,"id":868304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lin, Qiulian","contributorId":294476,"corporation":false,"usgs":false,"family":"Lin","given":"Qiulian","email":"","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":868306,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241240,"text":"70241240 - 2023 - Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","interactions":[],"lastModifiedDate":"2023-05-25T15:52:09.064418","indexId":"70241240","displayToPublicDate":"2023-02-23T08:30:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","docAbstract":"We determine optimal on‐fault earthquake spatial distributions using a combinatorial method that minimizes the long‐term cumulative stress resolved on the fault. An integer‐programming framework was previously developed to determine the optimal arrangement of a millennia‐scale earthquake sample that minimizes the misfit to a target slip rate determined from geodetic data. The resulting cumulative stress from just slip‐rate optimization, however, can greatly exceed fault strength estimates. Therefore, we add an objective function that minimizes cumulative stress and broad stress constraints to limit the solution space. We find that there is a trade‐off in the two objectives: minimizing the cumulative stress on a fault within fault strength limits concentrates earthquakes in specific areas of the fault and results in excursions from the target slip rate. Both slip‐rate and stress objectives can be combined in either a weighted or lexicographic (hierarchical) method. Using a combination of objectives, we demonstrate that a Gutenberg–Richter sample of earthquakes can be arranged on a constant slip‐rate finite fault with minimal stress and slip‐rate residuals. We apply this method to determine the optimal arrangement of earthquakes on the variable slip‐rate Nankai megathrust over 5000 yr. The sharp decrease in slip rate at the Tokai section of the fault results in surplus cumulative stress under all scenarios. Using stress optimization alone restricts this stress surplus to the northeast end of the fault at the expense of decreasing the slip rate away from the target slip rate at the southwest end of the fault. A combination of both slip‐rate and stress objectives provides an adequate fit to the data, although alternate model formulations for the fault are needed at the Tokai section to explain persistent excess cumulative stress. In general, incorporating stress objectives and constraints into the integer‐programming framework adds an important aspect of fault physics to the resulting earthquake rupture forecasts.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220175","usgsCitation":"Geist, E.L., and Parsons, T.E., 2023, Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1025-1038, https://doi.org/10.1785/0120220175.","productDescription":"14 p.","startPage":"1025","endPage":"1038","ipdsId":"IP-144689","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":414280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":15543,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866628,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248834,"text":"70248834 - 2023 - Identifying research in support of the management and control of dreissenid mussels in the western United States","interactions":[],"lastModifiedDate":"2023-09-22T12:25:55.939686","indexId":"70248834","displayToPublicDate":"2023-02-23T07:23:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Identifying research in support of the management and control of dreissenid mussels in the western United States","docAbstract":"On February 9–10, 2022, the Pacific States Marine Fisheries Commission, U.S. Fish and Wildlife Service, U.S. Geological Survey, and Washington State University hosted a workshop to establish research priorities that support the implementation of action items listed in a current invasive species management plan, the Quagga and Zebra Mussel Action Plan (QZAP) 2.0, that are intended to limit the establishment and spread of quagga and zebra mussels in the western United States. The workshop focus was on developing research priorities for the thematic areas that are addressed in QZAP 2.0: 1) early detection monitoring, 2) prevention and containment, 3) control and management, and 4) rapid response. In addition, research priorities were developed for a fifth thematic area that addressed dreissenid mussel biology. Forty scientists participated in the two-day workshop. Prior to the workshop, participants were asked to review and rank research priorities that were established for a previous version of the QZAP and to offer suggestions on emerging research priorities. During the workshop, subject matter experts presented information describing current knowledge of research and information associated with the thematic areas of early detection monitoring, prevention and containment, rapid response, control and management, and biology in the context of strategies and actions listed in QZAP 2.0. The rankings of previous research priorities and suggestions of emerging priorities were then reviewed, and a revised list of research priorities was formed. The list of research priorities is presented by thematic area.
