Identifying population genetic structure is useful for inferring evolutionary process and comparing the resulting structure with subspecies boundaries can aid in species management. The American Kestrel (Falco sparverius) is a widespread and highly diverse species with 17 total subspecies, only 2 of which are found north of U.S./Mexico border (F. s. paulus is restricted to southeastern United States, while F. s. sparverius breeds across the remainder of the U.S. and Canadian distribution). In many parts of their U.S. and Canadian range, American Kestrels have been declining, but it has been difficult to interpret demographic trends without a clearer understanding of gene flow among populations. Here we sequence the first American Kestrel genome and scan the genome of 197 individuals from 12 sampling locations across the United States and Canada in order to identify population structure. To validate signatures of population structure and fill in sampling gaps across the U.S. and Canadian range, we screened 192 outlier loci in an additional 376 samples from 34 sampling locations. Overall, our analyses support the existence of 5 genetically distinct populations of American Kestrels—eastern, western, Texas, Florida, and Alaska. Interestingly, we found that while our genome-wide genetic data support the existence of previously described subspecies boundaries in the United States and Canada, genetic differences across the sampled range correlate more with putative migratory phenotypes (resident, long-distance, and short-distance migrants) rather than a priori described subspecies boundaries per se. Based on our results, we suggest the resulting 5 genetically distinct populations serve as the foundation for American Kestrel conservation and management in the face of future threats.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/auk/ukaa051","usgsCitation":"Ruegg, K.C., Brinkmeyer, M., Bossu, C.M., Bay, R., Anderson, E.C., Boal, C.W., Dawson, R.D., Eschenbauch, A., McClure, C.J., Miller, K.E., Morrow, L., Morrow, J.R., Oleyar, M.D., Ralph, B., Schulwitz, S., Swem, T., Therrien, J.F., Van Buskirk, R., Smith, T.B., and Heath, J.A., 2021, The American Kestrel (Falco sparverius) genoscape: Implications for monitoring, management, and subspecies boundaries: Ornithology, v. 138, no. 2, ukaa051, https://doi.org/10.1093/auk/ukaa051.","productDescription":"ukaa051","ipdsId":"IP-115588","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/auk/ukaa051","text":"Publisher Index Page"},{"id":395779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","volume":"138","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-24","publicationStatus":"PW","contributors":{"editors":[{"text":"Therrien, J-F.","contributorId":275699,"corporation":false,"usgs":false,"family":"Therrien","given":"J-F.","email":"","affiliations":[{"id":51980,"text":"Hawk Mountain Sanctuary","active":true,"usgs":false}],"preferred":false,"id":834226,"contributorType":{"id":2,"text":"Editors"},"rank":16}],"authors":[{"text":"Ruegg, K. C.","contributorId":275671,"corporation":false,"usgs":false,"family":"Ruegg","given":"K.","email":"","middleInitial":"C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":834208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinkmeyer, M.","contributorId":275672,"corporation":false,"usgs":false,"family":"Brinkmeyer","given":"M.","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":834209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bossu, C. M.","contributorId":275674,"corporation":false,"usgs":false,"family":"Bossu","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":834315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bay, R.","contributorId":275673,"corporation":false,"usgs":false,"family":"Bay","given":"R.","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, E. C.","contributorId":275675,"corporation":false,"usgs":false,"family":"Anderson","given":"E.","email":"","middleInitial":"C.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":834212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834213,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dawson, R. D.","contributorId":275676,"corporation":false,"usgs":false,"family":"Dawson","given":"R.","email":"","middleInitial":"D.","affiliations":[{"id":49840,"text":"University of Northern British Columbia","active":true,"usgs":false}],"preferred":false,"id":834214,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eschenbauch, A.","contributorId":275681,"corporation":false,"usgs":false,"family":"Eschenbauch","given":"A.","email":"","affiliations":[{"id":56878,"text":"Central Wisconsin Kestrel Research","active":true,"usgs":false}],"preferred":false,"id":834215,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McClure, C. J. W.","contributorId":275685,"corporation":false,"usgs":false,"family":"McClure","given":"C.","email":"","middleInitial":"J. W.","affiliations":[{"id":56879,"text":"The Pergrine Fund","active":true,"usgs":false}],"preferred":false,"id":834216,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, K. 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D.","contributorId":275693,"corporation":false,"usgs":false,"family":"Oleyar","given":"M.","email":"","middleInitial":"D.","affiliations":[{"id":35596,"text":"HawkWatch International","active":true,"usgs":false}],"preferred":false,"id":834220,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ralph, B.","contributorId":275694,"corporation":false,"usgs":false,"family":"Ralph","given":"B.","email":"","affiliations":[{"id":56883,"text":"Yosemite Area Audubon Society","active":true,"usgs":false}],"preferred":false,"id":834221,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schulwitz, S.","contributorId":275695,"corporation":false,"usgs":false,"family":"Schulwitz","given":"S.","affiliations":[{"id":36583,"text":"The Peregrine Fund","active":true,"usgs":false}],"preferred":false,"id":834222,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Swem, T.","contributorId":275696,"corporation":false,"usgs":false,"family":"Swem","given":"T.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834223,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Therrien, J. F.","contributorId":243502,"corporation":false,"usgs":false,"family":"Therrien","given":"J.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834316,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Van Buskirk, Rich","contributorId":275812,"corporation":false,"usgs":false,"family":"Van Buskirk","given":"Rich","email":"","affiliations":[],"preferred":false,"id":834317,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Smith, T. B.","contributorId":275697,"corporation":false,"usgs":false,"family":"Smith","given":"T.","email":"","middleInitial":"B.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834224,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Heath, J. A.","contributorId":275698,"corporation":false,"usgs":false,"family":"Heath","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":834225,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70219423,"text":"70219423 - 2021 - A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2021-04-15T15:26:51.238749","indexId":"70219423","displayToPublicDate":"2021-04-06T10:17:12","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem","docAbstract":"The Yellowstone Ecosystem Subcommittee (YES) asked the Interagency Grizzly Bear Study Team (IGBST) to re-assess a technique used in annual population estimation and trend monitoring of grizzly bears in the Greater Yellowstone Ecosystem (GYE). This technique is referred to as the Chao2 approach and estimates the number of females with cubs-of-the-year (hereafter, females with cubs) and, in association with other demographic data, is used by the IGBST to produce annual population estimates. Females with cubs are an easily recognizable population segment, and trends for this reproductive segment of the population are assumed to be representative of trend for the entire population.