","language":"English","publisher":"Reabic","doi":"10.3391/mbi.2023.14.3.05","usgsCitation":"Counihan, T., DeBruyckere, L., Bollens, S., Phillips, S., Thom, T., and Shemai, B., 2023, Identifying research in support of the management and control of dreissenid mussels in the western United States: Management of Biological Invasions, v. 14, no. 3, p. 459-466, https://doi.org/10.3391/mbi.2023.14.3.05.","productDescription":"8 p.","startPage":"459","endPage":"466","ipdsId":"IP-144926","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":444381,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2023.14.3.05","text":"Publisher Index Page"},{"id":421067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":883831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeBruyckere, Lisa","contributorId":207531,"corporation":false,"usgs":false,"family":"DeBruyckere","given":"Lisa","email":"","affiliations":[{"id":37555,"text":"Creative Resource Strategies, LLC","active":true,"usgs":false}],"preferred":false,"id":883832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bollens, Stephen M.","contributorId":181850,"corporation":false,"usgs":false,"family":"Bollens","given":"Stephen M.","affiliations":[],"preferred":false,"id":883833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Stephen","contributorId":156280,"corporation":false,"usgs":false,"family":"Phillips","given":"Stephen","affiliations":[{"id":20304,"text":"Pacific States Marine Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":883834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thom, Theresa","contributorId":224436,"corporation":false,"usgs":false,"family":"Thom","given":"Theresa","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":883835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shemai, Barak","contributorId":330000,"corporation":false,"usgs":false,"family":"Shemai","given":"Barak","email":"","affiliations":[{"id":78764,"text":"U.S. Fish and Wildlife Service - Southwest Region Aquatic Invasive Species Coordinator Box 1306. Albuquerque, NM 87103","active":true,"usgs":false}],"preferred":false,"id":883836,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241040,"text":"70241040 - 2023 - Incorporation of real-time earthquake magnitudes estimated via peak ground displacement scaling in the ShakeAlert Earthquake Early Warning system","interactions":[],"lastModifiedDate":"2023-05-25T15:50:57.475532","indexId":"70241040","displayToPublicDate":"2023-02-23T07:19:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Incorporation of real-time earthquake magnitudes estimated via peak ground displacement scaling in the ShakeAlert Earthquake Early Warning system","docAbstract":"The United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real‐time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real‐time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD‐derived magnitude estimates (
The U.S. Geological Survey, in cooperation with the Macoupin County Soil and Water Conservation District and the American Farmland Trust, undertook a monitoring effort from 2017 to 2021 in the upper Macoupin Creek watershed. The monitoring effort was to determine and characterize nitrogen, phosphorus, and suspended-sediment concentrations, loads, and yields for a 566.7 square kilometer area of the Macoupin Creek watershed at two locations on upper Macoupin Creek bracketing a segment of the watershed where increased implementation of conservation land-use practices was planned. Two monitoring stations were established, consisting of an upstream site (Macoupin Creek at Highway 108 near Carlinville, Illinois; U.S. Geological Survey streamgage 05586647) and a downstream site (Macoupin Creek at Highway 111 near Summerville, Ill.; U.S. Geological Survey streamgage 05586745). Data collected at these stations included continuous stream discharge and periodic samples for nutrients and suspended sediment. A Weighted Regressions on Time, Discharge, and Season–Kalman model was implemented to estimate daily concentrations for nitrate plus nitrite, total phosphorus, and suspended sediment for both monitoring stations. These daily concentrations were used in conjunction with the continuous stream discharge data to derive estimates of constituent flux, loads, and yields.
During the study period, the study area subbasin of the upper Macoupin Creek watershed reduced downstream nitrate and total phosphorus cummulative yields by approximately 54 and 21 percent, respectively; however, the cummulative yield of suspended sediment increased by approximately 10 percent from inputs within the study area. These data indicate that nitrate and phosphorus transport is greater from the upstream subbasin and being diluted in the combined subbasin by lower transport from the study area, whereas suspended sediment is being contributed from the study area reach, presumably through surface runoff and streambank and streambed erosion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225131","collaboration":"Prepared in cooperation with the Macoupin County Soil and Water Conservation District and American Farmland Trust","usgsCitation":"Garcia, L.A., Terrio, P.J., and Manaster, A.E., 2023, Nutrient and suspended-sediment concentrations, loads, and yields in upper Macoupin Creek, Illinois, 2017–21: U.S. Geological Survey Scientific Investigations Report 2022–5131, 17 p., https://doi.org/10.3133/sir20225131.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-144304","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413286,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5131/images"},{"id":413285,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5131/sir20225131.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":413284,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5131/sir20225131.pdf","text":"Report","size":"2.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5131"},{"id":499487,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114379.htm","linkFileType":{"id":5,"text":"html"}},{"id":413345,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225131/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413289,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":413283,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5131/coverthb.jpg"},{"id":413288,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95IC7QS","text":"USGS data release","linkHelpText":"Nutrient and sediment concentrations, loads, and yields in the Upper Macoupin Creek watershed, water years 2018–2021"}],"country":"United States","state":"Illinois","otherGeospatial":"Upper Macoupin Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.666,\n 39.5\n ],\n [\n -90.666,\n 39\n ],\n [\n -89.5,\n 39\n ],\n [\n -89.5,\n 39.5\n ],\n [\n -90.666,\n 39.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Drinking Water Protection Section of the Minnesota Department of Health conducted reconnaissance monitoring of selected public water systems in Minnesota. Funding was obtained primarily from the Environment and Natural Resources Trust Fund. Sampling was conducted in 2019 and 2021. Laboratory analysis of samples was conducted for a variety of different contaminants of emerging concern (CECs), including selected pharmaceuticals, pesticides, PFAS, wastewater indicators and other parameters chosen for the physical and land use setting surrounding the sampling points. Sampling site and parameter selection were designed with several goals, as follows: Characterize occurrence and distribution of selected CECs in settings where such chemicals are most likely to be present; Determine if any such occurrences represent a public health concern; Compare results from coupled source water and finished (i.e., treated) water samples at public water system sites where such sampling is feasible; Assess if results from geologically vulnerable (sensitive subject to rapid recharge) and geologically non-vulnerable settings differ significantly. 306 samples were collected as part of the study, from three networks of public water systems differentiated on the basis of source water type (i.e., surface water or groundwater) and land use environment (agricultural and wastewater influenced). This report provides a preliminary, qualitative evaluation of the results. Additionally, more rigorous research will be conducted on these water quality data to evaluate the below findings in more detail. High-level findings from this assessment include the following: Very few samples exceeded health-based guidance for CECs; o When this occurred, MDH staff conducted follow up sampling at the system and provided technical advice about managing the situation. Only a fraction of the CECs analyzed were detected; o Of the 522 different CECs analyzed in the water samples, 161 were detected in one or more samples; o Additionally, most detections were at low levels; Among the CEC classes included in the analytical work, pesticides and PFAS were generally detected at a greater frequency than other CECs; o See Executive Summary Figure 1. The ten most commonly detected individual compounds include: o Tribromomethane, or bromoform, (a disinfection by-product) (70% of sites where analyzed); o norgestrel (a pharmaceutical) (69% of sites where analyzed); o lithium (68% of sites where analyzed); o Metolachlor SA (52%), Deethylatrazine (49%), atrazine (45%), and deisopropylatrazine (31%) (pesticides); o PFBA (44%) and PFHxS (27%) (PFAS compounds); and o 5-methyl benzotriazole (29%) (a benzotriazole). Some CECs were detected more frequently in samples collected from surface waters than those collected from groundwater sources; CEC concentrations were generally higher in vulnerable settings compared to nonvulnerable settings; Whether CECs were detected more frequently in the source water or finished water varied by CEC class. For example, o Benzotriazoles and pharmaceuticals were more frequently detected in source water samples than finished water samples; and o Tribromomethane, or bromoform, a common disinfection by-product, was more frequently found in finished water samples than in source water samples. This work prompted a series of programmatic changes and innovations: A response framework was established for helping the program and public water systems manage detections of unregulated CECs in drinking water; Results were forwarded to the program within MDH responsible for developing healthbased guidance in order to nominate specific compounds found in drinking water but for which limited or no risk advice is available; MDH is seeking support from the Clean Water Council to support the establishment of permanent capacity within the Drinking Water Protection Section to continue sampling efforts of this type.