The overarching objective of the analyses presented in this report was to provide a more accurate representation of the GYE grizzly bear population using the current methodologies in place. Specifically, we addressed two limitations of the current Chao2 approach: 1) underestimation bias associated with a distance criterion used to differentiate annual sightings of females with cubs into unique individuals and 2) limitations of the model-averaging approach to effectively distinguish among potential future population trajectories (decline, stability, and growth).
The first issue addressed in this report is the underestimation bias associated with the rule set that Knight et al. (1995) developed to differentiate sightings of females with cubs into unique individuals (i.e., unique family groups). The rule set was originally designed to be conservative by reducing the risk of identifying more females with cubs than actually existed, primarily through use of a distance criterion of 30 km to separate sightings of unique females. This approach resulted in an underestimation bias, and previous research demonstrated that this bias increases with increasing number of females with cubs. Using location data from radio-marked females with cubs, we evaluated alternative distance criteria by simulating scenarios with varying numbers of true females with cubs and sightings. Findings from these analyses demonstrate that bias in estimates of females with cubs can be substantially reduced by changing the 30-km distance criterion in the rule set to 16 km, which produced relatively unbiased estimates. Findings also indicate, however, the importance of adaptability with regard to the distance criteria because of the complex relationships and biases among the various parameters involved in estimation of unique females with cubs. The total number of annual sightings and the true number of females with cubs play particularly important roles. Whereas these analyses remind us that there is no perfect approach to estimating the number of females with cubs from sightings under various scenarios, they provide us with new tools to determine when and how to adapt the monitoring program.
The second issue we were tasked to investigate was the potential for improvement of the technique referred to as model-averaging, which serves to smooth relatively high variation in annual estimates. This technique was chosen by YES as the basis for monitoring the Yellowstone grizzly bear population, as described in the 2016 Conservation Strategy. This choice was made in part because the technique has been well documented and population estimates derived from counts of females with cubs are conservative. Using simulations of population trends, we demonstrate why the model-averaging technique currently used cannot distinguish between plausible future trend scenarios. As a suitable alternative to model averaging, we propose the use of generalized additive models (GAMs). Using a suite of simulated trend dynamics relevant to management, we demonstrate GAM performance for tracking trends in females with cubs within the context of the annual monitoring program. We demonstrate the ability to not only document directional changes in population trend but also patterns of stabilization or resiliency after such changes. Furthermore, the proposed monitoring framework provides objective measures useful for early detection of directional changes in trend. The new framework is flexible, allowing retrospective analysis of Chao2-based estimates and future applications to time series of other population metrics, such as vital rates.
The aforementioned updates provide us with new tools to determine when and how to adapt the monitoring program. Within the context of current monitoring protocols and effort, and considering the full suite of simulations presented in this report and previous studies, the IGBST plans to incorporate the following changes to the population monitoring protocol: 1) modify the distance criterion, starting with 16 km under current sampling conditions and 2) revise the population monitoring framework using GAMs as the basis for smoothing of annual estimates and detecting trends and changes in trend.
Implementation of the 16-km distance criterion combined with use of GAM techniques would affect some of the population metrics (e.g., annual population size and uncertainty, population trend, mortality rates) used to inform management responses. A primary consideration is that the 16-km distance criterion results in total population estimates derived from the Chao2 estimates that are greater than those we have reported in the past. This increase is due to a change in the implementation of the technique and more accurately represents the number of females with cubs in the GYE grizzly bear population. Additionally, interpretation of retrospective trend patterns may change due to the combination of a different distance criterion and enhanced trend monitoring based on the GAM approach we present here. Implementation will require relatively minor changes in the monitoring protocols described in Appendices B and C of the 2016 Conservation Strategy. Finally, we note that the IGBST has ongoing investigations into the merits of an Integrated Population Model (IPM), for which annual Chao2-based estimates are important input data. The IGBST plans to continue those investigations using the 16-km distance criterion to derive Chao2 estimates.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"van Manen, F.T., Ebinger, M.R., Haroldson, M.A., Bjornlie, D., Clapp, J., Thompson, D.J., Frey, K.L., Costello, C., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2021, A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem, viii, 77 p.","productDescription":"viii, 77 p.","ipdsId":"IP-126615","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":385125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384856,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/science/interagency-grizzly-bear-study-team?qt-science_center_objects=0#qt-science_center_objects"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone 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Katharine R.","contributorId":244265,"corporation":false,"usgs":false,"family":"Wilmot","given":"Katharine","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":813490,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":813491,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fortin-Noreus, Jennifer","contributorId":200746,"corporation":false,"usgs":false,"family":"Fortin-Noreus","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":813492,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hnilicka, Pat","contributorId":256935,"corporation":false,"usgs":false,"family":"Hnilicka","given":"Pat","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813493,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tyers, Daniel B.","contributorId":124587,"corporation":false,"usgs":false,"family":"Tyers","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":5129,"text":"U.S. Forest Service, 2327 University Way, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":813494,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70219448,"text":"70219448 - 2021 - Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","interactions":[],"lastModifiedDate":"2021-04-08T13:12:45.82359","indexId":"70219448","displayToPublicDate":"2021-04-06T08:10:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","docAbstract":"Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.
Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00256-8","usgsCitation":"Hance, D., Moriarty, K.M., Hollen, B.A., and Perry, R., 2021, Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states: Movement Ecology, v. 9, 17, 22 p., https://doi.org/10.1186/s40462-021-00256-8.","productDescription":"17, 22 p.","ipdsId":"IP-123520","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":452803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00256-8","text":"Publisher Index Page"},{"id":384927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.55224609375,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 41.88592102814744\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moriarty, Katie M.","contributorId":256976,"corporation":false,"usgs":false,"family":"Moriarty","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":51930,"text":"National Council for Air and Stream Improvement, Inc., Corvallis, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":813626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hollen, Bruce A.","contributorId":256977,"corporation":false,"usgs":false,"family":"Hollen","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":51933,"text":"USDI Bureau of Land Management, Regional Office, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":813627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220413,"text":"70220413 - 2021 - Prevalence of neonicotinoids and sulfoxaflor in alluvial aquifers in a high corn and soybean producing region of the Midwestern United States","interactions":[],"lastModifiedDate":"2021-05-13T12:51:05.599137","indexId":"70220413","displayToPublicDate":"2021-04-06T07:46:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Prevalence of neonicotinoids and sulfoxaflor in alluvial aquifers in a high corn and soybean producing region of the Midwestern United States","docAbstract":"Neonicotinoids have been previously detected in Iowa surface waters, but less is known regarding their occurrence in groundwater. To help fill this research gap, a groundwater study was conducted in eastern Iowa and southeastern Minnesota, a corn and soybean producing area with known heavy neonicotinoid use. Neonicotinoids were studied in alluvial aquifers, a hydrogeologic setting known to be vulnerable to surface-applied contaminants. Groundwater samples were analyzed from 40 wells for six neonicotinoid compounds (acetamiprid, clothianidin, dinotefuran, imidacloprid, thiacloprid, thiamethoxam), and sulfoxaflor. Samples were analyzed using liquid chromatography tandem mass spectrometry (LC/MS/MS) with both direct aqueous injection and solid phase extraction methods. Neonicotinoids were prevalent in the alluvial aquifers with 73% of the wells having at least one neonicotinoid detection. Clothianidin (68%, max: 391.7 ng/L) was the most commonly detected, followed by imidacloprid (43%, max: 6.7 ng/L) and thiamethoxam (3%, max: 0.2 ng/L). Acetamiprid, dinotefuran, sulfoxaflor, and thiacloprid were not detected during the study. The solid phase extraction method was more sensitive than direct aqueous injection, where only clothianidin detected in 23% of samples. SPE is the preferred method for detecting low concentrations of hydrophilic pesticides in water. This study documented that the combination of heavy chemical use overlying a hydrogeologic setting vulnerable to surface applied contaminants leads to transport of neonicotinoids into an important groundwater resource.
Multiple lines of evidence suggest climate change will result in increased precipitation variability and consequently more frequent extreme events. These hydroclimatic changes will likely have significant socioecological impacts, especially across water-limited regions. Here we present an analysis of daily meteorological observations from 1976 to 2019 at 337 long-term weather stations distributed across the western United States (US). In addition to widespread warming (0.2 °C ± 0.01°C/decade, daily maximum temperature), we observed trends of reduced annual precipitation (−2.3 ± 1.5 mm/decade) across most of the region, with increasing interannual variability of precipitation. Critically, daily observations showed that extreme-duration drought became more common, with increases in both the mean and longest dry interval between precipitation events (0.6 ± 0.2, 2.4 ± 0.3 days/decade) and greater interannual variability in these dry intervals. These findings indicate that, against a backdrop of warming and drying, large regions of the western US are experiencing intensification of precipitation variability, with likely detrimental consequences for essential ecosystem services.
Information concerning the availability, use, and quality of water in St. Martin Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 46.99 million gallons per day (Mgal/d) of water were withdrawn in St. Martin Parish, including about 35.91 Mgal/d from groundwater sources and 11.08 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture (32.28 Mgal/d), rice irrigation (6.44 Mgal/d), general irrigation (2.38 Mgal/d), and livestock uses (0.06 Mgal/d), accounted for about 88 percent (41.16 Mgal/d) of the total water withdrawn. Other categories of use included public supply, which accounted for about 10 percent (4.83 Mgal/d), rural domestic, which accounted for about 2 percent (0.81 Mgal/d), and industry, which accounted for less than 1 percent (0.18 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals in St. Martin Parish peaked in 1985 at more than 68 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213007","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of St. Martin Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3007, 6 p., https://doi.org/10.3133/fs20213007.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103365","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384877,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3007/fs20213007.pdf","text":"Report","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3007"},{"id":384878,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":384876,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3007/coverthb.jpg"}],"country":"United States","state":"Louisiana","county":"St. Martin 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Martin\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Iberville Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 589.87 million gallons per day (Mgal/d) of water were withdrawn in Iberville Parish in southeastern Louisiana: 30.86 Mgal/d from groundwater sources and 559.01 Mgal/d from surface-water sources. Withdrawals for industrial use accounted for about 77 percent (452.80 Mgal/d) of the total water withdrawn in 2016. Other use categories included power generation, which accounted for about 21 percent (124.54 Mgal/d), and aquaculture, which accounted for about 1 percent (7.50 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals peaked in 1980 at 1,429.78 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213014","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of Iberville Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3014, 6 p., https://doi.org/10.3133/fs20213014.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103366","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384875,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":384873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3014/coverthb.jpg"},{"id":384874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3014/fs20213014.pdf","text":"Report","size":"1.