","language":"English","publisher":"Minnesota Department of Health","collaboration":"Minnesota Department of Health, Minnesota Environment and Natural Resources Trust Fund","usgsCitation":"de Lambert, J., Overbo, A., Robertson, S., and Elliott, S.M., 2023, Data summary report: Unregulated contaminants monitoring project, 85 p.","productDescription":"85 p.","ipdsId":"IP-145896","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":414813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414798,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.health.state.mn.us/communities/environment/water/docs/ucmpreport.pdf"}],"country":"United 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Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867798,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246686,"text":"70246686 - 2023 - Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use","interactions":[],"lastModifiedDate":"2023-07-14T11:53:26.020041","indexId":"70246686","displayToPublicDate":"2023-02-23T06:50:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use","docAbstract":"Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.
Understanding plant community response to environmental change is a crucial aspect of biological conservation and restoration, but species-based approaches are limited in that they do not reveal the underlying mechanisms driving vegetation dynamics. An understanding of trait-environment relationships is particularly important in the case of invasive species which may alter abiotic conditions and available resources. This study is the first to measure the functional response of riparian plant communities to biocontrol of an invasive species. We focused on an invasive shrub, Tamarix (saltcedar), that is defoliated by a beetle that was released by the US Department of Agriculture along the Upper Colorado River (southwestern United States). We calculated community weighted means and functional dispersion of individual traits, multivariate functional dispersion and species diversity. We used linear mixed effect models (LME) to compare these metrics at paired vegetation patches dominated and not dominated by Tamarix during cycles of defoliation and refoliation over eight years. We found that community-weighted average trait values, species diversity and functional dispersion changed little in response to defoliation, and instead seemed to be responding to fluctuations in yearly precipitation. Average height and seed weight were greater in Tamarix-dominated patches relative to control patches. Functional dispersion followed a similar trajectory to species diversity, but was a more sensitive indicator of plant community change. We showed that riparian vegetation can be resilient to Tamarix biocontrol, and that defoliation might not necessarily always lead to substantial changes in ecosystem function.
Condit Dam was removed from river kilometer (rkm) 5.3 of the White Salmon River, Washington, in 2011 and 2012 after blocking upstream passage of anadromous fish for nearly 100 years. The dam removal opened habitat upstream and improved habitat downstream with addition of cobble and gravel to a reach depauperate of spawning and rearing habitat. We assessed juvenile anadromous salmonid abundance and distribution in the subbasin from 2016 through 2021 to evaluate the efficacy of natural recolonization. We sampled for outmigrant smolts and other life-history stages at a rotary screw trap at rkm 2.3 and for juvenile abundance at sites in Buck and Rattlesnake Creeks, two primary tributaries upstream from the former dam location.
We estimated smolt abundance of steelhead (Oncorhynchus mykiss) and coho salmon (O. kisutch) at the screw-trap site during most years of the study. High flow and missed trapping days in 2017 precluded estimates, and the trap was not fished during 2020 because of the onset of the COVID-19 pandemic. Steelhead smolt-abundance estimates ranged from 3,581 to 5,851 fish; coho salmon smolt-abundance estimates ranged from 1,093 to 1,773 fish, although in 2021, only 2 coho salmon smolt were captured and no estimate was made.