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3014"}],"country":"United States","state":"Louisiana","county":"Iberville Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.4853,30.4972],[-91.4604,30.4707],[-91.4535,30.4753],[-91.4524,30.4743],[-91.4143,30.4318],[-91.4127,30.4322],[-91.3947,30.4094],[-91.3947,30.3956],[-91.3714,30.3874],[-91.3371,30.3526],[-91.3202,30.3443],[-91.3144,30.3246],[-91.1419,30.3237],[-91.1382,30.3169],[-91.1329,30.315],[-91.1255,30.3127],[-91.1213,30.3132],[-91.1171,30.3145],[-91.1128,30.315],[-91.0943,30.319],[-91.0863,30.3199],[-91.0721,30.3203],[-91.0678,30.3212],[-91.0641,30.3207],[-91.0594,30.3202],[-91.052,30.3184],[-91.0461,30.317],[-91.0398,30.3174],[-91.025,30.3201],[-91.0213,30.3146],[-91.0218,30.3128],[-91.0599,30.2132],[-91.0536,30.2122],[-91.0691,30.1817],[-91.0718,30.162],[-91.0797,30.1634],[-91.0914,30.157],[-91.0904,30.136],[-91.0905,30.125],[-91.09,30.1126],[-91.09,30.109],[-91.1069,30.1081],[-91.1061,30.0628],[-91.2244,30.0256],[-91.2222,30.0307],[-91.2222,30.0394],[-91.2238,30.0407],[-91.2275,30.043],[-91.2306,30.0421],[-91.2391,30.0307],[-91.2423,30.0298],[-91.2544,30.0426],[-91.2591,30.0504],[-91.2638,30.0546],[-91.2686,30.061],[-91.3371,30.0602],[-91.3514,30.0602],[-91.3545,30.0611],[-91.3577,30.057],[-91.3625,30.0543],[-91.3672,30.0547],[-91.3688,30.0589],[-91.3725,30.0625],[-91.3725,30.0653],[-91.3688,30.0671],[-91.3698,30.0767],[-91.3719,30.0817],[-91.3766,30.0863],[-91.3777,30.095],[-91.3814,30.0978],[-91.3898,30.0996],[-91.3893,30.1024],[-91.3925,30.1028],[-91.4642,30.1029],[-91.4706,30.1097],[-91.4727,30.1143],[-91.4684,30.1184],[-91.4679,30.1212],[-91.4648,30.1235],[-91.4637,30.1253],[-91.4616,30.1317],[-91.4727,30.1431],[-91.469,30.1486],[-91.4748,30.1564],[-91.4753,30.166],[-91.4774,30.1711],[-91.4742,30.1875],[-91.4774,30.1921],[-91.4827,30.1939],[-91.488,30.198],[-91.4906,30.2017],[-91.4901,30.2063],[-91.4885,30.2109],[-91.4816,30.2186],[-91.4821,30.2214],[-91.4795,30.2246],[-91.4763,30.2255],[-91.4753,30.2287],[-91.479,30.2319],[-91.4922,30.2369],[-91.5176,30.2415],[-91.5271,30.2411],[-91.5429,30.2397],[-91.563,30.242],[-91.5757,30.2479],[-91.5884,30.257],[-91.5911,30.2639],[-91.5911,30.2671],[-91.5879,30.2731],[-91.59,30.2772],[-91.6043,30.2895],[-91.6213,30.3105],[-91.6282,30.3302],[-91.6271,30.3357],[-91.6266,30.3426],[-91.6245,30.3545],[-91.6266,30.3586],[-91.6457,30.365],[-91.6478,30.3677],[-91.6436,30.3727],[-91.6394,30.3741],[-91.6351,30.3778],[-91.6304,30.3846],[-91.6246,30.3947],[-91.624,30.4048],[-91.6267,30.4112],[-91.6273,30.4176],[-91.6326,30.4272],[-91.6379,30.4336],[-91.6373,30.4349],[-91.6374,30.4427],[-91.6405,30.4432],[-91.6432,30.4454],[-91.6453,30.4523],[-91.657,30.4587],[-91.6581,30.4646],[-91.6682,30.4747],[-91.6825,30.4779],[-91.6926,30.481],[-91.7016,30.4925],[-91.7011,30.4975],[-91.6253,30.4972],[-91.5839,30.4967],[-91.5818,30.4825],[-91.5701,30.4826],[-91.5568,30.483],[-91.5584,30.4885],[-91.5261,30.4972],[-91.4853,30.4972]]]},\"properties\":{\"name\":\"Iberville\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
The goal of the 3D Elevation Program (3DEP) is to complete nationwide data acquisition in 8 years, by 2023, to provide the first-ever national baseline of consistent high-resolution three-dimensional data—including bare earth elevations and three-dimensional point clouds—collected in a timeframe of less than a decade. Successful implementation of 3DEP depends on partnerships and the development and adoption of a unified Federal approach to acquiring data. The purpose of this document is to outline several best practices to aid the Federal 3DEP community in reaching a higher level of coordinated implementation, maximize Federal data investments, and reduce the number of years it will take to complete national coverage. The best practices are provided to Federal agencies as a checklist to assess the level of their participation and to inspire further adoption of Federal enterprise practices that will advance joint 3DEP coverage goals for the benefit of their missions and the Nation as a whole. It is anticipated that additional best practices will be defined and added as the effort matures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203062","usgsCitation":"Lukas, V., and Baez, V., 2021, 3D Elevation Program—Federal best practices: U.S. Geological Survey Fact Sheet 2020–3062, 2 p., https://doi.org/10.3133/fs20203062.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-118601","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":384879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3062/fs20203062.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3062"},{"id":383062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3062/coverthb.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (<) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 37.16031654673677\n ],\n [\n -89.296875,\n 37.33522435930639\n ],\n [\n -89.9560546875,\n 37.71859032558816\n ],\n [\n -90.8349609375,\n 36.914764288955936\n ],\n [\n -91.5380859375,\n 35.96022296929667\n ],\n [\n -92.1533203125,\n 34.66935854524543\n ],\n [\n -92.373046875,\n 33.50475906922609\n ],\n [\n -91.0546875,\n 33.100745405144245\n ],\n [\n -89.7802734375,\n 33.50475906922609\n ],\n [\n -88.9453125,\n 35.639441068973944\n ],\n [\n -88.9892578125,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Kansas River provides drinking water to about 800,000 people in northeastern Kansas. Water-treatment facilities that use the Kansas River as a water-supply source use chemical and physical processes during water treatment to remove contaminants before public distribution. Advanced notification of changing water-quality conditions near water-supply intakes allows water-treatment facilities to proactively adjust treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne, collected water-quality data at the Kansas River at Wamego (USGS site 06887500; hereafter referred to as the “Wamego site”) and De Soto (USGS site 06892350; hereafter referred to as the “De Soto site”) monitoring sites to update previously published regression models relating continuous water-quality sensor measurements, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Linear regression analysis was used to update and develop models for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and fecal indicator bacteria at the Wamego and De Soto monitoring sites using data collected during July 2012 through September 2019. The water-quality information documented in this report can be used as guidance for water-treatment processes and to characterize changes in water-quality conditions in the Kansas River over time that would not be otherwise possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211018","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Williams, T.J., 2021, Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019: U.S. Geological Survey Open-File Report 2021–1018, 18 p., https://doi.org/10.3133/ofr20211018.","productDescription":"Report: vii, 18 p.; Appendixes: 1–32; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120556","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1018/downloads","text":"Appendixes 1–32","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1018 Appendixes 1–32"},{"id":384811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1018/ofr20211018.pdf","text":"Report","size":"1.16 MB","description":"OFR 2021–1018"},{"id":384810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1018/coverthb.jpg"},{"id":384813,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.66845703124999,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 38.151837403006766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Emergent late Pliocene and Pleistocene shoreline deposits, morphologically identifiable Pleistocene shoreline units, and seaward-facing scarps characterize the easternmost Atlantic Coastal Plain (ACP) of the United States of America. In some areas of the ACP, these deposits, units, and scarps have been studied in detail. Within these areas, temporal and spatial data are sufficient for time-depositional frameworks for shoreline-evolution to have been developed and published. For other areas, such as the southeastern Atlantic Coastal Plain (SEACP), available data are conflicting and (or) insufficient to develop such a framework, or to make shoreline correlations. Differential epeirogenic uplift and shoreline deformation, resulting from mantle-flow and climate-induced isostatic adjustments, complicate regional shoreline correlations. In the SEACP, the topographically prominent Orangeburg Scarp (hereafter, the Scarp) rises tens of meters in elevation from southeastern Georgia to southeastern North Carolina. The degree to which the Scarp and shoreline units seaward of the Scarp are deformed continues to be debated, but there is general agreement that the lower Savannah River area (LSRA) of Georgia and South Carolina is the least deformed area of the SEACP.