Other species and life stages also were captured in the screw trap. Steelhead and coho salmon fry and parr, and Chinook salmon (O. tshawytscha) fry were captured, indicating the presence and likely use of improved habitat downstream from the former dam site by multiple life stages and spawning success upstream from the screw-trap site. Chinook salmon fry were captured, indicating spawning success upstream from the screw-trap site. Fry numbers varied greatly by day and year. Yearly variation in Chinook and coho salmon fry numbers may have been influenced by high flows following spawning causing redd scour and egg-to-fry mortality. Three bull trout (Salvelinus confluentus) were caught in the screw trap, one in June 2018, one in June 2019, and one in June 2021. All three bull trout showed smolt characteristics and were tagged with passive integrated transponders (PITs). The bull trout captured in June 2018 was detected at Bonneville Dam Corner Collector several days later, indicating likely anadromy. We also captured lamprey in the screw trap: 44 during 2018, 31 during 2019, and 11 during 2021; we believe most were adult brook lamprey (Lampetra richardsoni), although some could have been Pacific lamprey (Entosphenus tridentatus) macropthalmia.
We confirmed the presence of juvenile steelhead (through smolt origin data) and coho salmon in Mill, Buck, and Rattlesnake Creeks, which are all upstream from the former site of Condit Dam. Juvenile salmonid abundance sampling at a site in Buck Creek during 2016–20 indicated the presence of juvenile coho salmon in all years except 2020. Total salmonid abundance (steelhead and coho salmon combined) at the Buck Creek site each year exceeded abundance in sampling prior to dam removal in 2009 and 2010. Juvenile salmonid abundance sampling in Rattlesnake Creek during 2016–20 indicated the presence of juvenile coho salmon in 2017, 2018, and 2019. Total juvenile salmonid abundance at the Rattlesnake Creek site was highly variable, sometimes exceeding and sometimes less than abundance prior to dam removal during 2001–05. During the period covered by this report, adult salmonid returns to the Columbia River were decreasing, largely because of marine survival. The extent to which this basin-wide decrease affected adult returns and juvenile populations in the White Salmon River subbasin is not known.
Despite a period of poor marine survival, PIT-tagged smolt and juvenile steelhead and coho salmon from the screw trap and tributaries returned to Bonneville Dam. Smolt-to-adult return rates from the screw trap to Bonneville Dam were similar to those in other nearby rivers during this period. However, data are still incomplete for some years and sample sizes were low. Future tagging and monitoring would be beneficial to track this valuable metric.
Genetic samples from steelhead smolt and parr collected at the screw trap and some main-stem electrofishing during 2016 were analyzed for Genetic Stock Identification (GSI) by CRITFC. Preliminary data showed that White Salmon River fish were the most common at about 42 percent, with 19 percent typing to Hood River, Oregon stock, and about 26 percent typing to Skamania stock, a common hatchery stock in the area. Winter and summer runs were represented in the samples.