This paper synthesizes published and previously unpublished numerical age and stratigraphic data for emergent Pliocene and younger shoreline deposits in the LSRA in Georgia. Age data are applied to these shoreline deposits as they are delineated (map units) on the 1976 geologic map of Georgia by Lawton and others. Age assignments are based on stratigraphic position, fossil content, soil and weathering diagnostic properties, and numerical ages as determined by meteoric Beryllium‑10 paleosol residence time (10BePRT), optically stimulated luminescence (OSL), uranium disequilibrium series (U-series), amino acid racemization (AAR), and radiocarbon (14C) analyses. These data provide a preliminary Pliocene-Pleistocene geochronology for the Orangeburg Scarp and shoreline deposits seaward of the Scarp in the LSRA of Georgia. Minimum ages and age ranges indicate the following:
Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth’s climate. The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry. Here, we present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems. Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and/or plant-mediated fluxes from 15 aquatic ecosystems. We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design. We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources. We show that methane emissions increase from natural to impacted aquatic ecosystems and from coastal to freshwater ecosystems. We argue that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions.
","language":"English","publisher":"Nature Research","doi":"10.1038/s41561-021-00715-2","usgsCitation":"Rosentreter, J.A., Borges, A.V., Deemer, B., Holgerson, M.A., Liu, S., Song, C., Melack, J.M., Raymond, P.A., Duarte, C.M., Allen, G., Olefeldt, D., Poulter, B., Batin, T.I., and Eyre, B.D., 2021, Half of global methane emissions come from highly variable aquatic ecosystem sources: Nature Geoscience, v. 14, p. 225-230, https://doi.org/10.1038/s41561-021-00715-2.","productDescription":"6 p.","startPage":"225","endPage":"230","ipdsId":"IP-112683","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":487210,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://infoscience.epfl.ch/record/284804","text":"External Repository"},{"id":385016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosentreter, Judith A.","contributorId":257244,"corporation":false,"usgs":false,"family":"Rosentreter","given":"Judith","email":"","middleInitial":"A.","affiliations":[{"id":51987,"text":"Centre for Coastal Biogeochemistry, Southern Cross University, Lismore, NSW, 2480, Australia","active":true,"usgs":false}],"preferred":false,"id":813864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borges, Alberto V.","contributorId":257245,"corporation":false,"usgs":false,"family":"Borges","given":"Alberto","email":"","middleInitial":"V.","affiliations":[{"id":51988,"text":"University of Liege, Chemical Oceanography Unit, Liege, Belgium","active":true,"usgs":false}],"preferred":false,"id":813865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holgerson, Meredith A.","contributorId":257243,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":51986,"text":"Departments of Biology and Environmental Studies, St. Olaf College, Northfield, Minnesota, USA","active":true,"usgs":false}],"preferred":false,"id":813867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Shaoda","contributorId":257246,"corporation":false,"usgs":false,"family":"Liu","given":"Shaoda","email":"","affiliations":[{"id":51989,"text":"Yale School of Forestry and Environmental Studies, 195 Prospect Street, New Haven, CT, USA","active":true,"usgs":false}],"preferred":false,"id":813868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Song, Chunlin","contributorId":257247,"corporation":false,"usgs":false,"family":"Song","given":"Chunlin","email":"","affiliations":[{"id":51990,"text":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, China","active":true,"usgs":false}],"preferred":false,"id":813869,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Melack, John M.","contributorId":167466,"corporation":false,"usgs":false,"family":"Melack","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":24713,"text":"Bren School of Environmental Science and Management, University of California, Santa Barbara, California, USA","active":true,"usgs":false}],"preferred":false,"id":813870,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":813871,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duarte, Carlos M.","contributorId":222294,"corporation":false,"usgs":false,"family":"Duarte","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":813872,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allen, George H.","contributorId":257248,"corporation":false,"usgs":false,"family":"Allen","given":"George H.","affiliations":[{"id":51991,"text":"Department of Geography, Texas A&M University, College Station, TX, USA","active":true,"usgs":false}],"preferred":false,"id":813873,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Olefeldt, David","contributorId":169408,"corporation":false,"usgs":false,"family":"Olefeldt","given":"David","affiliations":[{"id":32365,"text":"Department of Renewable Resources, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":813874,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Poulter, Benjamin 0000-0002-9493-8600","orcid":"https://orcid.org/0000-0002-9493-8600","contributorId":200477,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":813875,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Batin, Tom I.","contributorId":257249,"corporation":false,"usgs":false,"family":"Batin","given":"Tom","email":"","middleInitial":"I.","affiliations":[{"id":51992,"text":"Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":813876,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Eyre, Bradley D.","contributorId":257250,"corporation":false,"usgs":false,"family":"Eyre","given":"Bradley","email":"","middleInitial":"D.","affiliations":[{"id":51987,"text":"Centre for Coastal Biogeochemistry, Southern Cross University, Lismore, NSW, 2480, Australia","active":true,"usgs":false}],"preferred":false,"id":813877,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70219467,"text":"70219467 - 2021 - Water reliability in the west -- SECURE Water Act Section 9503(C)","interactions":[],"lastModifiedDate":"2021-04-08T13:21:21.063231","indexId":"70219467","displayToPublicDate":"2021-04-05T08:17:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Water reliability in the west -- 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","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Technical Memorandum No. ENV-2021-001","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Bureau of Reclamation","collaboration":"U.S. Bureau of Reclamation, U.S. Geological Survey, University of Arizona","usgsCitation":"McGuire, M., Gangopadhyay, S., Martin, J.T., Pederson, G.T., Woodhouse, C.A., and Littell, J., 2021, Water reliability in the west -- SECURE Water Act Section 9503(C), 60 p.","productDescription":"60 p.","ipdsId":"IP-124595","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":384933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384913,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/climate/secure/2021secure.html"}],"country":"United 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gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":813689,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":813690,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":813691,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219301,"text":"ofr20211012 - 2021 - Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces","interactions":[],"lastModifiedDate":"2021-04-06T11:29:46.334003","indexId":"ofr20211012","displayToPublicDate":"2021-04-05T07:36:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1012","displayTitle":"Implementation Plan for the Southern Pacific Border and Sierra-Cascade Mountains Provinces","title":"Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces","docAbstract":"The National Cooperative Geologic Mapping Program (NCGMP) is publishing a strategic plan titled Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge (Brock and others, in press). The plan provides a vision, mission, and goals for the program during the years 2020–2030, which are:
In order to achieve the goals outlined in the strategic plan, the NCGMP has developed an implementation plan. This plan will guide the annual review of projects carried out by USGS staff (FEDMAP) described in the plan and the development of the annual FEDMAP prospectus that will ensure the effective application of the NCGMP strategy.