Juvenile salmonid sampling in the White Salmon River, Washington, following removal of Condit Dam, demonstrated that anadromous salmonids are using newly opened habitat upstream from the former dam site and improved lower river habitat. Steelhead and coho salmon smolts are being produced upstream from the former dam site, and some have returned to Bonneville Dam as adults. Chinook salmon spawning upstream from our smolt trap site are producing fry. These results are encouraging for success of the strictly natural recolonization strategy. However, declines in anadromous runs to the larger Columbia River Basin also likely have affected the White Salmon runs and our data may not reflect full capacity of the White Salmon River subbasin juvenile production. Continued abundance, distribution, and GSI monitoring will help to track the evolution of anadromous fish in the White Salmon River under a natural recolonization strategy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221117","collaboration":"Prepared in cooperation with Yakama Nation Fisheries and Mid-Columbia Fisheries Enhancement Group","usgsCitation":"Jezorek, I.G., and Hardiman, J.M., 2023, Juvenile salmonid monitoring to assess natural recolonization following removal of Condit Dam on the White Salmon River, Washington, 2016–21: U.S. Geological Survey Open-File Report 2022–1117, 23 p., https://doi.org/10.3133/ofr20221117.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-137364","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":413140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1117/coverthb.jpg"},{"id":413143,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1117/images"},{"id":413142,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221117/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1117"},{"id":413141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1117/ofr20221117.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1117"},{"id":499726,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114378.htm","linkFileType":{"id":5,"text":"html"}},{"id":413144,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1117/ofr20221117.XML"}],"country":"United States","state":"Washington","otherGeospatial":"White Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78283038944755,\n 46.100301136884156\n ],\n [\n -121.78283038944755,\n 45.67990372212273\n ],\n [\n -121.22276550358751,\n 45.67990372212273\n ],\n [\n -121.22276550358751,\n 46.100301136884156\n ],\n [\n -121.78283038944755,\n 46.100301136884156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
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1 Gifford Pinchot Drive
Madison, WI 53726
Email: water-science-school@usgs.gov
The U.S. Geological Survey (USGS) launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the surface and subsurface geologic mapping of the United States, with a focus on identifying areas that may have the potential to contain critical mineral resources. EarthMRI can inform strategies to ensure secure and reliable domestic critical mineral supplies for the United States as mandated by Executive Order 13817 and the Infrastructure and Jobs Act of 2021 (Public Law 117–58, 135 Stat. 529). Earth MRI is a collaborative effort between the USGS and the State geological surveys as represented by the Association of American State Geologists to identify, prioritize, and acquire new geoscience data for geographic areas, or focus areas, across the Nation that have potential to host critical mineral resources. Mapping of focus areas was based on a framework of mineral systems and their associated mineral deposit types that could possibly host critical minerals. Using readily available geologic, geophysical, geochemical, and mineral deposit data, teams of USGS scientists worked with representatives of State geological surveys in a series of workshops to outline focus areas that contain evidence of key features for one or more mineral systems. These areas can be used to guide future efforts to collect new geologic, geophysical, geochemical, and topographic data that focus on critical minerals through Earth MRI.
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\"name\": \"United States\"\n }\n }\n ]\n}","contact":"Earth Mapping Resources Initiative (Earth MRI)
Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: minerals@usgs.gov
Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity—twice the amount of connectivity as random—and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.220437","usgsCitation":"Cross, T.B., Tack, J.D., Naugle, D., Schwartz, M.D., Doherty, K., Oyler-McCance, S.J., Pritchert, R.D., and Fedy, B.C., 2023, The ties that bind the sagebrush biome: Integrating genetic connectivity into range-wide conservation of greater sage-grouse: Royal Society Open Science, v. 10, no. 2, 220437, 15 p., https://doi.org/10.1098/rsos.220437.","productDescription":"220437, 15 p.","ipdsId":"IP-136470","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444387,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.220437","text":"Publisher Index Page"},{"id":435437,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI7OGR","text":"USGS data release","linkHelpText":"Greater sage-grouse network-prioritized functional connectivity cumulative current map (raster)"},{"id":413667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.07007462178439,\n 48.995456978554444\n ],\n [\n -119.8990141514297,\n 48.995456978554444\n ],\n [\n -119.8990141514297,\n 36.393670817249514\n ],\n [\n -103.07007462178439,\n 36.393670817249514\n ],\n [\n -103.07007462178439,\n 48.995456978554444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":865592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":302682,"corporation":false,"usgs":false,"family":"Tack","given":"Jason","email":"jtack@usgs.gov","middleInitial":"D.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":865593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":865594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":865595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":865596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pritchert, Ronald D.","contributorId":218059,"corporation":false,"usgs":false,"family":"Pritchert","given":"Ronald","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":865598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fedy, Brad C.","contributorId":140877,"corporation":false,"usgs":false,"family":"Fedy","given":"Brad","email":"","middleInitial":"C.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":865599,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}