This publication describes the implementation plan of the NCGMP strategy for the southern Pacific Border and Sierra-Cascade Mountains provinces, as defined by Fenneman (1917, 1928, and 1946). This implementation plan focuses on the geology of California and a sliver of Nevada surrounding Lake Tahoe. The southern Pacific Border and Sierra-Cascade Mountains provinces encompass the varied landscapes of the high Sierra Nevada, the Central Valley, and Coast Ranges in northern and central California and the Peninsular Ranges, Continental Borderland, Los Angeles Basin-San Gabriel-San Bernardino valleys, western and central Transverse Ranges, and northernmost Salton Trough in southern California. Societal demands create a need for earth-science data in each of these landscapes. The broader San Francisco Bay area, Central Valley, Los Angeles-San Gabriel-San Bernardino lowlands, and the coastal lowlands that border the Peninsular Ranges are densely populated (about 30 million people) areas at high risk of natural hazards. The mountains of the Sierra Nevada, Peninsular Ranges, and Transverse Ranges, and the coast all provide numerous recreational opportunities that attract visitors from around the world, whereas previously these ranges attracted people to mine their resources. The agricultural capacity of the Central Valley is a critical resource for the Nation that is increasingly water limited.
The southern. Pacific Border and Sierra-Cascade Mountains provinces, at the edge of the North American continent, were profoundly influenced by subduction zone tectonics during the Mesozoic and early Cenozoic (ongoing in northernmost California) and subsequently by the inception, development, and present activity of the San Andreas transform margin system. Although the geology of this region is the poster child of fundamental conceptual models of subduction zone complexes, forearc basins, ophiolite obductions, magmatic arcs, and suspect terranes, as well as hosting one of Earth’s most notorious continental transform faults—the San Andreas Fault—important questions that have important societal consequences remain to be answered. Most of California’s population reside in these provinces and live within 30 miles of an active fault (according to www.earthquakeauthority.com) yet new faults continue to be discovered, highlighting the importance of deformation off the main San Andreas Fault. Bedrock, surficial, and three-dimensional (3D) geologic maps depicting stratigraphic structure and depth to crystalline basement rocks provide critical context and information for understanding fault rupture, distributed deformation, fault connectivity, and history in addition to providing crucial data that enable forecasting of shaking amplitude and length from hypothetical earthquake scenarios.
The tectonic evolution of California produced not only stunning mountains, with associated hazards from landslides and active volcanoes, but also fertile valleys that make California the top agricultural producer in the country in terms of cash receipts (according to www.ers.usda.gov/faqs). These valleys lie atop large basins that not only store groundwater but, in many cases, host oil and gas fields, contributing to the fourth highest hydrocarbon production by State in the country in 2016 (according to https://www.aei.org/carpe-diem/animated-chart-of-us-oil-production-by-state-1981-2017). Water is a key resource increasingly stressed by growing agricultural, industrial, and residential needs. Warmer and drier conditions have led to an increased reliance on extracting groundwater resources, whose availability and quality are dictated at the first order by the 3D spatial distribution of bedrock and Quaternary surficial deposits. Thus, assessment of this critical resource is inextricably tied to knowledge of the surficial and subsurface geologic structure and material types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211012","usgsCitation":"Langenheim, V.E., Graymer, R.W., Powell, R.E., Schmidt, K.M., and Sweetkind, D.S., 2021, Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces: U.S. Geological Survey Open-File Report 2021–1012, 11 p., https://doi.org/10.3133/ofr20211012.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-121693","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":384840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1012/ofr20211012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1012/covrthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.69486597787505\n ],\n [\n -117.20214843749999,\n 34.415973384481866\n ],\n [\n -116.806640625,\n 36.491973470593685\n ],\n [\n -119.35546875000001,\n 38.34165619279595\n ],\n [\n -119.3115234375,\n 39.30029918615029\n ],\n [\n -120.10253906249999,\n 40.212440718286466\n ],\n [\n -121.86035156249999,\n 42.06560675405716\n ],\n [\n -124.3212890625,\n 42.06560675405716\n ],\n [\n -124.541015625,\n 40.51379915504413\n ],\n [\n -123.70605468750001,\n 38.71980474264237\n ],\n [\n -122.607421875,\n 37.19533058280065\n ],\n [\n -121.59667968749999,\n 35.817813158696616\n ],\n [\n -120.58593749999999,\n 34.45221847282654\n ],\n [\n -117.94921874999999,\n 33.54139466898275\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -115.26855468749999,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Using a before-after study design in a stable, largely undisturbed pond habitat and a dataset spanning 33 years, we document and describe the decline of native Sonora mud turtles (Kinosternon sonoriense) after the introduction of non-native pond sliders (Trachemys scripta). The Sonora mud turtle population in Montezuma Well in central Arizona, USA, declined to less than 25% of previous numbers, from 372 ± 64 in 1983 to 80 ± 21 in 2011. We trapped and removed the non-native turtles between 2007 and 2012 and after removal of the non-natives, the Sonora mud turtle population increased to 139 ± 34 in 2015. The native turtles also significantly increased basking activity after removal of the non-natives, paralleling results of small-scale mesocosm studies showing that pond sliders negatively affect basking rates of native turtle species. Reproductive rates of female Sonora mud turtles (numbers of females with eggs) were lower during the period of peak non-native turtle abundance, and increased after removal of the non-native turtles. We hypothesize that the reduction in effective reproductive rate links interference competition (reflected in reduced basking rates) to the long-term decline of the native mud turtles. Results from the undisturbed natural system of Montezuma Well provide new insights on the overall occurrence, magnitude, and mechanisms of negative effects of introduced pond sliders on native turtle species. Sonora mud turtles are very different in their morphology, behavior, and ecology from pond sliders and from native turtles in other studies, suggesting that impacts of non-native pond sliders are more pervasive than previously thought.
","language":"English","publisher":"Reabic","doi":"10.3391/ai.2021.16.3.10","usgsCitation":"Drost, C.A., Lovich, J.E., Rosen, P.C., Malone, M., and Garber, S.D., 2021, Non-native Pond Sliders cause long-term decline of native Sonora Mud Turtles: A 33-year before-after study in an undisturbed natural environment: Aquatic Invasions, v. 16, no. 3, p. 542-570, https://doi.org/10.3391/ai.2021.16.3.10.","productDescription":"29 p.","startPage":"542","endPage":"570","ipdsId":"IP-101856","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":452811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/ai.2021.16.3.10","text":"Publisher Index Page"},{"id":436421,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EL65UI","text":"USGS data release","linkHelpText":"Sonora Mud Turtles and non-native turtles, Montezuma Well, Yavapai County, Arizona, 1983 - 2015"},{"id":387893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":821113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":821114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosen, Philip C.","contributorId":70311,"corporation":false,"usgs":true,"family":"Rosen","given":"Philip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":821132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malone, Matthew","contributorId":264216,"corporation":false,"usgs":false,"family":"Malone","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":821133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garber, Steven D.","contributorId":264217,"corporation":false,"usgs":false,"family":"Garber","given":"Steven","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":821134,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229019,"text":"70229019 - 2021 - Climate change may cause shifts in growth and instantaneous natural mortality of American Shad throughout their native range","interactions":[],"lastModifiedDate":"2022-02-25T13:01:46.692613","indexId":"70229019","displayToPublicDate":"2021-04-05T06:57:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Climate change may cause shifts in growth and instantaneous natural mortality of American Shad throughout their native range","docAbstract":"American Shad Alosa sapidissima is an anadromous species with populations ranging along the U.S. Atlantic coast. Past American Shad stock assessments have been data limited and estimating system-specific growth parameters or instantaneous natural mortality (M) was not possible. This precluded system-specific stock assessment and management due to reliance on these parameters for estimating other population dynamics (such as yield per recruit). Furthermore, climate-informed biological reference points remain a largely unaddressed need in American Shad stock assessment. Population abundance estimates of American Shad and other species often rely heavily on M derived from von Bertalanffy growth function (VBGF) parameters. Therefore, we developed Bayesian hierarchical models to estimate coastwide, regional, and system-specific VBGF parameters and M using data collected from 1982 to 2017. We tested predictive performance of models that included effects of various climate variables on VBGF parameters within these models. System-specific models were better supported than regional or coast-wide models. Mean asymptotic length (L∞) decreased with increasing mean annual sea surface temperature (SST) and degree days (DD) experienced by fish during their lifetime. Although uncertain, K (Brody growth coefficient) decreased over the same range of lifetime SST and DD. Assuming no adaptation, we projected changes in VBGF parameters and M through 2099 using modeled SST from two climate projection scenarios (Representative Concentration Pathways 4.5 and 8.5). We predicted reduced growth under both scenarios, and M was projected to increase by about 0.10. It is unclear how reduced growth and increased mortality may influence population productivity or life history adaptation in the future, but our results may inform stock assessment models to assess those trade-offs.
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Colorado Plateaus aquifers constitute one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213012","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Degnan, J.R., and Musgrove, M., 2021, Groundwater quality in the Colorado Plateaus aquifers, western United States: U.S. Geological Survey Fact Sheet 2021–3012, 4 p., https://doi.org/10.3133/fs20213012.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117821","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384828,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"},{"id":384827,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3012/fs20213012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384826,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3012/covrthb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.6875,\n 33.063924198120645\n ],\n [\n -105.64453124999999,\n 34.161818161230386\n ],\n [\n -105.16113281249999,\n 36.70365959719456\n ],\n [\n -105.0732421875,\n 37.78808138412046\n ],\n [\n -105.5126953125,\n 38.51378825951165\n ],\n [\n -104.94140625,\n 39.027718840211605\n ],\n [\n -105.205078125,\n 40.38002840251183\n ],\n [\n -106.3916015625,\n 42.71473218539458\n ],\n [\n -107.841796875,\n 43.13306116240612\n ],\n [\n -110.390625,\n 42.61779143282346\n ],\n [\n -110.9619140625,\n 40.94671366508002\n ],\n [\n -109.6875,\n 40.64730356252251\n ],\n [\n -110.9619140625,\n 39.87601941962116\n ],\n [\n -112.763671875,\n 37.23032838760387\n ],\n [\n -113.115234375,\n 36.73888412439431\n ],\n [\n -111.22558593749999,\n 36.4566360115962\n ],\n [\n -111.6650390625,\n 33.211116472416855\n ],\n [\n -109.6875,\n 33.063924198120645\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
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U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Stream Valley aquifers constitute one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213011","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Kingsbury, J.A., 2021, Groundwater quality in selected Stream Valley aquifers, western United States: U.S. Geological Survey Fact Sheet 2021–3011, 4 p., https://doi.org/10.3133/fs20213011.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117820","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384825,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"},{"id":384824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3011/fs20213011.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3011/covrthb.jpg"}],"country":"United States","state":"Colorado, Kansas, Missouri, Nebraska, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.615234375,\n 38.993572058209466\n ],\n [\n -92.98828125,\n 39.774769485295465\n ],\n [\n -94.5703125,\n 39.639537564366684\n ],\n [\n -95.49316406249999,\n 40.3130432088809\n ],\n [\n -97.0751953125,\n 42.97250158602597\n ],\n [\n -104.0625,\n 43.068887774169625\n ],\n [\n -104.1064453125,\n 40.94671366508002\n ],\n [\n -106.435546875,\n 39.470125122358176\n ],\n [\n -106.12792968749999,\n 38.09998264736481\n ],\n [\n -104.67773437499999,\n 37.020098201368114\n ],\n [\n -102.87597656249999,\n 36.94989178681327\n ],\n [\n -99.755859375,\n 36.73888412439431\n ],\n [\n -99.8876953125,\n 35.31736632923788\n ],\n [\n -96.5478515625,\n 33.94335994657882\n ],\n [\n -94.6142578125,\n 36.94989178681327\n ],\n [\n -94.52636718749999,\n 38.30718056188316\n ],\n [\n -91.2744140625,\n 38.54816542304656\n ],\n [\n -90.4833984375,\n 38.61687046392973\n ],\n [\n -90.615234375,\n 38.993572058209466\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
National Water-Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Edwards-Trinity aquifer system constitutes one of the important aquifers being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213010","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Musgrove, M., 2021, Groundwater quality in the Edwards-Trinity aquifer system: U.S. Geological Survey Fact Sheet 2021–3010, 4 p., https://doi.org/10.3133/fs20213010.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117819","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3010/fs20213010.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3010/covrthb.jpg"},{"id":384822,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"}],"country":"United States","state":"Arkansas, Oklahoma, Texas","otherGeospatial":"Edwards-Trinity Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.546875,\n 28.93124697186731\n ],\n [\n -98.052978515625,\n 29.31514119318728\n ],\n [\n -97.086181640625,\n 30.467614102257855\n ],\n [\n -96.04248046875,\n 31.644028945047822\n ],\n [\n -95.328369140625,\n 32.90726224488304\n ],\n [\n -94.031982421875,\n 33.687781758439364\n ],\n [\n -93.14208984375,\n 33.779147331286474\n ],\n [\n -92.515869140625,\n 34.08906131584994\n ],\n [\n -93.878173828125,\n 34.65128519895413\n ],\n [\n -95.9326171875,\n 34.397844946449865\n ],\n [\n -97.283935546875,\n 34.252676117101515\n ],\n [\n -98.69018554687499,\n 33.137551192346145\n ],\n [\n -99.51416015625,\n 32.37068286611427\n ],\n [\n -101.348876953125,\n 32.045332838858506\n ],\n [\n -104.578857421875,\n 31.644028945047822\n ],\n [\n -103.216552734375,\n 30.401306519203583\n ],\n [\n -101.326904296875,\n 29.878755346037977\n ],\n [\n -100.546875,\n 28.93124697186731\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
National Water-Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002
In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range-Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits.
Northeastern United States
Carnivora
We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6-year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient.
We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats (Lynx rufus) and coyotes (Canis latrans) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx (Lynx canadensis), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species.
We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community-wide hypotheses at macroecological scales and identify coarse-scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14112","usgsCitation":"Siren, A., Sutherland, C., Bernier, C., Royar, K., Kilborn, J.R., Callahan, C., Cliche, R., Prout, L.S., and Morelli, T.L., 2021, Abiotic stress and biotic factors mediate range dynamics on opposing edges: Journal of Biogeography, v. 48, no. 7, p. 1758-1772, https://doi.org/10.1111/jbi.14112.","productDescription":"15 p.","startPage":"1758","endPage":"1772","ipdsId":"IP-125344","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":452813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.14112","text":"Publisher Index Page"},{"id":410693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.47563781677009,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 45.75938677061683\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Siren, Alexej P. 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K.","affiliations":[],"preferred":false,"id":859342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutherland, Christopher","contributorId":300051,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","affiliations":[{"id":65006,"text":"University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":859343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernier, Chris","contributorId":300052,"corporation":false,"usgs":false,"family":"Bernier","given":"Chris","email":"","affiliations":[{"id":65007,"text":"Vermont Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":859344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royar, Kimberly","contributorId":300053,"corporation":false,"usgs":false,"family":"Royar","given":"Kimberly","email":"","affiliations":[{"id":65007,"text":"Vermont Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":859345,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kilborn, Jillian R.","contributorId":236780,"corporation":false,"usgs":false,"family":"Kilborn","given":"Jillian","email":"","middleInitial":"R.","affiliations":[{"id":47548,"text":"Universidad de La Frontera, Temuco, Chile","active":true,"usgs":false}],"preferred":false,"id":859346,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Catherine","contributorId":236779,"corporation":false,"usgs":false,"family":"Callahan","given":"Catherine","email":"","affiliations":[{"id":47548,"text":"Universidad de La Frontera, Temuco, Chile","active":true,"usgs":false}],"preferred":false,"id":859347,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cliche, Rachel","contributorId":300056,"corporation":false,"usgs":false,"family":"Cliche","given":"Rachel","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":859348,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prout, Leighlan S.","contributorId":300057,"corporation":false,"usgs":false,"family":"Prout","given":"Leighlan","middleInitial":"S.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":859349,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859350,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219324,"text":"fs20203061 - 2021 - The transformation of dryland rivers: The future of introduced tamarisk in the U.S.","interactions":[],"lastModifiedDate":"2023-06-08T13:14:04.708426","indexId":"fs20203061","displayToPublicDate":"2021-04-02T09:52:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3061","displayTitle":"The Transformation of Dryland Rivers: The Future of Introduced Tamarisk in the U.S.","title":"The transformation of dryland rivers: The future of introduced tamarisk in the U.S.","docAbstract":"Tamarix spp. (tamarisk or saltcedar), a shrub-like tree, was intentionally introduced to the U.S. from Asia in the mid-1800s. Tamarisk thrives in today’s human-altered streamside (riparian) habitats and can be found along wetlands, rivers, lakes, and streams across the western U.S. In 2001, a biological control agent, Diorhabda spp. (tamarisk leaf beetle), was released in six states, and has since spread throughout the southwestern U.S. and northern Mexico. Beetle defoliation of tamarisk has altered tamarisk’s water use and effectiveness as erosion control, as well as dynamics of native and nonnative plant and wildlife species. The full effects of the tamarisk leaf beetle on ecosystem function remain unknown. The U.S. Geological Survey collaborates with Tribal, State, Federal agencies, and other institutions to provide current, fact-based information on the effects of tamarisk and the tamarisk leaf beetle on managed resources, and provides sound science for conservation and restoration of riparian habitats in the southwestern U.S.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203061","usgsCitation":"Nagler, P.L., Hull, J.B., van Riper, C., Shafroth, P.B., and Yackulic, C.B., 2021, The Transformation of dryland rivers: The future of introduced tamarisk in the U.S.: U.S. Geological Survey Fact Sheet 2020–3061, 6 p., https://doi.org/10.3133/fs20203061.","productDescription":"6 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-122043","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":384841,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3061/covrthb.jpg"},{"id":384842,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3061/fs20203061.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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